Text books:

Course Number	MA1101
Course Credit (L-T-P-C)	3-1-0-4
Course Title	Calculus and Linear Algebra
Learning Mode	Lectures and Tutorials
Learning Objectives	To provide the essential knowledge of basic tools of Differential Calculus, Integral Calculus, Vector spaces and Matrix Algebra.
Course Description	This course provides a foundation for Calculus and Linear Algebra. Topics related to properties of single and two variable functions along with their applications will be discussed. In addition fundamentals of linear algebra and matrix theory with applications will also be discussed.
Course Content	Differential Calculus (12 Lectures) : Limit and continuity of one variable function (including ε-δ definition). Limit, continuity and differentiability of functions of two variables, Tangent plane and normal, Change of variables, chain rule, Jacobians, Taylor’s Theorem for two variables, Extrema of functions of two or more variables, Lagrange’s method of undetermined multipliers. Integral Calculus (10 Lectures) : Riemann integral for one variable functions, Double and Triple integrals, Change of order of integration. Change of variables, Applications of Multiple integrals such as surface area and volume. Vector Spaces (12 Lectures) : Vector spaces (over the field of real numbers), subspaces, spanning set, linear independence, basis and dimension. Linear transformations, range and null space, rank-nullity theorem, matrix of a linear transformation. Matrix Algebra (8 Lectures) : Elementary operations and their use in getting the rank, inverse of a matrix and solution of linear simultaneous equations, Orthogonal, symmetric, skew-symmetric, Hermitian, skew-Hermitian, normal and unitary matrices and their elementary properties, Eigenvalues and Eigenvectors of a matrix, Cayley-Hamilton theorem, Diagonalization of a matrix.
Learning Outcome	Students completing this course will be able to: 1. Understand various properties of functions such as limit, continuity and differentiability. 2. Learn about integrations in various dimension and their applications. 3. learn about the concept of basis and dimension of a vector space. 4. define Linear Transformations and compute the domain, range, kernel, rank, and nullity of a linear transformation. 5. compute the inverse of an invertible matrix. 6. solve the system of linear equations. 7. Apply linear algebra concepts to model, solve, and analyze real-world problems.
Assessment Method	Quiz /Assignment/ MSE / ESE

Textbooks:

Thomas, G. B., Hass, J., Heil, C. and Weir M. D., “Thomas’ Calculus”, 14th Ed., Pearson Education, 2018
Kreyszig, E., “Advanced Engineering Mathematics”, 10th Ed., Wiley India Pvt. Ltd, 2015

Reference Books:

Jain, R. K. and Iyenger, S. R. K., “Advanced Engineering Mathematics”, 5^th, Narosa Publishing House, 2017
Axler, S., “Linear Algebra Done Right”, 3rd Ed., Springer Nature, 2015
Strang, G., “Linear Algebra and Its Applications” 4th Ed., Cengage India Private Limited, 2005

4.0

CS1101

Course Number	CS1101
Course Credit	3-0-3-4.5
Course Title	Foundations of Programming
Learning Mode	Offline
Learning Objectives	· To understand the fundamental concepts of programming · To develop the basic problem-solving skills by designing algorithms and implementing them. · To learn about various data types, control statements, functions, arrays, pointers, and file handling. · To achieve proficiency in debugging and testing a C program
Course Description	This introductory course provides a solid foundation in programming principles and techniques. Designed for students with little to no prior programming experience, it covers fundamental concepts such as variables, data types, control structures, functions, and basic data structures. Students will learn to write, debug, and execute programs using a high-level programming language. Emphasis is placed on developing problem-solving skills, logical thinking, and the ability to write clear and efficient code. By the end of the course, students will be equipped with the essential skills needed to pursue more advanced studies in computer science and software development.
Course Outline	Introduction and Programming basics, Expressions Control and Iterative statements, Functions, Arrays, Recursion vs. Iteration Pointers, 2D-Array with pointers, Structures, String, Dynamic memory allocation, File handling, Contemporary programming languages, and applications Practical component: Lab to be conducted on a 3-hour slot weekly. It will be conducted with the theory course so the topics for problems given in the lab are already initiated in the theory class.
Learning Outcome	· Understanding of Basic Syntax and Structure in C language · Proficiency in Data Types, Operators, and Control Structures · Function Implementation and learn to use them appropriately · Efficient Use of Arrays and Strings · Pointer Utilization · Ability to perform dynamic memory allocation and deallocation using malloc (), calloc (), realloc (), and free () functions. · Structured data management with structures and unions · Exposure of file Handling · Learning debugging and error Handling
Assessment Method	Internal (Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Reading

Knuth, Donald E. The art of computer programming, volume 4A: combinatorial algorithms, part 1. Pearson Education India, 2011.
P.J. Deitel and H.M. Deitel, C How To Program, Pearson Education (7th Edition)
Brian W. Kernighan and Dennis M. Ritchie, The C Programming Language, Prentice−Hall
A. Kelley and I. Pohl, A Book on C, Pearson Education (4th Edition)

K. N. King, C PROGRAMMING A Modern Approach, W. W. Norton & Company

4.5

PH1101/PH1201

Course Number	PH1101/PH1201
Course Credit	3-1-3-5.5
Course Title	Physics
Learning Mode	Lectures and Tutorials
Learning Objectives	Complies with Program Goals 1 and 2
Course Description	This course deals with fundamentals in Classical mechanics, Waves and Oscillations and Quantum Mechanics. As a prerequisite, the mathematical preliminaries such as coordinate systems, vector calculus etc will be discussed in the beginning.
Course Outline	Orthogonal coordinate systems (Plane polar, Spherical, Cylindrical), concept of generalised coordinates, generalised velocity and phase space for a mechanical system, Introduction to vector operators, Gradient, divergence, curl and Laplacian in different co-ordinate systems. Central force problem and its applications. Rigid body rotation, vector nature of angular velocity, Finding the principal axes, Euler's equations; Gyroscopic motion and its application; Accelerated frame of reference, Fictitious forces. Potential energy and concept of equilibrium, Lennard-Jones and double-well potentials, Small oscillations, Harmonic oscillator, damped and forced oscillations, resonance and its different examples, oscillator states in phase space, coupled oscillations, normal modes, longitudinal and transverse waves, wave equation, plane waves, examples two- and three-dimensional waves. Michelson-Morley experiment, Lorentz transformation, Postulates of special theory of relativity, Time dilation and length contraction, Applications of special theory of relativity.
Learning Outcome	Complies with PLO 1a, 2a, 3a
Assessment Method	Quiz, Assignments and Exams

Suggested Readings:

Textbooks:

Engineering Mechanics, M. K. Harbola, 2^nd ed., Cengage, 2012
D. Kleppner and R. J. Kolenkow, An introduction to Mechanics, Tata McGraw-Hill, New Delhi, 2000.
I. G. Main, Oscillations and Waves
H. G. Pain, The Physics of Vibrations and Waves, 1968
Frank S. Crawford, Berkeley Physics Course Vol 3: Waves and Oscillations, McGraw Hill, 1966.

References:

R. P. Feynman, R. B. Leighton and M. Sands, The Feynman Lecture in Physics, Vol I, Narosa Publishing House, New Delhi, 2009.
David Morin, Introduction to Classical Mechanics, Cambridge University Press, NY, 2007.
P. C. Deshmukh, Foundations of Classical Mechanics, Cambridge University Press, 2019

5.5

CE1101/CE1201

Course code	CE1101/CE1201
Course Credit (L-T-P-C)	1-0-3-2.5
Course Title	Engineering Graphics
Learning Mode	Lectures and Practical
Learning Objectives	Complies with PLO-1a 1. The course on engineering drawing is designed to introduce the fundamentals of technical drawing as an important form of conveying information. 2. Apply principles of engineering visualization and projection theory to prepare engineering drawings, using conventional and modern drawing tools. 3. Practice drawing orthographic projections, isometric views, and sectional views, of simple and combined solids in different orientations.
Course Description	This course will introduce drawing as a tool to represent a complex three-dimensional object on two-dimensional paper through methods of projections. The course explains the use of different drafting tools and the importance of conventions for uniformity and standardization of the interpretation of the drawings.
Course Outline	Fundamental of engineering drawing, line types, dimensioning, and scales. Conic sections: ellipse, parabola, hyperbola; cycloidal curves. Principle of projection, method of projection, orthographic projection, plane of projection, first angle of projection, Projection of points, lines, planes and solids. Section of solids: Sectional views of simple solids- prism, pyramid, cylinder, cone, sphere; the true shape of the section. Methods of development, development of surfaces. Isometric projections: construction of isometric view of solids and combination of solids from orthographic projections. Introduction to AutoCad and solving isometric problems.
Learning Outcome	After attending this course, the following outcomes are expected: a) The student will understand the basic concepts of engineering drawing. b) The student will be able to use basic drafting tools, drawing instruments, and sheets. c) The student will be able to represent three-dimensional simple and combined solid objects on two-dimensional paper. d) The student will be able to visualize and interpret the orientation of simple and combine solid objects.
Assessment Method	Laboratory Assignments (30%), Mid-semester examination (25%) and End-semester examination (45%).

Suggested Readings:

Textbooks:

D. Bhatt, Engineering Drawing, Charotar Publishing House.
Agrawal & Agrawal, Engineering Drawing, McGraw Hill.
Jolhe, Engineering Drawing.

References:

Engineering Drawing and Design by David Madsen

2.5

EE1101/EE1201

Course Number	EE1101/EE1201
Course Credit	3-0-3-4.5
Course Title	Electrical Sciences
Learning Mode	Lectures and Experiments
Learning Objectives	Complies with Program goals 1, 2 and 3
Course Description	The course is designed to meet the requirements of all B. Tech programmes. The course aims at giving an overview of the entire electrical engineering domain from the concepts of circuits, devices, digital systems and magnetic circuits.
Course Outline	Circuit Analysis Techniques, Circuit elements, Simple RL and RC Circuits, Kirchoff’s law, Nodal Analysis, Mesh Analysis, Linearity and Superposition, Source Transformations, Thevenin’s and Norton’s Theorems, Time Domain Response of RC, RL and RLC circuits, Sinusoidal Forcing Function, Phasor Relationship for R, L and C, Impedance and Admittance, Instantaneous power, Real, reactive power and power factor. Semiconductor Diode, Zener Diode, Rectifier Circuits, Clipper, Clamper, UJT, Bipolar Junction Transistors, MOSFET, Transistor Biasing, Transistor Small Signal Analysis, Transistor Amplifier and their types, Operational Amplifiers, Op-amp Equivalent Circuit, Practical Op-amp Circuits, Power Opamp, DC Offset, Constant Gain Multiplier, Voltage Summing, Voltage Buffer, Controlled Sources, Instrumentation Amplifier, Active Filters and Oscillators. Number Systems, Logic Gates, Boolean Theorem, Algebraic Simplification, K-map, Combinatorial Circuits, Encoder, Decoder, Combinatorial Circuit Design, Introduction to Sequential Circuits. Magnetic Circuits, Mutually Coupled Circuits, Transformers, Equivalent Circuit and Performance, Analysis of Three-Phase Circuits, Power measurement in three phase system, Electromechanical Energy Conversion, Introduction to Rotating Machines (DC and AC Machines). Laboratory: Experiments to verify Circuit Theorems; Experiments using diodes and bipolar junction transistor (BJT): design and analysis of half -wave and full-wave rectifiers, clipping and clamping circuits and Zener diode characteristics and its regulators, BJT characteristics (CE, CB and CC) and BJT amplifiers; Experiment on MOSFET characteristics (CS, CG, and CD), parameter extraction and amplifier; Experiments using operational amplifiers (op-amps): summing amplifier, comparator, precision rectifier, Astable and Monostable Multivibrators and oscillators; Experiments using logic gates: combinational circuits such as staircase switch, majority detector, equality detector, multiplexer and demultiplexer; Experiments using flip-flops: sequential circuits such as non-overlapping pulse generator, ripple counter, synchronous counter, pulse counter and numerical display; Power Measurement by two Wattmeter method; Open and Short Circuit Tests of Transformer.
Learning Outcomes	Complies with PLO 1a, 2a and 3a
Assessment Method	Quiz, Assignments and Exams

Texts/References

K. Alexander, M. N. O. Sadiku, Fundamentals of Electric Circuits, 3rd Edition, McGraw-Hill, 2008.
H. Hayt and J. E. Kemmerly, Engineering Circuit Analysis, McGraw-Hill, 1993.
L. Boylestad and L. Nashelsky, Electronic Devices and Circuit Theory, 6th Edition, PHI, 2001.
M. Mano, M. D. Ciletti, Digital Design, 4th Edition, Pearson Education, 2008.
Floyd, Jain, Digital Fundamentals, 8th Edition, Pearson.
David V. Kerns, Jr. J. David Irwin, Essentials of Electrical and Computer Engineering, Pearson, 2004.
Donald A Neamen, Electronic Circuits; analysis and Design, 3rd Edition, Tata McGraw-Hill Publishing Company Limited.
Adel S. Sedra, Kenneth C. Smith, Microelectronic Circuits, 5th Edition, Oxford University Press, 2004.
E. Fitzgerald, C. Kingsley Jr., S. D. Umans, Electric Machinery, 6th Edition, Tata McGraw-Hill, 2003.
P. Kothari, I. J. Nagrath, Electric Machines, 3rd Edition, McGraw-Hill, 2004.
Del Toro, Vincent. "Principles of electrical engineering." (No Title) (1972).

4.5

HS1101

Probability Theory and Ordinary Differential Equations

Course Number	HS1101
Course Credit	L-T-P-W: 2-0-1-2.5
Course Title	English for Professionals
Learning Mode	Offline
Learning Objectives	This course aims to help the students (a) attain proficiency in written English through the construction of grammatically correct sentences, utilization of subject-verb agreement principles, mastery of various tenses, and effective deployment of active and passive voice to ensure coherent and impactful written expression; (b) enhance oral communication skills by honing public speaking abilities, acquiring strategies to deliver persuasive presentations, and cultivating a polished telephone etiquette, enabling confident and articulate verbal communication; (c) foster active listening capabilities by recognizing different types of listening, and applying proven methods and strategies to improve active listening skills; (d) strengthen reading skills, including comprehension, interpretation, and critical analysis, to grasp diverse written materials and derive meaning from various types of texts encountered in academic and professional contexts; (e) develop adeptness in written communication for business purposes, encompassing the understanding of essential writing elements, mastery of appropriate writing styles thereby enhancing prospects for successful job interviews and subsequent professional endeavors.
Course Description	This academic course on communication skills aims to equip students with fluency in spoken and written English for effective expression in both academic and professional settings. By focusing on essential communication principles and providing practical experiences, students develop clarity, precision, and confidence in their communication. Through interactive discussions and exercises, students enhance critical thinking and adaptability in diverse contexts. Upon completion, students will excel in formal presentations, group discussions, and persuasive writing, enhancing their overall communication proficiency.
Course Outline	Unit I: Introduction to professional communication – LSRW - Phonetics and phonology Sounds in English Language – production and articulation – rhythm and intonation – connected speech - Basic Grammar and Advanced Vocabulary Sounds in English Language – production and articulation – rhythm and intonation – connected speech – persuading and negotiating – brevity and clarity in language. Unit II: Characteristics of Technical Communication: Types of communication and forms of communication - Formal and informal communication Verbal and non-Verbal Communication – Communication barriers and remedies Intercultural communication – neutral language Unit III: Comprehension and Composition – summarization, precis writing Business Letter Writing CV/ Resume – E-Communication Unit IV: Statement of Purpose, Writing Project Reports, Writing research proposal, writing abstracts, developing presentations, interviews – combating nervousness Tutorial: Listening Exercises, Speaking Practice (GDs, and Presentations), and Writing Practice Learning Outcome · Attain proficiency in written English, enabling the construction of grammatically correct sentences and coherent written expression through the use of appropriate grammar, tenses, and voice. · Enhance oral communication skills, including public speaking, persuasive presentation, and polished telephone etiquette, fostering confident and articulate verbal expression. · Cultivate active listening abilities, recognizing different listening types, overcoming obstacles, and employing strategies for attentive and effective communication. · Develop proficient written communication skills for business purposes, demonstrating understanding of essential writing elements, appropriate styles, and the creation of reports, notices, agendas, and minutes that effectively convey information.
Assessment Method	Class test + Quiz = 20%; Mid-semester = 25%; Assignment = 15%; End semester = 40%

Suggested Reading

Balzotti, Jon. Technical Communication: A Design-Centric Approach. Routledge, 2022.
Kaul, Asha, Business Communication. PHI Learning Pvt. Ltd. 2009
Laplante, Phillip A. Technical Writing: A Practical Guide for Engineers, Scientists, and Nontechnical Professionals. CRC Press, 2018.
Lawson, Celeste, et al. Communication Skills for Business Professionals, Second Edition. CUP, 2019.
Sharon Gerson and Steven Gerson. Technical Writing: Process and Product (8th Edition), London: Longman, 2013
Rentz, Kathryn, Marie E. Flatley & Paula Lentz. Lesikar’s Business Communication Connecting in a Digital world, McGraw-Hill, Irwin.2012
Allan & Barbara Pease. The Definitive Book of Body Language, New York, Bantam,2004
Jones, Daniel. The Pronunciation of English, New Delhi, Universal Book Stall.2010
Savage, Alice. Effective Academic Writing. OUP. 2014
Swan and Alter. Oxford English grammar course. OUP. 201

2.5

TOTAL

23.5

Semester -II

Sl. No.

Subject Code

SEMESTER II

MA1201

Course Number	MA1201
Course Credit (L-T-P-C)	3-1-0-4
Course Title	Probability Theory and Ordinary Differential Equations
Learning Mode	Lectures and Tutorials
Learning Objectives	To introduce the basic concepts of probability, statistics, and Differential equations.
Course Description	This course aims to cover basic concepts of probability, statistics and ordinary differential equations. In particular, popular distributions, random sampling, various estimators and hypothesis testing will be discussed. Students will also get exposure to the linear ordinary differential equations and their solution techniques.
Course Content	Probability (12 Lectures): Random variables and their probability distributions, Cumulative distribution functions, Expectation and Variance, probability inequalities, Binomial, Poisson, Geometric, negative binomial distributions, Uniform, Exponential, beta, Gamma, Normal and lognormal distributions. Statistics (10 Lectures): Random sampling, sampling distributions, Parameter estimation, Point estimation, unbiased estimators, maximum likelihood estimation, Confidence intervals for normal mean, Simple and composite hypothesis, Type I and Type II errors, Hypothesis testing for normal mean. Ordinary Differential Equations (20 Lectures): First order ordinary differential equations, exactness and integrating factors, Picard's iteration, Ordinary linear differential equations of n-th order, solutions of homogeneous and non-homogeneous equations (Method of variation of parameters). Systems of ordinary differential equations, Power series methods for solutions of ordinary differential equations. Legendre equation and Legendre polynomials, Bessel equation and Bessel functions.
Learning Outcome	Students will get exposure and understanding of: 1. Random variables and their probability distributions 2. Understand popular distributions and their properties 3. Sampling, estimation and hypothesis testing 4. Solution of ordinary differential equations 5. Solution of system of ordinary differential equations 6. Special functions arising as power series solutions of ordinary differential equations
Assessment Method	Quiz /Assignment/ MSE / ESE

Text Books:

Hogg, R. V., Mckean, J. and Craig, A. T., “Introduction to Mathematical Statistics”, 8th Ed., Pearson Education India, 2021
M. Ross “An introduction to Probability Models, Academic Press INC, 11th edition.
Miller, I. and Miller, M., “John E. Freund's Mathematical Statistics with Applications”, 8th Ed., Pearson Education India, 2013
L. Ross, Differential equations, 3rd Edition, Wiley, 1984
W. E. Boyce and R. C. Di Prima, Elementary Differential equations and Boundary Value Problems, 7th Edition, Wiley, 2001.

CS1201

Course Number	CS1201
Course Credit	3-0-3-4.5
Course Title	Data Structure
Learning Mode	Offline
Learning Objectives	· Understand the principles and concepts of data structures and their importance in computer science. · Learn to implement various data structures and understand how different algorithms works. · Develop problem-solving skills by applying appropriate data structures to different computational problems. · Achieving proficiency in designing efficient algorithms.
Course Description	This course provides a comprehensive study of data structures and their applications in computer science. It focuses on the implementation, analysis, and use of various data structures such as arrays, linked lists, stacks, queues, trees, and graphs. Through theoretical concepts and practical programming exercises, this course aims to develop problem-solving and algorithmic thinking skills essential for advanced topics in computer science and software development.
Course Outline	· Introduction to Data Structure, · Time and space requirements, Asymptotic notations · Abstraction and Abstract data types · Linear Data Structure: stack, queue, list, and linked structure · Unfolding the recursion · Tree, Binary Tree, traversal · Search and Sorting, · Graph, traversal, MST, Shortest distance · Balanced Tree Practical component: Lab to be conducted on a 3-hour slot weekly. It will be conducted with the theory course so the topics for problems given in the lab are already initiated in the theory class.
Learning Outcome	· Understand Data Structure Fundamentals · Implement Basic Data Structures using a programming language · Analyse and Apply Algorithms · Design and Analyse Tree Structures · Understand the usage of graph and its related algorithms · Design and Implement Sorting and Searching Algorithms · Debug and Optimize Code
Assessment Method	Internal (Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Reading

Alfred V. Aho, John E. Hopcroft, Jeffrey D. Ullman, Data Structures and Algorithms, Published by Addison-Wesley
Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest and Clifford Stein., Introduction to Algorithms,
Mark Allen Weiss, Data Structures and Algorithm Analysis in Java
Robert Sedgewick and Kevin Wayne, Algorithms
Narasimha Karumanchi, Data Structures and Algorithms Made Easy

4.5

CH1201/CH1101

Course Number	CH1201/CH1101
Course Credit	3-1-3-5.5
Course Title	Chemistry
Learning Mode	Offline
Learning Objectives	The course aims to lay a foundation for all three branches of chemistry, viz. Organic, Inorganic, and Physical Chemistry. The course aims to nurture knowledge to appreciate the interface of chemistry with other science and Engineering branches by combining theoretical concepts and experimental studies.
Course Description	This course introduces basic organic chemistry, inorganic chemistry and Physical chemistry to understand fundamental laws that governs various reactions, reaction rates, equilibrium, and their applications in daily life through relevant experimentation.
Course Outline	Module 1: Thermodynamics: The fundamental definition and concept, the zeroth and first law. Work, heat, energy and enthalpies. Second law: entropy, free energy and chemical potential. Change of Phase. Third law. Chemical equilibrium. Conductance of solutions, Kohlrausch’s law-ionic mobilities, Basic Electrochemistry. Module 2: Coordination chemistry: Crystal field theory and consequences color, magnetism, J.T distortion. Bioinorganic chemistry: Trace elements in biology, heme and non-heme oxygen carriers, haemoglobin and myoglobin; Organometallic chemistry. Module 3: Stereo and regio-chemistry of organic compounds, conformational analysis and conformers, Molecules devoid of point chirality (allenes and biphenyls); Significance of chirality in living systems, organic photochemistry, Modern techniques in structural elucidation of compounds (UV–Vis, IR, NMR). Module 4 (Lab Component): Experiments based on redox and complexometric titrations; synthesis and characterization of inorganic complexes and nanomaterials; synthesis and characterization of organic compounds; experiments based on chromatography; experiments based on pH and conductivity measurement; experiment related to chemical kinetics and spectroscopy.
Learning Outcome	Students will be able to 1. identify organic and inorganic molecules and relate them to daily life applications through experiments. 2. understand important hypothesis, laws and their derivations to intercept physical phenomenon of chemical reactions and apply them in hands-on experiments. 3. understand the importance of organic and inorganic molecules in our body and environment. 4. know important analytical techniques to intercept chemical entity. 5. approach organic and inorganic synthesis as a skillset for drug manufacturing, calculate limiting reagents and yields, use various analytical tools to characterize organic compounds, interpret and ascertain data related to Physical chemistry aspects and know laboratory safety measures, risk factors and scientific report writing skills.
Assessment Method	Theory: 20% Quiz and assignment, 30% Mid sem and 50% End semester exams for theory part (4 credits). Lab: 60% lab report, lab performance and assignment, 20% End semester exam for practical part, 20% viva/quiz (1.5 credits). Overall Weightage: Theory (70%), Lab (30%).

Suggested Reading:

Text books:

Vogel's Qualitative Inorganic Analysis, G. Svehla, 7^th Edition, Revised, Prentice Hall, 1996.
J. Elias, S. S. Manoharan and H. Raj, "Experiments in General Chemistry", Universities Press (India) Pvt. Ltd., 1997.

A. J. Elias, A Collection of Interesting General Chemistry Experiments, revised edition, Universities Press (India) Pvt. Ltd., 2007.

Albert Cotton, G. Wilkinson, C. A. Murillo, M. Bochmann, Advanced Inorganic Chemistry - 6^th Edition New Delhi: Wiley India, 2008.
Mukkanti, Practical Engineering Chemistry, B.S. Publications, Hyderabad, 2009.
Shriver and Atkins inorganic chemistry / Peter Atkins, Tina Overton, Jonathan Rourke, Mark Weller, Fraser Armstrong-5^th Edition – Oxford: UOP. 2012.
Atkins’ Physical Chemistry, Peter Atkins, Julio de Paula, James Keeler, Oxford University Press, 11^th Edition 2017.
L. Kapoor, A Textbook of Physical Chemistry, Vol: 1, 2 (6^th Edition, 2019), Vol: 3 (5^th Edition, 2020) MaGraw Hill.
R. Chatwal, S. K. Anand, Instrumental Methods of Chemical Analysis, 5^th Edition, Himalaya Publications, 2023.

	PLO-1	PLO-2	PLO-3	PLO-4	PLO-5	PLO-6	PLO-7	PLO-8
CLO-1	X	X	X	X	X	X	X	X
CLO-2	X	X		X	X
CLO-3	X	X	X	X		X	X
CLO-4	X	X		X	X	X	X	X
CLO-5			X	X	X			X

5.5

ME1201/ME1101

Course Number	ME1201/ME1101
Course Credit	0-0-3-1.5
Course Title	Mechanical Fabrication
Learning Mode	Fabrication work – hands on fabrication work in Workshop
Learning Objectives	Complies with PLOs 3-4. · This course aims to develop the concepts and skills of various mechanical fabrication methods. · Fabrication of metallic and non-metallic components, fabrication using bulk and sheet metals, subtractive and additive manufacturing methods, and assemble the parts
Course Description	This course is designed to fulfil the need of hand on experience about various approaches (conventional and CNC, subtractive and additive) of mechanical fabrication approaches. Prerequisite: NIL
Course Outline	The jobs for various shops should be planned such that they are the parts of an assembled item. The student groups will fabricate different parts in various shops which will involve some amount of their creativeness/input particularly in design and/or planning. Various components as required for the assembled part can be made using the following shops: Sheet Metal Working: Development, sheet cutting and fabrication of designated job using sheet metal (ferrous/nonferrous); Joining of required portions by soldering, in case part is desired to be made leak proof. Pattern Making and Foundry: Making of suitable pattern (wood); making of sand mould, melting of non-ferrous metal/alloy (Al or Al alloys), pouring, solidification. Observation/identification of various defects appeared on the component. Joining: Butt/lap/corner joint job fabrication as required of low carbon steel plates; weld quality inspection by dye-penetration test (non-destructive testing approach)of the component made. Demonstration of semi-automatic Gas Metal Arc welding (GMAW). Conventional machining: Operations on lathe and vertical milling to fabricate the required component. The fabrication of the component should cover various lathe operations like straight turning, facing, thread cutting, parting off etc., and operations using indexing mechanism on vertical milling. CNC centre: Fundamentals of CNC programming using G and M code; setting and operations of job using CNC lathe or milling, tool reference, work reference, tool offset, tool radius compensation to fabricate the component with a designed profile on Al/Al-alloy plate. 3D printing (Fused Filament Fabrication): (2 weeks) Create the model, select appropriate slicing and path for fabrication of a 3D job by layer deposition (additive manufacturing approach) using polymeric material. Demonstration on pattern fabrication using 3D printing.
Learning Outcome	· This course would enable the students to develop the concept of design, fabrication (subtractive and additive) for various engineering applications. Fabrication of components and assemble them. · The practical skill and hands on experience for various fabrication methods from bulk, sheet metal using conventional as well as CNC machines.
Assessment Method	Fabrication of components in each of the shops required for assembly of the given part; submission of reports for each shop, and quiz assessment.

Text and Reference books:

Hajra Choudhury, HazraChoudhary and Nirjhar Roy, 2007, Elements of Workshop Technology, vol. I,Mediapromoters and Publishers Pvt. Ltd.
W A J Chapman, Workshop Technology, 1998, Part -1, 1st South Asian Edition, Viva Book Pvt Ltd.
N. Rao, 2009, Manufacturing Technology, Vol.1, 3rd Ed., Tata McGraw Hill Publishing Company.
Adithan, B.S. Pabla, 2012, CNC machines, New Age International Publishers

1.5

ME1202/ME1102

Introduction to Chemical Engineering

Course Number	ME1202/ ME1102
Course Number	Engineering Mechanics
L-T-P-C	3-1-0-4
Pre-requisites	Nil
Semester	Spring
Learning Mode	Lectures
Learning Objectives	Complies with PLOs 1, 4 · The objective of this first course in mechanics is to enable engineering students to analyze basic mechanics problems and apply vector-based approach to solve them.
Course Outline	1. Rigid body statics: Equivalent force system. Equations of equilibrium, Free body diagram, Reaction, Static indeterminacy. 2. Structures: 2D truss, Method of joints, Method of section. Beam, Frame, types of loading and supports, axial force, Bending moment, Shear force and Torque Diagrams for a member. 3. Friction: Dry friction (static and kinetic), wedge friction, disk friction (thrust bearing), belt friction, square threaded screw, journal bearings, Wheel friction, Rolling resistance. 4. Centroid and Moment of Inertia 5. Introduction to stress and strain: Definition of Stress, Normal and shear Stress. Relation between stress and strain, Cauchy formula. Stress in an axially loaded member and stress due to torsion in axisymmetric section
Learning Outcomes:	Following learning outcomes are expected after going through this course. · Learn and apply general mathematical and computer skills to solve basic mechanics problems. · Apply the vector-based approach to solve mechanics problems.
Assessment Method	Mid semester examination, End semester examination, Class test/Quiz, Tutorials

Reference Books

Shames, Engineering Mechanics: Statics and dynamics, 4th Ed, PHI, 2002.
P. Beer and E. R. Johnston, Vector Mechanics for Engineers, Vol I - Statics, 3rd Ed, Tata McGraw Hill, 2000.
L. Meriam and L. G. Kraige, Engineering Mechanics, Vol I - Statics, 5th Ed, John Wiley, 2002.
P. Popov, Engineering Mechanics of Solids, 2nd Ed, PHI, 1998.
P. Beer and E. R. Johnston, J.T. Dewolf, and D.F. Mazurek, Mechanics of Materials, 6th Ed, McGraw Hill Education (India) Pvt. Ltd., 2012.

IK1201

Indian Knowledge System (IKS)

TOTAL

22.5

Semester -III

Sl. No.

Subject Code

SEMESTER III

CB2101

Course Number	CB2101
Course Credit (L-T-P-C)	2-0-0-2
Course Title	Introduction to Chemical Engineering
Learning Mode	Classroom lectures
Learning Objectives	To build an interest and basic understanding on the scope and importance of Chemical Engineering subjects. To introduce the career and research opportunities in the area of Chemical Engineering.
Course Description	In this course, basic introduction to various subjects/topics comprising the broad discipline of chemical engineering and their applications in real scenarios will be highlighted. The course will also give an overview of recent developments and emerging areas of the field.
Course Content	Chemical process industries and the evolution of Chemical Engineering domain; Roles of chemical engineers– conventional and modern scenarios; Basics of units and dimensions; Equations of state; Material and energy balances; Introduction to unit operations and unit processes; Chemical reactions; Heat and mass transfer and process control; Safety in chemical industries including historical case studies; Sustainable development via chemical engineering; Emerging trends and R&D scenario in Chemical Engineering; Interaction with other engineering disciplines.
Learning Outcome	Knowledge on various subjects and their applications relevant for chemical engineers. Identifying the importance of safety and sustainable development in chemical engineering context. Awareness on research and development in chemical engineering.
Assessment Method	Assignments, Quiz, Mid-semester examination and End-semester examination

Text Books:

W. L. Badger, J. T. Banchero, Introduction to Chemical Engineering, McGraw Hill, 2017.
R. M. Felder, R. W. Rousseau, Elementary Principles of Chemical Processes, 3^rd Ed., Wiley & Sons, 2005.
W. McCabe, J. Smith, P. Harriott, Unit Operations of Chemical Engineering, 7^th Ed., McGraw Hill, 2005.

Reference Books:

J.M. Smith, H.C., Van Ness, M.M. Abbott, Introduction to Chemical Engineering Thermodynamics, 8^th Ed., McGraw Hill Education (India), 2018.
S. Pushpavanam, Introduction to Chemical Engineering, PHI Learning Ltd., 2012.

	CLO1	CLO2
PLO1	X
PLO2		X
PLO3

CB2102

Chemical Process Calculations

Course Number	CB2102
Course Credit (L-T-P-C)	3-1-2-5
Course Title	Fluid Mechanics
Learning Mode	Classroom lectures and practical
Learning Objectives	To build an understanding on the importance and scope of fluid in rest (statics) and fluid in motion (dynamics) in process systems. Learning basics of pressure development, fluid properties and their role in driving various types of flows and fluid response under different external/internal forces. To study governing equations and dimensionless groups which drive the flow and their applications in the designing of pipe networks, pumps, etc.
Course Description	The course helps to develop a basic understanding of fluid mechanics and its application in chemical engineering. Equations and concepts in fluid statics, kinematics, and dynamics are covered in the course.
Course Content	Introduction to fluid mechanics; Definition and types of fluids; System and control volume; Fluid as a continuum; Velocity and stress field; Newton’s law of viscosity; Newtonian and non-Newtonian fluids; Fluid statics; Hydrostatic force on submerged bodies; Buoyancy and stability; Streamlines, pathlines, streaklines; Rigid body motion; Flow kinematics: Eulerian and Lagrangian approach; Integral analysis: mass and momentum balances; Bernoulli equation; Differential analysis of flow; Navier-Stokes equation; Dimensional analysis using Buckingham PI theorem; Flow similarity and model studies; Unidirectional flow; Compressible and incompressible flows; Viscous and inviscid flow; Irrotational flow; Laminar and turbulent flow; Skin friction and form friction; Friction factor; Fully developed flow through pipes and ducts; Head losses; Potential flow; Boundary layer theory; Boundary layer separation; Flow around immersed bodies; Drag and lift; Flow through packed and fluidized beds, Compressible flow; Flow measurement: Venturi and orifice meter; Fluid transportation- pumps, blowers and compressors.
Learning Outcome	Development and application of governing equations and laws of fluid systems. Study on flow and pressure measuring equipment, frictional losses in pipes/conduits, laminar/turbulent flows, compressible/incompressible flows, boundary layer development and flows through porous beds. Illustrating the physical significance of pertinent non-dimensional groups through dimensional analysis.
Assessment Method	Assignments, Quiz, Mid-semester examination and End-semester examination

Text Books:

R.W. Fox, A.T. McDonald, P.J. Pritchard, Introduction to Fluid Mechanics, Wiley, 7^th Ed., 2009.
F.M. White, Fluid Mechanics, Mc-Graw Hill, 6^th Ed., 2008.

Reference Books:

M. Denn, Process Fluid Mechanics, Prentice Hall, 1979.
V.L. Streeter, Fluid Mechanics, 5^th Ed., Mc-Graw Hill, 1971.
R.B. Bird, W.E. Stewart, E. N. Lightfoot, Transport Phenomena, 2^nd Ed., Wiley, 2006.
J. M. Coulson, J. F. Richardson, J. R. Backhurst and J. H. Harker, Chemical Engineering, Vol. 1, 5^th Ed., Elsevier, 2015.
W. L. McCabe, J. C. Smith, P. Harriott, Unit Operations of Chemical Engineering, 7^th Ed., Mc-Graw Hill, 2005.

	CLO1	CLO2	CLO3
PLO1	X	X
PLO2	X	X
PLO3		X	X

CB2103

Heat Transfer

Course Number	CB2103
Course Credit (L-T-P-C)	3-0-3-4.5
Course Title	Heat Transfer
Learning Mode	Classroom lectures and practical
Learning Objectives	To understand the fundamentals of heat transfer mechanisms in fluids and solids and their applications to engineering problems. To understand the basic principle and mathematical formulation of various heat transfer equipment used in process industries. To perform experiments to understand the various heat transfer measurements that help to optimize the process parameters.
Course Description	This course helps to understand the fundamentals of heat transfer mechanisms in fluids and solids and their applications in various heat transfer equipment in process industries including conduction, convection and radiation problems, heat exchangers and evaporators.
Course Content	Introduction; Heat conduction; Fourier’s law; Thermal conductivity;; Thermal resistance; Critical thickness of insulation; One dimensional heat conduction; Heat generation; 2D heat conduction; Unsteady-state conduction, Various shapes (thin plate, thick plate, cylinder, sphere); Practical applications (Freezing of butter slab, meat loafs, Pasteurization of milk, Sterilization of canned jam/jelly); Heisler charts; extended surfaces; Laws of Black body radiation; Solid angle and radiation intensity; Radiation exchange between black and gray surfaces; Shape factor; electrical network analogy for thermal systems; Forced Convection; Differential equation of convection; Thermal boundary layer; Laminar and turbulent flow heat transfer; Natural Convection; Governing equations; Heat Exchangers; Types of heat exchangers; Overall heat transfer coefficient and fouling factor, Mean temperature difference; Effective-NTU approach; Condensation and boiling; Types of boiling; Correlations in saturated poll boiling; Film and drop condensation; Condensation on a vertical plate and horizontal tubes.
Learning Outcome	Ability to understand, analyze and solve conduction, convection and radiation problems. Ability to design and analyze the performance of heat exchangers, evaporators and other heat transfer equipment. Ability to conduct experiments and analyze the data to interpret the performance of the equipment.
Assessment Method	Assignments, Quiz, Mid-semester examination and End-semester examination.

Text Books:

D. Q. Kern, Process Heat Transfer, 1^st Ed. McGraw Hill, 2001.
S. P. Sukhatme, A Text Book of Heat Transfer, 4^th Ed., Universities press, 2005.
J. P. Holman, Heat Transfer, 10^th Ed., Mc-Graw Hill, 2017.

Reference Books:

W. H. McAdams, Heat Transmission, 2^nd revised Ed., Mc-Graw Hill, 1973.
H. Martin, Heat Exchangers, 1^st Ed., CRC press, 1988.
C. J. Geankoplis, Transport Processes and Unit Operations, 3^rd Ed., Prentice Hall India Pvt. Ltd., New Delhi, 2002.
J. M. Coulson, J. F. Richardson, J. R. Backhurst and J. H. Harker, Chemical Engineering, Vol-1, 5^th Ed., ‎ Butterworth-Heinemann Ltd, 1999.

	CLO1	CLO2	CLO3
PLO1	X	X
PLO2		X	X
PLO3			X

4.5

CB2104

Course Number	CB2104
Course Credit (L-T-P-C)	3-1-0-4
Course Title	Chemical Process Calculations
Learning Mode	Classroom lectures and tutorials
Learning Objectives	To learn the fundamental concepts of material balance and their applications. To learn the fundamental concepts of energy balance and their applications. To learn the overall concepts of combined material and energy balance and their diverse applications.
Course Description	This course is mainly about learning the concepts of material balance and energy balance and their applications (individual or combined) with reference to different chemical engineering systems/processes.
Course Content	Units and dimensions; Dimensional analysis, Rayleigh method and Buckingham method; Steady-state/unsteady state processes; Lumped and distributed processes; Single and multi-phase systems; Correlations for physical and transport properties; Equilibrium relations; Ideal gases and gaseous mixtures; Vapor pressure; Vapor liquid equilibrium; Some Thermodynamics cycle namely Rankine Cycle, Carnot Cycle etc.; Material balances: Non-reacting single-phase systems; Systems with recycle, purge and bypass; Processes involving vaporization and condensation; Intensive and extensive variables; Rate laws; Calculation of enthalpy change; Heat of reaction; Saturation humidity, humidity charts and their use; Energy balance calculations; Flow-sheet preparation; Degrees of freedom analysis.
Learning Outcomes	Familiarize with different units and dimensions. Analyse and comprehend steady-state and dynamic processes. Understand and calculate problems related to material balances. Understand and calculate problems related to energy balances. Understand and calculate problems related to combined material and energy balances.
Assessment Method	Assignments, Quiz, Mid-semester examination and End-semester examination

Text Books:

B. I. Bhatt; S. B. Thakore, Stoichiometry, McGraw Hill, 6^th Ed., 2021.
O. A. Hougen, K. M. Watson and R. A. Ragatz, Chemical Process Principles, CBS Publishers, Part-1, 2^nd Ed., 2004.
D. M. Himmelblau, Basic Principles and Calculations in Chemical Engineering, Prentice Hall of India, 8^th Ed., 2014.

Reference Books:

N. Chopey, Handbook of Chemical Engineering Calculations, Mc-Graw Hill, 3^rd Ed., 2004.
R. M. Felder and R. W. Rousseau, Elementary Principles of Chemical Processes, Wiley, 3^rd Ed., 2014.

	CLO1	CLO2	CLO3
PLO1	X	X
PLO2			X
PLO3			X

CB2105

Chemical Engineering Thermodynamics

Course Number	CB2105
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Chemical Engineering Thermodynamics
Learning Mode	Classroom lectures
Learning Objectives	To establish the basic thermodynamic principles and key relations for understanding the transfer processes. To familiarize with various real-world engineering examples for how thermodynamics is applied in engineering practice.
Course Description	This course covers the basic postulates of classical thermodynamics and their applications to open and closed systems, the equation of states, properties of pure fluids and mixtures, phase equilibrium, and chemical reaction equilibrium. Applications are discussed through extensive problem work relating to practical topics.
Course Content	Introduction and basic definitions: System, Property, Energy, Work, Heat; Equilibrium; Reversible and irreversible processes; Equations of state and prediction of volumetric properties of fluids from PVT relationss; First law and other basic concepts; Second law: Heat engines, refrigerators and heat pumps, Carnot Cycle; Entropy: entropy balance and changes of pure substances, liquids, solids, and gases; Third law; Thermodynamic property relations: Maxwell relations, Isentropic processes; Vapour-Liquid equilibria (VLE): Phase rule, Gibbs-Duhem equation; Raoult's law and modified Raoult’s Law, Henry's law, High-pressure VLE; Solution thermodynamics: fundamental property relation, chemical potential and phase equilibria, partial and molar, Fugacity and fugacity coefficient, Ideal solution model; Excess properties, activity coefficient, Chemical reaction equilibrium: Homogeneous and heterogeneous reactions; multi-reaction equilibria, equilibrium criteria to chemical reactions.
Learning Outcome	Development of an intuitive understanding of thermodynamics. Identify the problems that deal with the treatment of properties of solutions, phase equilibria and chemical reaction equilibria
Assessment Method	Assignments, Quiz, Mid-semester examination and End-semester examination

Text Books:

J. M. Smith, H. C. Van Ness, M. M. Abbott, Introduction to Chemical Engineering Thermodynamics, McGraw-Hill, 6^thEd., 2001.
S. I. Sandler, Chemical, Biochemical and Engineering Thermodynamics, Wiley India, 4^thEd., 2006.
Y. V. C. Rao, Chemical Engineering Thermodynamics, Universities Press, India, 1^stEd., 1997.

Reference Books:

J. M. Prausnitz, R. N. Lichtenthaler, E. G. Azevedo, Molecular Thermodynamics of Fluid-Phase Equilibria, Prentice Hall, 3^rdEd., 1998 .
J. W. Tester, M. Modell, Thermodynamics and its Applications, Prentice Hall, 3^rd Ed., 1999.
R. C. Reid, J. M. Prausnitz, B.E.Poling, Properties of Gases and Liquids, McGraw-Hill, 4^th Ed., 1987.

	CLO1	CLO2	CLO3
PLO1	X	X	X
PLO2	X	X
PLO3

HS21XX

HSS Elective-I

TOTAL

21.5

Semester -IV

Sl. No.

Subject Code

SEMESTER IV

CB2201

Mechanical Operations

Course Number	CB2201
Course Credit (L-T-P-C)	2-0-3-3.5
Course Title	Mechanical Operations
Learning Mode	Classroom lectures and practical
Learning Objectives	To understand various mechanical operations applicable in the Chemical Process Industries (CPIs). To learn the principle and construction of the equipment. To perform process evaluation of various unit operations.
Course Description	This course helps to understand basic principles of various mechanical operations, construction and working of the equipment including size measurement and classification methods and systems, fluid particle systems and equipment, size reduction equipment, solid-solid separation method system.
Course Content	Principles of crushing and grinding; Laws of crushing and grinding (Rittinger’s Law, Kick’s Law, Bond’s Law); Properties and handling of particulate solids: introduction, characterization of solid particles, determination of mean particle size, size distribution equations; Characteristics of industrial crushers and mills; Industrial screening; Effectiveness of screens; Cyclones; Fluid-particle mechanics; Free and hindered settling; Stoke’s Law and Newton’s Law; Industrial classifiers; Clarifiers and thickeners; Gravity separation, tabling and jigging; Floatation and its kinetics; Mixing of liquids and solids; Power requirement in mixing; Principles of filtration: Ergun Equation and Kozeny-Carman Equation; Filtration equipment; Transportation and storage of solids: introduction, storage techniques (bulk storage, bin storage, hoppers, silos), bulk solids conveying; Grade efficiency: measurement techniques, cut size, sharpness cut, construction of grade efficiency curve.
Learning Outcomes	Ability to understand size measurement and classification methods and systems. Ability to understand fluid particle systems and equipment. Ability to select suitable size reduction equipment, solid-solid separation method system. Ability to analyse separation, filtration processes and solid-liquid separation equipment and systems.
Assessment Method	Assignments, Quiz, Mid-semester examination and End-semester examination.

Text Books:

W. L. McCabe, J. C. Smith and P. Harriott, Unit Operations of Chemical Engineering, Mc-Graw Hill, 7 ^th Ed., 2005.
J. M. Coulson, J. F. Richardson, J. R. Backhurst and J. H. Harker, Chemical Engineering, Elsevier, Vol-2, 5 ^th Ed., 2015.
C. J. Geankoplis, Transport Processes and Unit Operations, Prentice Hall India Pvt. Ltd., New Delhi, 3 ^rd Ed., 2002.

Reference Books:

A. M. Gaudin, Principles of Mineral dressing, Mc-Graw Hill, 1939.
R. H. Perry and C. H. Chilton, Chemical Engineer’s Hand Book, Mc-Graw Hill, 8 ^th Ed., 2007.
A. F. Taggart, Handbook of Mineral Dressing: Ores and Industrial Minerals, Wiley, 1945.
Ghosal, Sanyal, and Dutta, Introduction to Chemical Engineering, McGraw Hill Education, 2014.

3.5

CB2202

Mass Transfer-I

Course Number	CB2202
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Mass Transfer-I
Learning Mode	Classroom lectures
Learning Objectives	To learn the fundamental concepts of Mass transfer operations. To develop numerical solutions to various mass transfer processes. To familiarize with various equipment that uses these principles for useful separation processes.
Course Description	Introduction to the fundamental aspects of mass transfer and their importance in chemical engineering processes are covered in this course. Calculations and phase diagrams in distillation, extraction, and absorption are taught in the course.
Course Content	Fick’s law of diffusion, Molecular diffusion, Gas- and liquid- phase diffusion coefficients; Mass transfer in convective system; Types and correlations for mass transfer coefficients; Eddy diffusion; Mass transfer theories; Equilibrium; Raoult’s and Henry’s law; Inter-phase mass transfer; Overall mass transfer coefficient; Contacting equipment – gas-liquid; Type of contacting equipment: plate, tray, bubble column, packed column, agitated vessel, spray tower; Design of packed tower; Flooding in packed towers; Equilibrium in gas-liquid systems; Gas absorption and stripping; Selection of solvent; Number of stages in a tray tower; Height equivalent to a theoretical plate; Distillation: Vapor-liquid equilibrium; Enthalpy-concentration plots; Flash vaporization; Batch distillation; Steam distillation; Continuous fractionation; McCabe-Thiele and Ponchon-Savarit methods; Liquid-liquid extraction (LLE); Solvent selection; Equipment for LLE; Solid-liquid extraction/leaching: rate, solid-liquid contacting strategy, equipment.
Learning Outcome	Identify, quantify and calculate various parameters relevant to simple mass transfer operations. Describe various equipment working on mass transfer principles. Design the basic equipment required for separation processes that are based on mass transfer principles.
Assessment Method	Assignments, Quizzes, Mid-semester examination and End-semester examination.

Text Books:

R. E. Treybal, Mass Transfer Operations, McGraw Hill, 3^rd Ed., 1980.
E. L. Cussler, Diffusion- Mass Transfer in Fluid Systems, Cambridge University Press, 1997.
B. K. Dutta, Principles of Mass Transfer and Separation Processes, PHI Learning Private Limited, 2009.

CB2203

Fundamentals of Biochemical Engineering

Course Number	CB2203
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Fundamentals of Biochemical Engineering
Learning Mode	Classroom lectures
Learning Objectives	To enhance skills in the areas of biochemical processes, to provide the fundamental background of biological systems, biochemical engineering. To make a better understanding of the microbial world and their growth. To make an expert of enzymes in kinetic analysis of biochemical reactions, apply the basic concepts of thermodynamics, mass and energy balances, reaction kinetics, and reactor design for biochemical processes.
Course Description	This course contains the basic concepts of biochemical products, the kinetics of enzymatic and microbial-related reactions, design and principles of bioreactors, types and principles of fermenters, and transport phenomena related to bioprocess systems.
Course Content	Introduction: definition and scope of biochemical engineering; Enzymes; Vitamins; Single-cell protein microbiology; Biocatalysts; Model reactions; Kinetics of enzyme catalyzed reactions; Kinetic models (structured and unstructured) of microbial growth and product formation; Fermenter types: Modeling of batch and continuous fermenter; Design and analysis of bioreactor; Mixing phenomena in bioreactors; Sterilization equipment; Batch and continuous sterilize design; Transport phenomena in bioprocess systems: gas-liquid mass transfer in cellular systems, bubble aeration and mechanical agitation, calculation of power consumption, correlation between oxygen transfer coefficient and operating variables, estimation of K_La in the fermentation process, factors affecting volumetric oxygen transfer, rheology of fermentation fluids; Biochemical product recovery and separation: membrane separation process (reverse osmosis, dialysis, ultrafiltration, chromatographic methods).
Learning Outcome	The students are expected to understand the basic importance and need for biochemical engineering and also the difference between bioprocesses and chemical processes. Ability to understand growth patterns and kinetics of microbe. Acquire knowledge of enzyme-catalyzed reaction and inhibition mechanisms.
Assessment Method	Assignments, Literature review, Simulation, Quiz, Mid-semester examination and End-semester examination

Text Books:

J.E. Bailey, D.F. Ollis, Biochemical Engineering Fundamentals, McGraw- Hill, 2^nd Ed., 1986.
S N Mukhopadhyay, Process Biotechnology Fundamentals, Viva Books Private Limited, 2^nd Ed., 2001.
Debabrata Das, Debayan Das, Biochemical Engineering, Jenny Stanford, 1^st Ed., 2019.

Reference Books:

P. M. Doran, Bioprocess Engineering Principles, Academic Press, 2^nd Ed., 2013.
D. G. Rao, Introduction to Biochemical Engineering. Tata McGraw-Hill, 1^st Ed 2005.
J. Nilsen, J. Villadsen, Bioreaction Engineering Principles, Plenum Press, 1994.
M. L. Shuler, F. Kargi, Bioprocess Engineering, Prentice Hall, 2^nd Ed., 1992.
P. F. Stanbury, A. Whitekar, Principles of Fermentation Technology, The Pergamon Press, 1984.

CB2204

Process Dynamics and Control

Course Number	CB2204
Course Credit (L-T-P-C)	3-0-2-4
Course Title	Process Dynamics and Control
Learning Mode	Lectures and practical
Learning Objectives	To study the different type of physical processes and its dynamics. To learn about the various components of a control system and the type of disturbances. To familiarize with different process instruments such as flow measurement, level measurement, temperature measurement, pressure measurement etc.
Course Description	This course introduces different kinds of control systems and identifies the suitable controller parameter for a stable system.
Course Content	The fundamental concepts of measurement and transduction; Quantification of pressure, temperature, level, flow, and composition; Sensors; Laplace Transform; System dynamics; Dynamic behavior; Linear systems; Systems of various orders, including first, second, and higher orders; Transfer function; Time constant; Gain; Standard inputs; Set point; Disturbance; Closed and open loop control; Block diagram; Feedback and feed forward configurations; Controllers and final control element; Effects of controller action on process response; Concept of stability, Routh test; Root locus; Poles and zeros; Zigler-Nichols approach; Experimental determination of process model; Frequency response: Design of controllers, Bode stability criterion; Advanced control strategies: cascade control, ratio control and feed forward control designs.
Learning Outcome	Mathematical modeling of different process systems and its dynamic behavior under various disturbances. Examine the stability of a control system using various methods. Identify the optimum parameters for the design of a controller.
Assessment Method	Assignments, Quizzes, Mid-semester examination and End-semester examination.

Text Books:

D. E. Seborg, T. F. Edgar, D. A. Mellichamp, F. J. Doyle III, Process Dynamics and Control, 4th Ed., Wiley, 2016.
G. Stephanopoulos, Chemical Process Control: An Introduction to Theory and Practice, PTR Prentice Hall Inc., 2008.

Reference Books:

W. L. Luyben, Process Modelling, Simulation and Control for Chemical Engineers, Sub Ed., McGraw Hill, 1989.
D. R. Coughanowr, S.E. LeBlanc, Process systems analysis and control, 3rd Ed. McGraw-Hill, 2009.

CB2205

Chemical Reaction Engineering-I

Course Number	CB2205
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Chemical Reaction Engineering-I
Learning Mode	Classroom Lectures
Learning Objectives	To learn about basics about type of reactions, contacting patterns, and different reactors.
Course Description	The course teaches the concepts of reaction rate, stoichiometry and equilibrium to the analysis of chemical reacting systems, derivation of rate expressions from reaction mechanisms and equilibrium or steady state assumptions, design of chemical reactors via synthesis of chemical kinetics, transport phenomena, and mass and energy balance.
Course Content	Introduction; Types of chemical reactions; Elementary and non-elementary homogeneous reactions; Order and molecularity of reactions; Arrhenius Equation and effect of temperature; Constant and varying volume batch reactor; Interpretation of batch reactor data; Reaction rate; Determination of rate constant and half-life; Differential and integral methods; Parallel and series reaction; Batch reactor; Plug-flow or tubular reactor; Continuous stirred tank reactor (CSTR); Performance equations; Recycle reactors; Design of parallel reactions and distribution of products; Autocatalytic reactions; Temperature and pressure effects for single and multiple reactions.
Learning Outcomes	Ability to read and analyze chemical reaction data, and generate rate expressions. Designing experiments involving chemical reactions with multiple reactants and products, recycle reactors. Develop skills to choose the right reactor among single, multiple, recycle reactor, etc. for isothermal/ non-isothermal/ adiabatic reactions.
Assessment Method	Assignments, Quizzes, Mid-semester examination and End-semester examination.

Text Books:

H. S. Fogler, Elements of Chemical Reaction Engineering, Prentice Hall, 4^th Ed., 2008.
O. Levenspiel, Chemical Reaction Engineering, Wiley Eastern, 3^rd Ed., 2003.

Reference Books:

J. M. Smith, Chemical Engineering Kinetics, McGraw Hill, 3^rd Ed., 1980.
L. D. Schmidt, The Engineering of Chemical Reactions, Oxford University Press, 1998.

XX22PQ

IDE-I

TOTAL

19.5

Semester -V

Sl. No.

Subject Code

SEMESTER V

CB3101

Mass Transfer-II

Course Number	CB3101
Course Credit (L-T-P-C)	3-0-3-4.5
Course Title	Mass Transfer-II
Pre-requisite	CB2202 (Mass Transfer-I)
Learning Mode	Lectures and practical
Learning Objectives	To learn specific application of the basic concepts of mass transfer Operations. To select appropriate operating conditions for unit operations involving mass transfer. To calculate yield and efficiency parameters for unit operations involving mass transfer.
Course Description	Engineering calculations related to mass transfer operations such as humidification, drying, crystallization, adsorption, and membrane separations are taught in this course. Relevant examples and numerical help the students to relate the theory to industrial applications.
Course Content	Humidification and Dehumidification: terms, definitions, psychrometric chart; Cooling towers: design of tower, tower height; Crystallization: solid-liquid equilibrium, crystal nucleation and growth, particle size distribution, crystallization equipment design; Drying of solids: drying rate curve, equilibrium, rate calculations; Drying equipment: classification, selection and design; Adsorption: characteristics, properties, and selection of adsorbents, adsorption isotherms, equipment for adsorption; Pressure swing adsorption; Chromatographic technique; Ion exchange; Membrane separation techniques: materials, types, preparation, and characterization of membranes; Membrane modules; Dialysis; Reverse osmosis; Micro-, ultra, and nano-filtration; Pervaporation; Multi-component distillation: key components, approximate design technique.
Learning Outcome	Identify mass transfer operations occurring in processes such as humidification, drying, crystallization, adsorption, and membrane separation processes. Quantify and calculate various parameters relevant to the above listed operations. Describe various equipment for the above processes.
Assessment Method	Assignments, Quizzes, Mid-semester examination and End-semester examination

Text Books:

R. E. Treybal, Mass Transfer Operations, McGraw Hill, 3^rd Ed., 1980.
B. K. Dutta, Principles of Mass Transfer and Separation Processes, PHI Learning Private Limited, 2009.

Reference Books:

W. McCabe, J. Smith, P. Harriott, Unit Operations of Chemical Engineering. McGraw-Hill, 7^th Ed., 2021.

2. C. J. Geankoplis, A. A. Hersel, D. H. Lepek. Transport Processes & Separation Process Principles, Pearson Education Limited, 5^th Ed., 2013.

4.5

CB3102

Chemical Process Technology

Course Number	CB3102
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Chemical Process Technology
Learning Mode	Lectures
Learning Objectives	To learn the process flow and instrumentation diagram. To learn the production of different chemical products, fuels and other specialty chemicals. To learn the related engineering challenges and their troubleshooting by selecting appropriate operating conditions including catalyst requirement for the synthesis/production of different useful products/chemicals.
Course Description	This course is mainly about learning the production process of different chemicals/materials/fuels/specialty chemicals; operating conditions including catalyst requirements and troubleshooting for different synthesis/production processes.
Course Content	Introduction and scope; A brief history and structure of the chemical industry (India and worldwide); Process flow and instrumentation diagrams: preparation, symbols; Introduction to the following Industries including the special features of design and operation; Fuels and industrial gases including Natural gas; Process for the conversion of biomass (biofuel and bio-based chemicals production); Petrochemical and downstream products; Polymer- production and processing; Fertilizer; Cement; Caustic chlorine; Coal based chemicals; Petroleum refining processes; Nitrogen and its derivatives; Sulphur and its derivatives, Phosphorus and its derivatives; Soaps and detergents; Glycerin; Sugar; Pulp and paper; Alcohol based chemicals; Specialty chemicals; Leather; Paint and pigments; Fermentation industries; Process intensification: introduction, structured catalytic reactors, reactive separation.
Learning Outcome	Familiarize with process flow and instrumentation diagrams including symbols. Gaining design and operation knowledge related to the process industries. Propose appropriate operating conditions including catalyst requirement for the synthesis/production of different useful products/chemicals.
Assessment Method	Assignments, Quizzes, Mid-semester examination and End-semester examination

Text Books:

I.D. Mall, Chemical Process Technology, CBS Publishers and Distributors Pvt Ltd., 2024.
C.E. Dryden, Outlines of Chemical Technology, Edited and revised by M. Gopala Rao and Marshall Sitting, 3rd Ed., Affiliated East-West Press, 1997.
G.T. Austin, R.N. Shreve, Chemical Process Industries, 5th Ed., McGraw Hill, 1984.

Reference Books:

H. Groggins, Unit Processes in Organic Synthesis, 5th Ed., McGraw Hill, 2001.
A. Moulijn, M. Makkee, A.V Diepen, Chemical Process Technology, 2nd Ed., Wiley, 2015.
F. Kirk-Othmer, Encyclopedia of Chemical Technology, 5th Ed., Wiley Interscience, 2004.
H. Gary, G.E. Handwerk, Petroleum Refining: Technology and Economics, 1st Ed., Marcel Dekker, 2001.

CB3103

Process Equipment Design

Course Number	CB3103
Course Credit (L-T-P-C)	1-2-0-3
Course Title	Process Equipment Design
Pre-requisite	CB2103 (Heat Transfer), CB2202 (Mass Transfer-I)
Learning Mode	Classroom lectures and tutorials
Learning Objectives	To build a preliminary understanding on mechanical design of process vessels used at various stages for the product development. To familiarize the emergence of pressure/stresses and their importance in equipment design. To study about different parts of pressure vessels and their structural optimization.
Course Description	In this course, the mechanical aspects of designing process equipment and their constructional parts have been covered using basic concepts of solid mechanics which is very useful for appropriate sizing and thickness calculations.
Course Content	Introduction; Design preliminaries- design pressure, maximum allowable working pressure, design temperature, design stress; Pressure vessels; Stress and strain; Poisson’s ratio; Thin and thick vessels; Open and closed vessels; Factor of safety; Corrosion allowance; Weld joint efficiency; Theories of failure; Design of cylindrical and spherical vessels under internal pressure; Heads and closures; Non-standard flanges; Process vessels and pipes under external pressure; Tall vessels; Design of supports for process vessels; Thick walled high pressure vessels; Mechanical and flow aspects in shell and tube heat exchanger; Air-cooled heat exchanger; Condensers and boilers; Tray/plate column design; Mechanical properties of materials; Material specifications; Equipment fabrication and testing.
Learning Outcome	Knowledge on various forms of stresses in pressure vessels and their relation. Mechanical designing of different parts/components used in heat exchangers or in separation units such as nuts/bolts, flanges, heads, shell, etc. Consideration and elementary sizing calculation on tall, horizontal/vertical vessels, and their constructional supports.
Assessment Method	Assignments, Quiz, Mid-semester examination and End-semester examination.

Text Books:

B.C. Bhattacharya, Introduction to Chemical Equipment Design, Mechanical Aspects, CBS Publisher and Distributor, 1^st Ed., 2012.
V.V. Mahajani, S.B. Umarji, Joshi’s Process Equipment Design, Macmillan India Ltd., 4^th Ed., 2009.
G. Towler, R. Sinnott, Butterworth-Heinemann, Chemical Engineering Design: Principles, Practice and Economics of Plant and Process Design, 4^th Ed., An Imprint of Elsevier Inc., 2005.

Reference Books:

M. Walas, Chemical Process Equipment: Selection and Design, Butterworth-Heinemann, 1999.
Sinnott, G. Towler, Chemical Engineering Design, 5^th Ed, Elsevier, 2009.
S. Peters, K.D. Timmerhaus, R.E. West, Plant Design and Economics for Chemical Engineers, 5^th Ed., McGraw Hill Education (India), 2003.
H. Perry, D.W. Green, Perry’s Chemical Engineer’s Handbook, 7^th Ed., McGraw Hill, 1998.

CB3104

Chemical Reaction Engineering-II

Course Number	CB3104
Course Credit (L-T-P-C)	3-0-2-4
Course Title	Chemical Reaction Engineering-II
Learning Mode	Lectures
Pre-requisite	CB2205 (Chemical Reaction Engineering-I)
Learning Objectives	To learn about reaction kinetics for single, multiple, isothermal, non-isothermal reactions and reactor design procedures.
Course Description	This course describes the non-ideality in a reactor and also, covers the topics related to catalyzed reactions
Course Content	Non-ideality in non-ideal flow reactors; Residence time distribution (RTD) experiments and analysis; Micro-and macro-mixing in reactors; methods for generating E, F and C curves; Different parameters model (zero, one, two and three) including tanks-in-series model, segregation model and dispersion models; Diagnostic methods for analysis of flow patterns in the reactors; non-isothermal reactors; Non-catalytic fluid-solid reactions; Application of fluid bed reactors and their design consideration; Heterogeneous Catalysis; Catalysts deactivation and poisoning; Diffusion effects in catalysis; Design of fluid-solid catalytic reactors; Thermal effects and cases of runaway reaction and its analysis; Strategies for stable reactor operations; Design of multi-phase/heterogeneous reactors; packed bed reactors.
Learning Outcome	Ability to analyse chemical reactors and reaction systems. Designing experiments involving chemical reactors, analysing and interpreting data. Ability to solve problems of mass transfer with reaction in solid catalysed reactions. Design and sizing of industrial scale reactors on the basis of kinetic data obtained at lab scale.
Assessment Method	Assignments, Quizzes, Mid-semester examination and End-semester examination

Text Books:

H. S. Fogler, Elements of Chemical Reaction Engineering, Prentice Hall, 4^th Ed., 2008.
O. Levenspiel, Chemical Reaction Engineering, Wiley Eastern, 3^rd Ed., 2003.
L. D Schmidt, The Engineering of Chemical Reactions, Oxford University Press, 1998

Reference Books:

J. M. Smith, Chemical Engineering Kinetics, McGraw Hill, 3^rd Ed., 1980.

CB3105

Fundamentals of Biochemical Engineering

Course Number	CB2203
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Fundamentals of Biochemical Engineering
Learning Mode	Classroom lectures
Learning Objectives	To enhance skills in the areas of biochemical processes, to provide the fundamental background of biological systems, biochemical engineering. To make a better understanding of the microbial world and their growth. To make an expert of enzymes in kinetic analysis of biochemical reactions, apply the basic concepts of thermodynamics, mass and energy balances, reaction kinetics, and reactor design for biochemical processes.
Course Description	This course contains the basic concepts of biochemical products, the kinetics of enzymatic and microbial-related reactions, design and principles of bioreactors, types and principles of fermenters, and transport phenomena related to bioprocess systems.
Course Content	Introduction: definition and scope of biochemical engineering; Enzymes; Vitamins; Single-cell protein microbiology; Biocatalysts; Model reactions; Kinetics of enzyme catalyzed reactions; Kinetic models (structured and unstructured) of microbial growth and product formation; Fermenter types: Modeling of batch and continuous fermenter; Design and analysis of bioreactor; Mixing phenomena in bioreactors; Sterilization equipment; Batch and continuous sterilize design; Transport phenomena in bioprocess systems: gas-liquid mass transfer in cellular systems, bubble aeration and mechanical agitation, calculation of power consumption, correlation between oxygen transfer coefficient and operating variables, estimation of K_La in the fermentation process, factors affecting volumetric oxygen transfer, rheology of fermentation fluids; Biochemical product recovery and separation: membrane separation process (reverse osmosis, dialysis, ultrafiltration, chromatographic methods).
Learning Outcome	The students are expected to understand the basic importance and need for biochemical engineering and also the difference between bioprocesses and chemical processes. Ability to understand growth patterns and kinetics of microbe. Acquire knowledge of enzyme-catalyzed reaction and inhibition mechanisms.
Assessment Method	Assignments, Literature review, Simulation, Quiz, Mid-semester examination and End-semester examination

Text Books:

J.E. Bailey, D.F. Ollis, Biochemical Engineering Fundamentals, McGraw- Hill, 2^nd Ed., 1986.
S N Mukhopadhyay, Process Biotechnology Fundamentals, Viva Books Private Limited, 2^nd Ed., 2001.
Debabrata Das, Debayan Das, Biochemical Engineering, Jenny Stanford, 1^st Ed., 2019.

Reference Books:

P. M. Doran, Bioprocess Engineering Principles, Academic Press, 2^nd Ed., 2013.
D. G. Rao, Introduction to Biochemical Engineering. Tata McGraw-Hill, 1^st Ed 2005.
J. Nilsen, J. Villadsen, Bioreaction Engineering Principles, Plenum Press, 1994.
M. L. Shuler, F. Kargi, Bioprocess Engineering, Prentice Hall, 2^nd Ed., 1992.
P. F. Stanbury, A. Whitekar, Principles of Fermentation Technology, The Pergamon Press, 1984.

3.5

XX31PQ

IDE-II

TOTAL

Semester -VI

Sl. No.

Subject Code

SEMESTER VI

CB3201

Process Plant Design and Economics

Course Number	CB3201
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Process Plant Design and Economics
Learning Mode	Lectures
Learning Objectives	To learn design principles and economics as applied in various chemical engineering processes and operations. To integrate all the knowledge gained in the chemical engineering course. curriculum and apply this understanding to solving real-life process problems.
Course Description	This course covers the basic concepts of various process parameters in engineering economics and plant design in developing a techno-economic process and plant design.
Course Content	Introduction to plant design; General design consideration, process design and development; Analysis of cost estimation: cash flow, production costs, capital investment, cost indexes; Estimation of capital investment and total product cost; Interest; Time value of money; Cash flow patterns; Taxes and fixed charges; Profitability standards; Methods for calculating profitability, Alternative investments, and replacement; Developing a conceptual design and finding the best; Input information; Batch versus continuous; Input-output structure and recycle structure of the flowsheet; Application of separation system; Application of heat exchanger network design principles; Cost diagrams and quick screening of process alternatives; Case study; Techno-economic feasibility and report writing.
Learning Outcome	Develop the ability to design new or improve existing processes and plants. Develop a broad spectrum of knowledge and intellectual skill to design new or modified products that will benefit society.
Assessment Method	Assignments, Quizzes, Mid-semester examination and End-semester examination.

Text Books:

J. Douglas, Conceptual Design of Chemical Processes, McGraw Hill, 1989.
M.S. Peters, K.D. Timmerhaus, R.E. West, Plant Design and Economics for Chemical Engineers, McGraw Hill Education, 5^th Ed., 2003.

Reference Books:

L.T. Biegler, I.E. Grossmann, A.W. Westerberg, Systematic Methods of Chemical Process Design, Prentice Hall, 1997.
R. Smith, Chemical Process Design, McGraw Hill, 1995.
E.E. Ludwig, Applied Project Engineering, Gulf Publishing Company, 2^nd Ed., 1988.

CB3202

Transport Phenomena

Course Number	CB3202
Course Credit (L-T-P-C)	3-1-0-4
Course Title	Transport Phenomena
Learning Mode	Lectures and Tutorials
Prerequisite	CB2102 (Fluid Mechanics), CB2103 (Heat Transfer), CB2202 (Mass Transfer-I)
Learning Objectives	To develop a correspondence among all the transport processes involving heat, mass, and momentum exchange. To identify generalized fundamental equations dealing with all the basic laws of convective and diffusive transport of quantities and highlighting the analogy/relation among them.
Course Description	This course develops a relation among all the transport processes (momentum, heat, concentration) through a general transport equation and analogous relations.
Course Content	Introduction; Vector and tensor analysis; Gradient, divergence and curl; Shear stress and rate of deformation tensors; Material derivative; Continuum theory; Molecular transport mechanisms; Newton’s law of viscosity; Fourier’s law of heat conduction; Fick’s law of diffusion; Transport in laminar flow in one dimension; Reynolds transport theorem; Development of continuity (mass conservation) equation; Momentum conservation; Energy conservation; Scalar transport equation; Velocity, temperature and concentration profiles; Equations of change for isothermal, non-isothermal and multi-component systems. Equations of motion for free- and forced-convection (heat/mass); Development of boundary layer equations; Momentum, energy and mass transport in boundary layers with relevant analogies; Interphase and unsteady-state transport.
Learning Outcome	Understanding the role of vectors and tensors in transport processes. Comprehensive derivation of conservation equations based on control volume and control mass formulation and their solution under steady/unsteady conditions. In-depth knowledge on boundary layer formation, its significance, equations and solution.
Assessment Method	Assignments, Quizzes, Mid-semester examination, End-semester examination.

Text Books:

R.B. Bird, W.E. Stewart, E.N. Lightfoot, Transport Phenomena, Wiley, 2^nd Ed., 2006.
F.P. Incropera, D.P. Dewitt, Fundamentals of Heat and Mass Transfer, John Wiley & Sons Inc., 5^th Ed., 2010.

Reference Books:

P.J. Pritchard, R.W. Fox, A.T. McDonald, Introduction to Fluid Mechanics, John Wiley & Sons Inc., 8^th Ed., 2011.
E.L. Cussler, Diffusion: Mass Transfer in Fluid Systems, Cambridge University Press, 3^rd Ed., 2009.

CB3203

Numerical Methods in Chemical Engineering

Course Number	CB3203
Course Credit (L-T-P-C)	3-1-0-4
Course Title	Numerical Methods in Chemical Engineering
Learning Mode	Classroom Lectures and Tutorials
Learning Objectives	Familiarization with various mathematical models and numerical techniques. Integration of mathematical modeling with computational tools. Formulation of real-life problems associated with heat and mass transfer, fluid mechanics and chemical reaction engineering.
Course Description	This course introduces the various numerical methods for solving different mathematical problems and how to formulate chemical engineering problems and apply them to solve them computationally.
Course Content	Solution of simultaneous linear equations; Matrix representation; Cramer’s rule; Gauss elimination; Matrix inversion; LU decomposition; Non-linear equations- Bisection method, Regular-Falsi method, Newton-Raphson method, Fixed-point iteration method; Eigen values and eigen vectors of matrices: Jacobi method, Power methods; Statistical analysis of data: curve fitting, approximation of functions; Interpolation: finite difference operators, difference tables, Newton's forward/backward difference, Lagrange interpolation, Newton’s divided difference interpolation; Numerical integration: Trapezoidal and Simpson's rules for integration; Differentiation using forward/backward/central difference formula; Ordinary differential equations - initial and boundary value problems: Euler method, Euler modified method, Runge-Kutta methods; Partial differential equations; Error and stability analysis in numerical computing; Implementation of numerical methods through programming.
Learning Outcome	Solving a variety of complex mathematical problems. Developing confidence in problem-solving capability using various computational tools. Modeling of real-world chemical engineering problems and solving them using numerical techniques.
Assessment Method	Assignments, Quizzes, Mid-semester examination and End-semester examination

Text Books:

S. C. Chapra, R. P. Canale, Numerical Methods for Engineers, Tata-McGraw-Hill, 7^th Ed., 2015.
S. K. Gupta, Numerical Methods for Engineers, New Age International, 1^st Ed., 2001.

Reference Books:

A. Constantinides, Applied Numerical Methods with Personal Computers, McGraw-Hill, 1^st Ed., 1987.
F. Gerald, P. O. Wheatley, Applied Numerical Methods, Pearson Education, 7^th Ed., 2003.
R.M. Somasundaram, R. M. Chandrasekaran, Numerical Methods with C++ Programming, Prentice-Hall of India, 1^st Ed., 2005.
W.H. Press, S.A. Teukolsky, W.T. Vellerling, B.P. Flannery, Numerical Recipes in FORTRAN: The Art of Scientific Programming, Cambridge University Press, 2^nd Ed., 1992.

CB3204

AI/ML for Chemical Engineers

Course Number	CB3204
Course Credit (L-T-P-C)	1-0-4-3
Course Title	AI/ML for Chemical Engineers
Learning Mode	Classroom lectures and practical
Learning Objectives	Deliver background on AI in chemical engineering and allied systems. To learn about artificial intelligence basics and applications. To learn about AI/ML application for prediction/classification in chemical engineering problems.
Course Description	This course gives the overview of artificial intelligence and machine learning algorithms in the context of chemical engineering problems.
Course Content	Introduction to artificial intelligence: history, definition and scope, scope in chemical engineering; Knowledge: knowledge representation, heuristic knowledge, rule-based knowledge; Decision trees; Object oriented programming; Artificial neural networks: types, training methods, uses, data fitting; Application of AI in modeling: AI in chemical process modeling, AI in optimization of chemical process, Application of neural networks in chemical process control; Modelling real-world processes: Deep and shallow knowledge integrated with approximate reasoning in a diagnostic expert system; Application of AI techniques in fault detection and diagnosis of chemical engineering; Case studies; AI in chemical engineering: recent trends; Development in large scale systems of self-organizing intelligent agents, Introduction to IIoT.
Learning Outcome	Gain fundamental understanding of the application of AI in chemical and allied engineering. Learn to develop AI model equations, approaches for chemical and allied engineering systems. Learn to write basic codes of AI for simple systems.
Assessment Method	Assignments, Literature review, Simulations, Quiz, Mid-semester examination and End-semester examination.

Text Books

T. E. Quantrille, Y. A. Liu. Artificial Intelligence in Chemical Engineering, Elsevier, 2012.
M. L. Mavrovouniotis, Artificial Intelligence in Process Engineering, Academic Press, 1990.

Reference Books

V. Venkatasubramanian, The Promise of Artificial Intelligence in Chemical Engineering: Is It Here, Finally? AIChE, Vol. 65, 2019.
M. Gopal, Applied Machine Learning, McGraw-Hill Education, 2018.
K. P. Murphy, Machine Learning: A Probabilistic Perspective, MIT Press, 2012.
A. Smola, S. V. N. Vishwanathan, Introduction to Machine Learning, Cambridge University, UK, 2008.

CB3205

Chemical Plant Safety and Hazards

Course Number	CB3205
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Chemical Plant Safety and Hazards
Learning Mode	Classroom lectures
Learning Objectives	To conduct assessments and to produce safe operational working procedures in industries and research laboratories. To apply the principles and approach of inherently safer design to reduce and eliminate hazards, lowering the risk of new or currently operating chemical plants. To plan emergency procedures and disaster management.
Course Description	The course helps students learn and analyze different risks associated with chemical process plants. This course will also help students understand how to work in emergencies.
Course Content	Engineering ethics, accidents, loss statistics, acceptable risk, and inherent safety; Identification, classification, and assessments of various hazards and safety audits; Reactivity, instability, and explosiveness of materials; Hazard indices, hazard assessment and operability (HAZOP); Case studies; Seven significant disasters; Consequences analysis: Discharge model, flash and evaporation, and dispersion models; Explosion and fires: Unconfined vapor cloud explosion and flash fires, physical explosion, BLEVE and fireball, confined explosion, pool fire & jet fire; Effect models: Toxic gas effects, thermal effects, explosion effects, evasive actions; Risk estimates: Risk indices, individual and societal risks; Emergency planning and disaster management plan; Emergency work planning, and procedures; Disaster management planes.
Learning Outcome	Students will be able to identify the typical sources of risk in process plants by hazard identification and examination of case studies and to perform chemical process safety analysis on a proposed process.
Assessment Method	Assignments, Quizzes, Mid-semester examinations, and End-semester examination

Text Books:

D.A. Crowl, J. F. Louvar, Chemical Process Safety, Fundamentals with Applications, 2nd Ed, Prentice Hall, 2002.
F. Crawley, Malcolm Preston, Brian Tyler, HAZOP Guide to Best Practice, 2nd Edition, IChemE, 2008.
J.W. Vincoli, J. Hoboken, Basic Guide to System Safety, Wiley & Sons, Inc., New Jersey, 2014.

Reference Books:

A.M. Flynn, L. Theodore, M. Dekker, Health, Safety and Accident Management in the Chemical Process Industries, Inc. NW, 2002.
AIChE, Guidelines for Chemical Process Quantitative Risk Analysis. 2nd edition, John Wiley & Sons, Inc., Hoboken, New Jersey, 2000.

CB32XX

DE-I

TOTAL

Semester -VII

Semester -VIII

Catalysis Science and Engineering

Department Elective - I

Sl. No.

Subject Code

Course

CB3206

Course Number	CB3206
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Catalysis Science and Engineering
Learning Mode	Lectures
Learning Objectives	Provides students with basic concepts regarding the design, operation, and process related to the use of catalysis, basic characterization techniques, and reaction engineering
Course Description	This course contains the basic understanding of heterogeneous and homogeneous catalysis. Also, various characterization techniques and the underlying working principle.
Course Content	Fundamentals of solid catalysts and their relevant characterization techniques (such as surface area analyzer, X- ray diffraction, FTIR, Raman, XPS, electron microscopy, thermal analysis) for estimation of chemical and physical properties; Synthesis methods of catalysts; Types of catalytic reactors and effect of external and internal transport resistances; Catalyst deactivation; Study of different industrial catalysts such as for Steam Reforming and Petroleum Refining; Environmental Catalysis; Hydrogenation and oxidation catalysis, Homogeneous catalysis: Enzyme catalysis, Zeolites catalysts; Polymerization catalysts; Carbon nanotubes; Nano metal or metal oxide catalysts; Phase transfer catalysts; Design of catalysis- supported and non-supported; Molecular Modeling.
Learning Outcome	Able to analyze the basic principles and techniques of catalytic reaction engineering
Assessment Method	Assignments, Quizzes, Mid-semester examination and End-semester examination

Text Books:

R.J. Farrauto, C.H. Bartholomew, Fundamentals of Industrial Catalytic Processes, Blackie Academic & Professional, 2^nd Ed., 1997.
H.S. Fogler, Elements of Chemical Reaction Engineering, Prentice Hall, 4^th Ed., 2008.
J.J. Carberry, Chemical and catalytic reaction Engineering, Dover Publications, 2001.

References Books:

J.M. Smith, Chemical Engineering Kinetics, McGraw Hill, 3^rd Ed., 1980.
D.M. Ruthven, Principle of adsorption & adsorption processes, John Wiley & sons, 1^st Ed., 1984.
C.H. Bartholomew, R. J. Farrauto, Fundamentals of Industrial Catalytic Processes, Wiley- VCH, 2^nd Ed., 1997.
B. Viswanathan, S. Sivasanker, A.V. Ramaswamy, Catalysis: Principles & Applications, Narosa Publishing House, 2002.
J.M. Thomas, W.J. Thomas, Principles and Practice of Heterogeneous Catalysis, VCH, 2^nd Ed., 1997.
L.D. Schmidt, The Engineering of Chemical Reactions, Oxford University Press, 1998.

CB3207

Biopharmaceutical Downstream Processing

Course Number	CB3207
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Biopharmaceutical Downstream Processing
Learning Mode	Classroom lectures
Learning Objectives	To learn theory and design of liquid chromatography step. Learning application of basic concepts of mass transfer principles in liquid chromatography separation process. Learning of quality-by-design (QbD) based preparative chromatography process development.
Course Description	Syllabus addresses preparative chromatography for industrial separation and purification of proteins and explains the physicochemical phenomena involved in the liquid chromatography step. Presents Quality-by-Design (QbD) model-based approaches for chromatography process development initiated by FDA regulatory authorities.
Course Content	Introduction to biomolecules; Analytical characterization of therapeutic biomolecules: high performance liquid chromatography, mass spectrophotometry, capillary electrophoresis, near infrared spectroscopy, UV spectroscopy; Unit operations in therapeutic protein production: Upstream and downstream processing; Preparative liquid chromatography; and Modes: affinity, reverse-phase, size exclusion, ion-exchange, hydrophobic interaction, multimodal chromatography; Stages in operation of liquid chromatography step; Zone movement in chromatography column; Height equivalent to Theoretical Plate (HETP); Mode of operation: Linear Gradient Elution (LGE) and Flow-through mode chromatography; Stationary phase properties characterization; Binding of protein to stationary phase: distribution coefficient and binding sites; Chromatography process development: one-factor-at-a-time (OFAT), design of experiments (DoE), high throughput process development (HTPD) plate study; Quality-by-design (QbD) model based approaches; Process chromatography: process development and optimization, scale-up, and intensification.
Learning Outcome	Identifying the applications of different modes of liquid chromatography in therapeutic protein purification. Learning upstream and downstream unit operations and QbD based chromatography modeling involved in manufacture of therapeutic proteins
Assessment Method	Assignments, Quiz, Mid-semester examination and End-semester examination

Text Books:

G. Guiochon, A. Felinger, D. G. Shirazi, A. M. Katti, Fundamentals of Preparative and Non-Linear Chromatography, 2^ndEd., Elsevier, 2006.
A. Staby, A. S. Rathore, S. Ahuja, Preparative Chromatography for Separation of Proteins. John Wiley & Sons, 2017.

Reference books:

G. Carta, A. Jungbauer, Protein chromatography: Process Development and Scale-up, John Wiley & Sons, 2020.
A. S. Rathore, A. Velayudhan, Scale-up and Optimization in Preparative Chromatography, Taylor & Francis, 2002.

CB3208

Material Science and Engineering

Course Number	CB3208
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Material Science and Engineering
Learning Mode	Classroom lectures
Learning Objectives	To build an understanding on the basic models of elementary structures, material classification, and material properties. Analyze phase diagrams of binary and multi-component mixtures. Evaluate material properties and their failure mechanisms to identify potential applications.
Course Description	The course will provide basic understanding of the concepts of material science, and to identify materials suitable for various engineering applications based on their properties.
Course Content	Structure of atoms; Rutherford and Bohr’s models; Bonding in solids; Types of solids; Crystal systems; Bravais lattices; Miller indices; Crystal defects; Determination of crystal structure; Properties of engineering materials; Mechanical properties and methods of measurements; Poisson’s ratio; Stress-strain relation; True stress and true strain; Technological properties; Phase diagrams and transformations; Iron and iron carbide phase diagrams; Eutectic systems; Solid solutions; Heat treatment of metals and alloys; Non-ferrous metals and alloys; Alloys for specialized applications: High temperature; Nuclear applications; Corrosion resistance; Types and application of non-metallic materials: Ceramics; Polymers; Composite materials; Material failure: Fracture; Griffith theory; Crack propagation; Fatigue; Creep curves; Thermal, electrical, optical and magnetic properties of material; Materials for chemical industries: equipment, catalysts, adsorbents, membranes; Novel materials: 2D materials, nanomaterials.
Learning Outcome	Explain material selection based on various properties and their requirements. Evaluate suitability of different materials for specific engineering applications.
Assessment Method	Assignments, Quiz, Mid-semester examination and End-semester examination

Text Books:

W. D. Callister, D. G. Rethwisch, Callister’s Material Science and Engineering, Wiley Publishers, 10^th Ed., 2019.
V. Raghavan, Materials Science and Engienering: A First Course, PHI Learning, 6^th Ed., 2015.

Reference Books:

B.S. Mitchell, An Introduction to Materials Engineering and Science for Chemical and Materials Engineers, Wiley- Interscience, 1^st Ed., 2003.
S. Zhang, L. Li, A. Kumar, Materials Characterisation Techniques, CRC press, 2008.
J. Roesler, H. Harders, M. Baeker, Mechanical Behaviour of Engineering Materials: Metals, Ceramics, Polymers, and Composites, Springer-Verlag, 2007.
R. J Young, P. A. Lovell, Introduction to Polymers, CRC Press, 3^rd Ed., 2011.

CB3209

Introduction to Microfluidics Technology

Course Number	CB3209
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Introduction to Microfluidics Technology
Learning Mode	Classroom lectures
Learning Objectives	To develop the skills and techniques for handling the systems in micro- and nanoscale. To develop the fundamental knowledge on various microfabrication techniques. To gain required skills for the designing of fluidic circuits for various biomedical applications.
Course Description	This course gives basic understanding on: Principles of incompressible fluid mechanics and challenges, slip effects, lubrication theory, and electrokinetic phenomena at the micro and nanoscale. Biomedical applications illustrating fabrication techniques and experimental methods.
Course Content	Introduction; Fundamentals: scaling laws, microfluidics vs. macrofluidics; Micro-scale fluid mechanics: dynamics at small scales, interfacial phenomena and surface effects in microchannels; Intermolecular forces: surface tension, wetting, contact angle; Governing equations at small scale: low Reynolds number flows, Electrokinetic phenomena; Continuum approach and deviations: Knudsen number and transition to non-continuum flows, slip boundary; Constitutive relations: rheological models, thermal effects; Low-Reynolds flows: characteristics, Stokes drag, transition; Couette and Poiseuille flows in microchannels, Capillary flows; Lab-on-Chip: concepts, sensing and detection technologies in healthcare, environment, and point-of-care diagnostics; Electrokinetics; Microfabrication techniques: oxidation, photolithography, spin coating, etching, wafer bonding, polymer microfabrication on PMMA/PDMS substrates, micromolding, and hot embossing; Bio-microfluidics: drug delivery systems, point-of-care devices, bio-sensing technologies.
Learning Outcome	Understand microfluidics technology and lab-on-a-chip systems. Master basic fluid mechanics at small scales. Know basic multi-physics for microfluidic applications. Apply standard fabrication technologies for microfluidics.
Assessment Method	Assignments, Quiz, Mid-semester examination, End-semester examination

Text Books:

N.T. Nguyen, S.T. Werely, Fundamentals and Applications of Microfluidics, Artech house Inc., 2002.
P. Tabeling, Introduction to Microfluidics, Oxford University Press Inc., 2005.
S. Chakraborty, Microfluidics and Microfabrication, Springer, 2010.

Reference Books:

S. Colin, Microfluidics, John Wiley & Sons, 2009.
M.J. Madou, Fundamentals of Microfabrication, CRC press, 2002.
H. Bruus, Theoretical Microfluidics, Oxford University Press Inc., 2008.
B.J. Kirby, Micro- and Nanoscale Fluid Mechanics: Transport in Microfluidic Devices, Cambridge University Press, 2010.

Industrial Pollution Control

Department Elective - II

Sl. No.

Subject Code

Course

CB4101

Text Books:

G. M Masters, W. P. Ela, Introduction to Environmental Engineering and Science, Pearson, 3^rd Ed., 2015.
P. A. Vesilind, S. M. Morgan, Introduction to Environmental Engineering, Nelson Engineering, 2^nd Ed., 2003.
N. D. Nevers, Air Pollution Control Engineering, McGraw-Hill, 1994.

Reference Books:

C. N. Swayer, P. L. Mcarty, C. F. Perkin, Chemistry for Environmental Engineering and Science, McGraw-Hill, 2003.
L. Theodore, Air Pollution Control Equipment Calculations, John Wiley & Sons, 2006.
B. Sportisse, Fundamentals of Air Pollution: From Process to Modelling, Springer, 2014.
S. J. Arceivala, S. R. Asolekar, Wastewater Treatment for Pollution Control and Reuse, Tata McGraw-Hill, 2008.
D. Mara, Domestic Wastewater Treatment in Developing Countries, Earthscan, 2003.

Course Number	CB4101
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Industrial Pollution Control
Learning Mode	Classroom lectures
Learning Objectives	To acquire a basic knowledge about various types of industrial pollutants, and their impact on the ecosystem. To provide the opportunity for students to understand the principles of industrial pollution control technologies to minimize pollution.
Course Description	The subject gives a brief description of the principles and mechanisms of pollutant removal, the processes, and design of conventional as well as advanced technologies applied in treatment and control.
Course Content	Introduction: industrial pollution, characterization of effluents, environmental norms and regulations; Air pollution: types, sources, standards and limits, Atmospheric dispersion- Lapse rate; Plume and classification of plume; Dispersion models; Ground and elevated sources with and without reflection; Calculation for plume rise; Gaseous emission control by absorption and adsorption; Particulate pollutants control by mechanical separation and electrostatic precipitation; Design and efficiency of cyclones; Electrostatic precipitators; Fabric filters and scrubbers; Automobile emission control; Water Pollution: Sources; Pollution laws and limits; Classification of industrial wastewaters; Pretreatment and primary treatment techniques; Physical and chemical processes of water treatment; Anaerobic and aerobic treatment methods; trickling filter; Activated sludge process; Aeration systems; Sludge separation disposal; Solid waste pollution: Sources; Composition; Properties of solid wastes; Collection; Handling and storage of solid wastes; Various methods for processing of solid waste (Land-filling technique, Trench, Ramp methods, etc.).
Learning Outcomes	To understand strategies, legal requirements, and appropriate mitigation and treatment technologies for industrial pollution control. To comprehend the process design of selected treatment technologies. To explain the principles of physical, chemical, and biological treatment processes.
Assessment Method	Assignments, Quiz, Mid-semester examination, and End-semester examination

CB4102

Introduction to Computational Biology

Course Number	CB4102
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Introduction to Computational Biology
Learning Mode	Lectures
Learning Objectives	Develop knowledge and understanding of statistical biophysics, molecular modeling, molecular biophysics, and chemical biophysics.
Course Description	Thermodynamics and molecular-level description are essential to analyze bio-systems and to gain valuable insights. This course will cover different aspects to provide a comprehensive understanding to a biological engineer to design and develop a novel compound.
Course Content	Statistical thermodynamics- definition and application; Macrostates and microstates; Boltzmann distribution law; Partition function and its relation to thermodynamics; Ensemble and time average; Structure and thermodynamic properties of macromolecules- Structural properties of protein and its stability; Driving forces in protein folding; Force fields for macromolecules- covalent, ionic, electrostatic and Van der Walls interactions; Conformational search in macromolecules; Calculation of the entropy and the free energy in biological macromolecules; Introduction to molecular dynamics simulation- building realistic model for biological macromolecules, topology and parameter files, steps in molecular dynamics, periodic boundary conditions and property analysis; Drug design and development- structure and property, 3D structure visualization, pharmacokinetic properties, ligand-receptor binding affinity; Case studies- structure based drug designing and other biomolecular systems.
Learning Outcome	Students will be able to understand the classical and statistical aspects of bio-systems. This molecular-level knowledge will help in the modification and design of bio-systems.

Text Books:

Frenkel, B. Smit, Understanding Molecular Simulation: From Algorithms to Applications, Academic Press, 2^nd Ed., 2001.
E. Tuckerman, Statistical Mechanics: Theory and Molecular Simulation, OUP Oxford, 2010.
I. Branden, J. Tooze, Introduction to Protein Structure, Garland Science; 2^nd Ed., 1999.

Reference Books:

C. Rapaport, The Art of Molecular Dynamics Simulation, Cambridge University Press, 2^nd Ed., 2004.
Voet, J.G. Voet. Biochemistry, Wiley, 4^th Ed., 2010.
M. Lesk, Introduction to Protein Architecture: The Structural Biology of Proteins, Oxford, 2000.

CB4103

Molecular Modeling and Simulation

Course Number	CB4103
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Molecular Modeling and Simulation
Learning Mode	Classroom lectures
Learning Objectives	To learn the fundamental concept of atomistic simulation (Monte Carlo and Molecular Dynamics), and its applications. To gain knowledge about the statistical thermodynamic to relate the macroscopic properties from molecular level information. To familiarize with the various methodologies, simulation techniques, and different simulation packages to solve engineering problems and to optimize the processes.
Course Description	This course introduces different methods of molecular simulation to understand the structural and dynamical behavior of a system at nanoscale.
Course Content	Introduction; Statistical thermodynamics: Concept of ensemble, Derivation of partition function; Molecular modeling: Force fields; Short-range forces; Cutoff; Correction; Electrostatic forces; Molecular dynamics simulation: Verlet method; Leap-frog method; Velocity Verlet; Thermostats; Maxwell-Boltzmann distribution; Velocity rescaling; Nosé-Hoover method; Monte Carlo simulation: Metropolis method; Markov chains; Acceptance ratio; Different moves; Translation, Rotation, Volume change; Monte Carlo Simulation for simple LJ system; Equilibration and Production cycle; Boundary conditions; Molecular dynamic simulation for simple LJ system; Radial distribution function; Diffusion; Viscosity; Preparation and simulation of molecular systems such as water and alkanols.
Learning Outcome	Understanding on building of a system at atomic scale. Modeling of a system using molecular interaction parameters. Evaluate the various structural and dynamical properties of the systems.
Assessment Method	Assignments, Literature review, Quiz, Mid-semester examination and End-semester examination.

Text Books:

D. Frankel and B. Smit, Understanding Molecular Simulation: From algorithm to Applications, 2^nd Ed, Elsevier, 2002.
M.P. Allen and D. J. Tildesley, Computer Simulation of Liquids (Reprint/Revised), Clarendon Press, 1989.
D. McQuarrie, Statistical Thermodynamics (Reprint/Revised), University Science Books, 1991.

Reference Books:

D.C. Rapaport, The Art of Molecular Dynamics Simulation, 2^nd Edition, 2004.
D. Chandler, Introduction to Modern Statistical Mechanics, OUP USA, 1987.
Y.V.C Rao, Postulational and Statistical thermodynamics, Allied Publishers Pvt. Ltd., 1^st edition, 1994.

Electrochemical Energy Systems

Department Elective - III

Sl. No.

Subject Code

Course

CB4104

Course Number	CB4104
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Electrochemical Energy Systems
Learning Mode	Classroom lectures
Learning Objectives	To introduce the fundamental elements of electrochemistry and the principles of operation of electrochemical energy conversion and storage. To focus on power storage in various types of batteries, their design, and potential/drawbacks.
Course Description	This course describes the basic principles of design and operation of electrochemical energy storage cells. The topics cover the mathematical models of electrochemical energy storage and conversion. Various issues, limitations, important terminology, and performance characteristics in batteries have also been taken care of.
Course Content	Fundamentals of electrochemistry; Transport equations; Characteristics; Primary and secondary batteries; Supercapacitors; Kinetics of electrochemical cells; Electromotive force (EMF); Redox potential; Faraday’s law; Nernst equation; Battery design and performance parameters; Voltage, capacity, and performance curve; C-rate, state of charge (SoC), depth of discharge (DoD), Ragone diagram; Lithium-batteries and electrode materials; Design of separator, electrolyte, current collector; Types and fabrication of lithium cells; Cell degradation; Solid-electrolyte interface (SEI); Self-discharge; Limitations of lithium-ion cells; Battery module, pack design and battery management system (BMS), Types of thermal management; Other commercial rechargeable batteries such as Li-air, Li-sulfur, Na-ion and future aspects; Redox flow batteries; Solid state batteries.
Learning Outcome	After the course the student will be able to: Understand the thermodynamics in electrolyte solutions. Understand the concept of the electrochemical cell and various electrochemical energy storage solutions. Understand the most common electrochemical reactions measurement techniques.
Assessment Method	Assignments, Quiz, Mid-semester examination, End-semester examination

Text Books:

S. Bagotsky, A.M. Skundin, Y.M. Volfkovich, Electrochemical Power Sources: Batteries, Fuel cells, and Supercapacitors. John Wiley & Sons, 2015.
Job, Electrochemical Energy Storage: Physics and Chemistry of Batteries, De Gruyter, 2020.

Reference Books:

A.J. Bard, L.R. Faulkner, Electrochemical Methods: Fundamentals and Applications, 2^nd Ed., John Wiley and Sons. Inc. New York, 2000.
A. Braun, Electrochemical Energy Systems: Foundations, Energy Storage and Conversion, De Gruyter, 2019.

CB4105

Fertilizer Technology

Course Number	CB4105
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Fertilizer Technology
Learning Mode	Classroom lectures
Learning Objectives	To understand the basic concepts of fertilizer for agriculture and the manufacturing process. Design of ammonia reactor and urea prilling tower. Analyses and examines different fertilizers for different agricultural purposes.
Course Description	The course will introduce the basic concepts of fertilizer for agriculture and the manufacturing process.
Course Content	Definition of fertilizer; Nutrient requirements for paddy, wheat, sugarcane plants, vegetables, etc. A natural way of fixing nitrogen; Nitrogen cycle, Carbon cycle; Different nitrogen-fixing plants, bacteria, and algae. Role of C/N ratio in the growth of different plants; Organic manure; Production of ammonia: its feed preparation; Limitations of using different feed materials for hydrogen generation, reforming process, and reformer design; Partial oxidation process and partial oxidation reactor design; Removal of impurities from synthesis gas; CO removal and shift reactor design; CO₂ removal methods; Design of CO₂ absorber; NH₃ synthesis loop design, and design considerations for different types of NH₃ Reactors; Phosphate fertilizers: different methods of production; NPK: production and drying of NPK fertilizers, and bio-fertilizer; fertilizer coating; Urea production: special features of urea reactor, prilling tower design.
Learning Outcome	This program will enable the students to learn fertilizer manufacturing, including new/modified fertilizer products and new techniques, which will help in agricultural production.
Assessment Method	Assignments, Quizzes, Mid-semester examination, and End-semester examination

Text Books:

V. Sauchelli, Chemistry and Technology of Fertilizers, Reinhold Publications, 1960.
M. Gopala Rao Sitting Marshal, Dryden’s Outlines of Chemical Technology, Affiliated East West Press (Pvt) Ltd, 3^rd Ed., New Delhi, 1997.
G.T. Austin, Shreve’s Chemical Process Industries, McGraw Hill publication, 5^th Ed., New Delhi, 2017.

Reference Books:

A.F. Gustafson, Handbook of Fertilizers: Their Sources, Make-up Effects, and use, Agrobios publications, Jodhpur, 3^rd Ed., 2012
V. Gowarikar, V.N.Krishnamurthy, Sudha Gowariker, Manik Dhanorkar, Kalyani Paranjape, The Fertilizer Encyclopaedia, John Wiley & Sons, 2008.
N.S. Subba Rao, Bio fertilizers in Agriculture, Oxford & IBH Publishing Company, 4^th Ed. 2017

CB4106

Nanomaterials

Course Number	CB4106
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Nanomaterials
Learning Mode	Classroom lectures
Learning Objectives	To build an understanding on the use, advantages, and classification of nanomaterials. Identify production and characterization techniques of nanomaterials.
Course Description	The course will provide basic understanding of the concepts of material science, and to identify materials suitable for various engineering applications based on their properties.
Course Content	Introduction to nanomaterials and nanostructures: definitions, history, classifications, Properties of nanomaterials and their size dependence: mechanical, thermal, electrical, magnetic, optical properties; Synthesis routes of nanomaterials: chemical, electrochemical, gas-phase, thin films, mechanical methods, sol-gel methods, nanolithography; Characterization of nanomaterials: size, SEM, TEM, Scanning tunneling microscopy; Atomic force microscopy; Spectroscopy techniques; X-ray diffraction; Applications: Chemical and biosensing; Catalysis; Fuel-cells and other energy related applications; Biological applications; Multiscale hierarchical structures and their applications; Nanomaterials and structures in nature.
Learning Outcome	Explain production and applications of nanomaterials and nanostructures. Evaluate performance of nanomaterials.
Assessment Method	Assignments, Quiz, Mid-semester examination and End-semester examination

Text Books:

M. F. Ashby, P. Ferreira, D. L. Schodek, Nanomaterials, Nanotechnologies and Design: An Introduction for Engineers and Architects, Butterworth-Heinemann, 2009.
F. J. Owens, C. P. Poole Jr., The physics and Chemistry of Nanosolids. John Wiley & Sons, 2008.
D. Vollath, Nanomaterials: An Introduction to Synthesis, Properties and Applications, John Wiley & Sons, 2^nd Ed., 2013.

Reference Books:

Z. L. Wang, Y. Liu, Z. Zhang, Handbook of Nanophase and Nanostructured Materials: Materials Systems and Applications, Springer New York, Volume 4, 2003.
G. A. Ozin, A. C. Arsenault, L. Cademartiri, Nanochemistry: A Chemical Approach to Nanomaterials. Royal Society of Chemistry, 2015.
E. L. Wolf, Nanophysics and Nanotechnology: An Introduction to Modern Concepts in Nanoscience. John Wiley & Sons, 3^rd Ed., 2015.

CB4107

Combustion Engineering and Technology

Course Number	CB4107
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Combustion Engineering and Technology
Learning Mode	Lectures
Learning Objectives	To understand the nature of fuel based on its physico-chemical properties. To learn fundamentals of thermochemistry of combustion process. To evaluate calorific values of fuels and identify their application in various combustion technologies.
Course Description	This course describes the fundamentals of solid, liquid, and gaseous fuels. It covers their origins, classification, preparation processes, and key physico-chemical properties. The course explores the thermodynamics of combustion for various fuels and discusses their applications in combustion technologies.
Course Content	Introduction: history of fuels, types and properties; Definition and characteristics of solid fossil fuels: classification of coal, composition, basis; Coal mining; Different type of combustion techniques of coal; Coal gasification; Gaseous fuels: Natural gas and liquid petroleum gas; Producer gas; Hydrogen; Water gas; Acetylene; Other fuel gases; Combustion: Stoichiometry, thermodynamics; Nature and types of combustion processes: Mechanism and kinetics of combustion; Ignition temperature; Explosion range; Flash and fire points; Calorific value calculations of fuels; Adiabatic flame temperature calculation; Flame properties; Combustion burners and furnaces; Internal combustion engines.
Learning Outcome	Identifying types of different fuels and its properties. Deep understanding of thermodynamics and kinetics of combustion processes. Identify application of fuels in various combustion processes.
Assessment Method	Assignments, Quizzes, Mid-semester examination and End-semester examination

Text Books:

R. A. Dave, Modern Petroleum Technology, John Wiley & Sons. Ltd., Upstream, 1, 6^th Ed., 2002.
A. G. Lucas, Modern Petroleum Technology, John Wiley & Sons. Ltd., Downstream, 2, 6^th Ed., 2002.
I. Glassman, Combustion, Elsevier 2^ndEd., 2012.

Reference Books:

J. Griswold, Fuels Combustion and Furnaces, Mc-Graw Hill Book Company Inc., 1^st Ed., 1946.
S. Sarkar, Fuels and Combustion, Universities Press, 3^rd Ed., 2009.
W.L. Nelson, Petroleum Refinery Engineering, Mc-Graw Hill Book Company, 4^th Ed., 1968.
B.K. Bhaskar Rao, Modern Petroleum Refining Processes, Oxford & IBH Publishing Co. Pvt. Ltd., 4^thEd., 2003.

Energy Storage: Technologies and Applications

Department Elective - IV

Sl. No.

Subject Code

Course

CB4201

Membrane Separation

Course Number	CB4201
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Membrane Separation
Learning Mode	Classroom lectures
Pre-requisite	CB2202 (Mass Transfer-I)
Learning Objectives	To identify applicability of membranes in different separation processes. Compare different membrane techniques and examine their applicability for specific purpose. Evaluate membrane separation processes for their suitability with regard to various conventional and novel separation techniques.
Course Description	The course will provide introduction to different types of membranes, their production and characterization, and various membrane-based technologies utilized in chemical and allied industries.
Course Content	Introduction to membrane processes; Classifications of membrane separation techniques; Polymer based membranes and their applications; Polymers used in membranes; Inorganic membranes and their applications; Techniques for preparation of membranes; Composite membranes- production and applications; Characterization of membranes; Transport in porous and non-porous membranes; Types and applications of osmotic membrane technologies; Microfiltration, Basic principles, modules, transport, and applications; Ultrafiltration; Nanofiltration; Other membrane techniques- electrodialysis, pervaporation, ion-exchange membranes, membrane distillation units, membrane crystallizers, membrane reactors; Membrane fouling; Concentration polarization; Membrane recycling.
Learning Outcome	Identifying the applications of membrane technology in separation processes. Learning membrane production and characterization techniques. Identify various applications of membranes, including novel separation techniques.
Assessment Method	Assignments, Quiz, Mid-semester examination and End-semester examination.

Text Books:

R.W. Baker, Membrane Technology and Application, John Wiley and Sons Ltd., 2004.
S. Heinrich, Introduction to Membrane Science and Technology, John Wiley & Sons, 2011.
J. Mulder, M. Marcel, Basic Principles of Membrane Technology, Springer Netherlands, 2013.

Reference Books:

W. S. Ho, K. K. Sirkar, Membrane Handbook, Vol 1, Springer US, 2012.
E. Nagy, Basic Equations of Mass Transport Through a Membrane Layer, Elsevier, 2^nd Ed., 2018.
K. Nath, Membrane Separation Processes, PHI Learning Pvt. Ltd., 2017.

CB4202

Course Number	CB4202
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Energy Storage: Technologies and Applications
Learning Mode	Classroom lectures
Learning Objectives	To provide understanding on various energy storage methodologies, their potential and fundamental design aspects. To discover the advantages of energy storage and learn how to make informed decisions on energy storage systems. To provide a suitable and economic energy storage solution based on demand-supply.
Course Description	This course describes the various methods and applications of energy storage to achieve sustainable energy solutions. Different types of energy storage systems and their importance has been covered herein.
Course Content	Need; Scope; Basic concepts; General aspects of thermodynamics, Thermal energy storage: Sensible heat; Latent heat; Heat pumps; Phase change materials; Storage for renewable energy: Solar and wind; Reversible chemical reactions; Electromagnetic energy storage; Hydrogen storage; Flywheels; Compressed air; Pumped-hydro power; Electrochemical energy storage; Rechargeable batteries; Lead-acid battery; Electrodes in lithium systems; Electric vehicles, battery pack and thermal management; Sodium/potassium-ion; Lithium-air; Sulfur-air and zinc-air battery; Fuel cells and microbial fuel cells (MFCs); Super capacitors; Medium to large scale applications; Energy savings and smart grids; Hybrid storage systems; Recent advances and applications.
Learning Outcome	On successful completion of the course students will be able to: Discuss the scientific principles underpinning the operation of energy storage systems. Resolve the intermittency of renewable energy sources by utilizing problem solving skills in energy storage engineering and grid integration.
Assessment Method	Assignments, Quiz, Mid-semester examination, End-semester examination

Text Books:

I. Dincer, M.A. Rosen, Thermal Energy Storage: Systems and Applications, Wiley, 2^nd Ed., 2011.
R.A. Huggins, Energy Storage, Springer, 2010.

Reference Books:

R. Zito, Energy Storage: A New Approach, Wiley, 2010.
A.F. Zobaa, Energy Storage: Technologies and Applications, InTech, 2023.
A. Thumann, D.P. Mehta, Handbook of Energy Engineering, CRC Press, 2008.
J.W. Twidell, A.D. Weir, Renewable Energy Resources, E & F N Spon, London, 1986.

CB4203

Process Integration

Course Number	CB4203
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Process Integration
Learning Mode	Classroom lectures
Learning Objectives	To study major applications process integration in the industrial and research realms. To review specific literature on mathematical models developed in process integration operations. To learn energy integration, water integration principles for industrial problems
Course Description	This course will primarily concentrate on the advancement of mathematical models and methodologies for process integration, which are extensively employed in chemical engineering research and industry. The focus will be on establishing a connection between the knowledge base developed in the process integration course and real-world illustrations.
Course Content	Process Integration; Targeting for energy; Pinch analysis; Hot composite curves; Cold composite curves; Grand composite curves; Area, unit and cost targeting; Heat exchanger network design and evolution: Heat exchanger design, Pinch design method, Retrofit design; Mathematical optimization techniques: linear programming, mixed integer linear programming; Production planning; Inventory management; Process integration of different systems: Fired heater; Cogeneration and utility system; Solar thermal; Batch Process; Distillation column; Evaporators; Resource management; Water management; Limiting composite curves; Source composite curves; Resource allocation networks; Hydrogen management; Environmental management; Recent developments.
Learning Outcome	Energy integration, water integration, production planning and other resource integration aspects.
Assessment Method	Assignments, Literature review, Quiz, Mid-semester examination and End-semester examination.

Text Books:

I.C. Kemp, Pinch Analysis and Process Integration-A User Guide on Process Integration for the Efficient Use of Energy, Elsevier, 2007.
U.V. Shenoy, Heat Exchanger Network Synthesis: Processes Optimization by Energy and Resource Analysis, Gulf Publishing Company, Houston, 1995.

Reference Books:

B.D. Linnhoff, W. Townsend, D. Boland, G.F. Hewitt, B.E.A. Thomas, A.R. Guy, R.H. Marsland, User Guide on Process Integration for the Efficient Use of Energy, The Institution of Chemical Engineers, Rugby, UK, 1982.
J.M. Douglas, Conceptual Design of Chemical Processes, McGraw-Hill, New York, 1988.

Department Elective - V

Department Elective - V

Sl. No.

Subject Code

Course

CB4204

Renewable Energy Sources

Course Number	CB4204
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Renewable Energy Sources
Learning Mode	Classroom Lectures
Learning Objectives	To provide understanding on various sources of renewable energy and their production and applications. To discover the advantages of renewable energy sources and learn how to make decisions on alternative to conventional fossil fuels. To provide suitable designs of various renewable energy plant installations based on feasibility.
Course Description	This course describes the various sources and applications of renewable energy to achieve the sustainable energy solutions. Various types of renewable energy plant designs and their productions are covered.
Course Content	Biofuels: classification of biofuels; Biomass production; Energy generation via fermentation, gasification, pyrolysis and combustion; Aerobic and anaerobic biogas generation processes; Feed stock; Bio-gas composition; Biogas plant design and principle of operation. Hydrogen Energy: Electrolytic and thermo-chemical hydrogen production; Metal hydrides and storage of hydrogen; Economics and technical feasibility. Solar Energy: Solar radiation, availability, measurement and estimation; Solar collectors (liquid flat- plate collector, air heater and concentrating collector) and thermal storage; Steady state operation; Photovoltaic solar cell; Hybrid systems; Solar distillation; Solar drying; Ocean thermal energy conversion; Geothermal; Tidal energy; Power generation through OTEC; Wind energy: Wind power plant design; Horizontal axis/vertical axis wind turbines.
Learning Outcomes	On successful completion of the course students will be able to: Understand on various sources of renewable energy and their production and applications. Discover the advantages of renewable energy sources and make decisions on alternative to conventional fossil fuels. Provide suitable designs of various renewable energy plant installations based on feasibility.
Assessment Method	Assignments, Quiz, Mid-semester examination, End-semester examination.

Text Books:

V. R. Koteswara Rao, Energy Resources-Conventional and Non-Conventional, 2nd Ed., BS Publications, 2006.
H. Khan, Non-Conventional Energy Resources, 2nd Ed., Tata McGraw Hill, 2009.
S. Solanki, Renewable Energy Technologies: A Practical Guide for Beginners, Second Printing, PHI Learning Private Limited, 2009.

Reference Books:

Mukherjee, S. Chakrabarti, Fundamentals of Renewable Energy Systems, New Age International (P) Limited, 2005.
Merick, R. Marshall, Energy, Present and Future Options, Vol. I and II, John Wiley and Sons, 2001.

CB4205

Advanced Separation Processes

Course Number	CB4205
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Advanced Separation Processes
Learning Mode	Classroom lectures
Pre-requisite	CB2202 (Mass Transfer I)
Learning Objectives	To study major applications of mass transfer operations in the industrial and research realms. To review specific literature on mathematical models developed in mass transfer operations.
Course Description	The course will focus on the development of mathematical models and techniques of mass transfer used in chemical engineering research and industry. Emphasis will be on connecting the knowledge base created in the undergraduate chemical engineering course on mass transfer with real world examples. The course will involve literature review and numerical simulation of specific mass transfer units.
Course Content	Introduction to diffusion; Lumped and distributed models; Equations for steady-state and unsteady state mass transfer operations in thin film; Semi-infinite falling film; Diffusion in porous media; Interphase mass transfer; Boundary layer theory; Two-film theory; Mass transfer with first order homogeneous reaction- steady and unsteady state diffusion; Mass transfer and reaction in packed bed; Enhanced distillation techniques– azeotropic, extractive, steam, and reactive distillations; Adsorption processes; Isotherms; Breakthrough curves; Thermal and pressure swing adsorption; Continuous adsorption processes; Chromatography and ion-exchange processes; Crystallization: Phase diagrams; Cooling; Evaporative; Anti-solvent crystallization; Impact of mixing and mass transfer on crystallization process; Population balance model to study crystal size distribution.
Learning Outcome	Development of numerical models for steady and unsteady-state mass transfer processes. Analyze flow diagrams and processes of enhanced distillation processes. Analyzing process parameters for separation processes such as adsorption and crystallization
Assessment Method	Assignments, Literature review, Quiz, Mid-semester examination and End-semester examination

Text Books:

E.L. Cussler, Diffusion: Mass Transfer in Fluid Systems, Cambridge University Press, 3^rd Ed., 2009.
J. D. Seader, E. J. Henley, D. K. Roper, Separation Process Principles: With Applications Using Process Simulators, John Wiley & Sons, 4^th Ed., 2016.

Reference Books:

C. J. Geankoplis, A. A. Hersel, D. H. Lepek. Transport Processes and Separation Process Principles, Pearson Education Limited, 5^th Ed., 2013.
B. K. Dutta, Principles of Mass Transfer and Separation Processes, PHI Learning Private Limited, 2009.

CB4206

Fluidization Engineering

Course Number	CB4206
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Fluidization Engineering
Learning Mode	Classroom lectures
Prerequisite	CB2102 (Fluid Mechanics)
Learning Objectives	The aim of this course is to provide basic knowledge about fluidization and aerodynamics of gas-solid systems as well as mathematical tools that enable the simulation of basic fluidized systems. To learn about characteristics of fluidization and their relation to design a fluidized system for newer applications.
Course Description	This course mainly covers the basic principles of fluidization phenomena and introduces the learner to the fundamental and practical aspects of basic fluidization operations for industrial application.
Course Content	Introduction to fluidization: definition and basic concepts, applications and significance in industrial processes, history and development; Principles of fluid mechanics relevant to fluidized beds; Fluid flow regimes (laminar, turbulent) in fluidized systems; Bed expansion and pressure drop calculations; Fluidized bed behavior: types of fluidized beds (bubbling, turbulent, circulating); Fluidization regimes (minimum fluidization velocity, transition velocity); Characteristics of particles and their influence on fluidization (Geldart groups); Heat and mass transfer in fluidized beds; Hydrodynamics of fluidized beds: flow regimes and hydrodynamic characteristics, particle mixing and segregation, bed stability; Particle technology in fluidization: particle size distribution and its effects, particle properties (shape, density) and their influence on fluidization, fluidized bed reactors and their applications; Design and operation: design considerations (bed height, diameter, distributor design); distributors, gas jets and pumping power; Solid movement, mixing, segregation and staging; Scale-up and modeling approaches; Case studies of industrial applications.
Learning Outcome	Students will be able to understand the importance of fluidization. Students will be able to describe the various applications of fluidization and their types. Students will be able to explain the mass and heat transfer processes occurring in various modes of fluidization.
Assessment Method	Assignments, Quiz, Mid-semester examination, End-semester examination

Text Books:

D. Kunii, O. Levenspiel, Fluidization Engineering, 2^nd Ed., Butterworth-Heinemann Ltd., 1991.
L.G. Gibilaro, Fluidization‐dynamics, Butterworth‐Heinemann, 2001.

Reference Books:

W.C. Yang, Fluidization, Solids Handling, and Processing: Industrial Applications, Noyes Publishing, 1999.
J.R. Grace, X. Bi., N. Ellis, Essentials of Fluidization Technology, Wiley-VCH, 2020.
S.K. Majumder, Hydrodynamics and Transport Processes of Inverse Bubbly Flow, 1^st Ed. Elsevier, Amsterdam, 2016.
D. Gidaspow, Multiphase Flow and Fluidization: Continuum and Kinetic Theory Description, Elsevier Science & Technology, 1993.

Department Elective - VI

Department Elective - VI

Sl. No.

Subject Code

Course

CB4207

Energy Management

Course Number	CB4207
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Energy Management
Learning Mode	Lectures
Learning Objectives	To learn the various principles or techniques for modeling of various energy systems. To familiarize with the different simulation and optimization techniques for energy management. To learn rules for energy conservation.
Course Description	This course covers energy management aspects of various processes and introduces different techniques to understand these systems.
Course Content	Importance of energy management; Energy audit: method, analysis of plant data, energy balance, laws of thermodynamics, measurements, portable and on line instruments; Utility Systems: boiler -efficiency testing, excess air control, steam distribution & use- steam traps, condensate recovery, flash steam utilization; Thermal insulation; Electrical systems: Demand control, power factor correction, load scheduling/shifting; Motor drives- efficiency testing, energy efficient motors, motor speed control; Efficient windows; Energy conservation in pumps, Fans (flow control); Compressed air systems; Refrigeration & air conditioning systems; Waste heat recovery: heat pipes, heat pumps; Cogeneration - concept, options (steam/gas turbines/diesel engine based); selection criteria; control strategy; Heat exchanger networking- concept of pinch, target setting, problem table approach, composite curves; Demand side management; Production planning and management.
Learning Outcome	Energy management, planning and conservation aspects
Assessment Method	Assignments, Quizzes, Mid-semester examination and End-semester examination

Text Books:

L. C. Witte, P. S. Schmidt, D. R. Brown, Industrial Energy Management and Utilization, Hemisphere Publ, Washington,1988.
I.G.C. Dryden, The Efficient Use of Energy, Butterworths, London, 1982.
D. Steve, W.C. Turner, Energy Management Handbook, Wiley, New York, 2004.

Reference Books:

Technology Menu for Efficient energy use- Motor drive systems, Prepared by National Productivity Council and Center for & Environmental Studies- Princeton Univ., 1993.
Industrial Energy Conservation Manuals, MIT Press, Mass, 1982

CB4208

Heterogeneous Catalysis: Fundamentals and Applications

Course Number	CB4208
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Heterogeneous Catalysis: Fundamentals and Applications
Learning Mode	Classroom lectures
Pre-requisite	CB2205 (Chemical Reaction Engineering-I)
Learning Objectives	To understand the catalytic process and reaction engineering and their application in industrial processes To learn about the characterization techniques for heterogeneous catalysts Evaluate heat and mass transfer effects in heterogeneous catalysis.
Course Description	The course will introduce to the catalytic processes, and their industrial applications. The focus is to learn about the underlying heat and mass transfer resistances and their impact on the overall rate of product formation. Additionally, detailed characterization techniques are introduced.
Course Content	Introduction and understanding of catalytic reactions; Adsorption, inter-particle and intra-particle transport processes in porous and non-porous catalysts; Effect of heat and mass transfer resistance in heterogeneous catalytic reactions; Calculation of effective diffusivity and thermal conductivity in porous ctalysts; Synthesis of as-designed catalysts using methods such as sol-gel, precipitation, hydrothermal, mechanical milling etc.; Physical and chemical properties analysis of catalysts using methods such as BET surface area analyzer, X-ray diffraction, FTIR, X-ray photoelectron spectroscopy; Scanning and transmission electron microscopy; Understanding of other techniques such as Gas chromatograph; UV-Visible spectroscopy; Photoluminescence; Fundamentals of catalyst test and reactor types; Kinetics of catalyst deactivation; Emerging industrially relevant catalysts.
Learning Outcome	It will impart the concepts of catalytic reaction engineering and its application in industries.
Assessment Method	Assignments, Literature review, Quiz, Mid-semester examination, and End-semester examination

Text Books:

B.W. Wojciechowski, N.M. Rice, Experimental Methods in Kinetic Studies, Elsevier, 2003.
L. D. Schmidt, The Engineering of Chemical Reactions, Oxford University Press, 2004.
D. K. Chakrabarty, B. Vishwanathan, Heterogeneous Catalysis, New Age Science Ltd, 1^st Ed., 2007.

Reference Books:

M. A. Vennices, Kinetics of Catalytic Reactions, Springer, 1^st Ed., 2005.
B. Viswanathan, S. Sivasanker, A.V. Ramaswamy, Catalysis: Principles & Applications, CRC Press, 1^st Ed., 2002.
J.J. Carberry, Chemical and Catalytic Reaction Engineering, Dover Publications, 1^st Ed., 2001.
I. Chorkendorff, J.W. Niemantsverdriet, Concept of Modern Catalysis and Kinetics, Wiley-VCH, 2^nd Ed., 2003.
J. M. Thomas, W. J. Thomas, Principles and Practice of Heterogeneous Catalysis, Wiley-VCH, 2^nd Ed., 1997.

CB4209

Polymer Science and Technology

Course Number	CB4209
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Polymer Science and Technology
Learning Mode	Classroom lectures
Learning Objectives	Develop the students to decisively think and analyze complex problems related to polymer processing and accordingly should be able to develop new formulations. Prepare students to execute new ideas with the knowledge of polymer technology in the field of research and development.
Course Description	The course will provide basic understanding of the polymer science, their synthesis, classifications, rheology, mechanisms, and different types of polymer additives and their processing.
Course Content	Introduction to polymers: concepts and definitions, classification, chemical bonding and structure; Polymer properties: molecular weight, chemical structure and thermal transitions, phase behavior and thermodynamics; Polymer synthesis and polymerization: Synthesis of polymers, polymerization mechanisms and techniques; Viscoelasticity and rubber elasticity; Degradation and stability; Solution and mechanical properties; Degradation, stability, and environmental issues; Polymer additives and reinforcements; Polymer types and applications: blends, composites, thermoplastics, fibers, elastomers, thermosets, and specialty polymers; Unit operations in polymer processing; Advanced topics in polymer science: Rheology and analysis using non-Newtonian fluid models, applications of polymers in separations.
Learning Outcome	Students will be able to understand the relationships between polymer molecular weight, molecular weight distribution, and the properties of polymeric materials. Students will demonstrate an ability to distinguish different polymerization reactions and their mechanisms/kinetics. Students will be able to describe the viscoelastic behavior of polymers with respect to their chemical structures and molecular weights, and to construct a corresponding master curve from the experimental data.
Assessment Method	Assignments, Quiz, Mid-semester examination, End-semester examination

Text Books:

P.J. Flory, Principles of Polymer Chemistry, Asian Books, 2006.
R.O. Ebewele, Polymer Science and Technology, CRC Press, 1^st Ed., 2000.
J.R. Fried, Polymer Science & Technology, Prentice Hall of India, 3^rd Ed., 2014.

Reference Books:

F.W. Billmeyer (Jr.), Textbook of Polymer Science, 3^rd Ed., John Wiley & Sons, 2002.
P. Bahadur, N.V. Sastry, Principles of Polymer Science, Narosa Publishing House, 2002.
V.R. Gowariker, N.V. Viswanathan and J. Sreedhar, Polymer science, New Age International (P) Ltd., 2001.
M. Rubinstein, R.H. Colby, Polymer Physics, Oxford University Press, 2003.
N.K. Petchers, R.K. Gupta, A. Kumar, Fundamentals of Polymer Engineering, Marcel Dekker, 2^nd Ed., 2003.

CB4210

Petroleum Refinery Engineering

Course Number	CB4210
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Petroleum Refinery Engineering
Learning Mode	Lectures
Learning Objectives	To build understanding on fundamentals of petroleum refining and processing from basic chemical engineering concepts. Learning about crude oil and its thermo-physical properties and petroleum fractions. Learning about unit operations and unit processes used to convert crude oil to finished petroleum products.
Course Description	The course provides fundamentals of petroleum refining introducing refinery engineering topics from basic concepts and unit operations and unit processes.
Course Content	Introduction: overview, importance, historical background and evolution of refining technology, Role of refineries in the petroleum industry; Petroleum properties and composition: Composition of crude oil; Classification and physico-chemical properties of petroleum; Analysis and characterization techniques for crude oil; Refining processes such as atmospheric and vacuum distillation, fractionation, cracking, reforming and isomerization; Conversion of petroleum gas into motor fuel, aviation fuel, lubricating oils and petroleum waxes; Chemicals and clay treatment of petroleum products; Hydrotreating; Desulfurization; Refining operations: dehydration, desalting, gas separation, separation of light gases (methane, ethane, propane), natural gas production and gas sweetening; Tube still heater design; Product profile of petrochemicals; Petrochemical feed stocks; Olefin and aromatic hydrocarbons production; Treatment and upgrading of olefinic C4 and C5 cuts; Chemicals from C1 compounds; Ethylene and its derivatives; Propylene and its derivatives; Butadiene and butene; BTX chemicals.
Learning Outcome	Understanding the petroleum refinery engineering concepts. Quantification of thermos-physical properties of crude oils and petroleum fractions. Understanding the unit operations and unit processes used to convert crude oil to finished products.
Assessment Method	Assignments, Quizzes, Mid-semester examination, and End-semester examination

Text Books:

K. Bhaskara Rao, Modern Petroleum Refining Processes, Oxford & IBH Publishing, 6^th Ed., 2018.
L. Nelson, Petroleum Refinery Engineering, McGraw Hill, New York, 4^th Ed., 1981.
H. Altgelt, M.M. Boduszynski, Composition and Analysis of Heavy Petroleum Fractions, Taylor & Francis, 1994.

Reference Book:

J.H. Gary, G.E. Handwork, M.J. Kaiser, Petroleum refining: Technology and Economics, CRC Press, 5^thEd., 2007.

IDE floated by the Department (not applicable for B. Tech. Chemical Engineering students)

Sl. No.

Subject Code

Course

CB2206

Environmental Science and Engineering

Course Number	CB2206
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Environmental Science and Engineering
Learning Mode	Classroom lectures
Learning Objectives	To impart knowledge of the environment and different types of pollution, causes, and preventive measures against various pollutions.
Course Description	This course presents a broad introduction to Environmental Engineering. A set of fundamental principles, which are based on scientific fundamentals such as chemistry, biology, physics, and mathematics will be discussed. Applications including water quality engineering, air quality engineering, and hazardous waste management will be explained.
Course Content	Structure of environment; Pollution arises due to urbanization and industrialization; Mass and energy balance for environmental engineering systems; Accumulation of pollutants in air, water, and soil; Air pollution and its remediation: smog and fumigation, collection of particulate pollutants, analysis of air pollutants, control of particulate emission, various types of equipment to control air emissions; Water pollution and its remediation: origin of wastewater, types of water pollutants, adverse effects on the ecosystem, water pollution control equipment and instruments; Liquid and solid waste; Domestic and industrial waste; Dumping solid wastes: incineration, sanitary land field, collection, and disposal; Noise pollution: types, adverse effects of noise, permissible noise limits, measurement, and reduction of noise; Thermal pollution; Greenhouse effect; Acid precipitation; Ozone layer depletion; Life cycle analysis; Environmental quality objectives.
Learning Outcome	The students should be able to describe environmental challenges and identify solutions, evaluate design solution alternatives, describe the principles and methods of environmental impact assessment
Assessment Method	Assignments, Quizzes, Mid-semester examinations, and End-semester examination

Text Books:

G. Masters, W. Ela, W. Ela, Introduction to Environmental Engineering and Science, Pearson, 3^rd Ed., 2013.
P. A. Vesilind, S. M. Morgan, Introduction to Environmental Engineering, Thomson, 2004.
N. de Nevers, Air Pollution Control Engineering, McGraw-Hill, 1994.

Reference Books:

David Cornwell, Mackenzie Davis, Introduction to Environmental Engineering, McGraw‐Hill Education, 2012.
D L Russel, Practical Wastewater Treatment, John Wiley & Sons, 2006.
G. Kiely, Environmental Engineering, McGraw-Hill, 2006.
M. N. Rao, H. V. N. Rao, Air Pollution, Tata McGraw-Hill, New Delhi, 1993.

CB3106

Introduction to Sustainable Engineering

Course Number	CB3106
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Introduction to Sustainable Engineering
Learning Mode	Classroom lectures
Learning Objectives	Identify the various emissions and environmental impacts associated with commercial and non-commercial projects/processes. Calculate the carbon footprint of various products/processes. Evaluate new technologies that can help to mitigate the impacts of climate change.
Course Description	The course will introduce the climate and the reason for its deviation from the base timeline, current environmental policies, and the methods to mitigate the adverse effects of climate change.
Course Content	Global agreements; Sustainability- concepts and pillars; Sustainable materials; Design, and Energy; 17 Sustainable goals; Life cycle analysis. Environmental impact assessment; Industrial ecology; Biomimicry in sustainable engineering designs; The atmosphere and its constituents; Atmospheric lifetime; Atmospheric trace constituents- Sulfur, nitrogen, carbon, and halogen containing compounds; Mercury; Greenhouse gasses- sources and effects; Properties of atmospheric aerosol; Stratospheric aerosol, cloud condensation Nuclei, sizes of Atmospheric particles, mineral dust, biomass burning; Tropospheric Chemistry of chlorofluorocarbon (CFC) with ozone; Introduction to Carbon Footprint. ISO 14064.
Learning Outcome	It will impart the concepts of climate change, its effect on the environment, and its possible prevention methodology.
Assessment Method	Assignments, Literature review, Simulation, Quiz, Mid-semester examination and End-semester examination

Text Books:

J. H. Seinfeld, S.N. Pandis, Atmospheric Chemistry and Physics: From Air Pollution to Climate Change, 3rd edition, Wiley, 2006.
W. E. Kelly, B. Luke, N. R. Wright, Engineering for Sustainable Communities: Principles and Practices, American Society of Civil Engineers, 2017.

Reference books:

H. Bao-Jie. D. Prasad, G. Pgnata, J. Jupesta, Climate Change and Environmental Sustainability, Advance in Science, Technology & Innovation, Springer, 2022.
E. Adams, J. Connor, J. Ochsendorf, R. Nicolin, Design for Sustainability. Fall, Massachusetts Institute of Technology: MIT OpenCourseWare, 2006.
T. Letcher, Managing Global Warming: An Interface of Technology and Human Issues, Academic Press, 2018.

CB4108

Bioprocess Engineering

Course Number	CB4108
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Bioprocess Engineering
Learning Mode	Classroom lectures
Learning Objectives	Provides students with basic concepts regarding the design, operation, process optimization of various bioreactors. and fermenters.
Course Description	This course deals with the basic unit operation in bioprocesses, microbial growth kinetics, principles of bioreactors, bioreactors systems, basic bioreactor design, bioprocess control, and downstream operations.
Course Content	Introduction to bioprocess engineering; Upstream and downstream operations; Mass and energy balances in microbial processes; Enzyme technology: kinetics of enzyme-catalyzed reactions, immobilization, large-scale production; Fermentation processes: introduction, types, process parameters, design of fermenters, industrial application; Microbial growth: growth kinetic models, microbial behaviour in bioreactors; Necessity and optimization of growth media (Placket-Burman design); Fermenter and media sterilization; Cell disintegration and product recovery; Microbial cells and products separation technology: centrifugation, filtration; Purification techniques: precipitation, ultrafiltration, chromatography, electrophoresis; Integrated bio-reaction and bio-separation processes: membrane bioreactors, extractive fermentation, bioprocess instrumentation.
Learning Outcome	Able to analyze the basic principles and techniques of bioprocess engineering
Assessment Method	Assignments, Literature review, Simulation, Quiz, Mid-semester examination and End-semester examination

Text/Reference Books

J. E. Bailey, D. Olis, Biochemical Engineering Fundamentals, McGrew- Hill Book Co., 2010.
S. N. Mukhopadhyay, Process Biotechnology Fundamentals, Viva Books Private Limited, 2001.
A. T. Jackson, Process Engineering in Biotechnology, Prentice-Hall, 1991.
M. L. Shuler, Bioprocess Engineering Basic Concepts, Prentice Hall PTR, 2^nd Ed., 2014.
D.G. Rao, Introduction to Biochemical Engineering, Tata McGraw-Hill, 2005.
M, Doble, A. Kumar, Biotreatment of Industrial Effluents, Elsevier, 2008.

Minor in Chemical Engineering

Sl. No.

Semester

Code

Course

Sem III

CB2103

Heat Transfer

Course Number	CB2103
Course Credit (L-T-P-C)	3-0-3-4.5
Course Title	Heat Transfer
Learning Mode	Classroom lectures and practical
Learning Objectives	To understand the fundamentals of heat transfer mechanisms in fluids and solids and their applications to engineering problems. To understand the basic principle and mathematical formulation of various heat transfer equipment used in process industries. To perform experiments to understand the various heat transfer measurements that help to optimize the process parameters.
Course Description	This course helps to understand the fundamentals of heat transfer mechanisms in fluids and solids and their applications in various heat transfer equipment in process industries including conduction, convection and radiation problems, heat exchangers and evaporators.
Course Content	Introduction; Heat conduction; Fourier’s law; Thermal conductivity;; Thermal resistance; Critical thickness of insulation; One dimensional heat conduction; Heat generation; 2D heat conduction; Unsteady-state conduction, Various shapes (thin plate, thick plate, cylinder, sphere); Practical applications (Freezing of butter slab, meat loafs, Pasteurization of milk, Sterilization of canned jam/jelly); Heisler charts; extended surfaces; Laws of Black body radiation; Solid angle and radiation intensity; Radiation exchange between black and gray surfaces; Shape factor; electrical network analogy for thermal systems; Forced Convection; Differential equation of convection; Thermal boundary layer; Laminar and turbulent flow heat transfer; Natural Convection; Governing equations; Heat Exchangers; Types of heat exchangers; Overall heat transfer coefficient and fouling factor, Mean temperature difference; Effective-NTU approach; Condensation and boiling; Types of boiling; Correlations in saturated poll boiling; Film and drop condensation; Condensation on a vertical plate and horizontal tubes.
Learning Outcome	Ability to understand, analyze and solve conduction, convection and radiation problems. Ability to design and analyze the performance of heat exchangers, evaporators and other heat transfer equipment. Ability to conduct experiments and analyze the data to interpret the performance of the equipment.
Assessment Method	Assignments, Quiz, Mid-semester examination and End-semester examination.

Text Books:

D. Q. Kern, Process Heat Transfer, 1^st Ed. McGraw Hill, 2001.
S. P. Sukhatme, A Text Book of Heat Transfer, 4^th Ed., Universities press, 2005.
J. P. Holman, Heat Transfer, 10^th Ed., Mc-Graw Hill, 2017.

Reference Books:

W. H. McAdams, Heat Transmission, 2^nd revised Ed., Mc-Graw Hill, 1973.
H. Martin, Heat Exchangers, 1^st Ed., CRC press, 1988.
C. J. Geankoplis, Transport Processes and Unit Operations, 3^rd Ed., Prentice Hall India Pvt. Ltd., New Delhi, 2002.
J. M. Coulson, J. F. Richardson, J. R. Backhurst and J. H. Harker, Chemical Engineering, Vol-1, 5^th Ed., ‎ Butterworth-Heinemann Ltd, 1999.

4.5

Sem IV

CB2201

Mechanical Operations

Course Number	CB2201
Course Credit (L-T-P-C)	2-0-3-3.5
Course Title	Mechanical Operations
Learning Mode	Classroom lectures and practical
Learning Objectives	To understand various mechanical operations applicable in the Chemical Process Industries (CPIs). To learn the principle and construction of the equipment. To perform process evaluation of various unit operations.
Course Description	This course helps to understand basic principles of various mechanical operations, construction and working of the equipment including size measurement and classification methods and systems, fluid particle systems and equipment, size reduction equipment, solid-solid separation method system.
Course Content	Principles of crushing and grinding; Laws of crushing and grinding (Rittinger’s Law, Kick’s Law, Bond’s Law); Properties and handling of particulate solids: introduction, characterization of solid particles, determination of mean particle size, size distribution equations; Characteristics of industrial crushers and mills; Industrial screening; Effectiveness of screens; Cyclones; Fluid-particle mechanics; Free and hindered settling; Stoke’s Law and Newton’s Law; Industrial classifiers; Clarifiers and thickeners; Gravity separation, tabling and jigging; Floatation and its kinetics; Mixing of liquids and solids; Power requirement in mixing; Principles of filtration: Ergun Equation and Kozeny-Carman Equation; Filtration equipment; Transportation and storage of solids: introduction, storage techniques (bulk storage, bin storage, hoppers, silos), bulk solids conveying; Grade efficiency: measurement techniques, cut size, sharpness cut, construction of grade efficiency curve.
Learning Outcomes	Ability to understand size measurement and classification methods and systems. Ability to understand fluid particle systems and equipment. Ability to select suitable size reduction equipment, solid-solid separation method system. Ability to analyse separation, filtration processes and solid-liquid separation equipment and systems.
Assessment Method	Assignments, Quiz, Mid-semester examination and End-semester examination.

Text Books:

W. L. McCabe, J. C. Smith and P. Harriott, Unit Operations of Chemical Engineering, Mc-Graw Hill, 7^th Ed., 2005.
J. M. Coulson, J. F. Richardson, J. R. Backhurst and J. H. Harker, Chemical Engineering, Elsevier, Vol-2, 5^th Ed., 2015.
C. J. Geankoplis, Transport Processes and Unit Operations, Prentice Hall India Pvt. Ltd., New Delhi, 3^rd Ed., 2002.

Reference Books:

A. M. Gaudin, Principles of Mineral dressing, Mc-Graw Hill, 1939.
R. H. Perry and C. H. Chilton, Chemical Engineer’s Hand Book, Mc-Graw Hill, 8^th Ed., 2007.
A. F. Taggart, Handbook of Mineral Dressing: Ores and Industrial Minerals, Wiley, 1945.
Ghosal, Sanyal, and Dutta, Introduction to Chemical Engineering, McGraw Hill Education, 2014.

3.5

Sem V

CB3102

Chemical Process Technology

Course Number	CB3102
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Chemical Process Technology
Learning Mode	Lectures
Learning Objectives	To learn the process flow and instrumentation diagram. To learn the production of different chemical products, fuels and other specialty chemicals. To learn the related engineering challenges and their troubleshooting by selecting appropriate operating conditions including catalyst requirement for the synthesis/production of different useful products/chemicals.
Course Description	This course is mainly about learning the production process of different chemicals/materials/fuels/specialty chemicals; operating conditions including catalyst requirements and troubleshooting for different synthesis/production processes.
Course Content	Introduction and scope; A brief history and structure of the chemical industry (India and worldwide); Process flow and instrumentation diagrams: preparation, symbols; Introduction to the following Industries including the special features of design and operation; Fuels and industrial gases including Natural gas; Process for the conversion of biomass (biofuel and bio-based chemicals production); Petrochemical and downstream products; Polymer- production and processing; Fertilizer; Cement; Caustic chlorine; Coal based chemicals; Petroleum refining processes; Nitrogen and its derivatives; Sulphur and its derivatives, Phosphorus and its derivatives; Soaps and detergents; Glycerin; Sugar; Pulp and paper; Alcohol based chemicals; Specialty chemicals; Leather; Paint and pigments; Fermentation industries; Process intensification: introduction, structured catalytic reactors, reactive separation.
Learning Outcome	Familiarize with process flow and instrumentation diagrams including symbols. Gaining design and operation knowledge related to the process industries. Propose appropriate operating conditions including catalyst requirement for the synthesis/production of different useful products/chemicals.
Assessment Method	Assignments, Quizzes, Mid-semester examination and End-semester examination

Text Books:

I.D. Mall, Chemical Process Technology, CBS Publishers and Distributors Pvt Ltd., 2024.
C.E. Dryden, Outlines of Chemical Technology, Edited and revised by M. Gopala Rao and Marshall Sitting, 3rd Ed., Affiliated East-West Press, 1997.
G.T. Austin, R.N. Shreve, Chemical Process Industries, 5th Ed., McGraw Hill, 1984.

Reference Books:

H. Groggins, Unit Processes in Organic Synthesis, 5th Ed., McGraw Hill, 2001.
A. Moulijn, M. Makkee, A.V Diepen, Chemical Process Technology, 2nd Ed., Wiley, 2015.
F. Kirk-Othmer, Encyclopedia of Chemical Technology, 5th Ed., Wiley Interscience, 2004.
H. Gary, G.E. Handwerk, Petroleum Refining: Technology and Economics, 1st Ed., Marcel Dekker, 2001.

Sem VI

CB2202

Mass Transfer-I

Course Number	CB2202
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Mass Transfer-I
Learning Mode	Classroom lectures
Learning Objectives	To learn the fundamental concepts of Mass transfer operations. To develop numerical solutions to various mass transfer processes. To familiarize with various equipment that uses these principles for useful separation processes.
Course Description	Introduction to the fundamental aspects of mass transfer and their importance in chemical engineering processes are covered in this course. Calculations and phase diagrams in distillation, extraction, and absorption are taught in the course.
Course Content	Fick’s law of diffusion, Molecular diffusion, Gas- and liquid- phase diffusion coefficients; Mass transfer in convective system; Types and correlations for mass transfer coefficients; Eddy diffusion; Mass transfer theories; Equilibrium; Raoult’s and Henry’s law; Inter-phase mass transfer; Overall mass transfer coefficient; Contacting equipment – gas-liquid; Type of contacting equipment: plate, tray, bubble column, packed column, agitated vessel, spray tower; Design of packed tower; Flooding in packed towers; Equilibrium in gas-liquid systems; Gas absorption and stripping; Selection of solvent; Number of stages in a tray tower; Height equivalent to a theoretical plate; Distillation: Vapor-liquid equilibrium; Enthalpy-concentration plots; Flash vaporization; Batch distillation; Steam distillation; Continuous fractionation; McCabe-Thiele and Ponchon-Savarit methods; Liquid-liquid extraction (LLE); Solvent selection; Equipment for LLE; Solid-liquid extraction/leaching: rate, solid-liquid contacting strategy, equipment.
Learning Outcome	Identify, quantify and calculate various parameters relevant to simple mass transfer operations. Describe various equipment working on mass transfer principles. Design the basic equipment required for separation processes that are based on mass transfer principles.
Assessment Method	Assignments, Quizzes, Mid-semester examination and End-semester examination.

Text Books:

R. E. Treybal, Mass Transfer Operations, McGraw Hill, 3^rd Ed., 1980.
E. L. Cussler, Diffusion- Mass Transfer in Fluid Systems, Cambridge University Press, 1997.
B. K. Dutta, Principles of Mass Transfer and Separation Processes, PHI Learning Private Limited, 2009.

Reference Books:

W. McCabe, J. Smith, P. Harriott, Unit Operations of Chemical Engineering. McGraw-Hill, 7^th Ed., 2021.
C. J. Geankoplis, A. A. Hersel, D. H. Lepek. Transport Processes & Separation Process Principles, Pearson Education Limited, 5^th Ed., 2013.

Sem VI

CB2205

Chemical Reaction Engineering-I

Course Number	CB2205
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Chemical Reaction Engineering-I
Learning Mode	Classroom Lectures
Learning Objectives	To learn about basics about type of reactions, contacting patterns, and different reactors.
Course Description	The course teaches the concepts of reaction rate, stoichiometry and equilibrium to the analysis of chemical reacting systems, derivation of rate expressions from reaction mechanisms and equilibrium or steady state assumptions, design of chemical reactors via synthesis of chemical kinetics, transport phenomena, and mass and energy balance.
Course Content	Introduction; Types of chemical reactions; Elementary and non-elementary homogeneous reactions; Order and molecularity of reactions; Arrhenius Equation and effect of temperature; Constant and varying volume batch reactor; Interpretation of batch reactor data; Reaction rate; Determination of rate constant and half-life; Differential and integral methods; Parallel and series reaction; Batch reactor; Plug-flow or tubular reactor; Continuous stirred tank reactor (CSTR); Performance equations; Recycle reactors; Design of parallel reactions and distribution of products; Autocatalytic reactions; Temperature and pressure effects for single and multiple reactions.
Learning Outcomes	Ability to read and analyze chemical reaction data, and generate rate expressions. Designing experiments involving chemical reactions with multiple reactants and products, recycle reactors. Develop skills to choose the right reactor among single, multiple, recycle reactor, etc. for isothermal/ non-isothermal/ adiabatic reactions.
Assessment Method	Assignments, Quizzes, Mid-semester examination and End-semester examination.

Text Books:

H. S. Fogler, Elements of Chemical Reaction Engineering, Prentice Hall, 4^th Ed., 2008.
O. Levenspiel, Chemical Reaction Engineering, Wiley Eastern, 3^rd Ed., 2003.

Reference Books:

J. M. Smith, Chemical Engineering Kinetics, McGraw Hill, 3^rd Ed., 1980.
L. D. Schmidt, The Engineering of Chemical Reactions, Oxford University Press, 1998.

Total

Civil Engineering and Minor in Infrastructure Engineering

Program Learning Objectives:	Program Learning Outcomes:
Program Goal 1: Equip the students with strong foundation in civil and environmental engineering for both research and industrial scenarios.	Program Learning Outcome 1a: Student develops ability to design and conduct experiments. Program Learning Outcome 1b: Student is able to organize and analyze the experiment data to draw conclusions.
Program Goal 2: Provide scientific and technical knowledge in planning, design, construction, operation and maintenance of civil engineering infrastructure.	Program Learning Outcome 2: Students are able to (i) develop material and process specifications, (ii) analyze and design projects, (iii) perform estimate and costing and (iv) manage technical activities.
Program Goal 3: Prepares the students to apply knowledge in policy and decision making related to civil engineering infrastructure.	Program Learning Outcome 3a: Student develops understanding of professional and ethical responsibility. Program Learning Outcome 3b: Student is able to consider economic, environmental, and societal contexts while developing engineering solutions.
Program Goal 4: Prepare students to attain leadership careers to meet the challenges and demands in civil engineering practice.	Program Learning Outcome 4a: Students is prepared for leading roles/profiles in government sector, construction industry, consultancy services, NGOs, corporate houses and international organizations. Program Learning Outcome 4b: Student develops ability to identify, formulate, and solve engineering problems
Program Goal 5: Nurture interdisciplinary education for finding innovative solutions.	Program Learning Outcome 5: Student is able to solve complex engineering problems by applying principles of engineering and science.

Sl. No.

Subject Code

SEMESTER I

MA1101

Course Number	MA1101
Course Credit (L-T-P-C)	3-1-0-4
Course Title	Calculus and Linear Algebra
Learning Mode	Lectures and Tutorials
Learning Objectives	To provide the essential knowledge of basic tools of Differential Calculus, Integral Calculus, Vector spaces and Matrix Algebra.
Course Description	This course provides a foundation for Calculus and Linear Algebra. Topics related to properties of single and two variable functions along with their applications will be discussed. In addition fundamentals of linear algebra and matrix theory with applications will also be discussed.
Course Content	Differential Calculus (12 Lectures): Limit and continuity of one variable function (including ε-δ definition). Limit, continuity and differentiability of functions of two variables, Tangent plane and normal, Change of variables, chain rule, Jacobians, Taylor’s Theorem for two variables, Extrema of functions of two or more variables, Lagrange’s method of undetermined multipliers. Integral Calculus (10 Lectures): Riemann integral for one variable functions, Double and Triple integrals, Change of order of integration. Change of variables, Applications of Multiple integrals such as surface area and volume. Vector Spaces (12 Lectures): Vector spaces (over the field of real numbers), subspaces, spanning set, linear independence, basis and dimension. Linear transformations, range and null space, rank-nullity theorem, matrix of a linear transformation. Matrix Algebra (8 Lectures): Elementary operations and their use in getting the rank, inverse of a matrix and solution of linear simultaneous equations, Orthogonal, symmetric, skew-symmetric, Hermitian, skew-Hermitian, normal and unitary matrices and their elementary properties, Eigenvalues and Eigenvectors of a matrix, Cayley-Hamilton theorem, Diagonalization of a matrix.
Learning Outcome	Students completing this course will be able to: 1. Understand various properties of functions such as limit, continuity and differentiability. 2. Learn about integrations in various dimension and their applications. 3. learn about the concept of basis and dimension of a vector space. 4. define Linear Transformations and compute the domain, range, kernel, rank, and nullity of a linear transformation. 5. compute the inverse of an invertible matrix. 6. solve the system of linear equations. 7. Apply linear algebra concepts to model, solve, and analyze real-world problems.
Assessment Method	Quiz /Assignment/ MSE / ESE

Textbooks:

Thomas, G. B., Hass, J., Heil, C. and Weir M. D., “Thomas’ Calculus”, 14th Ed., Pearson Education, 2018
Kreyszig, E., “Advanced Engineering Mathematics”, 10th Ed., Wiley India Pvt. Ltd, 2015

Reference Books:

Jain, R. K. and Iyenger, S. R. K., “Advanced Engineering Mathematics”, 5^th, Narosa Publishing House, 2017
Axler, S., “Linear Algebra Done Right”, 3rd Ed., Springer Nature, 2015
Strang, G., “Linear Algebra and Its Applications” 4th Ed., Cengage India Private Limited, 2005

4.0

CS1101

Course Number	CS1101
Course Credit	3-0-3-4.5
Course Title	Foundations of Programming
Learning Mode	Offline
Learning Objectives	· To understand the fundamental concepts of programming · To develop the basic problem-solving skills by designing algorithms and implementing them. · To learn about various data types, control statements, functions, arrays, pointers, and file handling. · To achieve proficiency in debugging and testing a C program
Course Description	This introductory course provides a solid foundation in programming principles and techniques. Designed for students with little to no prior programming experience, it covers fundamental concepts such as variables, data types, control structures, functions, and basic data structures. Students will learn to write, debug, and execute programs using a high-level programming language. Emphasis is placed on developing problem-solving skills, logical thinking, and the ability to write clear and efficient code. By the end of the course, students will be equipped with the essential skills needed to pursue more advanced studies in computer science and software development.
Course Outline	Introduction and Programming basics, Expressions Control and Iterative statements, Functions, Arrays, Recursion vs. Iteration Pointers, 2D-Array with pointers, Structures, String, Dynamic memory allocation, File handling, Contemporary programming languages, and applications Practical component: Lab to be conducted on a 3-hour slot weekly. It will be conducted with the theory course so the topics for problems given in the lab are already initiated in the theory class.
Learning Outcome	· Understanding of Basic Syntax and Structure in C language · Proficiency in Data Types, Operators, and Control Structures · Function Implementation and learn to use them appropriately · Efficient Use of Arrays and Strings · Pointer Utilization · Ability to perform dynamic memory allocation and deallocation using malloc (), calloc (), realloc (), and free () functions. · Structured data management with structures and unions · Exposure of file Handling · Learning debugging and error Handling
Assessment Method	Internal (Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Reading

Knuth, Donald E. The art of computer programming, volume 4A: combinatorial algorithms, part 1. Pearson Education India, 2011.
P.J. Deitel and H.M. Deitel, C How To Program, Pearson Education (7th Edition)
Brian W. Kernighan and Dennis M. Ritchie, The C Programming Language, Prentice−Hall
A. Kelley and I. Pohl, A Book on C, Pearson Education (4th Edition)
K. N. King, C PROGRAMMING A Modern Approach, W. W. Norton & Company

4.5

PH1101/PH1201

Course Number	PH1101/PH1201
Course Credit	3-1-0-4
Course Title	Physics
Learning Mode	Lectures and Tutorials
Learning Objectives	Complies with Program Goals 1 and 2
Course Description	This course deals with fundamentals in Classical mechanics, Waves and Oscillations and Quantum Mechanics. As a prerequisite, the mathematical preliminaries such as coordinate systems, vector calculus etc will be discussed in the beginning.
Course Outline	Orthogonal coordinate systems (Plane polar, Spherical, Cylindrical), concept of generalised coordinates, generalised velocity and phase space for a mechanical system, Introduction to vector operators, Gradient, divergence, curl and Laplacian in different co-ordinate systems. Central force problem and its applications. Rigid body rotation, vector nature of angular velocity, Finding the principal axes, Euler's equations; Gyroscopic motion and its application; Accelerated frame of reference, Fictitious forces. Potential energy and concept of equilibrium, Lennard-Jones and double-well potentials, Small oscillations, Harmonic oscillator, damped and forced oscillations, resonance and its different examples, oscillator states in phase space, coupled oscillations, normal modes, longitudinal and transverse waves, wave equation, plane waves, examples two- and three-dimensional waves. Michelson-Morley experiment, Lorentz transformation, Postulates of special theory of relativity, Time dilation and length contraction, Applications of special theory of relativity.
Learning Outcome	Complies with PLO 1a, 2a, 3a
Assessment Method	Quiz, Assignments and Exams

Suggested Readings:

Textbooks:

Engineering Mechanics, M. K. Harbola, 2^nd ed., Cengage, 2012
D. Kleppner and R. J. Kolenkow, An introduction to Mechanics, Tata McGraw-Hill, New Delhi, 2000.
I. G. Main, Oscillations and Waves
H. G. Pain, The Physics of Vibrations and Waves, 1968
Frank S. Crawford, Berkeley Physics Course Vol 3: Waves and Oscillations, McGraw Hill, 1966.

References:

R. P. Feynman, R. B. Leighton and M. Sands, The Feynman Lecture in Physics, Vol I, Narosa Publishing House, New Delhi, 2009.
David Morin, Introduction to Classical Mechanics, Cambridge University Press, NY, 2007.
P. C. Deshmukh, Foundations of Classical Mechanics, Cambridge University Press, 2019

5.5

CE1101/CE1201

Course code	CE1101/CE1201
Course Credit (L-T-P-C)	1-0-3-2.5
Course Title	Engineering Graphics
Learning Mode	Lectures and Practical
Learning Objectives	Complies with PLO-1a 1. The course on engineering drawing is designed to introduce the fundamentals of technical drawing as an important form of conveying information. 2. Apply principles of engineering visualization and projection theory to prepare engineering drawings, using conventional and modern drawing tools. 3. Practice drawing orthographic projections, isometric views, and sectional views, of simple and combined solids in different orientations.
Course Description	This course will introduce drawing as a tool to represent a complex three-dimensional object on two-dimensional paper through methods of projections. The course explains the use of different drafting tools and the importance of conventions for uniformity and standardization of the interpretation of the drawings.
Course Outline	Fundamental of engineering drawing, line types, dimensioning, and scales. Conic sections: ellipse, parabola, hyperbola; cycloidal curves. Principle of projection, method of projection, orthographic projection, plane of projection, first angle of projection, Projection of points, lines, planes and solids. Section of solids: Sectional views of simple solids- prism, pyramid, cylinder, cone, sphere; the true shape of the section. Methods of development, development of surfaces. Isometric projections: construction of isometric view of solids and combination of solids from orthographic projections. Introduction to AutoCad and solving isometric problems.
Learning Outcome	After attending this course, the following outcomes are expected: 1. The student will understand the basic concepts of engineering drawing. 2. The student will be able to use basic drafting tools, drawing instruments, and sheets. 3. The student will be able to represent three-dimensional simple and combined solid objects on two-dimensional paper. 4. The student will be able to visualize and interpret the orientation of simple and combine solid objects.
Assessment Method	Laboratory Assignments (30%), Mid-semester examination (25%) and End-semester examination (45%).

Suggested Readings:

Textbooks:

D. Bhatt, Engineering Drawing, Charotar Publishing House.
Agrawal & Agrawal, Engineering Drawing, McGraw Hill.
Jolhe, Engineering Drawing.

References:

Engineering Drawing and Design by David Madsen

2.5

EE1101/EE1201

Course Number	EE1101/EE1201
Course Credit	3-0-3-4.5
Course Title	Electrical Sciences
Learning Mode	Lectures and Experiments
Learning Objectives	Complies with Program goals 1, 2 and 3
Course Description	The course is designed to meet the requirements of all B. Tech programmes. The course aims at giving an overview of the entire electrical engineering domain from the concepts of circuits, devices, digital systems and magnetic circuits.
Course Outline	Circuit Analysis Techniques, Circuit elements, Simple RL and RC Circuits, Kirchoff’s law, Nodal Analysis, Mesh Analysis, Linearity and Superposition, Source Transformations, Thevenin’s and Norton’s Theorems, Time Domain Response of RC, RL and RLC circuits, Sinusoidal Forcing Function, Phasor Relationship for R, L and C, Impedance and Admittance, Instantaneous power, Real, reactive power and power factor. Semiconductor Diode, Zener Diode, Rectifier Circuits, Clipper, Clamper, UJT, Bipolar Junction Transistors, MOSFET, Transistor Biasing, Transistor Small Signal Analysis, Transistor Amplifier and their types, Operational Amplifiers, Op-amp Equivalent Circuit, Practical Op-amp Circuits, Power Opamp, DC Offset, Constant Gain Multiplier, Voltage Summing, Voltage Buffer, Controlled Sources, Instrumentation Amplifier, Active Filters and Oscillators. Number Systems, Logic Gates, Boolean Theorem, Algebraic Simplification, K-map, Combinatorial Circuits, Encoder, Decoder, Combinatorial Circuit Design, Introduction to Sequential Circuits. Magnetic Circuits, Mutually Coupled Circuits, Transformers, Equivalent Circuit and Performance, Analysis of Three-Phase Circuits, Power measurement in three phase system, Electromechanical Energy Conversion, Introduction to Rotating Machines (DC and AC Machines). Laboratory: Experiments to verify Circuit Theorems; Experiments using diodes and bipolar junction transistor (BJT): design and analysis of half -wave and full-wave rectifiers, clipping and clamping circuits and Zener diode characteristics and its regulators, BJT characteristics (CE, CB and CC) and BJT amplifiers; Experiment on MOSFET characteristics (CS, CG, and CD), parameter extraction and amplifier; Experiments using operational amplifiers (op-amps): summing amplifier, comparator, precision rectifier, Astable and Monostable Multivibrators and oscillators; Experiments using logic gates: combinational circuits such as staircase switch, majority detector, equality detector, multiplexer and demultiplexer; Experiments using flip-flops: sequential circuits such as non-overlapping pulse generator, ripple counter, synchronous counter, pulse counter and numerical display; Power Measurement by two Wattmeter method; Open and Short Circuit Tests of Transformer.
Learning Outcomes	Complies with PLO 1a, 2a and 3a
Assessment Method	Quiz, Assignments and Exams

Texts/References

K. Alexander, M. N. O. Sadiku, Fundamentals of Electric Circuits, 3rd Edition, McGraw-Hill, 2008.
H. Hayt and J. E. Kemmerly, Engineering Circuit Analysis, McGraw-Hill, 1993.
L. Boylestad and L. Nashelsky, Electronic Devices and Circuit Theory, 6th Edition, PHI, 2001.
M. Mano, M. D. Ciletti, Digital Design, 4th Edition, Pearson Education, 2008.
Floyd, Jain, Digital Fundamentals, 8th Edition, Pearson.
David V. Kerns, Jr. J. David Irwin, Essentials of Electrical and Computer Engineering, Pearson, 2004.
Donald A Neamen, Electronic Circuits; analysis and Design, 3rd Edition, Tata McGraw-Hill Publishing Company Limited.
Adel S. Sedra, Kenneth C. Smith, Microelectronic Circuits, 5th Edition, Oxford University Press, 2004.
E. Fitzgerald, C. Kingsley Jr., S. D. Umans, Electric Machinery, 6th Edition, Tata McGraw-Hill, 2003.
P. Kothari, I. J. Nagrath, Electric Machines, 3rd Edition, McGraw-Hill, 2004.
Del Toro, Vincent. "Principles of electrical engineering." (No Title) (1972).

4.5

HS1101

Course Number	HS1101
Course Credit	L-T-P-W: 2-0-1-2.5
Course Title	English for Professionals
Learning Mode	Offline
Learning Objectives	This course aims to help the students (a) attain proficiency in written English through the construction of grammatically correct sentences, utilization of subject-verb agreement principles, mastery of various tenses, and effective deployment of active and passive voice to ensure coherent and impactful written expression; (b) enhance oral communication skills by honing public speaking abilities, acquiring strategies to deliver persuasive presentations, and cultivating a polished telephone etiquette, enabling confident and articulate verbal communication; (c) foster active listening capabilities by recognizing different types of listening, and applying proven methods and strategies to improve active listening skills; (d) strengthen reading skills, including comprehension, interpretation, and critical analysis, to grasp diverse written materials and derive meaning from various types of texts encountered in academic and professional contexts; (e) develop adeptness in written communication for business purposes, encompassing the understanding of essential writing elements, mastery of appropriate writing styles thereby enhancing prospects for successful job interviews and subsequent professional endeavors.
Course Description	This academic course on communication skills aims to equip students with fluency in spoken and written English for effective expression in both academic and professional settings. By focusing on essential communication principles and providing practical experiences, students develop clarity, precision, and confidence in their communication. Through interactive discussions and exercises, students enhance critical thinking and adaptability in diverse contexts. Upon completion, students will excel in formal presentations, group discussions, and persuasive writing, enhancing their overall communication proficiency.
Course Outline	Unit I: Introduction to professional communication – LSRW - Phonetics and phonology Sounds in English Language – production and articulation – rhythm and intonation – connected speech - Basic Grammar and Advanced Vocabulary Sounds in English Language – production and articulation – rhythm and intonation – connected speech – persuading and negotiating – brevity and clarity in language. Unit II: Characteristics of Technical Communication: Types of communication and forms of communication - Formal and informal communication Verbal and non-Verbal Communication – Communication barriers and remedies Intercultural communication – neutral language Unit III: Comprehension and Composition – summarization, precis writing Business Letter Writing CV/ Resume – E-Communication Unit IV: Statement of Purpose, Writing Project Reports, Writing research proposal, writing abstracts, developing presentations, interviews – combating nervousness Tutorial: Listening Exercises, Speaking Practice (GDs, and Presentations), and Writing Practice Learning Outcome · Attain proficiency in written English, enabling the construction of grammatically correct sentences and coherent written expression through the use of appropriate grammar, tenses, and voice. · Enhance oral communication skills, including public speaking, persuasive presentation, and polished telephone etiquette, fostering confident and articulate verbal expression. · Cultivate active listening abilities, recognizing different listening types, overcoming obstacles, and employing strategies for attentive and effective communication. · Develop proficient written communication skills for business purposes, demonstrating understanding of essential writing elements, appropriate styles, and the creation of reports, notices, agendas, and minutes that effectively convey information.
Assessment Method	Class test + Quiz = 20%; Mid-semester = 25%; Assignment = 15%; End semester = 40%

Suggested Reading

Balzotti, Jon. Technical Communication: A Design-Centric Approach. Routledge, 2022.
Kaul, Asha, Business Communication. PHI Learning Pvt. Ltd. 2009
Laplante, Phillip A. Technical Writing: A Practical Guide for Engineers, Scientists, and Nontechnical Professionals. CRC Press, 2018.
Lawson, Celeste, et al. Communication Skills for Business Professionals, Second Edition. CUP, 2019.
Sharon Gerson and Steven Gerson. Technical Writing: Process and Product (8th Edition), London: Longman, 2013
Rentz, Kathryn, Marie E. Flatley & Paula Lentz. Lesikar’s Business Communication Connecting in a Digital world, McGraw-Hill, Irwin.2012
Allan & Barbara Pease. The Definitive Book of Body Language, New York, Bantam,2004
Jones, Daniel. The Pronunciation of English, New Delhi, Universal Book Stall.2010
Savage, Alice. Effective Academic Writing. OUP. 2014
Swan and Alter. Oxford English grammar course. OUP. 201

2.5

TOTAL

23.5

Probability Theory and Ordinary Differential Equations

Sl. No.

Subject Code

SEMESTER II

MA1201

Course Number	MA1201
Course Credit (L-T-P-C)	3-1-0-4
Course Title	Probability Theory and Ordinary Differential Equations
Learning Mode	Lectures and Tutorials
Learning Objectives	To introduce the basic concepts of probability, statistics, and Differential equations.
Course Description	This course aims to cover basic concepts of probability, statistics and ordinary differential equations. In particular, popular distributions, random sampling, various estimators and hypothesis testing will be discussed. Students will also get exposure to the linear ordinary differential equations and their solution techniques.
Course Content	Probability (12 Lectures): Random variables and their probability distributions, Cumulative distribution functions, Expectation and Variance, probability inequalities, Binomial, Poisson, Geometric, negative binomial distributions, Uniform, Exponential, beta, Gamma, Normal and lognormal distributions. Statistics (10 Lectures): Random sampling, sampling distributions, Parameter estimation, Point estimation, unbiased estimators, maximum likelihood estimation, Confidence intervals for normal mean, Simple and composite hypothesis, Type I and Type II errors, Hypothesis testing for normal mean. Ordinary Differential Equations (20 Lectures): First order ordinary differential equations, exactness and integrating factors, Picard's iteration, Ordinary linear differential equations of n-th order, solutions of homogeneous and non-homogeneous equations (Method of variation of parameters). Systems of ordinary differential equations, Power series methods for solutions of ordinary differential equations. Legendre equation and Legendre polynomials, Bessel equation and Bessel functions.
Learning Outcome	Students will get exposure and understanding of: 1. Random variables and their probability distributions 2. Understand popular distributions and their properties 3. Sampling, estimation and hypothesis testing 4. Solution of ordinary differential equations 5. Solution of system of ordinary differential equations 6. Special functions arising as power series solutions of ordinary differential equations
Assessment Method	Quiz /Assignment/ MSE / ESE

Text Books:

Hogg, R. V., Mckean, J. and Craig, A. T., “Introduction to Mathematical Statistics”, 8th Ed., Pearson Education India, 2021
M. Ross “An introduction to Probability Models, Academic Press INC, 11th edition.
Miller, I. and Miller, M., “John E. Freund's Mathematical Statistics with Applications”, 8th Ed., Pearson Education India, 2013
L. Ross, Differential equations, 3rd Edition, Wiley, 1984
E. Boyce and R. C. Di Prima, Elementary Differential equations and Boundary Value Problems, 7th Edition, Wiley, 2001.

CS1201

Course Number	CS1201
Course Credit	3-0-3-4.5
Course Title	Data Structure
Learning Mode	Offline
Learning Objectives	· Understand the principles and concepts of data structures and their importance in computer science. · Learn to implement various data structures and understand how different algorithms works. · Develop problem-solving skills by applying appropriate data structures to different computational problems. · Achieving proficiency in designing efficient algorithms.
Course Description	This course provides a comprehensive study of data structures and their applications in computer science. It focuses on the implementation, analysis, and use of various data structures such as arrays, linked lists, stacks, queues, trees, and graphs. Through theoretical concepts and practical programming exercises, this course aims to develop problem-solving and algorithmic thinking skills essential for advanced topics in computer science and software development.
Course Outline	· Introduction to Data Structure, · Time and space requirements, Asymptotic notations · Abstraction and Abstract data types · Linear Data Structure: stack, queue, list, and linked structure · Unfolding the recursion · Tree, Binary Tree, traversal · Search and Sorting, · Graph, traversal, MST, Shortest distance · Balanced Tree Practical component: Lab to be conducted on a 3-hour slot weekly. It will be conducted with the theory course so the topics for problems given in the lab are already initiated in the theory class.
Learning Outcome	· Understand Data Structure Fundamentals · Implement Basic Data Structures using a programming language · Analyse and Apply Algorithms · Design and Analyse Tree Structures · Understand the usage of graph and its related algorithms · Design and Implement Sorting and Searching Algorithms · Debug and Optimize Code
Assessment Method	Internal (Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Reading

Alfred V. Aho, John E. Hopcroft, Jeffrey D. Ullman, Data Structures and Algorithms, Published by Addison-Wesley
Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest and Clifford Stein., Introduction to Algorithms,
Mark Allen Weiss, Data Structures and Algorithm Analysis in Java
Robert Sedgewick and Kevin Wayne, Algorithms
Narasimha Karumanchi, Data Structures and Algorithms Made Easy

4.5

CH1201/CH1101

Course Number	CH1101/CH1201
Course Credit	L-T-P-C: 3-1-3-5.5
Course Title	Chemistry
Learning Mode	Offline
Learning Objectives	The course aims to lay a foundation for all three branches of chemistry, viz. Organic, Inorganic, and Physical Chemistry. The course aims to nurture knowledge to appreciate the interface of chemistry with other science and Engineering branches by combining theoretical concepts and experimental studies.
Course Description	This course introduces basic organic chemistry, inorganic chemistry and Physical chemistry to understand fundamental laws that governs various reactions, reaction rates, equilibrium, and their applications in daily life through relevant experimentation.
Course Outline	Module 1: Thermodynamics: The fundamental definition and concept, the zeroth and first law. Work, heat, energy and enthalpies. Second law: entropy, free energy and chemical potential. Change of Phase. Third law. Chemical equilibrium. Conductance of solutions, Kohlrausch’s law-ionic mobilities, Basic Electrochemistry. Module 2: Coordination chemistry: Crystal field theory and consequences color, magnetism, J.T distortion. Bioinorganic chemistry: Trace elements in biology, heme and non-heme oxygen carriers, haemoglobin and myoglobin; Organometallic chemistry. Module 3: Stereo and regio-chemistry of organic compounds, conformational analysis and conformers, Molecules devoid of point chirality (allenes and biphenyls); Significance of chirality in living systems, organic photochemistry, Modern techniques in structural elucidation of compounds (UV–Vis, IR, NMR). Module 4 (Lab Component): Experiments based on redox and complexometric titrations; synthesis and characterization of inorganic complexes and nanomaterials; synthesis and characterization of organic compounds; experiments based on chromatography; experiments based on pH and conductivity measurement; experiment related to chemical kinetics and spectroscopy.
Learning Outcome	Students will be able to 1. identify organic and inorganic molecules and relate them to daily life applications through experiments. 2. understand important hypothesis, laws and their derivations to intercept physical phenomenon of chemical reactions and apply them in hands-on experiments. 3. understand the importance of organic and inorganic molecules in our body and environment. 4. know important analytical techniques to intercept chemical entity. 5. approach organic and inorganic synthesis as a skillset for drug manufacturing, calculate limiting reagents and yields, use various analytical tools to characterize organic compounds, interpret and ascertain data related to Physical chemistry aspects and know laboratory safety measures, risk factors and scientific report writing skills.
Assessment Method	Theory: 20% Quiz and assignment, 30% Mid sem and 50% End semester exams for theory part (4 credits). Lab: 60% lab report, lab performance and assignment, 20% End semester exam for practical part, 20% viva/quiz (1.5 credits). Overall Weightage: Theory (70%), Lab (30%).

Suggested Reading:

Text books:

Vogel's Qualitative Inorganic Analysis, G. Svehla, 7^th Edition, Revised, Prentice Hall, 1996.
J. Elias, S. S. Manoharan and H. Raj, "Experiments in General Chemistry", Universities Press (India) Pvt. Ltd., 1997.

A. J. Elias, A Collection of Interesting General Chemistry Experiments, revised edition, Universities Press (India) Pvt. Ltd., 2007.

Albert Cotton, G. Wilkinson, C. A. Murillo, M. Bochmann, Advanced Inorganic Chemistry - 6^th Edition New Delhi: Wiley India, 2008.
Mukkanti, Practical Engineering Chemistry, B.S. Publications, Hyderabad, 2009.
Shriver and Atkins inorganic chemistry / Peter Atkins, Tina Overton, Jonathan Rourke, Mark Weller, Fraser Armstrong-5^th Edition – Oxford: UOP. 2012.
Atkins’ Physical Chemistry, Peter Atkins, Julio de Paula, James Keeler, Oxford University Press, 11^th Edition 2017.
L. Kapoor, A Textbook of Physical Chemistry, Vol: 1, 2 (6^th Edition, 2019), Vol: 3 (5^th Edition, 2020) MaGraw Hill.
R. Chatwal, S. K. Anand, Instrumental Methods of Chemical Analysis, 5^th Edition, Himalaya Publications, 2023.

5.5

ME1201/ME1101

Course Number	ME1101/ME1201
Course Credit	L-T-P-C: 0-0-3-1.5
Course Title	Mechanical Fabrication
Learning Mode	Fabrication work – hands on fabrication work in Workshop
Learning Objectives	Complies with PLOs 3-4. · This course aims to develop the concepts and skills of various mechanical fabrication methods. · Fabrication of metallic and non-metallic components, fabrication using bulk and sheet metals, subtractive and additive manufacturing methods, and assemble the parts
Course Description	This course is designed to fulfil the need of hand on experience about various approaches (conventional and CNC, subtractive and additive) of mechanical fabrication approaches. Prerequisite: NIL
Course Outline	The jobs for various shops should be planned such that they are the parts of an assembled item. The student groups will fabricate different parts in various shops which will involve some amount of their creativeness/input particularly in design and/or planning. Various components as required for the assembled part can be made using the following shops: Sheet Metal Working: Development, sheet cutting and fabrication of designated job using sheet metal (ferrous/nonferrous); Joining of required portions by soldering, in case part is desired to be made leak proof. Pattern Making and Foundry: Making of suitable pattern (wood); making of sand mould, melting of non-ferrous metal/alloy (Al or Al alloys), pouring, solidification. Observation/identification of various defects appeared on the component. Joining: Butt/lap/corner joint job fabrication as required of low carbon steel plates; weld quality inspection by dye-penetration test (non-destructive testing approach)of the component made. Demonstration of semi-automatic Gas Metal Arc welding (GMAW). Conventional machining: Operations on lathe and vertical milling to fabricate the required component. The fabrication of the component should cover various lathe operations like straight turning, facing, thread cutting, parting off etc., and operations using indexing mechanism on vertical milling. CNC centre: Fundamentals of CNC programming using G and M code; setting and operations of job using CNC lathe or milling, tool reference, work reference, tool offset, tool radius compensation to fabricate the component with a designed profile on Al/Al-alloy plate. 3D printing (Fused Filament Fabrication): (2 weeks) Create the model, select appropriate slicing and path for fabrication of a 3D job by layer deposition (additive manufacturing approach) using polymeric material. Demonstration on pattern fabrication using 3D printing.
Learning Outcome	· This course would enable the students to develop the concept of design, fabrication (subtractive and additive) for various engineering applications. Fabrication of components and assemble them. · The practical skill and hands on experience for various fabrication methods from bulk, sheet metal using conventional as well as CNC machines.
Assessment Method	Fabrication of components in each of the shops required for assembly of the given part; submission of reports for each shop, and quiz assessment.

Text and Reference books:

Hajra Choudhury, HazraChoudhary and Nirjhar Roy, 2007, Elements of Workshop Technology, vol. I,Mediapromoters and Publishers Pvt. Ltd.
W A J Chapman, Workshop Technology, 1998, Part -1, 1st South Asian Edition, Viva Book Pvt Ltd.
N. Rao, 2009, Manufacturing Technology, Vol.1, 3rd Ed., Tata McGraw Hill Publishing Company.
Adithan, B.S. Pabla, 2012, CNC machines, New Age International Publishers

1.5

ME1202/ME1102

Course Number	ME1102/ME1202
Course Number	Engineering Mechanics
L-T-P-C	3-1-0-4
Pre-requisites	Nil
Semester	Spring
Learning Mode	Lectures
Learning Objectives	Complies with PLOs 1, 4 · The objective of this first course in mechanics is to enable engineering students to analyze basic mechanics problems and apply vector-based approach to solve them.
Course Outline	1. Rigid body statics: Equivalent force system. Equations of equilibrium, Free body diagram, Reaction, Static indeterminacy. 2. Structures: 2D truss, Method of joints, Method of section. Beam, Frame, types of loading and supports, axial force, Bending moment, Shear force and Torque Diagrams for a member. 3. Friction: Dry friction (static and kinetic), wedge friction, disk friction (thrust bearing), belt friction, square threaded screw, journal bearings, Wheel friction, Rolling resistance. 4. Centroid and Moment of Inertia 5. Introduction to stress and strain: Definition of Stress, Normal and shear Stress. Relation between stress and strain, Cauchy formula. Stress in an axially loaded member and stress due to torsion in axisymmetric section
Learning Outcomes:	Following learning outcomes are expected after going through this course. · Learn and apply general mathematical and computer skills to solve basic mechanics problems. · Apply the vector-based approach to solve mechanics problems.
Assessment Method	Mid semester examination, End semester examination, Class test/Quiz, Tutorials

Reference Books

Shames, Engineering Mechanics: Statics and dynamics, 4th Ed, PHI, 2002.
P. Beer and E. R. Johnston, Vector Mechanics for Engineers, Vol I - Statics, 3rd Ed, Tata McGraw Hill, 2000.
L. Meriam and L. G. Kraige, Engineering Mechanics, Vol I - Statics, 5th Ed, John Wiley, 2002.
P. Popov, Engineering Mechanics of Solids, 2nd Ed, PHI, 1998.
P. Beer and E. R. Johnston, J.T. Dewolf, and D.F. Mazurek, Mechanics of Materials, 6th Ed, McGraw Hill Education (India) Pvt. Ltd., 2012.

IK1201

Indian Knowledge System (IKS)

TOTAL

22.5

Sl. No.

Subject Code

SEMESTER III

CE2101

Geomatics Engineering

Course	CE2101: Geomatics Engineering
Course Credit (L-T-P-C)	3-1-2-5
Course Title	Geomatics Engineering
Learning Mode	Lectures
Learning Objectives	Complies with PLO- 1 and 4 Understand the need of survey for the start of any project Understand the utilities of basic survey equipment Understand the importance of precise measurement, errors and accuracy, Importance of levels, R.L. and advance methods for plotting the points on ground with the help of map.
Course Description	This course deals with the theoretical learning of basic and advance survey methods and finding the errors and accuracy of any measurement. It also deals with the learning of basic and advanced survey methods.
Course Outline	Lecture: Introduction to surveying; linear measurements; chain surveying; compass surveying; accuracy, precision and errors, leveling; plane table; contouring, theodolite surveying, tacheometric survey; trigonometrical surveying; triangulation; curves; advanced survey instruments; Electronic Distance Measurement, Total station and Global Positioning System, Introduction to photogrammetry and remote sensing. Practical: Chain (tapes) surveying; Offsets, Compass surveying; Plane table survey; Theodolite surveying: Vertical and horizontal angle measurements, Theodolite Traversing; Triangulation and correction of errors; Leveling, computation of earth work; Contours; Tacheometric surveying; Trigonometric surveying; Total station; Setting out of buildings; Layout of simple circular curves.
Learning Outcome	At the end of the course, the student will be able to gather the information on accurate measurement of lines, angles, areas. Finding the errors and accuracy. Purpose and utility of basic and advanced survey for any construction project.
Assessment Method	Assignments, Quizzes, Mid-semester examination and End-semester examination.

Textbooks/ Reference books:

C. Punmia, A.K. Jain & A.K. Jain, Surveying, Vol-I and Vol-II, Laxmi Publication Pvt., 1996.
K. Duggal, Surveying, Vol-I and Vol-II, Mc.Graw Hill Publication, 2013.
P. Kanetkar and S. V. Kulkarni, Surveying and Levelling, Vol-I and Vol-II, Pune Vidyarthi Griha Prakshan, 1972.
W. Schofield, Engineering Surveying, Butterworth, Heinemann, New Delhi, 2001.
IS Code Provisions.

5.0

CE2102

Structural Mechanics

Course	CE2102: Structural Mechanics
Course Credit (L-T-P-C)	3-1-0-4
Course Title	Structural Mechanics
Learning Mode	Lectures
Learning Objectives	Objective for learning this course are Lectures: Complies with PLO-1, 2, and 4 Understand the need of mechanics of material and structure for the design of any civil engineering project. 2. Equip the students with strong foundation in civil and environmental engineering for both research and industrial scenarios. 3. Provide scientific and technical knowledge in planning, design, construction, operation and maintenance of civil engineering infrastructure.
Course Description	The course discusses the basic mechanics and behavior of materials under loads, strains, and deformations with various examples.
Course Outline	Introduction to mechanics of materials and structures, Simple bending theory, flexural and shear stresses, Stress / Strain Transformation unsymmetrical bending, shear centre. Thin walled pressure vessels, uniform torsion, buckling of column, combined and direct bending stresses. Different types of structures, loads on the structural system, static and kinematic indeterminacy, Methods of Analysis: Equilibrium equations, compatibility requirements, Introduction to force and displacement methods, Analysis of trusses: plane truss, compound truss, complex truss and space truss, three hinged arches and suspension cables, Bending moment and shear force diagram, Deflection of Beams, various methods for calculation of deflection.
Learning Outcome	At the end of the course, student would be able to Lectures: 1. Understand the basics of the strength of materials. 2. Get an overview of structural engineering. 3. Study this course as a prerequisite for any civil engineering design-based courses.
Assessment Method	Assignments, Quizzes, Project work, Mid-semester examination and End-semester examination.

Textbooks/ Reference books:

Ferdinand P. Beer, E. Russell Johnston Jr., John T. DeWolf, Mechanics of Materials, McGraw-Hill Education, 8th edition (2020).
C. Hibbeler, Mechanics of Materials, Prentice Hall, 11th edition (2022).
N. Frantziskonis, Essentials of the Mechanics of Materials, Destech Pubns Inc., 3rd edition (2017).
S. Reddy, Basic Structural Analysis, Tata McGraw Hill, 3rd edition (2017).
C. Hibbeler, Structural Analysis, Pearson Education, 10th edition (2022).
P. Popov, Engineering Mechanics of Solids, Pearson, 2nd edition (1998).
S. Negi and R. S. Jangid, Structural Analysis, Tata McGraw Hill, New Delhi, 6th edition (2003).
S. Khurmi and N. Khurmi, Theory of structures, Schand, 10th edition, 2000.
M. Leet, C. M. Uang, J. T. Lanning, and A. M. Gilbert, Fundamentals of Structural Analysis, McGraw Hill, 5th edition, 2017.
K. Roy and S. Chakrabarty, Fundamentals of Structural Analysis, S Chand & Company, 2nd edition, 2003.

4.0

CE2103

Civil Engineering Materials

Course	CE2103 Fluid Mechanics
Course Credit (L-T-P-C)	3-1-2-5
Course Title	Fluid Mechanics
Learning Mode	Lectures and Practical
Learning Objectives	Complies with PLO-1, 2, 3 and 5 1. The course on fluid mechanics is devised to introduce fundamental aspects of fluid behaviour. 2. The students will be able to understand and apply the principles of mechanics i.e. conservation of mass, momentum and energy to fluid flow and deformation for civil engineering applications.
Course Description	This course will discuss the definition of fluid and its properties, basic concepts of fluid statics, kinematics and dynamics of fluid flow. The course uses differential fluid mechanics for the theoretical solution of various fluid flow systems.
Course Outline	Fluid properties; Pressure measurement; Hydrostatic forces on plane and curved surfaces; Buoyancy and equilibrium; Stability, metacentric height; Types of flow; Continuity; Energy and momentum equations; Velocity distribution and velocity coefficients, practical applications; Navier-Stoke equation; Shear stress and pressure gradient; Flow through pipes, Hagen-Poiseuille equation; Turbulence, Prandtl’s mixing length, eddy viscosity; Darcy-Weisbach equation for flow through pipes, friction factor, Moody diagram, minor losses, pipes in series and parallel, equivalent length, pipe network analysis; Water hammer; Boundary layer concept, drag coefficients, control of boundary layer; Dimensional analysis and similitude, Introduction to pumps and turbines.
Learning Objectives	Practical: Complies with PLOs 1, 2, 3 and 5 1. Hands-on experience in the measurement of hydro-meteorological data and environmental parameters influencing water resources. Students will learn to analyze and interpret the environmental data and understand the controls in hydraulic structures.
Course Description	This course exposes the students to experimental setups for measuring fluid properties, and visualize behaviour of the fluid under static, kinematic and dynamic conditions. It also gives exposure to measure flow parameters required in civil engineering applications.
Course Outline	Measurement of fluid pressure using various manometers and gauges, Experimental study on capillarity, Determination of coefficient of viscosity of a fluid using viscometer, Experimental study on the stability of floating bodies, Study on fluid pressure distribution on immersed bodies, Study of different types of flow using Reynold’s apparatus, Determination of friction factor in pipes using pipe friction apparatus, Experimental studies on centrifugal and reciprocating pumps, Experimental studies on impulse and reaction turbines.
Learning Outcome	After attending this course, the students are expected to know the following: 1. Fluid properties stress-strain relationship in fluids 2. Understand and apply the principles of conservation to fluid under static, kinematic and dynamic conditions 3. Should be able to understand the principles of flow measurement i.e. discharge, pressure, losses in flow and its application to civil engineering.
Assessment Method	Assignments, Quizzes, Mid-semester examination, and End-semester examination

Text Books/ Reference Book:

M. White, Fluid Mechanics, McGraw Hill, 1994.
Som, Biswas and Chakrabarty, Introduction to Fluid Mechanics and Fluid Machines, Tata McGraw-Hill Education
Modi P.N. & Seth, S.M. Hydraulics and Fluid Mechanics, Standard Book House, New Delhi 2013
Fundamentals of Fluid Mechanics by Munson, Young & Okiishi
L. Streeter and E.B. Wylie, Fluid Mechanics, McGraw Hill, 1997
S. Massey, Mechanics of Fluids, Van Nostrand Reinhold Co., 1979.
Frabzini, Fluid Mechanics with Engineering Applications, McGraw Hill, 1997.
H. Spurk, Fluid Mechanics – Problems and Solutions, Springer, 2003.
K. Khan, Fluid Mechanics and Machinery, Oxford Higher Education, 2015.

P. N. Modi and S.M. Seth, Hydraulics and Fluid Mechanics, Standard Book House, 1998.
K. L. Kumar, Engineering Fluid Mechanics, Eurasia Publishing Company (P) Ltd., New Delhi, 1999.
Annapureddy Domodara Reddy, “Fluid Mechanics and Hydraulic Machines Lab manual”, LAMBERT Academic Publications.
Madan Mohan Das, Mimi Das Saikia , Bhargab Mohan Das, “Hydraulics and Hydraulic Machines Textbook”, PHI Learning, 1st edition, 2013.

5.0

CE2104

Geology for Engineers

Course	CE2104 Geology for Engineers
Course Credit (L-T-P-C)	3-0-2-4
Course Title	Geology for Engineers
Learning Mode	Lectures & Practical
Learning Objectives	Complies with PLO- 1, 2, 3, 4 and 5. Students will be able to Understand about the fundamentals of earth system and their dynamics. Provide scientific and technical knowledge about earth minerals, groundwater system and geological structures. Identify the various geological structures and stress distribution due to various loading. Analyze the engineering properties of rocks and site investigation of various geological rock mass Perform laboratory and field observations to apply knowledge for better understanding of geology in engineering practices.
Course Description	The course is designed to provide both theoretical and practical knowledge to geology, its significance and application in Civil Engineering.
Course Outline	Theory: Introduction to Engineering Geology, Silicate Structures and Symmetry Elements, Origin and Formation of Rocks, Groundwater related Engineering Issues, Geological Structures, Stress Distribution, Geologic Hazards, Engineering Properties of Rocks, Geological Site Criteria, Site Investigation. Laboratory Experiments and Practices: Geological Maps; Geological Mapping; Apparent and true dips; Three point problems; Depth and thickness problems; Joints; Faults; Megascopic and Microscopic identification of minerals and rocks; Rock classification and engineering properties of rocks: refraction and resistivity methods; Field trip.
Learning Outcome	At the end of the course, student would be able to: Develop ability to understand the importance of geology and complexity associated. Understand the origin of rocks and their characteristics. Comprehend with various issues such as ground water, geological structures and geological hazards Analyze the stress and strain behaviour, engineering properties of rocks and site investigation of various geological rock mass Perform laboratory tests, field observations and apply knowledge for better understanding of geology and construction of structures in complex geological terrain
Assessment Method	Assignment, Quizzes, Mid-semester examination and End-semester examination.

Textbooks:

Gokhale, K. V. G. K., Principles of Engineering Geology, Revised Edⁿ., B S Publications, Hyderabad, 2019.
Singh, P., Engineering and General Geology, S. K. Kataria and Sons, Reprint, 2013.
Waltham, A. C., Foundations of Engineering Geology, Taylor & Francis, 3^rd, 2009.

Reference books:

Kehew, Alan E., General Geology for Engineers, Prentice Hall, 1988.
Kesavulu, N. Chenna, Textbook of Engineering Geology, Laxmi Publications Pvt Ltd., 3^rd Edⁿ. 2018.
Ramamurthy, T., Engineering in rocks for slopes, foundations and tunnels, Prentice Hall India, 2010.
All relevant Indian Standard (IS) and international codes.

4.0

HS21XX

HSS Elective - I

3.0

TOTAL

21.0

Semester -IV

Sl. No.

Subject Code

SEMESTER IV

CE2201

Structural Analysis

Course	CE2201: Structural Analysis
Course Credit (L-T-P-C)	3-0-2-4
Course Title	Structural Analysis
Learning Mode	Lectures and Practical
Learning Objectives	Objective for learning this course are Lectures: Complies with PLO-1, 2, and 4 1. Understand the determinate and indeterminate structures and develop abilities to idealize and analyses such structures. 2. Familiarity with the concept of influence line for solving problems with moving loads. 3. Understand the matrix method and its application for computer-based analysis of structure. Practical: Complies with PLO- 1 and 4 Calculation of influence line diagram for different type of structures. Application of software in advance structural analysis.
Course Description	Structural analysis is the determination of the effects of loads on physical structures and their components. It incorporates the fields of applied mechanics, materials science and applied mathematics to compute a structure’s deformations, internal forces, stresses, and support reactions. This is an important part of the engineering design of structures. Practical of the course will focuses on the understanding of behaviour and response of different civil engineering structures (beam, column, truss, frame, bridge) under static and moving loads.
Course Outline	Lecture: Introduction to structural analysis: determinate and indeterminate structures, Analysis of the indeterminate structures by force methods, flexibility coefficients, Energy methods: Principle of minimum potential energy, principle of virtual work, Reciprocal theorem, unit load method, Influence line and Rolling loads, beam, frames and arches, Muller-Breslau Principles and its applications to determinate and indeterminate structures. Analysis of Beams and Frames: Moment Area method, Slope deflection method, Three Moment Equation, Moments distribution methods, effect of symmetry and antisymmetry, sway correction, Matrix method of structural analysis, Displacement/ Stiffness methods. Practical: Bending moments and deflection analysis of determinate and indeterminate beams, Unsymmetrical bending and shear centre, Analysis of pin jointed frameworks, Bending moments in a portal frame, Column buckling, Flexural test on steel beam, Analysis of 2-D and 3-D truss, Analysis on suspension cable bridge, Influence line diagram of bridge under moving loads, Exposure to advanced structural analysis softwares.
Learning Outcome	At the end of the course, student would be able to Lectures: 1. Analyze determinate and indeterminate structures. 2. Use influence line diagram in design. Practical: 1. Estimate response of different civil engineering structures such as beam, column, truss, frame and bridge under static loads. 2. Determination of influence line diagram for bridge structure. 3. Able to use advanced structural analysis softwares.
Assessment Method	Assignments, Lab Reports, Quizzes, Project work, Mid-semester examination and End-semester examination.

Textbooks/ Reference books:

R .C. Hibbeler, Structural Analysis, Pearson Education, 10th edition (2022).
S. Reddy, Basic Structural Analysis, Tata McGraw Hill, 3rd edition (2017).
K. Wang, Intermediate Structural Analysis, McGraw Hill Education, 1st edition (2017).
S. Prakash Rao, Structural analysis: Unified approach, Universities Press, 1996.
H. Norris, J. B. Wilbur, S.Utku, Elementary Structural Analysis, Tata McGraw Hill, 4th edition (2003).
S. Negi and R. S. Jangid, Structural Analysis, Tata McGraw Hill, New Delhi, 6th edition (2003).
Weaver and J. M. Gere, Matrix analysis of framed structures, CBS Publishers, 2nd edition (2018).
S. Pandit and S.P. Gupta, Structural Analysis - A matrix approach, Tata McGraw Hill, 2nd edition (2008).
B. Kanchi, Matrix Methods of Structural analysis, Enlarged edition, Wiley Eastern Limited (2016).
S. Khurmi and N. Khurmi, Theory of structures, Schand, 10th edition, 2000.
M. Leet, C. M. Uang, J. T. Lanning, and A. M. Gilbert, Fundamentals of Structural Analysis, McGraw Hill, 5th edition, 2017.
K. Roy and S. Chakrabarty, Fundamentals of Structural Analysis, S Chand & Company, 2nd edition, 2003.

4.0

CE2202

Soil Mechanics

	CE2202: Soil Mechanics
Course Credit (L-T-P-C)	3-0-2-4
Course Title	Soil Mechanics
Learning Mode	Lectures and Practical
Learning Objectives	Complies with PLO- 1, 2, 3, and 5. Soil is most important material for Civil Engineers, therefore, learning the procedure to determine soil properties and gaining knowledge about variation of properties is of utmost important for any Civil Engineering students Following are the learning objectives Students should be appraised of importance of Soil Mechanics for various Civil Engineering applications. Take appropriate decision to determine important properties as per site condition and structure to be built. Should be able to find out whether soil investigation report provides right or wrong data.
Course Description	This course provides an overview of soil mechanics that is applicable in Civil Engineering for foundation design of any superstructure. Detailed soil characterizations, strength, permeability, and compaction properties are covered. Further, the course covers a detailed laboratory testing by using various method as per Indian Standard (IS code) test procedures to determine and understand the physical and engineering properties of soils for design of different civil engineering construction projects.
Course Outline	Lectures: Origin and Mineralogy, Classification, Index Properties, Consistency Limits, Effective Stress, Stress within Soil, Permeability, Compaction, Consolidation, Shear strength. Practical: Determination of water content, specific gravity, in-situ density, Relative density. Particle size distribution by sieve analysis and hydrometer, Atterberg’s limits, Standard and modified Proctor test, Determination of Coefficient of permeability; Shear strength parameters of soil using direct shear, vane shear test.
Learning Outcome	At the end of theory lectures, student would be able to: Identify type of soil based on soil property Determine settlement of soil from consolidation property Determine required OMC, MDD for compaction test results Determine shear strength properties of soil. Take appropriate decision on tests to be conducted for finding design parameters of particular foundation. At the end of practical classes, student would be able to: 1. Determine physical and engineering properties of soils 2. Classify the soil based on laboratory test results 3. Verify any soil investigation report 4. Decide type of test to be conducted for particular type of soil.
Assessment Method	Theory: Assignments, Quizzes, Mid-semester examination and End-semester examination. Practical: Lab Reports, Viva, Mid-semester examination and End-semester examination.

Textbooks:

V. N. S. Murthy, Geotechnical Engineering: Principles and Practices of Soil Mechanics and Foundation Engineering, CRC Press, Taylor & Francis Group, Third Indian Reprint, 2013.
R. F. Craig, Craig’s Soil Mechanics, Taylor & Francis Group, 7th Edition, 2004.
Indian Standard (IS) codes practices for soil testing.
J. Bardet, Experimental Soil Mechanics, Upper Saddle River, Prentice Hall, USA, 1992.
D. Fratta, J. Aguettant, and L. R. Smith, Soil Mechanics Laboratory Testing, Boca Raton, CRC Press, USA, 2007.

Reference books:

Gopal Ranjan, and A. S. R. Rao, Basic and Applied Soil Mechanics, New Age International Publishers, 2nd Edition 2000.
K. Terzaghi, R. B. Peck and G. Mesri, Soil Mechanics in Engineering Practice, John Wiley & Sons, 1996.
B.M. Das, Principle of Geotechnical Engineering, Cengage Learning, eighth Edition, 2013.
All other relevant IS and international codes such as BS code, ASTM etc.

4.0

CE2203

Course	CE2203 Civil Engineering Materials
Course Credit (L-T-P-C)	3-0-2-4
Course Title	Civil Engineering Materials
Learning Mode	Lectures and Practical
Learning Objectives	Complies with PLO- 2 1. To provide fundamental knowledge in civil engineering materials. 2. Provide scientific and technical knowledge to prepare students to address civil engineering materials-related challenges in the field. 3. To train students to meet the current and future demand for civil engineering materials for construction industries.
Course Description	This course will discuss fundamental concepts in civil engineering materials. The course will cover theory and real-world practices in materials used in construction industries, their operations, and execution. The principal properties of building materials and up-to-date knowledge of the manufacturing of civil engineering materials will be discussed.
Course Outline	Introduction to building materials, Cement: Chemical composition, manufacturing, physical characteristics, hydration, properties of cement compounds, different types of cements, Aggregate: Coarse and fine aggregates, Influence of aggregate on the properties of concrete, aggregate selection. Fresh Concrete: Batching, Mixing, workability, effect of admixture, Hardened Concrete: mechanical properties of hardened concrete, Water-cement ratio, Porosity, Curing of concrete, High performance concrete, Design of concrete mix: IS code recommendation. Flyash. Brick: Raw materials, drying and burning, Strength and durability, mortar for masonry and strength of masonry, Timber, Seasoning and conversions, properties, tests, defects in timbers, FRPs: Chemical compositions, mechanical and physical properties, Various types of FRPs, Metals: steel for reinforced concrete and prestressed concrete construction, structural steel sections, Deterioration of building materials: Corrosion, chloride and sulphate attack on concrete, alkali-aggregate reaction, acid aggregate reactions Practical: Cement tests: normal consistency, initial and final set time; Coarse and fine aggregate tests: specific gravity, Sieve analysis, Los Angeles/Deval’s abrasion, Flakiness and elongation, Impact test;, fineness modulus, moisture content, SSD condition, unit weight and bulking of sand; Concrete tests: workability, strength, admixtures, mix design; Brick tests: moisture absorption, compressive strength, flyash.
Learning Outcome	At the end of the course, student would be able to: 1. Understand the physical and engineering properties, principles, testing, and standards of civil engineering materials used in construction. 2. Design mix of concrete for various construction industries. 3. The use of different civil engineering materials subjected to different construction scenarios and needs. 4. Understand the in-depth knowledge of mechanisms and factors influencing the manufacturing of civil engineering materials.
Assessment Method	Assignment, Quizzes, Mid-semester examination and End-semester examination .

Textbooks:

S. Somayaji, Civil Engineering Materials, Prentice Hall, New Jersey, 2001.
M. Neville and J. J. Brooks, Concrete Technology, Pearson Education, Fourth Indian reprint, 2004.

S. Shetty, Concrete Technology, S. Chand and Company Ltd. 2005.

M. S. Mamlouk and J. P. Zaniewski, Materials for Civil and Construction Engineers, Pearson, Prentice Hall, Second edition, 2006.
P.C Varghese, Building Materials, Publisher: ‎ Prentice Hall India Learning Private Limited; 2nd edition (1 January 2015)

Reference books:

All relavent IS Codes.
Jackson and R. K. Dhir, Civil Engineering materials, Macmillan Fourth edition 1997.
C. Aitcin, High Performance Concrete, E & Fn Spon, 1998.
F. Shackelford and M. K. Muralidhara, Introduction to Material science for Engineers, Pearson Education, Sixth edition, 2007.
Haimei Zhang, Building materials in civil engineering, Publisher: ‎Woodhead Publishing (9 May 2011).
Parbin Singh, Civil engineering materials,Publisher ‏: ‎ S K Kataria and Sons; Reprint 2013 edition.
K Duggal, Building Materials, New Age International Publisher, 4th edition.

4.0

CE2204

Water Resources Engineering-I

Course	CE2204 Water Resources Engineering-I
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Water Resources Engineering-I
Learning Mode	Lectures
Learning Objectives	Lectures: Complies with PLOs 1, 2, 3 and 5 1. Students will be enabled to understand the fundamental principles governing open channel hydraulics for the design of engineering systems. 2. The students will be exposed to the application of water conveyance and water retention/detention structures in the context of agriculture.
Course Description	This course offers an introduction and analysis of flows in open channels with emphasis on applying efficient solution techniques.
Course Outline	Flow through open channels: Uniform flow, Critical flow, Gradually Varied flow, Rapidly Varied flow, Spatially Varied flow, Unsteady flow. Pumps and turbines, surges, water hammer. Flow Measurement: Pressure, Velocity and Discharge measurement techniques. Introduction to Hydraulic Structures, importance and uses.
Learning Outcome	At the end of the course, students would be able to understand: 1. The governing principles of gravity flow system 2. The application of open channels in irrigation. 3. The methods of flow measurement and various components of the hydrologic cycle that affect the movement detention/retention of water resources.
Assessment Method	Assignments, Quizzes, Mid-semester examination, and End-semester examination

Text Books/ Reference Book:

K Subramaniya, Flow in Open Channels, McGraw Hill, 1997.
H. Chaudhury, Open channel flow, Second Edition. Springer (2008).
Rajesh Srivastava, Flow through open channels, Oxford University Press (2008).
Sturm, 2001, Open-Channel Hydraulics, McGraw Hill
V.T. Chow, Open-channel hydraulics, McGraw Hill Publications (1959, 1973).
Modi P.N. & Seth, S.M. Hydraulics and Fluid Mechanics, Standard Book House, New Delhi 2013
Todd D.K., Ground Water Hydrology, John Wiley and Sons, 2000

T. Chow, D.R. Maidment, and L.W. Mays, Applied Hydrology, McGraw Hill, 1998

3.0

CE2205

Numerical Methods in Civil Engineering

Course	CE2205: Numerical Methods in Civil Engineering
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Numerical Methods in Civil Engineering
Learning Mode	Lectures
Learning Objectives	Complies with PLO- 1 Understanding applicability of numerical methods to solve engineering problems Knowledge about various types of errors Solving simultaneous equations Basic of Matlab
Course Description	The course comprises a comprehensive method of numerical analysis. Minimization of error in numerical calculations, solving simultaneous equations, numerical differentiation, numerical integration are covered.
Course Outline	Introduction to Numerical Methods: Objectives of numerical methods, sources of error in numerical solutions: truncation error, round off error, order of accuracy - Taylor series expansion. Roots of Equations: Graphical Methods, Bisection Method, Simple Fixed-Point Iteration, Newton-Raphson Method, Secant Method, modified Secant Method. Direct Solution of Linear systems: Naive Gauss elimination, LU Decomposition, matrix Inverse, error analysis and system condition, Gauss-Seidel, Gauss-Jordon, Jacobi iteration, Factorization, Cholesky decomposition. diagonal dominance, condition number, ill-conditioned matrices. Numerical Optimization: Newton’s method in one and multiple dimension, Gradient Method. Curve Fitting: Linear Regression, Polynomial Regression, interpolation, spline fitting and their Civil Engineering application. Numerical Calculus: Trapezoidal and Simpson’s rule for integration and their application. Solving Differential Equation: Euler’s method, Runge-Kutta method, Boundary-Value and Eigenvalue Problem and their application, solving partial differential equation. Applicability of Numerical Methods in Civil Engineering: Exposure to software packages like MATLAB.
Learning Outcome	At the end of the course, student would be able to: Solve simultaneous equations. Write programs using Matlab to numerical solve engineering problems Apply various methods for numerical differentiation Apply various methods for numerical integration Apply various methods of interpolation
Assessment Method	Assignments, Quizzes, Mid-semester examination and End-semester examination .

Textbooks/ Reference books:

S. Chapra and R. Canale, Numerical Methods for Engineers, 6th Ed., McGraw Hill, 2010.
S. Guha and R. Srivastava, Numerical Methods: For Engineering and Science, 1st Ed., Oxford University Press, 2010.
D. Dahlquist, and A. Bork, Numerical Methods, Prentice-Hall, Englewood Cliffs, NJ, 1974.
K. E. Atkinson, Numerical Analysis, John Wiley, Low Price Edition, 2004.
J. D. Hoffman, Numerical Methods for Engineers and Scientists, McGraw‐Hill, 2001.
S. C. Chapra, Applied Numerical Methods with MATLAB for Engineers and Scientists, McGraw‐Hill 2008.

3.0

XX22PQ

IDE - I

3.0

TOTAL

21.0

Design of Reinforced Concrete Structures

Sl. No.

Subject Code

SEMESTER V

CE3101

Course	CE3101: Design of Reinforced Concrete Structures
Course Credit (L-T-P-C)	3-0-2-4
Course Title	Design of Reinforced Concrete Structures
Learning Mode	Lectures and Practical
Learning Objectives	Objective for learning this course are Lectures: Complies with PLO-1, 2, 3 and 4 1. Understand the structural load calculation and design process of concrete structure as per Indian standards/ codes (IS 875 and IS 456: 2000). 2. Understand the design process of different concrete structural components such as beam, column, slab etc. 3. Develop abilities to design and reinforce detailing of concrete structures. 4. Learn analysis and design procedure of RC building. Practical: Complies with PLO- 1, 3 and 4 Use of different sensors and devices for destructive and non-destructive testing. Learn behaviour of different RC structural elements under different loading scenario.
Course Description	In this course, fundamental components of entire structures that are controlled by bending, shear, axial forces, or a combination of them, are identified. The limits of collapse and serviceability will be introduced after a brief description of various design approaches. The design will be carried out in accordance with IS 456:2000. Practical of the course will cover destructive and non-destructive testing, testing of RC structural elements and reinforcement bars.
Course Outline	Lecture: Introduction to reinforced concrete structures, Basic material properties: stress-strain relation of concrete and reinforcing steel, Design philosophy: assumptions and code of practice, limit state method. Theory of singly reinforced members in bending, Design of simply supported and continuous beams with rectangular and flanged section, Limit state of collapse in shear, torsion, Design for bond, Design of one-way and two-way slab systems, Design of columns under uniaxial and biaxial bending, Design of footings and staircase. Introduction to bridges. Practical: Destructive and non-destructive test (UPV & rebound hammer); semi-destructive (core cutting), Testing of reinforcement bar (mild, HYSD & TMT) in tension; Bend and rebend of TMT bar, RCC beam with three and four point-loading (under-reinforced, balanced and over-reinforced).
Learning Outcome	At the end of the course, student would be able to Lectures: 1. Identify the method of analysis for indeterminate structures. 2. Determine the reactions and forces in indeterminate structures using approximate and exact analysis methods. 3. Use of influence line diagram. 4. Analyses the multi-storey frames. Practical: 1. Be familiar with the materials used for building structures. 2. Learn different testing methods of materials used in the concrete structures. 3. Understand the working principle of various sensors and devices to measure physical response of structures. 4. Develop the various concepts of structural analysis through experiments and hands-on.
Assessment Method	Assignments, Lab Reports, Quizzes, Project work, Mid-semester examination and End-semester examination.

Textbooks/ Reference books:

S. U. Pillai and D. Menon, Reinforced Concrete Design, Tata McGraw-Hill, 4th edition, 2021.
N Subramanian, Design of Reinforced Concrete Structures, Oxford, 2013.
P. C. Varghese, Limit State Design of Reinforced Concrete, Prentice Hall India, 2nd edition, 2008.
M. L. Gambhir, Fundamentals of Reinforced Concrete Design, Prentice Hall India, 2006.
A. K. Jain, Reinforced Concrete: Limit State Design, Nem Chand and Bros., 6th edition, 2002.
J. G. MacGregor and J. K. Wight, Reinforced Concrete: Mechanics and Design, Prentice Education India, 6th edition, 2016.
R. Park and T. Paulay, Reinforced Concrete Structures, John Wiley and Sons, 1975.
P. M. Ferguson, J. E. Breen and J. O. Jirsa, Reinforced Concrete Fundamentals, John Wiley and Sons, 5th edition, 1988.
J. C. McCormac and R. H. Brown, Design of Reinforced Concrete, John Wiley and Sons, 9th edition, 2014.
N. Krishnaraju, Advanced Reinforced Concrete Design, CBS Publisher, 2013.
IS 456 : Plain and Reinforced Concrete - Code of Practice, BIS.
IS 875 : Part 1 : Code of Practice For Design Loads (Other Than Earthquake)For Buildings And Structures Part 1 Dead Loads - Unit Weights of Building Material And Stored Materials.
IS 875 : Part 2 : Code of Practice for Design Loads (Other Than Earthquake) For Buildings And Structurres: Part 2 Imposed Loads.
IS 875 : Part 5 : Code of Practice For Design Loads (Other Than Earthquake) For Buildings And Structures Part 5 Special Loads And Combinations.

4.0

CE3102

Foundation Engineering

	CE3102 Foundation Engineering
Course Credit (L-T-P-C)	3-0-2-4
Course Title	Foundation Engineering
Learning Mode	Lectures and Practical
Learning Objectives	Theory complies with PLO- number 1, 2, 3, and 4. The objectives are to Understand the soil exploration and sub-surface ground investigation. Determine earth pressure, stability of retaining walls and slopes for structural design. Evaluate and analyze bearing capacity of soils for application in the different foundations design. Understand various important soil stabilization and ground improvement techniques for engineering applications. Practical complies with PLO- number 1, 2, 3, and 4. The objectives are to Investigate the shear strength behaviour of various types of soil at different conditions. Determine volume change behaviour of soils. Examine the strength of subgrade soils to be used for design and analysis of highway projects Investigate the bearing capacity and settlement behaviour of soils and identify the sub-surface soil behaviour using field tests. Apply the knowledge to identify the suitability of soils for infrastructure construction and further improvement if required to achieve the goal.
Course Description	The main objectives of this course are to understand, determine and analyze the engineering properties of soils and their application on the foundation design of structures. Further, the course covers a detailed advance laboratory testing by using laboratory and large scale investigation as per standard test procedures to determine and understand the engineering properties of soils and grounds for design of different civil engineering construction projects.
Course Outline	Lectures: Fundamentals of soil exploration and sub-surface ground investigation, Earth Pressure & Retaining Walls, Foundations classification, Shallow Foundation Analysis & Design: Shallow and deep Foundation; Pier and well foundations, Introduction to Mat/Raft foundation, Introduction to ground improvement techniques. Practical: Unconfined Compressive Strength (UCS) of soil; Shear strength parameters of soils using Triaxial tests; Swelling pressure of soils; Volume change behaviour of soils by using one dimensional oedometer test; California Bearing Ratio (CBR) of soils; Plate load test; Standard penetration test for soils; Static cone penetration test.
Learning Outcome	At the end of theory lectures, student would be able to: Analyze the problem related to ground investigation and foundation engineering. Determine earth pressure, stability of retaining walls and slopes for structural design. Evaluate bearing capacity of soils for application in the foundations design. Analyze the capacity and design of deep foundations Understand various important soil stabilization and ground improvement techniques for engineering applications. At the end of practical classes, student would be able to: 1. Investigate the shear strength behaviour of various types of soil at different conditions. 2. Determine volume change behaviour of soils. 3. Examine the strength of subgrade soils to be used for design and analysis of highway projects 4. Investigate the bearing capacity and settlement behaviour of soils and identify the sub-surface soil beaviour using field tests. 5. Apply the knowledge to identify the suitability of soils for infrastructure construction and further improvement if required to achieve the goal.
Assessment Method	Theory: Assignments, Quizzes, Mid-semester examination and End-semester examination. Practical: Lab Reports, Viva, Mid-semester examination and End-semester examination.

Textbooks:

V. N. S. Murthy, Geotechnical Engineering: Principles and Practices of Soil Mechanics and Foundation Engineering, CRC Press, Taylor & Francis Group, Third Indian Reprint, 2013.
J.E. Bowles, Foundation Analysis and Design, McGraw-Hill, 2001.
V. N. S. Murthy, Advanced foundation Engineering, CBS Publishers & Distributers, 2011.
D. Fratta, J. Aguettant, and L. R. Smith, Soil Mechanics Laboratory Testing, Boca Raton, CRC Press, USA, 2007.

Reference books:

Gopal Ranjan, and A. S. R. Rao, Basic and Applied Soil Mechanics, New Age International Publishers, 2nd Edition 2000.
K. Terzaghi, R. B. Peck and G. Mesri, Soil Mechanics in Engineering Practice, John Wiley & Sons, 1996.
B.M. Das, Principle of Geotechnical Engineering, Cengage Learning, eighth Edition, 2013.
J. Bardet, Experimental Soil Mechanics, Upper Saddle River, Prentice Hall, USA, 1992.
All relevant Indian Standard (IS) and international codes.

4.0

CE3103

Transportation Engineering-I

Course	CE3103: Transportation Engineering - I
Course Credit (L-T-P-C)	3-1-2-5
Course Title	Transportation Engineering-I
Learning Mode	Lectures and Practical
Learning Objectives	Lectures: Complies with PLO- 1, 2, 3, 4 To provide fundamental knowledge in transportation engineering. Train students to plan, design and operate transportation facilities in industry. Provide scientific and technical knowledge, to prepare students to address transportation problems in field. Practical: Complies with PLO- 1, 3, 4 Learn laboratory tests required to evaluate bitumen used in road construction. Learn laboratory tests required to evaluate aggregate used in road construction. Learn bituminous mix design. To conduct filed studies for obtained traffic data (speed, flow) To estimate traffic flow density using indirect methods
Course Description	This course will discuss fundamental concepts in transportation engineering. Course will cover theory and real world practice in planning, design, construction and operation in road transportation. Practical will focuses on the tests to measure engineering properties of aggregate and bitumen to evaluate them for road construction. Course will also cover tests to measure traffic stream characteristics.
Course Outline	Lectures: Introduction to transportation engineering; Road plans; Factors controlling highway alignment; Vehicle and driver characteristics, PIEV theory; Pavement materials and characterization: subgrade soil, aggregates, bituminous and modified binders, straight-run bitumen, cutback bitumen, tar; Pavement analysis and design: Flexible pavements, Rigid pavements; Geometric design of Highways: Cross sectional elements, Horizontal alignment, Vertical alignment; Analysis of Traffic Flow, Mixed traffic (PCU), Design of Traffic facilities. Practical: Evaluation of road aggregates for various properties: Blending of aggregate, specific gravity, crushing value, Evaluation of bitumen for various properties: Softening point test, Penetration test, Viscosity test, Ductility test, Flash and fire point test, Stripping test; Bituminous mix design- Marshal mix design method; Headway studies: Free flow, Intermediate flow, High flow; Speed-Volume studies; O-D survey.
Learning Outcome	At the end of the course, from lectures students would be able to: 1. Understand engineering properties of road construction materials. 2. Design flexible and rigid pavements using Indian Codes. 3. Design highway geometrics 4. Identify factors influencing drivers behaviour. 5. Understand basic traffic stream parameters and traffic flow models. From practical students would be able to: Test aggregates to determine its engineering properties to check its acceptability in road construction. Test bitumen to determine its engineering properties to check its acceptability in road construction. Conduct Marshall mix design. Build fundamental diagrams of traffic flow Differentiate time mean speed and space mean speed Understand difference between microscopic and macroscipic variables and how to collect such data in real-field
Assessment Method	Assignments, Quizzes, Mid-semester examination and End-semester examination.

Textbooks (Lectures):

P. Chakroborty and A. Das, Principles of Transportation Engineering, Prentice Hall India, 2003.
A.T. Papagiannakis and E.A. Masad, Pavement Design and Materials, John Wiley & Sons Inc, 2012.
C. J. Khisty and B. K. Lall, Transportation Engineering: An Introduction, Prentice Hall India, 2003.
L. R. Kadiyali, Traffic Engineering and Transport Planning, Khanna Publishers, 1987.

Reference books (Lectures):

Relavent IRC codes.
F. L. Mannering, W. P. Kilareski, and S.S. Washburn, Principles of Highway engineering and traffic analysis, John Wiley and Sons, 2005. C. S.
Papacostas and P. D. Prevedouros, Transportation Engineering and Planning, Prentice Hall India, 2001.
J. H. Banks, Introduction to Transportation Engineering, McGraw-Hill, 2002.
S. K. Khanna and C. E. G. Justo, Highway Engineering, Nem Chand Bros., 2002.
Y. H. Huang, Pavement Analysis and Design, Pearson Education, India 2008.

Textbooks (Practical):

N. A. Harold, Highway materials, Soil and Concrete, Prentice Hall, 2004.
C. S. Papacostas and P.D. Prevedouros, Transportation Engineering and Planning, Hall India, 2001

Reference books (Practical):

IS Codes and IRC Codes.
R.P. Roess, W.R. McShane, and E.S. Prassas, Traffic Engineering, Prentice Hall, 1990.

5.0

CE3104

Environmental Engineering - I

Course	CE3104: Environmental Engineering - I
Course Credit (L-T-P-C)	3-0-2-4
Course Title	Environmental Engineering - I
Learning Mode	Lectures and Practical
Learning Objectives	Complies with PLOs 1, 4 and 5 1. Understand and apply principles of population forecasting, water demand estimation, and the design of water supply and sewerage systems. 2. Analyze the physical, chemical, and biological characteristics of water and wastewater, incorporating fundamental concepts of microbiology and environmental chemistry. 3. Gain insight into the foundational aspects of water and wastewater treatment processes, factoring in stoichiometry, chemical kinetics, and equilibrium principles. 4. Identify the environmental ramifications arising from solid waste, air pollution, and noise pollution through the formulation of targeted mitigation strategies. 5. To understand the knowledge and principles of determination of different water and wastewater quality parameters and to understand the basis of the water and wastewater treatment process.
Course Description	This course provides a foundational understanding of Environmental Engineering principles and practices. It covers key topics such as water supply, wastewater management, environmental chemistry, microbiology, and pollution control. Students will gain insights into the environmental impacts of solid waste, air pollution, and noise pollution. The practical course explores the knowledge and principles of determining different water and wastewater quality parameters. It also enables an understanding of the relationships between different parameters and their effect on water and wastewater treatment.
Course Outline	Theory Introduction, population forecasting and estimation of future water demand, water supply and distribution, physical, chemical and biological characteristics of water and wastewater, generation and collection of wastewater, design principles for sewerage systems. Basic microbiology, metabolic processes and their role in the environment., microorganisms in natural water systems, biological oxidation of organic matter. Fundamental concepts in environmental chemistry, stoichiometry and kinetics of chemical reactions, equilibrium constant and solubility products, pH and alkalinity and their significance in water chemistry. Process layout for water and wastewater treatment. Introduction to solid waste management, air pollution and noise pollution. Practical Introduction to Laboratory: Identification of Common/ General/ Facilities/ Equipment/ Chemicals/ Glassware; Weighing Chemicals and Making up Solutions. Examination of Water/ Wastewater: Analytical methods of commonly encountered water/wastewater quality parameters; Determination of pH, Eh, turbidity and conductivity; Determination of alkalinity, sulfate, solids, chloride and hardness of water/wastewater; Determination of COD, DO and BOD of water, Optimum coagulant dose and Determination of Pathogenic content (TC & FC) of water/wastewater. Advance Instrumentations for Environmental Analysis: Demonstration of atomic absorption spectrometer, microwave digester, centrifuge, ion chromatography, TOC analyzer, ICPMS, etc.
Learning Outcome	Students will gain knowledge in: Theory: 1. Demand for water supply to households, industry and public services. 2. Environmental microbiology: microbial metabolism, roles in natural water, and organic matter oxidation 3. Appling the environmental chemistry concepts: stoichiometry, kinetics, equilibrium, pH, and alkalinity in water and waste water analysis. 4. Develop skills for environmental impact assessment and mitigation: treatment processes, waste management, pollution control. Practical: 1. Interpret the quality of water and wastewater before and after treatment. 2. Demonstrate the process involved in the treatment of water and wastewater. 3. Analyse the water and wastewater quality parameters using advanced instruments
Assessment Method	Lecture: Assignments, Quizzes, Mid-semester examination, and End-semester examination. Practical: Lab Reports, Lab written Examination and Practical Examination with Viva-voce

Text Books:

S. Peavy, D. R. Rowe and George Tchobanoglous, Environmental Engineering, McGraw-Hill International Ed., 1985.
L. Davis and D. A. Cornwell, Introduction to Environmental Engineering, McGraw-Hill, Inc., 2014.
J. McGhee, Water Supply and Sewerage, McGraw-Hill Inc., 1991.
N. Sawyer, P. L. McCarty and G. F. Parkin, Chemistry for Environmental Engineers, McGraw- Hill, 1994.
Garg, S.K., Environmental Engineering (Vol. I) Water Supply Engineering, Khanna Publishers, 37^th edition, 2024
Garg, S.K., Environmental Engineering (Vol. II) Sewage Waste Disposal and Air Pollution Engineering, Khanna Publishers, 40^th edition, 2024
Laboratory Manual.

Reference Books:

Metcalf & Eddy, Wastewater Engineering- Treatment and Reuse (Revised by G. Tchobanoglous, F. L. Burton and H. D. Stensel), Tata McGraw Hill, 4th Edn., 2004.
J. Arceivala and S. R. Asolekar, Wastewater Treatment for Pollution Control and Reuse, Tata McGraw Hill, 2006.
Manual on Sewerage and Sewage Treatment Systems, Central Public Health & Environmental Engineering Organisation, Ministry of Housing and Urban Development, Govt. of India, 2013.
Manual on Water Supply and Treatment Systems (Drink from Tap): Revised and Updated, Ministry of Housing and Urban Affairs, Govt. of India, 2024.
APHA, Standard Methods Examination of Water and Wastewater, American Public Health Association, Washington DC, 2012.
Radojevic and V. N. Bashkin, Practical Environmental Analysis. Royal Environmental Analysis, 1999.

4.0

XX31PQ

IDE - II

3.0

TOTAL

20.0

Design of Steel Structures

Sl. No.

Subject Code

SEMESTER VI

CE3201

Course	CE3201 Design of Steel Structures
Course Credit (L-T-P-C)	3-1-0-4
Course Title	Design of Steel Structures
Learning Mode	Lectures & Tutorials
Learning Objectives	Lectures: Complies with PLO-1, 2, 3 and 4 1. Understand the structural load calculation and design process of components of steel structures as per Indian standards/ codes (IS 875 and IS 800: 2007). 2. Provide scientific and technical knowledge for planning, analysis, and design procedure of steel truss and steel building. Tutorials: Complies with PLO- 1, 3 and 4 1. To equip the students with the analysis and design of steel structures using advance analysis and design software. 2. To equip the students with basic understanding of detailing of structural steel elements.
Course Description	Designing industrial steel structures is the focus of this course, and it covers all the relevant topics, such as material specifications, connections, and the basic design and detailing of structural components. The design will be carried out in accordance with IS 800.
Course Outline	Introduction: Steel structures, material properties, Limit states and design philosophies. Loads, partial safety factors and load combinations. Section classification. Design of tension and compression members. Effective length factor: Sway and Non-sway frames, Built-up columns - Battens and lacings. Design of laterally supported and unsupported beams, built-up beams, Plate girders and design of stiffeners. Design of beam-column of members: effect of axial load on flexure behaviour, P-M interaction and moment amplification, and bi-axial bending. Connections: Structural fasteners - rivets, bolts and welds, strength under combined stresses, simple and eccentric connections. Detailing of axial members, beams, plate girders and columns. Case study of steel buildings.
Learning Outcome	At the end of the course, student would be able to: Lectures: 1. Understand behaviour of structural steel members 2. Estimation of various design loads including wind loads 3. Identify and interpret the appropriate relevant design codes 4. Familiar with design and fabrication of steel members Tutorials: 1. Apply the knowledge of design to detail connections. 2. Analyse, design and detail structural elements in the steel buildings and truss using a commercially available software.
Assessment Method	Assignments, Quizzes, Project work, Mid-semester examination and End-semester examination.

Textbooks/ Reference books:

Subramanian, N. (2018). Steel Structures: Design and Practice, Oxford University Press.
Duggal, S.K. (2019). Limit State Design of Steel Structures, 3rd edition, McGraw Hill.
Bhavikatti, S. S. (2017). Design of Steel Structures (by Limit State Method as Per IS: 800—2007), 5^th edition, IK International.
Shiyekar, M. R. (2013). Limit State Design of Steel Structures, PHI Learning.
Sai Ram, K. S. (2020). Design of Steel Structures, 3rd ed., Pearson Education.
Gambhir, M. L. (2013). Fundamentals of Structural Steel Design, McGraw Hill.
Segui, W.T. (2017). Design of Steel Structures, 6th ed., Cengage Learning.
Galambous, T. V. and Surovek, A. E. (2008). Structural Stability of Steel: Concepts and Applications for Structural Engineers, Wiley.
IS 800: Indian Standard General Construction in Steel — Code of Practice, BIS, New Delhi.
IS 875: Part 3: Design Loads (Other than Earthquake) for Buildings and Structures - Code of Practice Part 3 Wind Loads, BIS, New Delhi.

4.0

CE3202

Infrastructure Drawing and Estimation

Course	CE3202: Infrastructure Drawing and Estimation
Course Credit (L-T-P-C)	1-2-0-3
Course Title	Infrastructure Drawing and Estimation
Learning Mode	Lectures & Tutorials
Learning Objectives	Complies with PLO- number 2 & 4 1. The course also aims at giving the students an idea on keeping cost of material and labour into consideration while designing a building. 2. The course shall provide the students an ability to read and interpret drawings and cost estimates.
Course Description	This course is aimed at providing the students a critical understanding of building planning from foundation level to the rooms, orientation, facade design and overall estimation of the cost. This course is aimed at giving the students an ability to read and interpret the building layouts, blueprints and budget estimates so that they can design structurally strong and cost-effective buildings in future.
Course Outline	Components of buildings: plan, elevation and section of buildings; Drawing of various details of residential buildings; Types of building: residential, industrial; brick masonry. Estimation: types of estimates, plinth area estimate, cubical content estimate, unit rate estimate, central line method, short wall - long wall method; estimate of other structures- estimate of bituminous and cement concrete roads, estimating of septic tank, estimating of irrigation works – aqueduct, syphon, etc., modes of measurement, estimation of buildings, specifications and analysis of rates.
Learning Outcome	At the end of the course, student would be able to: 1. Prepare drawing and layout of single, multi roomed and single to multi storeyed buildings. 2. Read and interpret building layouts. 3. Understand the costing and estimation issues involved in building design and planning and other structures.
Assessment Method	Assignment, Quizzes, Mid-semester examination and End-semester examination.

Text books:

B. N. Dutta, Estimating and Costing in Civil Engineering, UBS Publishers & Distributors Pvt. Ltd., 2003.
S. S. Bhavikatti, M. V. Chitawa, Building Planning and Drawing, I K International Publishing House Pvt. Ltd, 2014.
M.G Shah, C.M Kale, Principles of Building Drawing, Macmillan Publishers India Limited, 2000.
N. Kumara Swamy, A. Kameswara Rao, Building Planning and Drawing, Charotar Publishing House Pvt. Ltd. - Anand; 7th Revised edition (2013).
D.D. Kohli, and R.C. Kohli, A Text Book of Estimating and Costing (Civil), S.Chand & Company Ltd., 2004

Reference Book:

H. Banz, Building Construct. Details Prac. Drawings, CBS; 1ST edition, 2005.
G. H. Cooper, Building Construction and Estimating, McGraw-Hill, 1971.
B.P. Verma, Civil Engineering Drawing& House Planning, Khanna Publishers, 2010.
Latest version of DSR

3.0

CE3203

Construction Planning and Management

Course	CE3203: Construction Planning and Management
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Construction Planning and Management
Learning Mode	Lectures
Learning Objectives	1. Study the role and responsibilities of civil engineer as project manager. 2. 3. Study the life cycle of a construction project and the key activities in each phase. 4. Study various types of contracts and bidding procedure. 5. Study planning techniques such as Gantt charts, CPM, and PERT to construction projects. 6. Identify, assess, and mitigate risks in construction projects. 7. Apply principles and practices of quality control in construction. 8. Understand the legal and ethical considerations in construction management.
Course Description	This course introduces undergraduate students to the fundamental concepts and techniques of construction planning and management. It covers a broad range of topics, including project life cycle, planning techniques, resource management, and risk analysis. The course aims to equip students with the skills necessary to effectively plan, coordinate, and control construction projects from inception to completion.
Course Outline	Construction as industry and its challenges, Role of construction management, Methods of construction managements, Basic requirements of construction management: Learning structures, Life cycle of construction projects: Examples of real projects and its learning requirements. Contracts: Different types of contracts, notice inviting tenders, contract document, departmental method of construction, rate list, security deposit and earnest money, conditions of contract, arbitration, administrative approval, technical sanction; contract laws and handling of contracts, commissioning of project. Time management tools: Lists, bar chats, CPM and PERT. Introduction to network-based project management techniques: Defining activities and their interdependence, activity duration estimation: resource driven estimates, duration driven estimates; drawing of network using AOA and AON methods, calculation of project duration and critical path; fast tracking, crashing. Quality Management and Construction safety, Use of information technology in construction industries, Automation in construction industry: a general discussion.
Learning Outcome	Upon successful completion of this course, students will be able to: · Understand various phases in life cycle of a project · Understand the difference between different types of contracts · Understand how to award the contract · Identify activities in a project and estimate activity durations · Understand planning techniques such as Gantt charts, CPM, and PERT to construction projects. · Apply quality control principles to ensure the quality of construction projects. · Navigate legal and ethical issues in construction management.
Assessment Method	Assignments, Quizzes, Mid-semester examination and End-semester examination.

Textbooks

F. Harris, R. McCaffer and F. Edum-Fotwe, Modern Construction Management, Blackwell Publishing, 2006.
C. J. Schexnayder and R. E. Mayo, Construction Management Fundamentals, McGraw Hill, New Delhi, 2003.
K.K. Chitkara, Construction project management: planning, scheduling and controlling, Tata McGraw-Hill, 2008.

Reference books

J. Singh, Heavy Constructon-Planning, equipment and methods, Oxford & IBH Publishing Co. Pvt 1993.
R.L. Peurifoy & C.J. Schexnayder, Construction planning and equipment, and methods, 6th ed, McGraw-Hill, 2006.
D.S. Berrie and B.C. Paulson, Professional construction management including C.M., Design construct and general contracting, Third edition, McGraw Hill International edition, 1992.
L.S. Srinath, PERT and CPM principles and Applications, Third edition, Affiliated east-west press Pvt Ltd, 2001.
D.G. Carmichael, Construction engineering Networks: Techniques, planning and management, Ellis Horwood Publishers Chichester 1989.
Relevant Govt. manual and guidelines.

3.0

CE3204

Environmental Engineering - II

Course	CE3204: Environmental Engineering - II
Course Credit (L-T-P-C)	3-1-0-4
Course Title	Environmental Engineering - II
Learning Mode	Lectures and Tutorials
Learning Objectives	Complies with PLOs 1, 2 and 5 1. Design and implement key water treatment units. 2. Understand and design the preliminary, primary and secondary wastewater treatment systems. 3. Apply principles for wastewater stabilization and sludge treatment. 4. Enhance practical knowledge through site visits to treatment facilities.
Course Description	This course covers the design and operation of water and wastewater treatment systems, including physico-chemical processes, filtration, disinfection, and sludge treatment. Students will learn to implement treatment technologies and apply principles of stabilization and pollution control. Practical knowledge is enhanced through site visits to treatment facilities.
Course Outline	Water Treatment: Engineering design of physico-chemical processes. Sedimentation, coagulation, and flocculation techniques. Granular media filtration systems. Disinfection methods. Water softening processes. Manganese and iron removal methods. Adsorption and ion exchange technologies. Wastewater Treatment: Preliminary, primary, and secondary treatment units. Aerobic and anaerobic processes. Objectives, theoretical foundations, and design principles of aeration units. Sludge treatment and disposal methodologies. Design and operation of wastewater stabilization ponds, aerated ponds, and oxidation ditches. Site-visits to Water and Wastewater Treatment Works.
Learning Outcome	· Recognise the common physical, chemical and biological unit operations encountered in treatment processes. · Illustrate the fundamentals of water and wastewater treatment. · Able to apply key theories and principles for the design and selection of appropriate technology in water and wastewater treatment. · Integrate theory with practice through site visits to understand real-world applications.
Assessment Method	Assignments, Quizzes, Mid-semester examination, and End-semester examination.

Text Books:

S. Peavy, D. R. Rowe and George Tchobanoglous, Environmental Engineering, McGraw-Hill International Ed., 1985.
M. Montgomery, Water Treatment Principles and Design, John Wiley & Sons, 1985.
L. Davis and D. A. Cornwell, Introduction to Environmental Engineering, McGraw-Hill, Inc., 2014.
J. McGhee, Water Supply and Sewerage, McGraw-Hill Inc., 1991.
Garg, S.K., Environmental Engineering (Vol. I) Water Supply Engineering, Khanna Publishers, 37^th edition, 2024
Garg, S.K., Environmental Engineering (Vol. II) Sewage Waste Disposal and Air Pollution Engineering, Khanna Publishers, 40^th edition, 2024

Reference Books:

Metcalf & Eddy, Wastewater Engineering- Treatment and Reuse (Revised by G. Tchobanoglous, F. L. Burton and H. D. Stensel), Tata McGraw Hill, 4th Edn., 2004.
J. Arceivala and S. R. Asolekar, Wastewater Treatment for Pollution Control and Reuse, Tata McGraw Hill, 2006.
Manual on Sewerage and Sewage Treatment Systems, Central Public Health & Environmental Engineering Organisation, Ministry of Housing and Urban Development, Govt. of India, 2013.
Manual on Water Supply and Treatment Systems (Drink from Tap): Revised and Updated, Ministry of Housing and Urban Affairs, Govt. of India, 2024.

4.0

CE3205

Water Resources Engineering - II

Course	CE3205 Water Resources Engineering - II
Course Credit (L-T-P-C)	3-0-2-4
Course Title	Water Resources Engineering - II
Learning Mode	Lectures and Practical
Learning Objectives	Complies with PLOs 1, 2 and 5 1. To elucidate the importance of water resources. 2. To provide a basic understanding of the occurrence, distribution, and movement of water in the earth-atmosphere system. 3. To understand the principles of hydro-meteorological data and its measurements.
Course Description	This course offers a comprehensive overview of the various components of the hydrologic cycle and the techniques for hydro-metrological observations. Through this course, students will understand the fundamentals of quantitative assessment of water resources and apply water budget concepts required in designing irrigation and flood control structures.
Course Outline	Introduction: Water as a resource, Hydrologic cycle, water budget, world water quantities. Hydrometeorology: Constituents of atmosphere, general circulation and hydro-metrological observations. Catchment: Stream pattern, description of basin, classification of watersheds and streams. Precipitation and Abstractions: Forms of precipitation, data analysis, rain gauge networks; Infiltration process, infiltration indices and Horton's equation; Evaporation and Evapotranspiration, Pan evaporation, empirical equations for estimating evaporation and evapotranspiration; Transpiration. Runoff and Hydrographs: Rainfall-runoff relations, time area concept, flow duration curve, mass curve, flow hydrograph, Unit Hydrograph (UH), its analysis, S-curve hydrograph. Floods and Routing: Concepts of return period, flood frequency analysis, Gumbel's and Log Pearson Type I & II distributions, Rational method, risk, reliability, and safety factor; Hydrologic storage routing Groundwater Hydrology: Types of aquifers and properties, Darcy's law, steady flow in a confined and unconfined aquifer. Irrigation Engineering: Crop water requirements, Irrigation methods.
Learning Objectives	Practical: Complies with PLOs 1, 2, 3 and 5 1. Hands-on experience in the measurement of hydro-meteorological data and environmental parameters influencing water resources. Students will learn to analyze and interpret the environmental data and understand the controls in hydraulic structures.
Course Description	This course exposes the students to experimental setups for measuring environmental parameters influencing water resources. It also gives exposure to the physical modelling of hydraulic structures and associated measurements related to the control of these hydraulic structures.
Course Outline	Rainfall measurement; Groundwater sampling and groundwater level measurement; Infiltration test; Evaporation test. Flow line Visualization; Flood Hydrograph; Groundwater Abstraction, Open Channel Flow; Hydraulic Jump; Flow over Weirs (Broad Crested, Sharp crested, Crump); Forces on a sluice gate; Critical Depth and discharge measurement
Learning Outcome	At the end of the course, students would be able to understand: 1. Importance of water as a critical resource 2. Various components of the hydrologic cycle that affect the movement of water in the earth-atmosphere system. 3. The techniques for taking hydro-meteorological observations. 4. Applications of the hydro-meteorological observations and concepts in the management of water resources and its applications.
Assessment Method	Assignments, Quizzes, Mid-semester examination, and End-semester examination

Text Books/ Reference Book:

K Subramaniya, Engineering Hydrology, McGraw Hill, 4^th Edition, 2015.

Ven Te Chow, David R. Maidment, Larry W. Mays, Applied Hydrology, McGraw Hill, 1998.
P Jaya Rami Reddy, A Textbook of Hydrology, University Science Press, 3^rd Edition, 2023.
Todd D.K., Ground Water Hydrology, John Wiley and Sons, 2000.

Tim Davie, Nevil Wyndham Quinn, Fundamentals of Hydrology, Routledge, 2019.

4.0

CE3206

Transportation Engineering - II

Course	CE3206: Transportation Engineering - II
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Transportation Engineering - II
Learning Mode	Lectures
Learning Objectives	Complies with PLO number – 1, 2, and 4 1. Understand the fundamentals of urban transportation planning 2. Gain knowledge of railway engineering principles. 3. Introduce students to the field of Airport Engineering. 4. Understand airport markings and air traffic control lighting and signing.
Course Description	This course provides an introduction to urban transportation planning, railway engineering and airport engineering.
Course Outline	Transportation Planning: Introduction to urban transportation planning; Urban transportation planning process; Introduction to urban transportation model system; Evaluation of Transportation Systems: Economic analysis; Environmental impact assessment; Financial analysis. Railway Engineering: Introduction to railway engineering, History of Indian Railways, Nomenclature of locomotives, Track capacity, Track modulus, Railway alignment; Axle load and dynamic augmentation factor, Components of railway track structure and its functions; Geometric design. Airport Engineering: Introduction to Airport Engineering, Site selection, Geometric designs, Air traffic control lighting and signing, Runway and Taxiway signs and markings, Airport pavement design, Maintenance and Evaluation of pavements.
Learning Outcome	At the end of the course, student would be able to: 1. Describe the key components of urban transportation planning. 2. Calculate track capacity and understand track modulus. 3. Apply geometric design principles to railway alignment. 4. Design runways and taxiways.
Assessment Method	Assignments, Quizzes, Mid-semester examination and End-semester examination.

References:

R. Kadiyali Traffic Engineering and Transport Planning
Coenraad Esveld., “Modern Rail Track Design”, MRT productions.
Buddhima Indraratna, Wadud Salim, Cholachat Rujikiatkamjorn, “Advanced Rail Geotechnology - Ballasted Track”, CRC Press, 2011.
C. and Arora.S.P, “A Text Book of Railway Engineering”, Dhanpat Rail Publications, 2013
S. Mundrey, Railway track engineering. Mc. Graw Hill.
Saxena, S.C., "Airport Engineering – Planning and Design", CBS Publishers.
Horonjeff R., McKelvey F.X., Sproule W., Young S. "Planning and Design of Airports", 5th Ed. New York: McGraw-Hill.
H. Huang, Pavement Analysis and Design (2nd Edition), Pearson Education, India
T. Papagiannakis and E.A. Masad, Pavement Design and Materials, John Wiley & Sons, Inc.

3.0

TOTAL

21.0

Sl. No.	Subject Code	SEMESTER VII	L	T	P	C
1.	CE41XX	Departmental Elective – I	3	0	0	3.0
2.	CE41XX	Departmental Elective – II	3	0	0	3.0
3.	XX41PQ	IDE-III	3	0	0	3.0
4.	HS41XX	HSS Elective II	3	0	0	3.0
5.	CE4198	Summer Internship*	0	0	12	3.0
6.	CE4199	Project – I	0	0	12	6.0
TOTAL			12	0	24	21.0

* For specific cases of internship after 6^th Semester, the performance evaluation would be made on joining the VII^th Semester and graded accordingly in the VII^th Semester:

Note :

a) (i) Summer internship (*) period of at least 60 days’ (8 weeks) duration begins in the intervening vacation between semester VI and VII that may be done in industry / R&D / Academic Institutions including IIT Patna. The evaluation would comprise combined grading based on host supervisor evaluation, project internship report after plagiarism check and seminar presentation at the Department (DAPC to coordinate) with equal weightage of each of the three components stated herein.

a) (ii) Further, on return from internship, students will be evaluated for internship work through combined grading based on host supervisor evaluation, project internship report after plagiarism check, and presentation evaluation by the parent department with equal weightage of each component.

b) (i) In the VII^th semester, students can opt for a semester long internship on recommendation of the DAPC and approval of the Competent Authority.

b) (ii) On approval of semester long internship, at the maximum two courses (properly mapped/aligned syllabus) at par with institute electives may be opted from NPTEL and / or SWAYAM and the other two more should be done at the institute through course overloading in any other semester (either before or after the internship) and/or during following summer semester.
b) (iii) The candidates opting two courses from NPTEL and / or SWAYAM would be required to appear in the examination at the Institute as scheduled in the Academic Calendar.

Introduction to Bridge Engineering

Sl. No.	Subject Code	SEMESTER VIII	L	T	P	C
1.	CE42XX	Departmental Elective – III	3	0	0	3.0
2.	CE42XX	Departmental Elective – IV	3	0	0	3.0
3.	CE42XX	Departmental Elective – V	3	0	0	3.0
4.	CE4299	Project – II	0	0	16	8.0
TOTAL			9	0	16	17.0
GRAND TOTAL (including Semester I & II)			167.0

Department Elective-I

Department Elective-I

Sl. No.

Subject Code

Subject

CE4101

Course	CE4101Introduction to Bridge Engineering
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Introduction to Bridge Engineering
Learning Mode	Lectures
Learning Objectives	Lectures: Complies with PLO-1, 2, 3 and 4 1. Apply the fundamental principles of bridge engineering, including load distribution, dead and live load analyses etc. to evaluate the performance of different types of bridges. 2. Design of various bridge components following various Indian as well as international standards and safety regulations. 3. To become proficient in using advanced computational tools and software for the modelling, simulation considering dynamic loading like wind and earthquake.
Course Description	This course offers a comprehensive exploration of bridge engineering and design, covering fundamental principles, methodologies, and practical applications. This course covers key aspects including structural analysis, material selection, construction techniques, and environmental considerations.
Course Outline	Introduction: Classification of Bridges, General Features of Design, IRC Loading (viz. 70R, Class AA tracked and wheeled vehicle), Design Codes, Working Stress Method, Limit State Method of Design as per IS456 and IRC 112; Analysis & Design: Consideration of various loading (dead load, vehicular load etc.), Slab bridge, Box Culvert, T-beam bridge, Box Girder bridge.
Learning Outcome	At the end of the course, student would be able to Lectures: 1. Understand behaviour of structural steel members. 2. Estimation of various design loads including wind loads. 3. Identify and interpret the appropriate relevant design codes. 4. Familiar with design and fabrication of steel members.
Assessment Method	Assignments, Quizzes, Project work, Mid-semester examination and End-semester examination.

Textbooks/ Reference books:

Swami Saran, Analysis and Design of Substructures: Limit State Design, 28 February 2018.
K. Rakshit, Design and Construction and Highway Bridges.
Raju N. K, Design of Bridges, 5Ed (Pb 2019) – 1 January 2019.
Daniel J. Inman, Charles R. Farrar, Vicente Lopes Junior, Valder Steffen Junior, Damage Prognosis: For Aerospace, Civil and Mechanical Systems, John Wiley & Sons, 2005.
Latest version of relevant IRC ( IRC6, IRC112 etc.) and IS (viz. IS456, IS800 etc.) codes.

CE4102

Prestressed and Precast Concrete Structures

Course	CE4102Prestressed and Precast Concrete Structures
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Prestressed and Precast Concrete Structures
Learning Mode	Lectures
Learning Objectives	Lectures: Complies with PLO-1, 2, 3 and 4 1. Familiarize with the concept of pre-stressed concrete and design of pre-stressed concrete structures. 2. Analyse prestressed concrete structural members and estimate the losses of prestress.
Course Description	The course deals with the design of pre-stressed concrete structures for various types of loading and will provides an understanding of behaviour of pre-stressed concrete members under various action of forces.
Course Outline	Analysis and design of beams - Rectangular, Flanged and I section, for limit state of flexure. Analysis and design of end blocks in post tensional members -primary and secondary distribution zones, Bursting and spalling tensions. Shear strength of prestressed concrete beams and design of shear reinforcement. Bond in prestressed concrete. Analysis and design of prestressed concrete structures such as concrete pipes and sleepers. Precast Structural Building components such as slab panels, beams, columns. Prefabricated building using precast load bearing and non-load bearing wall panels. Prefab systems, structural schemes, and their classification including design considerations. Joints - requirements of structural joints and their design considerations.
Learning Outcome	At the end of the course, student would be able to Lectures: 1. Become familiar with basic of pre-stressed concrete structure. 2. Understand the behaviour of pre-stressed concrete structural members structures under flexure, shear, axial forces, combined flexure and axial forces, and in-plane shear forces. 3. Learn the methods of pre-stressed concrete construction and detailing practices.
Assessment Method	Assignments, Quizzes, Project work, Mid-semester examination and End-semester examination.

Textbooks/ Reference books:

IS 1343: Code of Practice for Prestressed Concrete by Bureau of Indian Standards.
Guyon Y.: Prestressed Concrete, Vol. I & II, John Wiley and Sons, New York.
Krishna Raju, N.: Prestressed Concrete, Tata McGraw Hill Publications Company, New Delhi.
Lin T. Y.: Prestressed Concrete, Tata McGraw Hill, New Delhi.
Dayaratnam P. and Sarah P.: Prestressed Concrete Structures.
Elliott K. S.: Precast Concrete Structures, CRC Press; 2nd edition, 2019.

CE4103

Fundamentals of Solid Mechanics

Course	CE4103Fundamentals of Solid Mechanics
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Fundamentals of Solid Mechanics
Learning Mode	Lectures
Learning Objectives	Lectures: Complies with PLO-1, 2, 3 and 4 1. Understand the concept of deformation, linear and nonlinear measures of strain and stress. 2. Introduce failure theory of different materials. 3. Predict the behaviour elastic solids under different loading.
Course Description	The course deals with analysis of deformable bodies. This course provides the students an exposure for linear and non-linear analysis of solids, analysis of stress and strain, fundamental physical principles, constitutive relation of materials, and two-dimensional electrostatics problems.
Course Outline	Introduction: Suffix notation system, tensor algebra; Strain analysis: deformation and velocity gradients, Lagrangian and Eulerian description of strain, principal strains and strain invariants, compatibility conditions; Stress analysis: forces and moments, theory of stress, energetically conjugate stress and strain measures, plane stress and plane strain, principal stresses and stress invariants, compatibility equations, equilibrium equations; Fundamental physical principles: conservation of mass, linear momentum, angular momentum, and energy; Constitutive theory: St. Venant’s principal, linear elasticity and generalized Hook’s law, stress, strain and energy based failure theory, yield criteria; Introduction to elasticity: two-dimensional problems, torsion, buckling.
Learning Outcome	At the end of the course, student would be able to Lectures: 1. Understand the concept of deformation mechanisms in solid and different measures of strain and stress. 2. Gain knowledge on material model of liner elastic solid body. 3. Analysis of problem in elastic deformable body.
Assessment Method	Assignments, Quizzes, Project work, Mid-semester examination and End-semester examination.

Textbook/ Reference book:

S. Timoshenko and J.N. Goodier, Theory of Elasticity, McGraw Hill Book Company, International Ed, 1970.
L. S. Srinath. Advanced Mechanics of Solids, McGraw Hill Education, 2010.
Allan F. Bower. Applied Mechanics of Soilds, CRC Press, 2010.
Irving H. Shames and Francls A. Cozzarelli. Elastic and Inelastic Stress Analysis, Taylor & Francis Group; Revised edition, 1997.
Romesh C. Batra. Element of Continuum Mechanics, AIAA, 2012.

CE4104

Matrix Methods of Structural Analysis

Course	CE4104 Matrix Methods of Structural Analysis
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Matrix Methods of Structural Analysis
Learning Mode	Lectures
Learning Objectives	Objective for learning this course are Lecture: 1. Understand the analysis and design different type of structural elements under static loading. 2. Predict nonlinear behaviour of different structures and structural components under static loading.
Course Description	The course deals with advanced analysis methods of structures. This course provides the students an exposure for linear and non-linear analysis of structures.
Course Outline	Introduction Structures, loads and response; determinate and indeterminate structures; stiffness and flexibility; Analysis of Indeterminate structures; Force and displacement methods; Mathematical preliminaries; Matrix algebra; stiffness and flexibility matrices; Analysis of Trusses; Analysis of Beams; Analysis of plane frames; Implementation issues; Beyond matrix method: Introduction to nonlinear analysis.
Learning Outcome	At the end of the course, student would be able to Lecture: 1. Analyse structures for designing them. 2. Should be able to understand various types of elements used for structural analysis.
Assessment Method	Assignments, Quizzes, Project work, Lab report, Mid-semester examination and End-semester examination.

Textbooks/ Reference books:

R C Hibbeler, Structural Analysis, Ninth Edition, Pearson, 2017.
McGuire, R. H. Gallagher and R. D. Ziemian, Matrix Structural Analysis, Second Edition, Wiley, 2015.
Menon, Advanced Structural Analysis. Narosa, 2015.
Amin Ghali, Adam M Neville and Tom G Brown, ""Structural Analysis:A Unified Classical and Matrix Approach"", Sixth Edition, 2007, Chapman & Hall.

Department Elective-II

Department Elective-II

Sl. No.

Subject Code

Subject

CE4105

Stochastic Hydrology

Course	CE4105 Stochastic Hydrology
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Stochastic Hydrology
Learning Mode	Lectures
Learning Objectives	Complies with PLOs 1, 2, 3, 4 and 5 This course is designed to present an understanding of statistical tools applied to hydrologic problems. The objective of this course is to introduce the concepts of probability theory and stochastic processes with applications in hydrologic analysis and design.
Course Description	The course covers the modelling of hydrologic time series with specific techniques for data generation and hydrologic forecasting. Case study applications will be discussed.
Course Outline	Statistical methods in hydrology, probability distribution of hydrologic variables, hypothesis testing and goodness of fit, flood frequency analysis, single and multiple regression analysis, classification of time series, characteristics of hydrologic time series, statistical principles and techniques for hydrologic time series modelling, time series modelling of annual and periodic hydrologic time series (including AR, ARMA, ARIMA, and DARMA models), multivariate modelling of hydrologic time series, practical considerations in time series modelling applications.
Learning Outcome	By taking this course, students will be able to: 1. Analyse hydrological and climatological data using advanced statistical methods and characterize water resources and hydrometeorological data. 2. Analyse hydrologic time series, and perform frequency analysis to estimate the magnitude of an event, frequency of occurrence and associated uncertainty.
Assessment Method	Assignments, Quizzes, Mid-semester examination, and End-semester examination

Text Books/ Reference Book:

Haan, C.T., Statistical Methods in Hydrology, First East-West Press Edition, New Delhi, 1995.
Bras, R.L. and Rodriguez-Iturbe , Random Functions and Hydrology, Dover Publications, New York, USA, 1993.
Clarke, R.T., Statistical Models in Hydrology, John Wiley, Chinchester, 1994.
Kite, G.W., Frequency and Risk Analyses in Hydrology, Water Resources Publications, Fort Collins, CO, 1977.
Yevjevich V. , Probability and statistics in Hydrology, Water Resources Publications, Colorado, 1972.
Ang, A.H.S. and Tang, W.H., Probabilistic concepts in Engineering Planning Design, Vol. 1, Wiley, New York, 1975.

CE4106

Irrigation Engineering and Hydraulic Structures

Course	CE4106 Irrigation Engineering and Hydraulic Structures
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Irrigation Engineering and Hydraulic Structures
Learning Mode	Lectures
Learning Objectives	Complies with PLOs 1, 2, 3, 4 and 5 1. Students will understand the soil-water-crop management and the role of irrigation system. 2. The students will be exposed to the various irrigation and flood control structure and their design principles.
Course Description	This course offers an introduction to irrigation practices and the development, conservation, regulation and use of water resources through the design of hydraulic structures.
Course Outline	Introduction to irrigation: Necessity and scope, irrigation practices, soil-crop-water requirement. Water resource Development: reservoir planning. Dams: Types of dams, their function and design. Design of Spillways. Diversion work. Distribution System: Flow irrigation canal, theory and design of canals, canal outlet, canal regulation work, cross drainage work, canal head work. Introduction to river engineering.
Learning Outcome	At the end of the course, students would be able to understand: 1. The soil-water-crop relation and the need for irrigation. 2. The principles of design of hydraulic structures.
Assessment Method	Assignments, Quizzes, Mid-semester examination, and End-semester examination

Text Books/ Reference Book:

Modi P.N. Irrigation Water Resources And Water Power Engineering, Rajsons Publications, New Delhi 2013
Punamia, B. C., & Lal, B. B..Irrigation and water power engineering. 17th Edition, Laxmi Publications
Santosh Kumar Garg, Irrigation Engineering and Hydraulic Structure, Khanna publishers, 2023
R. N. Reddy , Irrigation Engineering, Daya Publishing House, 2010
P. Novak, A.I.B. Moffat, C. Nalluri, R. Narayanan, Hydraulic Structures, CRC Press, 2017
Relevant IS codes

CE4107

Elementary Soil Behaviour

	CE4107 Elementary Soil Behaviour
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Elementary Soil Behaviour
Learning Mode	Lectures
Learning Objectives	Complies with PLO- number 1, 3, 4, and 5. The objectives of this course are to 1. Understand the significance of different clay mineral in behaviour of soils and its determination. 2. Comprehend the arrangement of soil particles and its relevance in behaviour of soils. 3. Analyse the mechanism behind the physio-chemical interactions within soils. 4. Develop an understanding of the factors determining and controlling the engineering properties and behaviour of soils.
Course Description	This course is offered as an elective course in department. This course basically comprises with several topics which should be covered to deal with basicsof soil behaviour subjected to variation in climatic changes. Board topics are different clay mineral in behaviour of soils and its determination, arrangement of soil particles and its relevance in behaviour of soils, mechanism behind the physio-chemical interactions within soils, and factors determining and controlling the engineering properties and behaviour of soils under different conditions.
Course Outline	Identification and Classification of Clay Minerals; Origin and description of clay minerals; Determination of soil composition and fabric; Physio-Chemical Behaviour of Soil; Soil-chemical interactions; Microbially Induced Calcite Precipitation (MICP); Effective, Inter-granular and Total stress; Water–Air interactions in soils; Volume Change, Shear Strength and Deformation Behaviour.
Learning Outcome	At the end of the course, student would be able to: Recognize the significance of different clay mineral in behaviour of soils and its determination. Understand the arrangement of soil particles and its relevance in behaviour of soils. Analyse the mechanism behind the physio-chemical interactions within soils. 4. Apply knowledge for determining and controlling the engineering properties and behaviour of soils under different conditions, with an emphasis on Why they are What they are for research/professional perspectives as well as for societal needs.
Assessment Method	Assignments , Quizzes , Term-paper project, Mid-semester examination and End-semester examination.

Textbooks:

Mitchell, J. K. and Soga, K. Fundamentals of soil behaviour, Wiley, New York, 2005.
Yong, R. N. and Warkentin, B. P. Soil properties and behaviour, Elsevier, 2012.
Lambe, T.W. and Whitman, R.V. Soil mechanics, John Wiley and Sons, New York, 1979.

Reference books:

Grim, R. E. Applied clay mineralogy, McGraw Hill, New York, 1966.
Fredlund, D. G., Rahardjo, H. and Fredlund, M. D. Unsaturated soil mechanics in engineering practice, Wiley, 2012.
Malcom, D. Bolton A guide to soil mechanics, University Press (India) Pvt. Ltd., 2003.
All relevant codes and research papers.

CE4108

Fundamentals of Geoenvironmental Engineering

	CE4108 Fundamentals of Geoenvironmental Engineering
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Fundamentals of Geoenvironmental Engineering
Learning Mode	Lectures
Learning Objectives	Complies with PLO- number 1, 2, 3, and 4 1. Understanding methods of waste management and disposal 2. Learning methods of contaminated site characterisation 3. Learning methods of remedial measures of a contaminated site
Course Description	The course covers the source of various types of waste and its proper disposal, remediation of contamination sites. Municipal solid waste and industrial waste disposal techniques. Role of compacted unsaturated clay as liner material in landfill.
Course Outline	Production and classification of wastes, contaminated site characterization, Selection of waste disposal sites, selection criteria. Design of various landfill components such as liners, covers, leachate collection and removal, gas generation and management, ground water monitoring, stability analysis. Ash disposal facilities, dry disposal, wet disposal, design of ash containment system, stability of ash dykes, mine tailings. Planning, source control, soil washing, bioremediation, stabilization of contaminated soils and risk assessment approaches
Learning Outcome	At the end of the course, student would be able to: 1. Able to manage and dispose particular type of waste 2. Should be able to characterize contaminated site 3. Should be able to take appropriate remedial measures for a contaminated site
Assessment Method	Assignments, Quizzes, Mid-semester examination and End-semester examination.

Textbooks:

H D Sharma and K R Reddy, “Geoenvironmental Engineering: Site Remediation, waste containment, and emerging waste management technologies”, John Willey and Sons, 2004.
R N. Yong, “Geoenvironmental Engineering: Contaminated Ground: Fate of Pollutions and Remediation”, Thomson Telford, 2000.
D. G. Fredlund and H. Rahardjo, “Soil Mechanics for Unsaturated soils”, Wiley Publication, 1993.

Reference books:

R Kerry Rowe, R M Quigley, Richard W I Brachman and John R Booker, “Barrier Systems for Waste Disposal Facilities”, 2nd edn, CRC press, 2019.

L N Reddy and H.I. Inyang, “Geoenvironmental Engineering: Principles and Applications”, Marcel Dekker, 2000
James K Mitechell, K Soga, “Fundamentals of soil behaviour”, Wiley Publication, 2005.
Charles W.W.Ng, B Menzies, “Advanced unsaturated soil mechanics and engineering”, CRC Press, 2014.
All relevant IS and International Codes.

CE4109

Biogeotechnical Engineering

	CE4109 Biogeotechnical Engineering
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Biogeotechnical Engineering
Learning Mode	Lectures
Learning Objectives	Complies with PLO- number 1, 3, and 5. The objectives of this course are to 1. Understand the significance of geomicrobiology in geotechnical engineering. 2. Comprehend various biological process in ground/soil improvement. 3. Apply the knowledge for upscaling to develop sustainable geomaterials.
Course Description	This course combines the principles of environmental biotechnology and geotechnical engineering. Geotechnical engineers design, build, and maintain structures in the subsurface. This course will be able to provide combine and apply basic theory and concepts from soil mechanics and biology in engineering applications. This course also brings an understanding about various geomicrobiological process for soil improvement.
Course Outline	Introduction to Biogeotechnics, Biological process of the subsurface materials, Stoichiometry and kinetics of bio-chemical reactions, Microbially Induced Calcite Precipitation (MICP), Root-Inspired Foundations, Enzymatically Induced Calcite Precipitation (EICP), Self-healing materials, Termite mounds-, Snake- and Ant-Inspired Excavations, Microbial Ecology, Biofilms, and Zeolite Sorption, Production of bio-cements. Instrumentation and testing for evaluating biological process and geotechnical material behaviour, Upscaled model tests and field trails.
Learning Outcome	At the end of the course, student would be able to: 1. Understand the importance of geomicrobiology in geotechnical engineering. 2. Comprehend various bio-chemical reactions and their application in biological process for ground/soil improvement. 3. Investigate biological process and geotechnical behaviour. 4. Apply the knowledge for upscaling to develop sustainable geomaterials.
Assessment Method	Assignments , Quizzes , Term-paper project, Mid-semester examination and End-semester examination.

Textbooks:

Ehrlich, H., Newman, D. Geomicrobiology (5th ed.). Boca Raton: CRC Press (2021).
Hemond, Harold F., and Elizabeth J. Fechner. Chemical fate and transport in the environment. academic press, 2022.
Rittmann, Bruce E., and Perry L. McCarty. "Environmental biotechnology: principles and applications." (No Title) (2001).

Reference books:

Coduto, Donald P., Man-chu Ronald Yeung, and William A. Kitch. "Geotechnical engineering: principles and practices." (No Title) (2011).
Zheng, Chunmiao, and Gordon D. Bennett. Applied contaminant transport modeling. Vol. 2. New York: Wiley-Interscience, 2002.
All relevant codes and research papers

CE4110

Pavement Geotechnology

Course Number	CE4110: Pavement Geotechnology
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Pavement Geotechnology
Learning Mode	Lectures
Learning Objectives	Complies with program learning outcome 1a; 3a 1. Equip the students with a strong foundation and strengthen their knowledge in pavement geotechnics. 2. The student will be able to apply advanced theory and analysis for problem-solving in pavement geotechnics. 3. The student will prepare for further research and graduate study by critical thinking and improving research skills. 4. The student will be able to apply fundamentals in identifying, formulating, and solving complex engineering problems in pavement geotechnics.
Course Description	This coursework will provide practical insights for students in the field of Pavement Geotechnics. The students will be taught the recent developments and design principles to face current and future highway problems in relevance with pavement geotechnics.
Course Content	Geotechnical properties of geomaterials such as soil, rock, soil-rock mixture, and alternative geomaterials. Introduction to stabilized geomaterials. Introduction to various types of pavement, subgrades, materials, subbase, base, and asphalt concrete materials relevant to pavement Geotechnics, Elastic theories and stress distribution in pavements, Geosynthetic stabilized pavements, geotechnical parametric studies for AASHTO, MEPDG, and IRC designs.
Learning Outcome	Students would be able to learn the core principles of pavement designs and advanced sustainable pavement techniques. Exploration of alternative materials, design approaches, and innovation in pavement geotechnics will be disseminated through this course.

Textbooks:

Huang, Y. H. (2004). Pavement analysis and design, Second edition, Upper Saddle River, NJ: Pearson Prentice Hall.
Yoder, E. J., & Witczak, M. W. (1991). Principles of pavement design. John Wiley & Sons.
Mallick, R. B., & El-Korchi, T. (2008). Pavement engineering: principles and practice. CRC Press.
Frost, M. W., Jefferson, I., Faragher, E., Roff, T. E. J., & Fleming, P. R. (Eds.). (2003). Transportation Geotechnics: Proceedings of the Symposium Held at The Nottingham Trent University School of Property and Construction on 11 September 2003. Thomas Telford Publishing.
Ellis, E., Yu, H. S., McDowell, G., Dawson, A. R., & Thom, N. (Eds.). (2008). Advances in Transportation Geotechnics: Proceedings of the International Conference Held in Nottingham, UK, 25-27 August 2008. CRC Press.
Miura, S., Ishikawa, T., Yoshida, N., Hisari, Y., & Abe, N. (Eds.). (2012). Advances in Transportation Geotechnics 2. CRC Press.

Reference books:

Ferguson, B. K., & Ferguson, B. K. (2005). Porous pavements. Boca Raton, FL: Taylor & Francis.
Rogers, M., & Enright, B. (2016). Highway engineering. John Wiley & Sons.
Nikolaides, A. (2014). Highway engineering: Pavements, materials and control of quality. CRC Press.
Babu, G. L. S., Kandhal, P. S., Kottayi, N. M., Mallick, R. B., & Veeraragavan, A. (2019). Pavement Drainage: Theory and Practice. CRC Press.
Babu, G.L.S., (2006). An Introduction to Soil Reinforcement and Geosynthetics, Universities Press (India) Pvt. Ltd.
All relevant codes and standards

Department Elective-III

Department Elective-III

Sl. No.

Subject Code

Subject

CE4201

Elements of Remote Sensing and GIS

Course Number	CE4201: Elements of Remote Sensing and GIS
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Elements of Remote Sensing and GIS
Learning Mode	Lectures
Learning Objectives	Complies with PLO 1, 3 and 4
Course Description	This course shall give the 3^rd year B. Tech students of Civil & Environmental Engineering an insight of the emerging trends in remote sensing and GIS apart from the very basics of remote sensing and GIS to the data and sensor types and some hands on in utilization of remote sensing and GIS tools.
Course Content	Lecture: Remote sensing – history & development, definition, concept and principles. Energy resources, radiation principles, EM Radiation and EM Spectrum. Interaction of EMR with atmosphere and earth’s surface. Platforms – types and their characteristics Satellites and their characteristics – geo-stationary and sun- synchronous Earth Resources Satellites -LANDSAT, SPOT, IRS, IKONOS satellite series Meteorological satellites – INSAT, NOAA, GOES. Sensors – types and their characteristics, across track (whiskbroom) and along track (pushbroom) scanning Optical mechanical scanners – MSS, TM, LISS, WiFS, PAN Concept of resolution – spatial, spectral, temporal, radiometric Basic concept and principles of thermal, microwave and hyperspectral sensing. Fundamentals of GIS, open source GIS tools, Network analysis, raster and vector data formats, cartography.
Learning Outcome	After completion of course, students will be able to: • Describe various sources and characteristics of remote sensing data. • Explain application of remote sensing data in different domains such as urban, agriculture, disaster management etc. • Understand the remote sensing data processing techniques. • Apply the knowledge for economic, environmental and sustainable infrastructure development.
Assessment Method	Quiz, Clast Test, Term-paper project and Examination

Books Recommended

Campbell, J.B.2002: Introduction to Remote Sensing. Taylor & Francis Publications Drury, A., 1987: Image Interpretation in Geology.
Allen and Unwin Gupta, P.., 1990: Remote Sensing Geology.
Springer Verlag Jensen, R. 2000: Remote Sensing of the Environment: An Earth Resource Perspective. Prentice Hall.
Joseph George, 2003: Fundamentals of Remote Oxford Universities Press

Lillesand, T.M., and Kieffer, R.M., 1987: Remote Sensing and Image Interpretation, John Wiley.

CE4202

Introduction to Soil-Structure Interaction

	CE4202 Introduction to Soil Structure Interaction
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Introduction to Soil-Structure Interaction
Learning Mode	Lectures
Learning Objectives	Complies with PLO- number 1, 2, 3, and 4 1. To provide the knowledge of the basic concept of soil and structural interaction. 2. Equip the students with a basic foundation in civil engineering for both research and industrial scenarios. 3. Prepares the students to apply knowledge in policy and decision making related to civil engineering infrastructure under dynamic loading.
Course Description	This course intends to bridge the basic concepts with the advanced topics related to geotechnical engineering. Topics ranging from general concept of soil-structure interaction, beams on elastic foundation, modern concept of analysis of piles and pile groups are covered.
Course Outline	General soil-structure interaction problems. Contact pressures and soil-structure interaction for shallow foundations. Concept of sub grade modulus, Beams on elastic foundation concept, Curved failure surfaces, their utility and analytical/graphical predictions from Mohr-Coulomb envelope and circle of stresses. Earth pressure computations by friction circle method. Earth pressures on sheet piles, braced excavations. Design of supporting system of excavations. Arching in soils. Elastic and plastic analysis of stress distribution on yielding bases. Modern concept of analysis of piles and pile groups. Axially, laterally loaded piles and groups. Elastic continuum and elasto-plastic analysis of piles and pile groups. Non-linear load-deflection response.
Learning Outcome	At the end of the course, student would be able to: 1. Apply beam on elastic foundation concept in analysis and design of various problem related to geotechnical engineering. 2. Determine ultimate lateral resistance of piles by various approaches.
Assessment Method	Assignments, Quizzes, Mid-semester examination and End-semester examination.

Textbooks:

J. P. Wolf, Dynamic Soil-Structure Interaction, Prentice-Hall, 1985.

Reference books:

H. G. Poulos, and E. H. Davis, Pile Foundation Analysis and Design, Krieger Pub Co., 1990.
Structure Soil Interaction- State of Art Report, Institution of Structural Engineers, 1978.
All relevant IS and International Codes.

CE4203

Introduction to Underground Excavation

	CE4203: Introduction to Underground Excavation
Course Credit (L-T-P-C)	3-0-0-3.0
Course Title	Introduction to Underground Excavation
Learning Mode	Lectures and practical
Learning Objectives	Complies with PLO- number 1, 2 and 3. 1. Understand the principles of underground excavation design, including site investigation and geological mapping. 2. Gain proficiency in analyzing rock mass behavior and selecting appropriate support systems. 3. Learn excavation methods, tunnelling techniques, and their applications in various geological conditions. 4. Develop skills to design safe, cost-effective, and sustainable underground structures while considering geological, geotechnical, and structural factors.
Course Description	This course covers principles of underground excavation including rock mechanics, support systems, and excavation methods. Topics include ground behavior, stability analysis, tunnelling methods, and practical design considerations.
Course Outline	Introduction to Underground Excavations, Rock Mechanics Fundamentals, Site Investigation and Geotechnical Data Collection, Excavation Methods, Support Systems for Underground Excavations.
Learning Outcome	At the end of the course, student would be able to: 1. Understanding principles of rock mechanics for underground openings. 2. Ability to analyze and design support systems for stability and safety. 3. Proficiency in assessing geological conditions and their impact on excavation design. 4. Skill development in designing underground excavations for various engineering purposes like tunnels, mines, or underground structures.
Assessment Method	Assignments, Quizzes, Mid-semester examination and End-semester examination.

Textbooks:

Goodman, R. E. Introduction to rock mechanics, John Wiley and Sons, 1989.
Hoek, E., & Bray, J. D. Rock slope engineering, CRC Press, 1981.
Hoek, E, & Brown, E. Underground excavations in rock, CRC Press, 1980.

References:

Singh, B., & Goel, R. K. Engineering rock mass classification, Elsevier, 2011.
Jaeger, J. C., Cook, N. G., & Zimmerman, R. Fundamentals of rock mechanics, John Wiley & Sons, 2009.
Debasis, D., & Kumar, V. A. Fundamentals and applications of rock mechanics, PHI Learning Pvt. Ltd. New Delhi, India, 2016.
All relevant IS and international codes.

CE4204

Multiphysical Processes in Fractured Rocks

	CE4204: Multiphysical Processes in Fractured Rocks
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Multiphysical Processes in Fractured Rocks
Learning Mode	Lectures and practical
Learning Objectives	Complies with PLO- number 1, 2 and 3 1. Understand the coupling mechanisms between various processes (e.g., fluid flow, heat transfer, and mechanical deformation) in fractured geological media. 2. Analyze the impact of fractures on the behavior of fluid flow, heat transfer, and mechanical deformation in geological formations. 3. Apply numerical modeling techniques to simulate coupled processes in fractured media and predict their behavior under different conditions. 4. Develop strategies for managing and controlling coupled processes to optimize resource extraction, geological storage, or environmental remediation in fractured geological environments.
Course Description	The Coupled Processes in Fractured Geological Media course delves into the complex interactions occurring within fractured rock formations. Students explore coupled hydro-mechanical-chemical processes occurring in subsurface environments.
Course Outline	Introduction to Fractured Geological Media, Rock Mechanics Fundamentals, Hydrological Processes in Fractured Media, Thermal-Hydrological-Mechanical (THM) Coupling, Geomechanical-Fluid Interaction
Learning Outcome	At the end of the course, student would be able to: 1. Grasp the complex interactions between fluid flow, heat transfer, and mechanical deformation in fractured geological formations. 2. Analyze coupled processes influencing subsurface systems such as groundwater flow, geothermal energy, and hydrocarbon reservoirs. 3. Develop skills to model and simulate coupled phenomena to solve real-world problems in fractured media.
Assessment Method	Assignments, Quizzes, Mid-semester examination and End-semester examination.

Textbooks:

Goodman, R. E. Introduction to rock mechanics, John Wiley and Sons, 1989.
R. Pusch. Waste Disposal in Rock. Elsevier. 1994
Coupled Processes Associated with Nuclear Waste Repositories" by Jacques Delay, Peter A. Witherspoon, François X. Dégerine
Randall F. Barron and Brian R. Barron. Design for Thermal Stresses. Wiley, 2011
Fractured Rock Hydrogeology" by John M. Sharp Jr.

References:

Hoek, E., & Bray, J. D. Rock slope engineering, CRC Press, 1981.
Hoek, E, & Brown, E. Underground excavations in rock, CRC Press, 1980.
Singh, B., & Goel, R. K. Engineering rock mass classification, Elsevier, 2011.

"Coupled Processes in Subsurface Deformation, Flow, and Transport" edited by George Pinder, Catherine A. Peters

CE4205

Rock Engineering for Hydropower Projects

	CE4205: Rock Engineering for Hydropower Projects
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Rock Engineering for Hydropower Projects
Learning Mode	Lectures
Learning Objectives	Complies with PLO- number 1, 2 and 3 1. Understand the geological processes shaping river valleys and the behavior of rock masses within them. 2. Analyze and assess geological hazards such as landslides, rockfalls, and erosion affecting river valley infrastructure. 3. Develop skills in rock slope stability analysis, support design, and mitigation measures specific to river valley environments.
Course Description	Rock Engineering for Hydropower Projects covers the geotechnical aspects of river valley infrastructure. Students learn about slope stability, rock mechanics, and foundation design tailored to river environments.
Course Outline	Introduction to Hydropower Projects, Rock mechanics principles,Geological Considerations, Rock Mechanics Fundamentals, Design of hydropower Structures, Case Studies, Instrumentation and Monitoring
Learning Outcome	At the end of the course, student would be able to: 1. Understand principles of rock mechanics relevant to river valley projects. 2. Analyze geological conditions to design stable structures for dams, tunnels, and slopes. 3. Apply engineering techniques for rock stabilization and slope reinforcement.
Assessment Method	Assignments, Quizzes, Mid-semester examination and End-semester examination.

Textbooks:

Goodman, R. E. Introduction to rock mechanics, John Wiley and Sons, 1989.
Hoek, E., & Bray, J. D. Rock slope engineering, CRC Press, 1981.
Hoek, E, & Brown, E. Underground excavations in rock, CRC Press, 1980.

References:

Singh, B., & Goel, R. K. Engineering rock mass classification, Elsevier, 2011.
Jaeger, J. C., Cook, N. G., & Zimmerman, R. Fundamentals of rock mechanics, John Wiley & Sons, 2009.
Debasis, D., & Kumar, V. A. Fundamentals and applications of rock mechanics, PHI Learning Pvt. Ltd. New Delhi, India, 2016.
All relevant IS and International Codes.

CE4206

Fundamentals of Forensic Geotechnical Engineering

	CE4206 Fundamentals of Forensic Geotechnical Engineering
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Fundamentals of Forensic Geotechnical Engineering
Learning Mode	Lectures
Learning Objectives	Complies with PLO- number 1, 3, 4 & 5. The learning objectives of this course are as follows: 1. To deal with investigations of different failures of engineered projects or facilities or structures related to civil engineering. 2. To analyze failures related to civil engineering, geotechnical, geoenvironmental and geological domains for professional practice, codes of analysis and design and implementation.
Course Description	This course is designed to understand and examine the various failures of civil engineering project due to different physical, environmental and geological causes. Further, knowledge gathered from this course will help in improving professional practice, developing codal provision and design and implementation.
Course Outline	Introduction, Types of failure and damages, Preliminary investigations and information, Interaction between neighboring Structures, Planning the investigations, Site investigations, Settlement and failures of sub structures due to physical and environmental causes, Foundation design in difficult soil and climatic conditions, Ground water moisture related problems of substructures, Repairs and crack diagnosis, Case studies of Leaning Instability, Bearing Capacity Failure etc.
Learning Outcome	At the end of the course, student would be able to: 1. Understand the necessity and importance of forensic investigation in geotechnical engineering for various projects. 2. To deal with investigations of different failures of engineered projects or facilities or structures related to civil engineering. 3. To comprehend the techniques for mitigation of the failure damage. 4. To analyze failures related to civil engineering, geotechnical, geoenvironmental and geological domains for professional practice, codes of analysis and design and implementation.
Assessment Method	Assignments, Quizzes, Mid-semester examination and End-semester examination.

Textbooks:

Rao, V. V. S., and GL Sivakumar Babu, eds. Forensic Geotechnical Engineering. India: Springer India, 2016.
Puzrin, Alexander M., Eduardo E. Alonso, and Núria M. Pinyol. Geomechanics of failures. Dordrecht, The Netherlands: Springer, 2010.
Iwasaki, Y. Instrumentation and Monitoring for Forensic Geotechnical Engineering. Forensic Geotechnical Engineering (2016): 145-163.

Reference books:

Day, Robert W. Forensic geotechnical and foundation engineering. McGraw-Hill, 2011.
Alonso, Eduardo E., Núria M. Pinyol, and Alexander M. Puzrin. Geomechanics of failures: advanced topics. Vol. 277. Berlin: Springer, 2010.
Lacasse, Suzanne. Forensic geotechnical engineering theory and practice. Forensic Geotechnical Engineering (2016): 17-37.
Franck, Harold, and Darren Franck. Forensic engineering fundamentals. Boca Raton, FL: CRC Press, 2013.
All relevant IS and international codes and research articles and reports.

CE4207

Ground Improvement for Civil Engineering Structures

	CE4207 Ground Improvement for Civil Engineering Structures
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Ground Improvement for Civil Engineering Structures
Learning Mode	Lectures
Learning Objectives	Complies with PLO- number 1, 3, 4 & 5 1. Understand the necessity and importance of ground improvement for various projects. 2. Identify the soils and select a suitable ground improvement technique. 3. Analysis and design the various ground improvement techniques. 4. Comprehend the construction methodology, equipment and quality control aspects 5. Introduction and application to national and international codal guidelines and provisions.
Course Description	Construction in weak and problematic soil is inevitable nowadays. The course addresses various ground improvement techniques along with principles, design issues and construction procedures.
Course Outline	Introduction and importance for ground improvements; Geotechnical materials, testing and design; Mechanical modifications; Preloading and vertical drains; Soil stabilization using additives; Grouting; Vibro techniques; Drainage and dewatering; Soil nailing; Underpinning, Introduction to geo-synthetics and reinforced earth; Behaviour of Reinforced earth walls; Geosynthetics in landfill system; Use of jute, coir, natural geotextiles, waste products as reinforcing material.
Learning Outcome	At the end of the course, student would be able to: 1. Understand the importance of ground improvement for various projects. 2. Recognize the problematic soil and select a suitable ground improvement technique. 3. Design the various ground improvement techniques. 4. Understand the construction methodology, equipment and quality control aspects. 5. Know the national and international codal guidelines and provisions.
Assessment Method	Assignments, Quizzes, Mid-semester examination and End-semester examination.

Textbooks:

Manfired R. Hausmann, Engineering Principles of Ground Modification, McGraw-Hill Pub, Co., 1990.
Koerner, R.M. Designing with Geosynthetics, Prentice Hall, New Jersey, USA, 4th edition, 1999.

Reference books:

Jie Han, Principles and Practice of Ground Improvement, Wiley Publishers, 2015.
B.M. Das, Principle of Geotechnical Engineering, Cengage Learning, eighth Edition, 2013.
V. N. S. Murthy, Geotechnical Engineering: Principles and Practices of Soil Mechanics and Foundation Engineering, CRC Press, Taylor & Francis Group, Third Indian Reprint, 2013.
All relevant IS and International Codes.

Department Elective-IV

Department Elective-IV

Sl. No.

Subject Code

Subject

CE4208

Solid Waste Engineering

Course Number	CE4208 - Solid Waste Engineering
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Solid Waste Engineering
Learning Mode	Lectures
Learning Objectives	Complies with PLOs 2 and 3 1. To impart knowledge and develop understanding of integrated solid waste management by technological interventions 2. To train students to comprehend, understand, plan, and design various steps and processes in an integrated solid waste management framework 3. To provide scientific and technical knowledge, to prepare students to address current issues and challenges for integrated solid waste management in field
Course Description	This course will discuss fundamental concepts and engineering practices involved in integrated solid waste management. The course will provide a deeper understanding of municipal solid waste generation and influencing factors, composition, segregation and collection, processing and disposal techniques for integrated solid waste management with theory and real-life practices.
Course Content	Sources, composition and properties of municipal solid waste, Generation of solid waste, Rates of generation and factors affecting them; Specific waste streams: construction and demolition (C&D) waste, electronic waste (e-waste), etc.; Hazardous wastes and characteristics; Environmental legislations; Sustainable development goals (SDGs), Urban mining and circular economy concepts; Solid wastes management: Generation, on-site storage and processing including segregation, collection, separation, processing and disposal; On-site storage methods: containers, their type, size and location; Collection systems: Vehicles, routing, route balancing and transfer stations, Processing technique and equipment, Recovery of resources, Conversion products and energy, Biological digestion, Composting and vermicomposting, Recycling, Incineration and pyrolysis, Disposal of solid waste including sanitary landfill, Planning, site and design aspects of solid waste engineering.
Learning Outcome	At the end of the course, students would be able to: 1. Understand about the municipal solid waste generation and composition with the influencing factors 2. Comprehend and understand steps and processes involved in the solid waste management 3. Analyze and understand the current issues and challenges for solid waste management 4. Comprehend, understand and apply various processing and disposal techniques for solid waste management
Assessment Method	Assignments, Quizzes, Mid Semester Examination and End Semester Examination

Text Books:

CPHEEO, Manual on Municipal Solid Waste Management, Central Public Health & Environmental Engineering Organisation (CPHEEO), Ministry of Housing and Urban Affairs, Govt. of India, 2016.
Vesilind, P., Worrel, W. and Ludwig, C., Solid Waste Engineering: A Global Perspective, CL Engineering, First Edition, 2016.
LaGrega, M., Buckingham, P. and Evans, J., Hazardous Waste Management, Medtech, 2015.

Reference Books:

Tchobanoglous, G., Theisen, H. and Vigil, S.A., Integrated Solid Waste Management: Engineering Principles and Management Issues, McGraw Hill, 2014.
Charles A. Wentz, Hazardous Waste Management, McGraw-Hill, 1995.

CE4209

Air Pollution Engineering

Course Number	CE4209 - Air Pollution Engineering
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Air Pollution Engineering
Learning Mode	Lectures
Learning Objectives	Complies with PLOs 2,3 and 5: 1. To provide insights into causes and effects of air pollution 2. To understand the relationship of meteorological parameters and dispersion of pollutants 3. To equip students to address current issues and challenges in field of air pollution control
Course Description	The course will provide a deeper understanding of air pollutants and their fate and dispersion, measurements, monitoring and modelling on the backdrop of meteorology and environmental legislations. The course also helps to understand the impacts of air pollution on human health and various technological interventions for the improvement of air quality in real-life scenario.
Course Outline	Fundamentals of Atmosphere, Air Pollution, Classification of pollutants, Effects on human health and environment, Sources of Pollutants, Fate and transport of pollutants, Criteria pollutants, Photochemical smog, Greenhouse gases, Global warming and climate change, Indoor air pollution. Meteorology: Elemental properties of atmosphere, Influence of meteorological parameters on air quality, Effect of atmospheric pollutant on meteorological parameters, Dispersion of air pollutants, Atmospheric modelling, Box model, Gaussian plume dispersion model, Atmospheric cleansing processes. Air quality and emission standards. Air quality index (AQI) and health risk.
Learning Outcome	At the end of the course, students would be able to: · Understand the atmosphere and origin, fate and transport of air pollutants · Comprehend and understand the influence of meteorological factors · Understand the concept of causes and effects of air pollution with the dispersion modelling of the pollutants
Assessment Method	Assignments, Quizzes, Mid-semester and End-semester Examination

Text books:

Davis, W.T., Fu, J.S. and Godish, T., Air Quality, CRC Press, 2021.
de Nevers, N., Air Pollution Control Engineering, Waveland Press, 2016.
S. Peavy, D. R. Rowe and George Tchobanoglous, Environmental Engineering, McGraw-Hill International Ed., 1985.
Wark, K., Warner, C.F. and Davis, W., Air Pollution: Its Origin and Control, Pearson, 1998.

Reference Books:

Pandis, S.N. and Seinfeld, J.H., Atmospheric Chemistry and Physics: From Air Pollution to Climate Change, Wiley, 2016.
Stull, R.B., Meteorology for Scientist and Engineers, Third Edition, Brooks/Cole, 2015.
Cooper, C.D. and Alley, F.C., Air Pollution Control: A Design Approach, Waveland Press, 2010.
Introduction to Atmospheric Chemistry, Daniel J. Jacob, Princeton University Press, 1999.
IPCC, 2007 Fourth Assessment Report, Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change

CE4210

Pavement Evaluation and Management

Course	CE4210
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Pavement Evaluation and Management
Learning Mode	Lectures
Learning Objectives	Complies with PLO number – 1, 2, and 4 To gain a thorough understanding of the key activities involved in pavement management within a highway organization. To develop practical skills in the collection and analysis of data essential for effective pavement management. To learn the fundamental practices and strategies required for managing highway pavements.
Course Description	The course will enable the students to learn the concepts and principles of pavement management system and principles, the use of cutting-edge equipment for performance data collection, and the application of tools and techniques for road asset management. Through various case studies and practical experience with big data analytics in asset management, students will gain comprehensive knowledge in this field.
Course Outline	Introduction to pavement management systems; Components of pavement management systems; Pavement distress mechanisms and surveys; Pavement performance prediction - concepts, modelling techniques, Comparison of different deterioration models; Pavement maintenance and rehabilitation strategies; Rehabilitation budget planning; Ranking and optimisation methodologies; Alternate pavement design strategies and economic evaluation; Reliability concepts in pavement engineering; life cycle costing; emerging trends in road asset management; Case Studies on Construction, maintenance, rehabilitation, reconstruction strategies.
Learning Outcome	At the end of the course, student would be able to: Understand the fundamentals of the pavement management systems and the state of art equipment for performance data collection, tools, and techniques. Know the strategic programming for pavement performance, rehabilitation, and maintenance. Appreciate the concept of pavement preservation techniques and cost analysis. Understand the process of implementing a pavement management system for highways.
Assessment Method	Assignments, Quizzes, Mid-semester examination and End-semester examination.

Textbooks:

Ralph Haas and Ronald. W. Hudson with Lynne Cowe Falls, 'Pavement Asset Management', Scrivener Publishing, Wiley, 2015.
Huang, Y. H. "Pavement analysis and design." Pearson, 2004.
Papagianna, A. T. and Masad, E. A. “Pavement Design and Materials.” John Wiley & Sons, Inc., 2008.

Reference books:

Relevant publications/codes from IRC, AASHTO, Transportation Research Board, National Institute of Standards and Technology and US Army Corps of Engineers.
Miller, John S., and William Y. Bellinger. Distress identification manual for the long-term pavement performance program. No. FHWA-RD-03-031. United States. Federal Highway Administration. Office of Infrastructure Research and Development, 2003.

CE4211

Pavement Materials

Course	CE4211
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Pavement Materials
Learning Mode	Lectures
Learning Objectives	Complies with PLO number – 1, 2, and 4 To understand the characteristic properties of materials used in road construction. To learn techniques for evaluating the performance of road construction materials. To gain knowledge in the design of asphalt and concrete mixes. To understand alternative materials used in road construction..
Course Description	The course provides a foundational understanding of the behavior of various materials used in pavement construction. It covers the characterization, testing, and engineering properties of these materials, emphasizing their practical applications in the field. Additionally, the course will explore current practices and future trends in pavement materials.
Course Outline	Soil: Introduction to soil as a highway material; Classification of soils; Consistency Limits; Soil compaction and role of moisture; Mechanical properties of soil; Introduction to expansive soils, relevant tests, and soil stabilization techniques; Aggregates: Aggregate origin, types, production, and quarrying operation; Classification of aggregates; Aggregate gradation and gradation parameters; Theories of aggregate blending; Minerology of aggregates and its importance; Aggregate shape and texture: quantification and importance; Aggregate strength properties, and relevant tests; Bituminous Materials: Production of bitumen; Physical and rheological properties of bitumen; Introduction to viscoelasticity; Chemistry of bitumen; Ageing of bitumen; Grading of bitumen, and relevant tests; Bitumen modification; Introduction of bitumen emulsion; Introduction to cutback bitumen. Bituminous Mixtures: Production of bituminous mixtures: Laboratory and Plant; Role of bituminous mixture and desirable properties; Volumetrics of bituminous mixture; Mix design of bituminous mixture: Marshall and Superpave methods; Mechanical tests and characterization of bituminous mixtures; Introduction to performance based mix design concepts; Mix design of cold bituminous mixtures; Mix design of hot recycled mixtures; Cement: Production of cement; Theory of hydration and importance of different hydration products; Physical and chemical properties of cement; Types of cement; Pozzolanic and geopolymer materials as alternate cement; Concrete Mix Design: Concrete proportioning and importance of various constituents; Introduction and mix design of pavement quality concrete, Dry lean concrete and Pervious concrete; Alternative Pavement Materials: State of the art on various alternative materials for construction of flexible and rigid pavements.
Learning Outcome	At the end of the course, student would be able to: Understand various conventional and recycled materials utilized in pavement construction. Develop the ability to select and design appropriate materials for road construction. Assess pavement materials based on their performance-related properties.
Assessment Method	Assignments, Project Work, Quizzes, Mid-semester examination and End-semester examination.

Textbooks:

Athanassios Nikolaides. “Highway Engineering: Pavements, Materials and Control of Quality”. CRC Press, T&F, 2015.

Papagianna, A. T. and Masad, E. A. “Pavement Design and Materials.” John Wiley & Sons, Inc., 2008.

Brown, Kandhal, Roberts, Kim, Lee and KennedyHot Mix Asphalt Materials, Mixture Design, and Construction, NCAT (Third Edition).
Bituminous Road Construction in India, Prithvi Singh Kandhal, PHI publications.

Reference books:

Relevant codes/standards from Indian Roads Congress (IRC), Bureau of Indian Standards (BIS), American Society of Testing Materials (ASTM), and American Association of State Highway and Transportation Officials (AASHTO).

MORTH. “Ministry of Road Transportation & Highways Specifications for Road and Bridge Works.” 2013.

CE4212

Introduction to Traffic Flow Modelling and Intelligent Transportation systems

Course Number	CE4212
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Introduction to Traffic Flow Modelling and Intelligent Transportation systems
Learning Mode	Lectures
Learning Objectives	To gain insight into theory and modeling of traffic flow operations To understand various data collection strategies of ITS To understand evaluation of the ITS applications To apply latest technologies in solving congestion related problems
Course Description	The purpose of this subject is to introduce students to the basic elements of intelligent transportation systems (ITS), focusing on technological, systems and institutional aspects.
Course Content	Traffic Characteristics: Macroscopic variables, microscopic variables, Relationships between micro and macroscopic variables; Microscopic Traffic Flow Models, Static/Equilibrium Traffic Stream Models, Dynamic Traffic Flow Models. Introduction to ITS; Advanced traveler information systems; transportation network operations; commercial vehicle operations and intermodal freight; public transportation applications; ITS and safety, ITS and security, ITS as a technology deployment program, research, development and business models, ITS and sustainable mobility, travel demand management, electronic toll collection, and ITS and road-pricing.
Learning Outcome	At the end of the course, the student will be able to gather the information on 1. Identify differences between microscopic and macroscopic variables 2. Learn how macroscopic models are derived from microscopic principles 3. Differences between intrusive and non-intrusive technologies 4. Various performance evaluation strategies of ITS applications, 5. Relevance of ITS in the context of developing countries especially with the national mission of smart cities, 6. Understand the differences between various functional areas of ITS etc.
Assessment Method	Assignments, Term Projects, Technical paper presentations, quizzes, mid-semester examination and end-semester examination

References:

Adolf D. May. Traffic Flow Fundamentals. Prentice-Hall International (1990)
Daganzo, C.F. Fundamentals of transportation and traffic operations. Vol. 30. Oxford: Pergamon, 1997.
Daiheng Ni, Traffic Flow Theory, PHI 5. Partha Chakraborty, Animesh Das: Principles of Transportation Engineering, 2nd Edition by PHI.
Henry Lieu. Revised Monograph on Traffic Flow Theory, Federal Highway Administration Research and Technology, 2017.
Joseph S. Sussman: Perspectives on Intelligent Transportation Systems (ITS), Springer; 2005th edition (April 7, 2005)
Robert Gordon, Intelligent Transportation Systems: Functional Design for Effective Traffic Management, Springer 2016.

CE4213

Design of Transportation Facilities and Safety

Course Number	CE4213
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Design of Transportation Facilities and Safety
Learning Mode	Lectures
Learning Objectives	To gain insights on how to design transportation facilities and to impart safety into them.
Course Description	The purpose of this subject is · To introduce the design concepts to develop transportation facilities · To introduce safety and accident data analysis
Course Content	Introduction to transportation facilities – considerations and requirements; Design of At-grade Inter-sections - sight distance consideration and principles of design, channelisation, mini round-abouts, layout and design of round-abouts; Design of signalised intersections, signal coordination, interchange design templates, entrance and exit ramps, acceleration and deceleration lanes, Bicycle and Pedestrian Facility Design; Parking Layout and Design; Terminal Layout and Design. Accident prevention through better planning, Designing for safety, Highway operation and accident counter measures, Road safety checklists, accident data analysis and its prediction models. Proof Check of IRC37-2018 with Actual Traffic Data and Soil Conditions using KENLAYER and IITPAVE; Proof Check of IRC58-2015 with Actual Traffic Data and Soil Conditions.
Learning Outcome	At the end of the course, the student will be able to gather the information on 1. Identify differences between microscopic and macroscopic variables 2. Learn how macroscopic models are derived from microscopic principles 3. Differences between intrusive and non-intrusive technologies 4. Various performance evaluation strategies of ITS applications, 5. Relevance of ITS in the context of developing countries especially with the national mission of smart cities, 6. Understand the differences between various functional areas of ITS etc.
Assessment Method	Assignments, Term Projects, Technical paper presentations, quizzes, mid-semester examination and end-semester examination

References:

Guidelines for the design of interchanges in urban areas (IRC:92-1985), The Indian Roads Congress.
Roadside design guide, American Association of State Highway Officials.
Manual of geometric design standards for Canadian roads, Transportation Associations of Canada.
Pline, J.L., Traffic Engineering Handbook, Institute of Transportation Engineers.
Manual on Uniform Traffic Control Devices, Federal Highway Administration.
Highway Capacity Manual 2010, Transportation Research Board.
K. Khanna and C.E.G. Justo, Highway Engineering, Khanna Publishers, Roorkee,
IRC 37-2018.
IRC 58-2015.

Department Elective-V

Department Elective-V

Sl. No.

Subject Code

Subject

CE4214

Introduction to Geotechnical Earthquake Engineering

	CE4214 Introduction to Geotechnical Earthquake Engineering
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Introduction to Geotechnical Earthquake Engineering
Learning Mode	Lectures
Learning Objectives	Complies with PLO- number 1, 2, 3, and 4 1. To provide the basic knowledge of the geotechnical earthquake engineering. 2. Equip the students with a strong foundation in civil engineering for both research and industrial scenarios. 3. Prepares the students to apply knowledge in policy and decision making related to civil engineering infrastructure. 4. Prepare students to attain leadership careers to meet the challenges and demands in civil engineering practice.
Course Description	This course intends to bridge the basic concepts with the advanced topics related to geotechnical engineering. Topics ranging from continental drift, seismic hazard analysis, wave propagation, liquefaction assessment, and seismic slope stability are covered. The course started with the basic knowledge regarding the wave propagation.
Course Outline	Introduction, Significant historical earthquakes, Continental drift and plate tectonics, Internal structure of earth, Sources of seismic activity, Size of the earthquake, Ground motion parameters, Seismic hazard analysis, Wave propagation, Dynamic soil properties and Measurement of dynamic soil properties, Ground response analysis, Local site effects, Evaluation of liquefaction hazards, and Seismic slope stability analysis.
Learning Outcome	At the end of the course, student would be able to: 1. Design earthquake resistant structure using various methods available along with the method suggested in the IS code. 2. Liquefaction potential assessment using IS code and other methods in practice. 3. Perform seismic hazard analysis for any site.
Assessment Method	Assignments, Quizzes, Mid-semester examination and End-semester examination.

Textbooks:

Kramar S.L, Geotechnical Earthquake Engineering, Prentice Hall International series, Pearson Education Pvt. Ltd.
J.E. Bowles, Foundation Analysis and Design, McGraw-Hill, 2001.

Reference books:

Ikuo Towhata, Geotechnical Earthquake Engineering, Springer series, 2008.
All relevant IS and International Codes.

CE4215

Structural Dynamics and Earthquake Engineering

Course	CE4215Structural Dynamics and Earthquake Engineering
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Structural Dynamics and Earthquake Engineering
Learning Mode	Lectures
Learning Objectives	Lectures: Complies with PLO-1, 2, 3 and 4 1. To develop a fundamental technical understanding on engineering vibration and earthquake in general. 2. To learn the basics of dynamics analysis pertaining to the field of earthquake engineering. 3. To make familiar with various elementary concepts of earthquake-resistant design.
Course Description	The course aims at developing detailed understanding with the design of various structures against seismic loading. This course provides the students an exposure for earthquake resistant designing of structures which are not usually covered in undergraduate design courses.
Course Outline	Single Degree of Freedom System (SDOF): equation of motion, free undamped and damped response, undamped and damped response to harmonic loading, response to arbitrary periodic, step, pulse excitations and ground motion; Multi Degree of Freedom System (MDOF): equations of motion; stiffness matrix; lumped and consistent mass matrix; proportional and Rayleigh damping matrix; Earthquake Engineering: causes of earthquakes and seismic waves, magnitude, intensity and energy release, earthquake characteristics, liquefaction and seismic risk, EQ response of structures, single-degree-of freedom dynamics, concept of response spectra, design response spectrum, idealization of structures, response spectrum analysis, equivalent lateral force concepts, philosophy of EQ resistant design, ductility, redundancy & over-strength, damping, supplemented damping, EQ behaviour of concrete, steel and masonry structures.
Learning Outcome	At the end of the course, student would be able to Lectures: 1. Understand the fundamental principles of vibrational motion and the mathematical models used to describe it. 2. Analyse and solve vibration problems in single-degree-of-freedom (SDOF), multi-degree-of-freedom (MDOF) systems. 3. Develop essential understanding earthquake loading on structures and apply the knowledge of structural dynamics. 4. Learn seismic design practices in real-life applications and introduce various codes of practice.
Assessment Method	Assignments, Quizzes, Project work, Mid-semester examination and End-semester examination.

Textbooks/ Reference books:

Villaverde, Fundamental Concepts of Earthquake Engineering, CRC Press, 1st edition, 2009.

A. K. Chopra, Dynamics of Structures: Theory and Applications to Earthquake Engineering, Prentice Hall, 4th edition, 2015.
T. K. Datta, Seismic Analysis of Structures, Wiley, 1st edition, 2011.
S. K. Duggal, Earthquake Resistant Design of Structures, Oxford Univ. Press, 2013.
M. Shrikhande and P. Agarwal, Earthquake Resistant Design of Structures, Prentice hall India, 2006.
R. W. Clough and J. Penzien, Dynamics of Structures, McGraw-Hill, 1975, 2nd edition, 1992.
N. M. Newmark and E. Rosenblueth, Fundamentals of Earthquake Engineering, Prentice Hall, 1971.
D. Key, Earthquake Design Practice for Buildings, Thomas Telford, London, 1988.
T. Paulay, Seismic Design of Reinforced Concrete and Masonry Buildings, John Wiley & Sons Inc, 1st edition, 1992.
Latest Indian standards for seismic design: IS1893, IS13920, IS456.

CE4216

Rehabilitation of Structures

Course	CE4216Rehabilitation of Structures
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Rehabilitation of Structures
Learning Mode	Lectures
Learning Objectives	Lectures: Complies with PLO-1, 2, 3 and 4 1. Understand the background of condition assessment, repair, and strengthening of structures. 2. Attain knowledge of rehabilitation of existing building.
Course Description	The course deals with the evaluation and strengthening of existing structures. The students shall learn about various techniques for the strengthening of structures.
Course Outline	Distress identification and repair management: causes of distress in structures. Preliminary inspection: planning stage, visual inspection and detailed inspection; Evaluation of concrete buildings: destructive testing systems, non-destructive testing techniques, semi-destructive testing techniques, and estimation of damage. Evaluation of strength of existing structures and analysis necessary to identify critical sections; Surface repair and retrofitting techniques; Strengthening techniques: beam shear capacity strengthening, column strengthening, and flexural strengthening. Guidelines for seismic rehabilitation of existing buildings, seismic vulnerability and strategies for seismic retrofit.
Learning Outcome	At the end of the course, student would be able to Lectures: 1. Introduce the application of different techniques for evaluation and retrofitting of buildings. 2. Present fundamental principles and methodologies for the design of various retrofitting techniques. 3. NDT techniques for condition assessment of structures for identifying damages in structures. 4. Select retrofitting strategy suitable for distress and formulate guide lines for repair management of deteriorated structures.
Assessment Method	Assignments, Quizzes, Project work, Mid-semester examination and End-semester examination.

Textbooks/ Reference books:

ASCE/SEI 41-23 Seismic Evaluation and Retrofit of Existing Buildings. 2023.
Varghese P.C., “Maintenance, Repair & Rehabilitation and Minor Works of Buildings” 1st Edition, PHI Learning Private Ltd., New Delhi., 2014.
Santhakumar A.R., “Concrete Technology” Oxford University Press, 2007, New Delhi
CPWD Handbook on Repair and Rehabilitation of RCC buildings, Govt. of India Press, New Delhi.
Emmons, P.H., “Concrete Repair and Maintenance”, Galgotia Publication. 2001.
Bungey, S., Lillard, G. and Grantham, M.G., “Testing of Concrete in Structures”, Taylor and Francis. 2001.
Malhotra, V.M. and Carino, N.J., “Handbook on Non-destructive Testing of Concrete”, CRC Press. 2004.
Bohni, H., “Corrosion in Concrete Structures”, CRC Press. 2005.
ATC- 40: Seismic Evaluation and Retrofit of Concrete Buildings, Vol. 1 & 2. 1997.
J.N. Priestley, Seible, F. and Calvi, G.M., “Seismic Design and Retrofit of Bridges”, John Wiley. 1996.

CE4217

Introduction to Structural Health Monitoring

Course	CE4217Introduction to Structural Health Monitoring
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Introduction to Structural Health Monitoring
Learning Mode	Lectures
Learning Objectives	Lectures: Complies with PLO-1, 2, 3 and 4 1. To learn the contingent need of structural monitoring in an infrastructurally heavy and old country like India. 2. To develop fundamental concepts on health monitoring of various structures. 3. To be familiar with some frequently used techniques in health monitoring.
Course Description	This course explores structural monitoring technologies for assessing the condition and performance of various structures. Some basic techniques are described. Non-destructive tests are focused. Some vibration-based methods are demonstrated. Moreover, the course covers emerging trends including sensor technology and data analytics for prognostic care.
Course Outline	Intro duction to Structural Health Monitoring (SHM): Definition & requirement for SHM, SHM of a bridge, monitoring historical buildings; Non-Destructive Testing (NDT): Classification of NDT procedures, visual inspection, half-cell electrical potential methods, Rebound Hammer Test, electro-magnetic methods, radiographic Testing, ultrasonic testing, Infra-Red thermography, ground penetrating radar etc; Vibration-based monitoring: Frequency-domain and time-domain analysis, Experimental modal analysis, application of damage detection methods on civil infrastructures.
Learning Outcome	At the end of the course, student would be able to Lectures: 1. Be fundamentally strong on structural condition assessment. 2. Be equipped with the work principle of various NDTs. 3. Develop proficiency in deploying sensor technologies and data acquisition systems to monitor the health of various structures. 4. To analyse collected data, detect structural damage, and make informed decisions regarding maintenance and safety measures.
Assessment Method	Assignments, Quizzes, Project work, Mid-semester examination and End-semester examination.

Textbooks/ Reference books:

Daniel J. Inman, Charles R. Farrar, Vicente Lopes Junior, Valder Steffen Junior, Damage Prognosis: For Aerospace, Civil and Mechanical Systems, John Wiley & Sons, 2005.
Chee-Kiong Soh, Yaowen Yang, Suresh Bhalla (Eds.), Smart Materials in Structural Health Monitoring, Control and Biomechanics, Springer, 2012.

IDE (Available to students of B. Tech. other than Dept. of Civil and Environmental Engineering)

Sl. No.

Subject Code

Subject

CE2206

Construction Technology and Management

Course	CE2206
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Construction Technology and Management
Learning Mode	Lectures
Learning Objectives	Complies with PLO- 2 1. To provide fundamental knowledge in construction technologies and material. 2. Provide scientific and technical knowledge to prepare students to address civil engineering materials-related challenges in the field. 3. Study the role and responsibilities of a construction manager. 4. Study the life cycle of a construction project and the key activities in each phase. 5. Study various types of contracts and bidding procedures
Course Description	This course will discuss fundamental concepts in construction technologies and civil engineering materials. The course will cover theory and real-world practices in materials used in construction industries, their operations, and execution.
Course Outline	Introduction to construction technologies and building materials. Properties of cement and aggregates and their types. Properties of fresh concrete and hardened concrete and design of concrete mix. Properties of bricks, masonry, timber, FRPs, structural steel and other building materials. Construction as industry and its challenges, Role of construction management, Methods of construction managements, Basic requirements of construction management, Life cycle of construction projects. Contracts and its types. Introduction to time management tools: List and Bar Charts, CPM and PERT. Quality Management and Construction safety, Use of information technology and automation in construction industries.
Learning Outcome	At the end of the course, student would be able to: 1. Understand the physical and engineering properties, principles, testing, and standards of civil engineering materials used in construction. 2. Understand various construction technologies 3. The use of different civil engineering materials subjected to different construction scenarios and needs. 4. Understand various phases in life cycle of a project 5. Understand the difference between different types of contracts and how to award the contract 6. Understand construction planning techniques and time management
Assessment Method	Assignment, Quizzes, Mid-semester examination and End-semester examination .

Textbooks:

S. Somayaji, Civil Engineering Materials, Prentice Hall, New Jersey, 2001.

S. Shetty, Concrete Technology, S. Chand and Company Ltd. 2005.

M. S. Mamlouk and J. P. Zaniewski, Materials for Civil and Construction Engineers, Pearson, Prentice Hall, Second edition, 2006.
P.C Varghese, Building Materials, Publisher: ‎ Prentice Hall India Learning Private Limited; 2nd edition (1 January 2015)
F. Harris, R. McCaffer and F. Edum-Fotwe, Modern Construction Management, Blackwell Publishing, 2006.
C. J. Schexnayder and R. E. Mayo, Construction Management Fundamentals, McGraw Hill, New Delhi, 2003

Reference books:

All relavent IS Codes.
Jackson and R. K. Dhir, Civil Engineering materials, Macmillan Fourth edition 1997.
Haimei Zhang, Building materials in civil engineering, Publisher: ‎Woodhead Publishing (9 May 2011).
Parbin Singh, Civil engineering materials, Publisher ‏: ‎ S K Kataria and Sons; Reprint 2013 edition.
K Duggal, Building Materials, New Age International Publisher, 4th edition.
S. Berrie and B.C. Paulson, Professional construction management including C.M., Design construct and general contracting, Third edition, McGraw Hill International edition, 1992.

CE3105

Green Building

Course Number	CE3110: Green Building
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Green Building
Learning Mode	Lectures
Learning Objectives	Complies with PLOs 2, 3,4 and 5: 1. Understand the principles of green building and sustainable design. 2. Learn about the various green building rating systems and certifications. 3. Gain knowledge of energy-efficient building systems and materials. 4. Explore sustainable construction practices and technologies.
Course Description	This course provides an in-depth understanding of green building principles, sustainable design, and construction practices. Students will explore the environmental, economic, and social impacts of buildings and learn how to design and construct buildings that are energy-efficient, environmentally friendly, and sustainable.
Course Outline	Introduction to Green Building, Sustainable Sites (Site Selection & Planning), Energy Efficiency in Buildings, Sustainable Materials and Resources, Waste Management and Recycling, Water Efficiency and Management, Indoor Environmental Quality, Sustainable Construction Practices, Green Building Rating Systems and Certifications, Case Studies and Future Trends.
Learning Outcome	By the end of this course, students will be able to: 1. Understand Green Building Concepts and Site Planning 2. Implement Energy, Resource, and Water Efficiency 3. Enhance Indoor Environmental Quality 4. Navigate Certifications and Analyze Case Studies
Assessment Method	Assignments, Quizzes, Mid-semester and End-semester Examination

Textbooks and Reference books:

IGBC, Introduction to Green Buildings & Built Environment, Indian Green Building Council, BS Publications / BSP Books, 2022.
G Harihara Iyer, Green Building Fundamentals, Notion Press, 2022.
Abe Kruger and Carl Seville, Green Building: Principles and Practices in Residential Construction, Delmar Cengage Learning, 2012.
Michael Bauer, Peter Mösle, and Michael Schwarz, Green Buildings: A Guide for Sustainable Architecture, Springer-Verlag Berlin Heidelberg, 2010.
India’s Energy Conservation Building Code (ECBC), 2017.
TERI, Sustainable Building Design Manual, Volume 1 and 2, Energy and Resources Institute (TERI).
Minsitry of Power, Energy Conservation Building Code 2018, Revised Version, Bureau of Energy Efficiency, 2018.
Indian Building Congress, Practical Handbook on Energy Conservation in Buildings, 1 st ed. Nabhi Publication, 2008.
Green Rating for Integrated Habitat Assessment (GRIHA).
IGBC Rating Systems.
Supplementary Materials: Articles, case studies, and industry reports on green building.

CE4111

Smart Transportation

Course	CE4111
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Smart Transportation
Learning Mode	Lectures
Learning Objectives	1. Understand the fundamental concepts of smart transportation 2. Explore the technologies that enable smart transportation, including sensors, IoT, and AI. 3. Analyse the impact of autonomous vehicles and connected infrastructure. 4. Evaluate the role of data analytics in optimizing transportation systems.
Course Description	This course introduces undergraduate students to the fundamental concepts of smart transportation. It explores the technologies, methodologies, and innovations transforming transportation into more efficient, safer, and environmentally friendly systems. Students will learn about smart transportation systems, autonomous vehicles, smart infrastructure, data analytics in transportation, and the societal impacts of these advancements.
Course Outline	Introduction to Smart Transportation: Definition and significance, overview of traditional and smart transportation systems. Intelligent Transportation Systems (ITS) and its components. Sensors and IoT in Transportation, Data in Transportation: importance, basic methods of data collection, and data analysis tools and techniques; Introduction to Autonomous and Connected Vehicles; Smart Infrastructure and its components (smart traffic lights, smart roads), Smart traffic management solutions, Introduction to smart public transit systems, and innovations in public transportation (e.g., contactless payments, real-time tracking), Introduction to safety technologies in transportation, In-depth analysis of selected smart transportation projects.
Learning Outcome	At the end of the course, student would be able to: 1. Understand the difference between traditional and smart transportation systems 2. Understand various techniques used to collect automated traffic data 3. Understand various technologies deployed for improving the efficiency of traffic management and public transportation 4. Understand the difference between connected and autonomous vehicles and its implications in real world
Assessment Method	Assignment, Quizzes, Mid-semester examination and End-semester examination .

Textbooks:

"Smart Transport for Cities and Nations: The Rise of Self-Driving and Connected Transport" by Graeme A. Dandy
"Introduction to Intelligent Systems in Traffic and Transportation" by T. P. S. Sreejith, K. S. Easwarakumar
"The Fourth Industrial Revolution" by Klaus Schwab (for context on technological impacts)

CE4112

Industrial Pollution and Control

Course Number	CE4112
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Industrial Pollution and Control
Learning Mode	Lectures
Learning Objectives	Complies with PLOs 2 and 4 1. To learn about industrial manufacturing processes and associated industrial effluent and emission characteristics 2. To discuss various pollution control and treatment techniques with their applicability based on industrial effluent and emission characteristics 3. To devise a treatment chain to treat an industrial effluent and emission for a manufacturing industry 4. To discuss the best available technologies implemented for pollution control in manufacturing industries from real-life case studies
Course Description	This course will provide overview of manufacturing processes with material and water inputs along with the associated industrial waste and emission characteristics. Further, the course aims to cover pollution control techniques for industrial effluent and emission with real-life case studies.
Course Content	Industry types and associated industrial pollution, Industrial wastes and emissions, Characteristics of wastes including hazardous waste and emission; Process modifications and alternate raw materials, Cleaner production, Waste minimization, Recycle and reuse; Best available technology (BAT), Zero liquid discharge (ZLD), Effluent treatment plant (ETP) and common effluent treatment plant (CETP), Physico-chemical and biological treatment of industrial effluents, Industrial air pollution control techniques – gravity settler, cyclone separator, electrostatic precipitator, fabric filter, scrubbers; Environmental auditing and performance, Environmental management plan; Selected case studies of pollution control in manufacturing industries.
Learning Outcome	At the end of the course, students would be able to: 1. Understand about the manufacturing processes with raw materials water inputs and associated effluent and emission generation and characteristics 2. Analyze and understand the current issues and challenges in the industrial sector for pollution control in an interdisciplinary manner 3. Comprehend and implement effluent treatment and emission control techniques for manufacturing industries 4. Learn and apply various emerging industrial pollution control practices and techniques for sustainability
Assessment Method	Assignments, Quizzes, Mid Semester Examination and End Semester Examination

Text Books:

de Nevers, N., Air Pollution Control Engineering, Waveland Press, 2010.
Eckenfelder Jr., W.W., Industrial Water Pollution Control, 3^rd Edition, McGraw-Hill, 2000.
Wise, D.L. and Trantolo, D.J. (eds.), Process Engineering for Pollution Control and Waste Minimization, 1^st Edition, Marcel Dekker, 1994.

Reference Books:

Metcalf & Eddy, Wastewater Engineering - Treatment and Reuse(Revised by Tchobanoglous, G., Burton, F.L. and Stensel, H.D.), Tata McGrawHill, 2004.
Wark, K. and Warner, C. F., Air Pollution ‐ Its Origin and Control, Harper & Row, 1981.

Minor in Infrastructure Engineering

Minor

Sl. No.

Subject Code

Subject

CE2102

Structural Mechanics

Course	CE2102: Structural Mechanics
Course Credit (L-T-P-C)	3-1-0-4
Course Title	Structural Mechanics
Learning Mode	Lectures
Learning Objectives	Objective for learning this course are Lectures: Complies with PLO-1, 2, and 4 Understand the need of mechanics of material and structure for the design of any civil engineering project. 2. Equip the students with strong foundation in civil and environmental engineering for both research and industrial scenarios. 3. Provide scientific and technical knowledge in planning, design, construction, operation and maintenance of civil engineering infrastructure.
Course Description	The course discusses the basic mechanics and behavior of materials under loads, strains, and deformations with various examples.
Course Outline	Introduction to mechanics of materials and structures, Simple bending theory, flexural and shear stresses, Stress / Strain Transformation unsymmetrical bending, shear centre. Thin walled pressure vessels, uniform torsion, buckling of column, combined and direct bending stresses. Different types of structures, loads on the structural system, static and kinematic indeterminacy, Methods of Analysis: Equilibrium equations, compatibility requirements, Introduction to force and displacement methods, Analysis of trusses: plane truss, compound truss, complex truss and space truss, three hinged arches and suspension cables, Bending moment and shear force diagram, Deflection of Beams, various methods for calculation of deflection.
Learning Outcome	At the end of the course, student would be able to Lectures: 1. Understand the basics of the strength of materials. 2. Get an overview of structural engineering. 3. Study this course as a prerequisite for any civil engineering design-based courses.
Assessment Method	Assignments, Quizzes, Project work, Mid-semester examination and End-semester examination.

Textbooks/ Reference books:

Ferdinand P. Beer, E. Russell Johnston Jr., John T. DeWolf, Mechanics of Materials, McGraw-Hill Education, 8th edition (2020).
C. Hibbeler, Mechanics of Materials, Prentice Hall, 11th edition (2022).
N. Frantziskonis, Essentials of the Mechanics of Materials, Destech Pubns Inc., 3rd edition (2017).
S. Reddy, Basic Structural Analysis, Tata McGraw Hill, 3rd edition (2017).
C. Hibbeler, Structural Analysis, Pearson Education, 10th edition (2022).
P. Popov, Engineering Mechanics of Solids, Pearson, 2nd edition (1998).
S. Negi and R. S. Jangid, Structural Analysis, Tata McGraw Hill, New Delhi, 6th edition (2003).
S. Khurmi and N. Khurmi, Theory of structures, Schand, 10th edition, 2000.
M. Leet, C. M. Uang, J. T. Lanning, and A. M. Gilbert, Fundamentals of Structural Analysis, McGraw Hill, 5th edition, 2017.
K. Roy and S. Chakrabarty, Fundamentals of Structural Analysis, S Chand & Company, 2nd edition, 2003.

CE2203

Civil Engineering Materials

Course	CE2203 Civil Engineering Materials
Course Credit (L-T-P-C)	3-0-2-4
Course Title	Civil Engineering Materials
Learning Mode	Lectures and Practical
Learning Objectives	Complies with PLO- 2 1. To provide fundamental knowledge in civil engineering materials. 2. Provide scientific and technical knowledge to prepare students to address civil engineering materials-related challenges in the field. 3. To train students to meet the current and future demand for civil engineering materials for construction industries.
Course Description	This course will discuss fundamental concepts in civil engineering materials. The course will cover theory and real-world practices in materials used in construction industries, their operations, and execution. The principal properties of building materials and up-to-date knowledge of the manufacturing of civil engineering materials will be discussed.
Course Outline	Introduction to building materials, Cement: Chemical composition, manufacturing, physical characteristics, hydration, properties of cement compounds, different types of cements, Aggregate: Coarse and fine aggregates, Influence of aggregate on the properties of concrete, aggregate selection. Fresh Concrete: Batching, Mixing, workability, effect of admixture, Hardened Concrete: mechanical properties of hardened concrete, Water-cement ratio, Porosity, Curing of concrete, High performance concrete, Design of concrete mix: IS code recommendation. Flyash. Brick: Raw materials, drying and burning, Strength and durability, mortar for masonry and strength of masonry, Timber, Seasoning and conversions, properties, tests, defects in timbers, FRPs: Chemical compositions, mechanical and physical properties, Various types of FRPs, Metals: steel for reinforced concrete and prestressed concrete construction, structural steel sections, Deterioration of building materials: Corrosion, chloride and sulphate attack on concrete, alkali-aggregate reaction, acid aggregate reactions Practical: Cement tests: normal consistency, initial and final set time; Coarse and fine aggregate tests: specific gravity, Sieve analysis, Los Angeles/Deval’s abrasion, Flakiness and elongation, Impact test;, fineness modulus, moisture content, SSD condition, unit weight and bulking of sand; Concrete tests: workability, strength, admixtures, mix design; Brick tests: moisture absorption, compressive strength, flyash.
Learning Outcome	At the end of the course, student would be able to: 1. Understand the physical and engineering properties, principles, testing, and standards of civil engineering materials used in construction. 2. Design mix of concrete for various construction industries. 3. The use of different civil engineering materials subjected to different construction scenarios and needs. 4. Understand the in-depth knowledge of mechanisms and factors influencing the manufacturing of civil engineering materials.
Assessment Method	Assignment, Quizzes, Mid-semester examination and End-semester examination .

Textbooks:

S. Somayaji, Civil Engineering Materials, Prentice Hall, New Jersey, 2001.
M. Neville and J. J. Brooks, Concrete Technology, Pearson Education, Fourth Indian reprint, 2004.

S. Shetty, Concrete Technology, S. Chand and Company Ltd. 2005.

M. S. Mamlouk and J. P. Zaniewski, Materials for Civil and Construction Engineers, Pearson, Prentice Hall, Second edition, 2006.
P.C Varghese, Building Materials, Publisher: ‎ Prentice Hall India Learning Private Limited; 2nd edition (1 January 2015)

Reference books:

All relavent IS Codes.
Jackson and R. K. Dhir, Civil Engineering materials, Macmillan Fourth edition 1997.
C. Aitcin, High Performance Concrete, E & Fn Spon, 1998.
F. Shackelford and M. K. Muralidhara, Introduction to Material science for Engineers, Pearson Education, Sixth edition, 2007.
Haimei Zhang, Building materials in civil engineering, Publisher: ‎Woodhead Publishing (9 May 2011).
Parbin Singh, Civil engineering materials,Publisher ‏: ‎ S K Kataria and Sons; Reprint 2013 edition.
K Duggal, Building Materials, New Age International Publisher, 4th edition.

CE3103

Transportation Engineering-I

Course	CE3103: Transportation Engineering - I
Course Credit (L-T-P-C)	3-1-2-5
Course Title	Transportation Engineering-I
Learning Mode	Lectures and Practical
Learning Objectives	Lectures: Complies with PLO- 1, 2, 3, 4 To provide fundamental knowledge in transportation engineering. Train students to plan, design and operate transportation facilities in industry. Provide scientific and technical knowledge, to prepare students to address transportation problems in field. Practical: Complies with PLO- 1, 3, 4 Learn laboratory tests required to evaluate bitumen used in road construction. Learn laboratory tests required to evaluate aggregate used in road construction. Learn bituminous mix design. To conduct filed studies for obtained traffic data (speed, flow) To estimate traffic flow density using indirect methods
Course Description	This course will discuss fundamental concepts in transportation engineering. Course will cover theory and real world practice in planning, design, construction and operation in road transportation. Practical will focuses on the tests to measure engineering properties of aggregate and bitumen to evaluate them for road construction. Course will also cover tests to measure traffic stream characteristics.
Course Outline	Lectures: Introduction to transportation engineering; Road plans; Factors controlling highway alignment; Vehicle and driver characteristics, PIEV theory; Pavement materials and characterization: subgrade soil, aggregates, bituminous and modified binders, straight-run bitumen, cutback bitumen, tar; Pavement analysis and design: Flexible pavements, Rigid pavements; Geometric design of Highways: Cross sectional elements, Horizontal alignment, Vertical alignment; Analysis of Traffic Flow, Mixed traffic (PCU), Design of Traffic facilities. Practical: Evaluation of road aggregates for various properties: Blending of aggregate, specific gravity, crushing value, Evaluation of bitumen for various properties: Softening point test, Penetration test, Viscosity test, Ductility test, Flash and fire point test, Stripping test; Bituminous mix design- Marshal mix design method; Headway studies: Free flow, Intermediate flow, High flow; Speed-Volume studies; O-D survey.
Learning Outcome	At the end of the course, from lectures students would be able to: 1. Understand engineering properties of road construction materials. 2. Design flexible and rigid pavements using Indian Codes. 3. Design highway geometrics 4. Identify factors influencing drivers behaviour. 5. Understand basic traffic stream parameters and traffic flow models. From practical students would be able to: Test aggregates to determine its engineering properties to check its acceptability in road construction. Test bitumen to determine its engineering properties to check its acceptability in road construction. Conduct Marshall mix design. Build fundamental diagrams of traffic flow Differentiate time mean speed and space mean speed Understand difference between microscopic and macroscipic variables and how to collect such data in real-field
Assessment Method	Assignments, Quizzes, Mid-semester examination and End-semester examination.

Textbooks (Lectures):

P. Chakroborty and A. Das, Principles of Transportation Engineering, Prentice Hall India, 2003.
A.T. Papagiannakis and E.A. Masad, Pavement Design and Materials, John Wiley & Sons Inc, 2012.
C. J. Khisty and B. K. Lall, Transportation Engineering: An Introduction, Prentice Hall India, 2003.
L. R. Kadiyali, Traffic Engineering and Transport Planning, Khanna Publishers, 1987.

Reference books (Lectures):

Relavent IRC codes.
F. L. Mannering, W. P. Kilareski, and S.S. Washburn, Principles of Highway engineering and traffic analysis, John Wiley and Sons, 2005. C. S.
Papacostas and P. D. Prevedouros, Transportation Engineering and Planning, Prentice Hall India, 2001.
J. H. Banks, Introduction to Transportation Engineering, McGraw-Hill, 2002.
S. K. Khanna and C. E. G. Justo, Highway Engineering, Nem Chand Bros., 2002.
Y. H. Huang, Pavement Analysis and Design, Pearson Education, India 2008.

Textbooks (Practical):

N. A. Harold, Highway materials, Soil and Concrete, Prentice Hall, 2004.
C. S. Papacostas and P.D. Prevedouros, Transportation Engineering and Planning, Hall India, 2001

Reference books (Practical):

IS Codes and IRC Codes.
R.P. Roess, W.R. McShane, and E.S. Prassas, Traffic Engineering, Prentice Hall, 1990.

CE3202

Infrastructure Drawing and Estimation

Course	CE3202: Infrastructure Drawing and Estimation
Course Credit (L-T-P-C)	1-2-0-3
Course Title	Infrastructure Drawing and Estimation
Learning Mode	Lectures & Tutorials
Learning Objectives	Complies with PLO- number 2 & 4 1. The course also aims at giving the students an idea on keeping cost of material and labour into consideration while designing a building. 2. The course shall provide the students an ability to read and interpret drawings and cost estimates.
Course Description	This course is aimed at providing the students a critical understanding of building planning from foundation level to the rooms, orientation, facade design and overall estimation of the cost. This course is aimed at giving the students an ability to read and interpret the building layouts, blueprints and budget estimates so that they can design structurally strong and cost-effective buildings in future.
Course Outline	Components of buildings: plan, elevation and section of buildings; Drawing of various details of residential buildings; Types of building: residential, industrial; brick masonry. Estimation: types of estimates, plinth area estimate, cubical content estimate, unit rate estimate, central line method, short wall - long wall method; estimate of other structures- estimate of bituminous and cement concrete roads, estimating of septic tank, estimating of irrigation works – aqueduct, syphon, etc., modes of measurement, estimation of buildings, specifications and analysis of rates.
Learning Outcome	At the end of the course, student would be able to: 1. Prepare drawing and layout of single, multi roomed and single to multi storeyed buildings. 2. Read and interpret building layouts. 3. Understand the costing and estimation issues involved in building design and planning and other structures.
Assessment Method	Assignment, Quizzes, Mid-semester examination and End-semester examination.

Text books:

B. N. Dutta, Estimating and Costing in Civil Engineering, UBS Publishers & Distributors Pvt. Ltd., 2003.
S. S. Bhavikatti, M. V. Chitawa, Building Planning and Drawing, I K International Publishing House Pvt. Ltd, 2014.
M.G Shah, C.M Kale, Principles of Building Drawing, Macmillan Publishers India Limited, 2000.
N. Kumara Swamy, A. Kameswara Rao, Building Planning and Drawing, Charotar Publishing House Pvt. Ltd. - Anand; 7th Revised edition (2013).
D.D. Kohli, and R.C. Kohli, A Text Book of Estimating and Costing (Civil), S.Chand & Company Ltd., 2004

Reference Book:

H. Banz, Building Construct. Details Prac. Drawings, CBS; 1ST edition, 2005.
G. H. Cooper, Building Construction and Estimating, McGraw-Hill, 1971.
B.P. Verma, Civil Engineering Drawing& House Planning, Khanna Publishers, 2010.
Latest version of DSR

TOTAL

Chemical Science and Technology

Program Learning Objectives:	Program Learning Outcomes (PLO):
Program Goal 1: Fundamental Understanding: To impart knowledge and proficiency in an advanced level of theoretical and practical aspects in the major fields of Chemical Science and Technology.	Program Learning Outcome 1: PLO-1: Students will acquire knowledge and demonstrate understanding of the core concepts, principles, and processes across the fields of chemistry and Chemical technology. Program Learning Outcome 2: PLO-2: Students will be able to recognize when information is needed and have the ability to locate, evaluate, and use the needed information for a wide range of purposes pertaining to Organic, Inorganic, Physical, Polymer, Industrial, Analytical and Material Chemistry
Program Goal 2: Basic Training for Research and Industry: To provide quality training for conducting fundamental and advanced research in Chemistry and technology development. Ethics in scientific research and publication.	Program Learning Outcome 3: PLO-3: Students will learn the critical thinking skills necessary to apply the scientific method and develop problem-solving skills. This includes: applying scientific inquiry and hypothesis building strategy, designing and conducting investigative experiments, applying quantitative reasoning skills to answer scientific questions. Ethics in scientific research and publication. Program Learning Outcome 4: PLO-4: Students will learn to employ critical thinking and scientific inquiry in the performance, design, interpretation and documentation of laboratory experiments, at a level suitable to succeed at an entry-level position in chemical industry or a chemistry graduate program.
Program Goal 3: Skill Enhancement: To focus on skill enhancement in the core chemistry with practical expert hands. This will make students employable in academia and industries.	Program Learning Outcome 5: PLO-5: Students will synthesize knowledge, use quantitative reasoning and data to address issues in global scale to help them developing good skill in core chemistry suitable for getting employed in academia and industries.
Program Goal 4: Communication Skill: To develop various communication skills such as reading, listening, speaking, etc. This will help in expressing ideas and views clearly and effectively.	Program Learning Outcome 6: PLO-6: Students will learn how to read and understand research papers, make presentations and communicate to a large audience, develop the ability to work collaboratively.
Program Goal 5: Social Awareness: To make understand social, economic, health and environmental issues related to chemical science and technology and develop methods and means to abate and create awareness in society.	Program Learning Outcome 7: PLO-7: Students will have awareness on various global problems related to chemistry, such as global warming, climate change, environmental pollution, energy crisis, etc. Program Learning Outcome 8: PLO-8: Students will be able to use their intellectual skills to devise and develop solutions to environmental problems in their communities to apply fact-based chemical science and technology solutions to situations relevant to everyday life in areas such as education, human health, the natural environment, technological advances and policy.

Sl. No.

Subject Code

SEMESTER I

MA1101

Course Number	MA1101
Course Credit (L-T-P-C)	3-1-0-4
Course Title	Calculus and Linear Algebra
Learning Mode	Lectures and Tutorials
Learning Objectives	To provide the essential knowledge of basic tools of Differential Calculus, Integral Calculus, Vector spaces and Matrix Algebra.
Course Description	This course provides a foundation for Calculus and Linear Algebra. Topics related to properties of single and two variable functions along with their applications will be discussed. In addition fundamentals of linear algebra and matrix theory with applications will also be discussed.
Course Content	Differential Calculus (12 Lectures): Limit and continuity of one variable function (including ε-δ definition). Limit, continuity and differentiability of functions of two variables, Tangent plane and normal, Change of variables, chain rule, Jacobians, Taylor’s Theorem for two variables, Extrema of functions of two or more variables, Lagrange’s method of undetermined multipliers. Integral Calculus (10 Lectures): Riemann integral for one variable functions, Double and Triple integrals, Change of order of integration. Change of variables, Applications of Multiple integrals such as surface area and volume. Vector Spaces (12 Lectures): Vector spaces (over the field of real numbers), subspaces, spanning set, linear independence, basis and dimension. Linear transformations, range and null space, rank-nullity theorem, matrix of a linear transformation. Matrix Algebra (8 Lectures): Elementary operations and their use in getting the rank, inverse of a matrix and solution of linear simultaneous equations, Orthogonal, symmetric, skew-symmetric, Hermitian, skew-Hermitian, normal and unitary matrices and their elementary properties, Eigenvalues and Eigenvectors of a matrix, Cayley-Hamilton theorem, Diagonalization of a matrix.
Learning Outcome	Students completing this course will be able to: 1. Understand various properties of functions such as limit, continuity and differentiability. 2. Learn about integrations in various dimension and their applications. 3. learn about the concept of basis and dimension of a vector space. 4. define Linear Transformations and compute the domain, range, kernel, rank, and nullity of a linear transformation. 5. compute the inverse of an invertible matrix. 6. solve the system of linear equations. 7. Apply linear algebra concepts to model, solve, and analyze real-world problems.
Assessment Method	Quiz /Assignment/ MSE / ESE

Textbooks:

Thomas, G. B., Hass, J., Heil, C. and Weir M. D., “Thomas’ Calculus”, 14th Ed., Pearson Education, 2018
Kreyszig, E., “Advanced Engineering Mathematics”, 10th Ed., Wiley India Pvt. Ltd, 2015

Reference Books:

Jain, R. K. and Iyenger, S. R. K., “Advanced Engineering Mathematics”, 5^th, Narosa Publishing House, 2017
Axler, S., “Linear Algebra Done Right”, 3rd Ed., Springer Nature, 2015
Strang, G., “Linear Algebra and Its Applications” 4th Ed., Cengage India Private Limited, 2005

4.0

CS1101

Course Number	CS1101
Course Credit	3-0-3-4.5
Course Title	Foundations of Programming
Learning Mode	Offline
Learning Objectives	· To understand the fundamental concepts of programming · To develop the basic problem-solving skills by designing algorithms and implementing them. · To learn about various data types, control statements, functions, arrays, pointers, and file handling. · To achieve proficiency in debugging and testing a C program
Course Description	This introductory course provides a solid foundation in programming principles and techniques. Designed for students with little to no prior programming experience, it covers fundamental concepts such as variables, data types, control structures, functions, and basic data structures. Students will learn to write, debug, and execute programs using a high-level programming language. Emphasis is placed on developing problem-solving skills, logical thinking, and the ability to write clear and efficient code. By the end of the course, students will be equipped with the essential skills needed to pursue more advanced studies in computer science and software development.
Course Outline	Introduction and Programming basics, Expressions Control and Iterative statements, Functions, Arrays, Recursion vs. Iteration Pointers, 2D-Array with pointers, Structures, String, Dynamic memory allocation, File handling, Contemporary programming languages, and applications Practical component: Lab to be conducted on a 3-hour slot weekly. It will be conducted with the theory course so the topics for problems given in the lab are already initiated in the theory class.
Learning Outcome	· Understanding of Basic Syntax and Structure in C language · Proficiency in Data Types, Operators, and Control Structures · Function Implementation and learn to use them appropriately · Efficient Use of Arrays and Strings · Pointer Utilization · Ability to perform dynamic memory allocation and deallocation using malloc (), calloc (), realloc (), and free () functions. · Structured data management with structures and unions · Exposure of file Handling · Learning debugging and error Handling
Assessment Method	Internal (Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Reading

Knuth, Donald E. The art of computer programming, volume 4A: combinatorial algorithms, part 1. Pearson Education India, 2011.
P.J. Deitel and H.M. Deitel, C How To Program, Pearson Education (7th Edition)
Brian W. Kernighan and Dennis M. Ritchie, The C Programming Language, Prentice−Hall
A. Kelley and I. Pohl, A Book on C, Pearson Education (4th Edition)
K. N. King, C PROGRAMMING A Modern Approach, W. W. Norton & Company

4.5

PH1101/PH1201

Course Number	PH1101/PH1201
Course Credit	3-1-0-4
Course Title	Physics
Learning Mode	Lectures and Tutorials
Learning Objectives	Complies with Program Goals 1 and 2
Course Description	This course deals with fundamentals in Classical mechanics, Waves and Oscillations and Quantum Mechanics. As a prerequisite, the mathematical preliminaries such as coordinate systems, vector calculus etc will be discussed in the beginning.
Course Outline	Orthogonal coordinate systems (Plane polar, Spherical, Cylindrical), concept of generalised coordinates, generalised velocity and phase space for a mechanical system, Introduction to vector operators, Gradient, divergence, curl and Laplacian in different co-ordinate systems. Central force problem and its applications. Rigid body rotation, vector nature of angular velocity, Finding the principal axes, Euler's equations; Gyroscopic motion and its application; Accelerated frame of reference, Fictitious forces. Potential energy and concept of equilibrium, Lennard-Jones and double-well potentials, Small oscillations, Harmonic oscillator, damped and forced oscillations, resonance and its different examples, oscillator states in phase space, coupled oscillations, normal modes, longitudinal and transverse waves, wave equation, plane waves, examples two- and three-dimensional waves. Michelson-Morley experiment, Lorentz transformation, Postulates of special theory of relativity, Time dilation and length contraction, Applications of special theory of relativity.
Learning Outcome	Complies with PLO 1a, 2a, 3a
Assessment Method	Quiz, Assignments and Exams

Suggested Readings:

Textbooks:

Engineering Mechanics, M. K. Harbola, 2^nd ed., Cengage, 2012
D. Kleppner and R. J. Kolenkow, An introduction to Mechanics, Tata McGraw-Hill, New Delhi, 2000.
I. G. Main, Oscillations and Waves
H. G. Pain, The Physics of Vibrations and Waves, 1968
Frank S. Crawford, Berkeley Physics Course Vol 3: Waves and Oscillations, McGraw Hill, 1966.

References:

R. P. Feynman, R. B. Leighton and M. Sands, The Feynman Lecture in Physics, Vol I, Narosa Publishing House, New Delhi, 2009.
David Morin, Introduction to Classical Mechanics, Cambridge University Press, NY, 2007.

3. P. C. Deshmukh, Foundations of Classical Mechanics, Cambridge University Press, 2019

5.5

CE1101/CE1201

Course code	CE1101/CE1201
Course Credit (L-T-P-C)	1-0-3-2.5
Course Title	Engineering Graphics
Learning Mode	Lectures and Practical
Learning Objectives	Complies with PLO-1a 1. The course on engineering drawing is designed to introduce the fundamentals of technical drawing as an important form of conveying information. 2. Apply principles of engineering visualization and projection theory to prepare engineering drawings, using conventional and modern drawing tools. 3. Practice drawing orthographic projections, isometric views, and sectional views, of simple and combined solids in different orientations.
Course Description	This course will introduce drawing as a tool to represent a complex three-dimensional object on two-dimensional paper through methods of projections. The course explains the use of different drafting tools and the importance of conventions for uniformity and standardization of the interpretation of the drawings.
Course Outline	Fundamental of engineering drawing, line types, dimensioning, and scales. Conic sections: ellipse, parabola, hyperbola; cycloidal curves. Principle of projection, method of projection, orthographic projection, plane of projection, first angle of projection, Projection of points, lines, planes and solids. Section of solids: Sectional views of simple solids- prism, pyramid, cylinder, cone, sphere; the true shape of the section. Methods of development, development of surfaces. Isometric projections: construction of isometric view of solids and combination of solids from orthographic projections. Introduction to AutoCad and solving isometric problems.
Learning Outcome	After attending this course, the following outcomes are expected: 1. The student will understand the basic concepts of engineering drawing. 2. The student will be able to use basic drafting tools, drawing instruments, and sheets. 3. The student will be able to represent three-dimensional simple and combined solid objects on two-dimensional paper. 4. The student will be able to visualize and interpret the orientation of simple and combine solid objects.
Assessment Method	Laboratory Assignments (30%), Mid-semester examination (25%) and End-semester examination (45%).

Suggested Readings:

Textbooks:

D. Bhatt, Engineering Drawing, Charotar Publishing House.
Agrawal & Agrawal, Engineering Drawing, McGraw Hill.
Jolhe, Engineering Drawing.

References:

Engineering Drawing and Design by David Madsen

2.5

EE1101/EE1201

Course Number	EE1101/EE1201
Course Credit	3-0-3-4.5
Course Title	Electrical Sciences
Learning Mode	Lectures and Experiments
Learning Objectives	Complies with Program goals 1, 2 and 3
Course Description	The course is designed to meet the requirements of all B. Tech programmes. The course aims at giving an overview of the entire electrical engineering domain from the concepts of circuits, devices, digital systems and magnetic circuits.
Course Outline	Circuit Analysis Techniques, Circuit elements, Simple RL and RC Circuits, Kirchoff’s law, Nodal Analysis, Mesh Analysis, Linearity and Superposition, Source Transformations, Thevenin’s and Norton’s Theorems, Time Domain Response of RC, RL and RLC circuits, Sinusoidal Forcing Function, Phasor Relationship for R, L and C, Impedance and Admittance, Instantaneous power, Real, reactive power and power factor. Semiconductor Diode, Zener Diode, Rectifier Circuits, Clipper, Clamper, UJT, Bipolar Junction Transistors, MOSFET, Transistor Biasing, Transistor Small Signal Analysis, Transistor Amplifier and their types, Operational Amplifiers, Op-amp Equivalent Circuit, Practical Op-amp Circuits, Power Opamp, DC Offset, Constant Gain Multiplier, Voltage Summing, Voltage Buffer, Controlled Sources, Instrumentation Amplifier, Active Filters and Oscillators. Number Systems, Logic Gates, Boolean Theorem, Algebraic Simplification, K-map, Combinatorial Circuits, Encoder, Decoder, Combinatorial Circuit Design, Introduction to Sequential Circuits. Magnetic Circuits, Mutually Coupled Circuits, Transformers, Equivalent Circuit and Performance, Analysis of Three-Phase Circuits, Power measurement in three phase system, Electromechanical Energy Conversion, Introduction to Rotating Machines (DC and AC Machines). Laboratory: Experiments to verify Circuit Theorems; Experiments using diodes and bipolar junction transistor (BJT): design and analysis of half -wave and full-wave rectifiers, clipping and clamping circuits and Zener diode characteristics and its regulators, BJT characteristics (CE, CB and CC) and BJT amplifiers; Experiment on MOSFET characteristics (CS, CG, and CD), parameter extraction and amplifier; Experiments using operational amplifiers (op-amps): summing amplifier, comparator, precision rectifier, Astable and Monostable Multivibrators and oscillators; Experiments using logic gates: combinational circuits such as staircase switch, majority detector, equality detector, multiplexer and demultiplexer; Experiments using flip-flops: sequential circuits such as non-overlapping pulse generator, ripple counter, synchronous counter, pulse counter and numerical display; Power Measurement by two Wattmeter method; Open and Short Circuit Tests of Transformer.
Learning Outcomes	Complies with PLO 1a, 2a and 3a
Assessment Method	Quiz, Assignments and Exams

Texts/References

K. Alexander, M. N. O. Sadiku, Fundamentals of Electric Circuits, 3rd Edition, McGraw-Hill, 2008.
H. Hayt and J. E. Kemmerly, Engineering Circuit Analysis, McGraw-Hill, 1993.
L. Boylestad and L. Nashelsky, Electronic Devices and Circuit Theory, 6th Edition, PHI, 2001.
M. Mano, M. D. Ciletti, Digital Design, 4th Edition, Pearson Education, 2008.
Floyd, Jain, Digital Fundamentals, 8th Edition, Pearson.
David V. Kerns, Jr. J. David Irwin, Essentials of Electrical and Computer Engineering, Pearson, 2004.
Donald A Neamen, Electronic Circuits; analysis and Design, 3rd Edition, Tata McGraw-Hill Publishing Company Limited.
Adel S. Sedra, Kenneth C. Smith, Microelectronic Circuits, 5th Edition, Oxford University Press, 2004.
E. Fitzgerald, C. Kingsley Jr., S. D. Umans, Electric Machinery, 6th Edition, Tata McGraw-Hill, 2003.
P. Kothari, I. J. Nagrath, Electric Machines, 3rd Edition, McGraw-Hill, 2004.

Del Toro, Vincent. "Principles of electrical engineering." (No Title) (1972).

4.5

HS1101

Course Number	HS1101
Course Credit	L-T-P-W: 2-0-1-2.5
Course Title	English for Professionals
Learning Mode	Offline
Learning Objectives	This course aims to help the students (a) attain proficiency in written English through the construction of grammatically correct sentences, utilization of subject-verb agreement principles, mastery of various tenses, and effective deployment of active and passive voice to ensure coherent and impactful written expression; (b) enhance oral communication skills by honing public speaking abilities, acquiring strategies to deliver persuasive presentations, and cultivating a polished telephone etiquette, enabling confident and articulate verbal communication; (c) foster active listening capabilities by recognizing different types of listening, and applying proven methods and strategies to improve active listening skills; (d) strengthen reading skills, including comprehension, interpretation, and critical analysis, to grasp diverse written materials and derive meaning from various types of texts encountered in academic and professional contexts; (e) develop adeptness in written communication for business purposes, encompassing the understanding of essential writing elements, mastery of appropriate writing styles thereby enhancing prospects for successful job interviews and subsequent professional endeavors.
Course Description	This academic course on communication skills aims to equip students with fluency in spoken and written English for effective expression in both academic and professional settings. By focusing on essential communication principles and providing practical experiences, students develop clarity, precision, and confidence in their communication. Through interactive discussions and exercises, students enhance critical thinking and adaptability in diverse contexts. Upon completion, students will excel in formal presentations, group discussions, and persuasive writing, enhancing their overall communication proficiency.
Course Outline	Unit I: Introduction to professional communication – LSRW - Phonetics and phonology Sounds in English Language – production and articulation – rhythm and intonation – connected speech - Basic Grammar and Advanced Vocabulary Sounds in English Language – production and articulation – rhythm and intonation – connected speech – persuading and negotiating – brevity and clarity in language. Unit II: Characteristics of Technical Communication: Types of communication and forms of communication - Formal and informal communication Verbal and non-Verbal Communication – Communication barriers and remedies Intercultural communication – neutral language Unit III: Comprehension and Composition – summarization, precis writing Business Letter Writing CV/ Resume – E-Communication Unit IV: Statement of Purpose, Writing Project Reports, Writing research proposal, writing abstracts, developing presentations, interviews – combating nervousness Tutorial: Listening Exercises, Speaking Practice (GDs, and Presentations), and Writing Practice Learning Outcome · Attain proficiency in written English, enabling the construction of grammatically correct sentences and coherent written expression through the use of appropriate grammar, tenses, and voice. · Enhance oral communication skills, including public speaking, persuasive presentation, and polished telephone etiquette, fostering confident and articulate verbal expression. · Cultivate active listening abilities, recognizing different listening types, overcoming obstacles, and employing strategies for attentive and effective communication. · Develop proficient written communication skills for business purposes, demonstrating understanding of essential writing elements, appropriate styles, and the creation of reports, notices, agendas, and minutes that effectively convey information.
Assessment Method	Class test + Quiz = 20%; Mid-semester = 25%; Assignment = 15%; End semester = 40%

Suggested Reading

Balzotti, Jon. Technical Communication: A Design-Centric Approach. Routledge, 2022.
Kaul, Asha, Business Communication. PHI Learning Pvt. Ltd. 2009
Laplante, Phillip A. Technical Writing: A Practical Guide for Engineers, Scientists, and Nontechnical Professionals. CRC Press, 2018.
Lawson, Celeste, et al. Communication Skills for Business Professionals, Second Edition. CUP, 2019.
Sharon Gerson and Steven Gerson. Technical Writing: Process and Product (8th Edition), London: Longman, 2013
Rentz, Kathryn, Marie E. Flatley & Paula Lentz. Lesikar’s Business Communication Connecting in a Digital world, McGraw-Hill, Irwin.2012
Allan & Barbara Pease. The Definitive Book of Body Language, New York, Bantam,2004
Jones, Daniel. The Pronunciation of English, New Delhi, Universal Book Stall.2010
Savage, Alice. Effective Academic Writing. OUP. 2014
Swan and Alter. Oxford English grammar course. OUP. 201

2.5

TOTAL

23.5

Probability Theory and Ordinary Differential Equations

Sl. No.

Subject Code

SEMESTER II

MA1201

Course Number	MA1201
Course Credit (L-T-P-C)	3-1-0-4
Course Title	Probability Theory and Ordinary Differential Equations
Learning Mode	Lectures and Tutorials
Learning Objectives	To introduce the basic concepts of probability, statistics, and Differential equations.
Course Description	This course aims to cover basic concepts of probability, statistics and ordinary differential equations. In particular, popular distributions, random sampling, various estimators and hypothesis testing will be discussed. Students will also get exposure to the linear ordinary differential equations and their solution techniques.
Course Content	Probability (12 Lectures): Random variables and their probability distributions, Cumulative distribution functions, Expectation and Variance, probability inequalities, Binomial, Poisson, Geometric, negative binomial distributions, Uniform, Exponential, beta, Gamma, Normal and lognormal distributions. Statistics (10 Lectures): Random sampling, sampling distributions, Parameter estimation, Point estimation, unbiased estimators, maximum likelihood estimation, Confidence intervals for normal mean, Simple and composite hypothesis, Type I and Type II errors, Hypothesis testing for normal mean. Ordinary Differential Equations (20 Lectures): First order ordinary differential equations, exactness and integrating factors, Picard's iteration, Ordinary linear differential equations of n-th order, solutions of homogeneous and non-homogeneous equations (Method of variation of parameters). Systems of ordinary differential equations, Power series methods for solutions of ordinary differential equations. Legendre equation and Legendre polynomials, Bessel equation and Bessel functions.
Learning Outcome	Students will get exposure and understanding of: 1. Random variables and their probability distributions 2. Understand popular distributions and their properties 3. Sampling, estimation and hypothesis testing 4. Solution of ordinary differential equations 5. Solution of system of ordinary differential equations 6. Special functions arising as power series solutions of ordinary differential equations
Assessment Method	Quiz /Assignment/ MSE / ESE

Text Books:

Hogg, R. V., Mckean, J. and Craig, A. T., “Introduction to Mathematical Statistics”, 8th Ed., Pearson Education India, 2021
M. Ross “An introduction to Probability Models, Academic Press INC, 11th edition.
Miller, I. and Miller, M., “John E. Freund's Mathematical Statistics with Applications”, 8th Ed., Pearson Education India, 2013
L. Ross, Differential equations, 3rd Edition, Wiley, 1984
E. Boyce and R. C. Di Prima, Elementary Differential equations and Boundary Value Problems, 7th Edition, Wiley, 2001.

CS1201

Course Number	CS1201
Course Credit	3-0-3-4.5
Course Title	Data Structure
Learning Mode	Offline
Learning Objectives	· Understand the principles and concepts of data structures and their importance in computer science. · Learn to implement various data structures and understand how different algorithms works. · Develop problem-solving skills by applying appropriate data structures to different computational problems. · Achieving proficiency in designing efficient algorithms.
Course Description	This course provides a comprehensive study of data structures and their applications in computer science. It focuses on the implementation, analysis, and use of various data structures such as arrays, linked lists, stacks, queues, trees, and graphs. Through theoretical concepts and practical programming exercises, this course aims to develop problem-solving and algorithmic thinking skills essential for advanced topics in computer science and software development.
Course Outline	· Introduction to Data Structure, · Time and space requirements, Asymptotic notations · Abstraction and Abstract data types · Linear Data Structure: stack, queue, list, and linked structure · Unfolding the recursion · Tree, Binary Tree, traversal · Search and Sorting, · Graph, traversal, MST, Shortest distance · Balanced Tree Practical component: Lab to be conducted on a 3-hour slot weekly. It will be conducted with the theory course so the topics for problems given in the lab are already initiated in the theory class.
Learning Outcome	· Understand Data Structure Fundamentals · Implement Basic Data Structures using a programming language · Analyse and Apply Algorithms · Design and Analyse Tree Structures · Understand the usage of graph and its related algorithms · Design and Implement Sorting and Searching Algorithms · Debug and Optimize Code
Assessment Method	Internal (Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Reading

Alfred V. Aho, John E. Hopcroft, Jeffrey D. Ullman, Data Structures and Algorithms, Published by Addison-Wesley
Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest and Clifford Stein., Introduction to Algorithms,
Mark Allen Weiss, Data Structures and Algorithm Analysis in Java
Robert Sedgewick and Kevin Wayne, Algorithms
Narasimha Karumanchi, Data Structures and Algorithms Made Easy

4.5

CH1201/CH1101

Course Number	CH1101/CH1201
Course Credit	L-T-P-C: 3-1-3-5.5
Course Title	Chemistry
Learning Mode	Offline
Learning Objectives	The course aims to lay a foundation for all three branches of chemistry, viz. Organic, Inorganic, and Physical Chemistry. The course aims to nurture knowledge to appreciate the interface of chemistry with other science and Engineering branches by combining theoretical concepts and experimental studies.
Course Description	This course introduces basic organic chemistry, inorganic chemistry and Physical chemistry to understand fundamental laws that governs various reactions, reaction rates, equilibrium, and their applications in daily life through relevant experimentation.
Course Outline	Module 1: Thermodynamics: The fundamental definition and concept, the zeroth and first law. Work, heat, energy and enthalpies. Second law: entropy, free energy and chemical potential. Change of Phase. Third law. Chemical equilibrium. Conductance of solutions, Kohlrausch’s law-ionic mobilities, Basic Electrochemistry. Module 2: Coordination chemistry: Crystal field theory and consequences color, magnetism, J.T distortion. Bioinorganic chemistry: Trace elements in biology, heme and non-heme oxygen carriers, haemoglobin and myoglobin; Organometallic chemistry. Module 3: Stereo and regio-chemistry of organic compounds, conformational analysis and conformers, Molecules devoid of point chirality (allenes and biphenyls); Significance of chirality in living systems, organic photochemistry, Modern techniques in structural elucidation of compounds (UV–Vis, IR, NMR). Module 4 (Lab Component): Experiments based on redox and complexometric titrations; synthesis and characterization of inorganic complexes and nanomaterials; synthesis and characterization of organic compounds; experiments based on chromatography; experiments based on pH and conductivity measurement; experiment related to chemical kinetics and spectroscopy.
Learning Outcome	Students will be able to 1. identify organic and inorganic molecules and relate them to daily life applications through experiments. 2. understand important hypothesis, laws and their derivations to intercept physical phenomenon of chemical reactions and apply them in hands-on experiments. 3. understand the importance of organic and inorganic molecules in our body and environment. 4. know important analytical techniques to intercept chemical entity. 5. approach organic and inorganic synthesis as a skillset for drug manufacturing, calculate limiting reagents and yields, use various analytical tools to characterize organic compounds, interpret and ascertain data related to Physical chemistry aspects and know laboratory safety measures, risk factors and scientific report writing skills.
Assessment Method	Theory: 20% Quiz and assignment, 30% Mid sem and 50% End semester exams for theory part (4 credits). Lab: 60% lab report, lab performance and assignment, 20% End semester exam for practical part, 20% viva/quiz (1.5 credits). Overall Weightage: Theory (70%), Lab (30%).

Suggested Reading:

Text books:

Vogel's Qualitative Inorganic Analysis, G. Svehla, 7^th Edition, Revised, Prentice Hall, 1996.
J. Elias, S. S. Manoharan and H. Raj, "Experiments in General Chemistry", Universities Press (India) Pvt. Ltd., 1997.

A. J. Elias, A Collection of Interesting General Chemistry Experiments, revised edition, Universities Press (India) Pvt. Ltd., 2007.

Albert Cotton, G. Wilkinson, C. A. Murillo, M. Bochmann, Advanced Inorganic Chemistry - 6^th Edition New Delhi: Wiley India, 2008.
Mukkanti, Practical Engineering Chemistry, B.S. Publications, Hyderabad, 2009.
Shriver and Atkins inorganic chemistry / Peter Atkins, Tina Overton, Jonathan Rourke, Mark Weller, Fraser Armstrong-5^th Edition – Oxford: UOP. 2012.
Atkins’ Physical Chemistry, Peter Atkins, Julio de Paula, James Keeler, Oxford University Press, 11^th Edition 2017.
L. Kapoor, A Textbook of Physical Chemistry, Vol: 1, 2 (6^th Edition, 2019), Vol: 3 (5^th Edition, 2020) MaGraw Hill.
R. Chatwal, S. K. Anand, Instrumental Methods of Chemical Analysis, 5^th Edition, Himalaya Publications, 2023.

5.5

ME1201/ME1101

Course Number	ME1101/ME1201
Course Credit	L-T-P-C: 0-0-3-1.5
Course Title	Mechanical Fabrication
Learning Mode	Fabrication work – hands on fabrication work in Workshop
Learning Objectives	Complies with PLOs 3-4. · This course aims to develop the concepts and skills of various mechanical fabrication methods. · Fabrication of metallic and non-metallic components, fabrication using bulk and sheet metals, subtractive and additive manufacturing methods, and assemble the parts
Course Description	This course is designed to fulfil the need of hand on experience about various approaches (conventional and CNC, subtractive and additive) of mechanical fabrication approaches. Prerequisite: NIL
Course Outline	The jobs for various shops should be planned such that they are the parts of an assembled item. The student groups will fabricate different parts in various shops which will involve some amount of their creativeness/input particularly in design and/or planning. Various components as required for the assembled part can be made using the following shops: Sheet Metal Working: Development, sheet cutting and fabrication of designated job using sheet metal (ferrous/nonferrous); Joining of required portions by soldering, in case part is desired to be made leak proof. Pattern Making and Foundry: Making of suitable pattern (wood); making of sand mould, melting of non-ferrous metal/alloy (Al or Al alloys), pouring, solidification. Observation/identification of various defects appeared on the component. Joining: Butt/lap/corner joint job fabrication as required of low carbon steel plates; weld quality inspection by dye-penetration test (non-destructive testing approach)of the component made. Demonstration of semi-automatic Gas Metal Arc welding (GMAW). Conventional machining: Operations on lathe and vertical milling to fabricate the required component. The fabrication of the component should cover various lathe operations like straight turning, facing, thread cutting, parting off etc., and operations using indexing mechanism on vertical milling. CNC centre: Fundamentals of CNC programming using G and M code; setting and operations of job using CNC lathe or milling, tool reference, work reference, tool offset, tool radius compensation to fabricate the component with a designed profile on Al/Al-alloy plate. 3D printing (Fused Filament Fabrication): (2 weeks) Create the model, select appropriate slicing and path for fabrication of a 3D job by layer deposition (additive manufacturing approach) using polymeric material. Demonstration on pattern fabrication using 3D printing.
Learning Outcome	· This course would enable the students to develop the concept of design, fabrication (subtractive and additive) for various engineering applications. Fabrication of components and assemble them. · The practical skill and hands on experience for various fabrication methods from bulk, sheet metal using conventional as well as CNC machines.
Assessment Method	Fabrication of components in each of the shops required for assembly of the given part; submission of reports for each shop, and quiz assessment.

Text and Reference books:

Hajra Choudhury, HazraChoudhary and Nirjhar Roy, 2007, Elements of Workshop Technology, vol. I,Mediapromoters and Publishers Pvt. Ltd.
W A J Chapman, Workshop Technology, 1998, Part -1, 1st South Asian Edition, Viva Book Pvt Ltd.
N. Rao, 2009, Manufacturing Technology, Vol.1, 3rd Ed., Tata McGraw Hill Publishing Company.
Adithan, B.S. Pabla, 2012, CNC machines, New Age International Publishers

1.5

ME1202/ME1102

Course Number	ME1102/ME1202
Course Number	Engineering Mechanics
L-T-P-C	3-1-0-4
Pre-requisites	Nil
Semester	Spring
Learning Mode	Lectures
Learning Objectives	Complies with PLOs 1, 4 · The objective of this first course in mechanics is to enable engineering students to analyze basic mechanics problems and apply vector-based approach to solve them.
Course Outline	1. Rigid body statics: Equivalent force system. Equations of equilibrium, Free body diagram, Reaction, Static indeterminacy. 2. Structures: 2D truss, Method of joints, Method of section. Beam, Frame, types of loading and supports, axial force, Bending moment, Shear force and Torque Diagrams for a member. 3. Friction: Dry friction (static and kinetic), wedge friction, disk friction (thrust bearing), belt friction, square threaded screw, journal bearings, Wheel friction, Rolling resistance. 4. Centroid and Moment of Inertia 5. Introduction to stress and strain: Definition of Stress, Normal and shear Stress. Relation between stress and strain, Cauchy formula. Stress in an axially loaded member and stress due to torsion in axisymmetric section
Learning Outcomes:	Following learning outcomes are expected after going through this course. · Learn and apply general mathematical and computer skills to solve basic mechanics problems. · Apply the vector-based approach to solve mechanics problems.
Assessment Method	Mid semester examination, End semester examination, Class test/Quiz, Tutorials

Reference Books

Shames, Engineering Mechanics: Statics and dynamics, 4th Ed, PHI, 2002.
P. Beer and E. R. Johnston, Vector Mechanics for Engineers, Vol I - Statics, 3rd Ed, Tata McGraw Hill, 2000.
L. Meriam and L. G. Kraige, Engineering Mechanics, Vol I - Statics, 5th Ed, John Wiley, 2002.
P. Popov, Engineering Mechanics of Solids, 2nd Ed, PHI, 1998.
P. Beer and E. R. Johnston, J.T. Dewolf, and D.F. Mazurek, Mechanics of Materials, 6th Ed, McGraw Hill Education (India) Pvt. Ltd., 2012.

IK1201

Indian Knowledge System (IKS)

TOTAL

22.5

Introduction to Quantum Chemistry

Sl. No.

Subject Code

SEMESTER III

CH2101

Organic Chemistry

Course Number	CH2101
Course Credit	L-T-P-C: 3-1-0-4
Course Title	Organic Chemistry
Learning Mode	Offline
Learning Objectives	The aim of this course is to lay down a strong foundation of modern organic chemistry, encompassing the mechanisms of organic transformations and various applications of organic chemistry with a focus on strategies and control.
Course Description	This course introduces basic organic chemistry with emphasis on reaction mechanism, stereochemical implications, reaction intermediates and their properties, landmark organic transformations, different types of organic reactions and reagents involved therein.
Course Outline	Module 1: Introduction to types of organic reactions; structure and stability of reactive intermediates: carbocations, carbanions, free radicals, carbenes, arynes and nitrene. Module 2: Methods of determining organic reaction mechanism: thermodynamic and kinetic requirements, transition state theory, Hammond postulate, Curtin-Hammett principle, kinetic vs. thermodynamic control reaction, isotope effects, substituent effects, Hammett linear free energy relationship, Taft equation. Module 3: Addition reaction to C=C and C=O; preliminary idea of radical reactions; Application of Oxidation and Reduction reactions and reagents, Name reactions (e.g. Asymmetric hydrogenation/oxidation, Suzuki coupling, Heck coupling etc.). Module 4: Mechanism of aromatic nucleophilic and electrophilic substitutions.
Learning Outcome	Students will be able to 1. identify, classify, organize, and analyze organic molecules. 2. draw structures of organic molecules, interpret molecular structure following organic chemical transformations. 3. acquire knowledge of the mechanistic pathways of the synthesis and reactions. 4. acquire the cognitive skill to functionalize aromatic compounds.
Assessment Method	Class test, assignment & quiz (20%), Mid sem examination (30%), End sem examination (50%).

Text books:

Sykes, A guide to mechanism in Organic Chemistry, 6^th Edition, Pearson Education, 2004.
V. Anslyn and D. A. Dougherty, Modern Physical Organic Chemistry, 1^st Edition, University Science Books, California, 2005.
Kürti and B. Czakó, Strategic Applications of Named Reactions in Organic Synthesis, 1^st Edition, Elsevier Academic Press, 2005.
A. Carey and R. A. Sundberg, Advanced Organic Chemistry, Part A: Structure and Mechanisms, 5^th Edition, Springer, New York, 2007.
A. Carey and R. A. Sundberg, Advanced Organic Chemistry: Part B: Reaction and Synthesis, 5^th Edition, Springer, New York, 2007.
B. Smith and J. March, March's Advanced Organic Chemistry, 7^th Edition, John Wiley and Sons, 2007.

CH2102

Inorganic Chemistry

Course Number	CH2102
Course Credit	L-T-P-C: 3-1-0-4
Course Title	Inorganic Chemistry
Learning Mode	Offline
Learning Objectives	After completion of the course the learners will be thorough with the concepts of inorganic acids and their conjugate bases and various related theories, compounds of B, Si, P, Se, Te, halogens and Xenon, redox reactions, Potential diagrams, and Frost–Ebsworth diagrams. Applications of redox reactions in industrial processes. Students will have a sound foundation of several important aspects of basic inorganic chemistry.
Course Description	To make students understand the concepts of Acid-Base Chemistry of inorganic species, the fundamentals of redox reactions, to predict course of reactions and their applications to industrial processes, the basic chemistry of selected p-block elements of B, Si, P, Se, Te, halogens and Xenon with reference to certain compounds in each case.
Course Outline	Module 1: Acid-Base Chemistry: Definitions and concepts- Brønsted Lowry, Lux-Flood, Solvent system, Lewis, Usanovich, Hard-Soft Acid and Base (classification, strength and relation with electronegativity). Module 2: Redox reactions and oxidation states, Reduction potentials and Gibbs energy, Disproportionation, Potential diagrams, Frost–Ebsworth diagrams. Module 3: The effect of complex formation or precipitation on M²⁺/M reduction potentials, Applications of redox reactions to industrial processes. Module 4: Chemistry of Boron, Silicon, Phosphorous and Sulphur. Module 5: Interhalogen compounds and polyhalogen ions, oxides and oxofluorides, oxoacids and salts of chlorine, and chemistry of xenon.
Learning Outcome	Students will be able to 1. identify inorganic acids and bases. 2. identify, balance and apply redox reactions for various practical applications. 3. learn the synthesis of selected compounds of p-block elements. 4. know and decipher the important uses and applications of various elements.
Assessment Method	Quiz and assignment (20%), Mid sem examination (30%), End sem examination (50%).

Suggested Reading:

Text Books:

Atkins, T. Overton, J. Rourke, M. Weller, and F. Armstrong, Shriver & Atkins' Inorganic Chemistry, 5^th Edition, Oxford University Press, 2010.
Catherine E. Housecroft, and Alan G. Sharpe, Inorganic Chemistry, Pearson; 5^th Edition, 2018.
E. Huheey, E. A. Keiter, R. L. Keiter and O. K. Medhi, Inorganic Chemistry: Principles of Structure and Reactivity, Imprint: Pearson Education, 5^th Edition, 2022.

Reference Book:

Albert Cotton, G. Wilkinson, C. A. Murillo, M. Bochmann, Advanced Inorganic Chemistry,

- 6^th Edition - New Delhi: Wiley India, 2008.

CH2103

Course Number	CH2103
Course Credit	L-T-P-C: 3-1-0-4
Course Title	Introduction to Quantum Chemistry
Learning Mode	Offline
Learning Objectives	To develop the concept of quantum mechanics, steps to get the energies and wave functions of model systems (particle in a box, harmonic oscillator etc.). To describe angular momentum in quantum mechanics. To demonstrate approximate methods such as perturbation and variation methods.
Course Description	This course demonstrates the concept of quantum mechanics starting from postulates, general principles, Schrödinger equation and its application on some model systems. Further the course describes the applications of variational methods and perturbation theory.
Course Outline	Module 1: The motivation for quantum mechanics, postulates and general principles of quantum mechanics, operators, and their properties. Module 2: Schrödinger equation and its application on some model systems: free-particle and particle in a box (1D and 3D), particle in a finite square well potential, tunneling, the harmonic oscillator, particle on a ring, the rigid rotor. Module 3: Approximate methods: The variation theorem, linear variation principle, time-independent perturbation theory, applications of variational methods and perturbation theory. Module 4: Angular momentum: eigenfunctions and eigenvalues of angular momentum operator, Ladder operator, addition of angular momenta.
Learning Outcome	Students will be able to 1. understand the origin and postulates of quantum mechanics. 2. solve for the energies and wave functions of model systems (particle in a box, harmonic oscillator, particle on a ring etc.). 3. to use approximation methods like variation theorem and variation method. 4. solve for the eigenfunctions and eigenvalues of angular momentum.
Assessment Method	Class test, assignment & quiz (20%), Mid sem examination (30%), End sem examination (50%).

Text Books:

L. Pilar, Elementary Quantum Chemistry, 2^nd Edition, Dover Publications, Inc. NY, 2003
W. Atkins and R. S. Friedman, Molecular Quantum Mechanics, 5^th Edition, Oxford University Press, 2010.

N. Levine, Quantum Chemistry, 7^th Edition, Pearson, 2013.
A. McQuarrie, Quantum Chemistry, Viva student Edition, Viva, 2020.

CH2104

Chemical Process Calculations

Course Number	CH2104
Course Credit	L-T-P-C : 3-1-2 5
Course Title	Fluid Mechanics
Learning Mode	Classroom lectures and practical
Learning Objectives	To build an understanding on the importance and scope of fluid in rest (statics) and fluid in motion (dynamics) in process systems. Learning basics of pressure development, fluid properties and their role in driving various types of flows and fluid response under different external/internal forces. To study governing equations and dimensionless groups which drive the flow and their applications in the designing of pipe networks, pumps, etc.
Course Description	The course helps to develop a basic understanding of fluid mechanics and its application in chemical engineering. Equations and concepts in fluid statics, kinematics, and dynamics are covered in the course.
Course Content	Introduction; Types of fluids; Non-Newtonian viscosity; Dimensional analysis (Buckingham PI theorem); Fluid statics; Hydrostatic force on submerged bodies; Rigid body motion; Kinematics of flow- Eulerian and Lagrangian descriptions; Integral analysis- mass and momentum balances; Bernoulli equation; Differential analysis of flow; Conservation of mass, linear, and angular momentum; Navier-Stokes equation; Unidirectional flow; Viscous flow; Turbulent flow; Skin friction and form friction; Friction factor; Flow through pipes and ducts; Potential flow; Boundary layer theory; Boundary layer separation; Flow around immersed bodies; Drag and lift; Flow through packed and fluidized beds, Compressible flow; Flow measurement; Fluid transportation- pumps, blowers and compressors.
Learning Outcome	Development and application of governing equations and laws of fluid systems. Study on flow and pressure measuring equipment, frictional losses in pipes/conduits, laminar/turbulent flows, compressible/incompressible flows, boundary layer development and flows through porous beds. Illustrating the physical significance of pertinent non-dimensional groups through dimensional analysis.
Assessment Method	Assignments, Quiz, Mid-semester examination and End-semester examination

Text Books:

N. de Nevers, Fluid Mechanics for Chemical Engineers, McGraw-Hill Education (India) Private Ltd., 2017.
R.W. Fox, A.T. McDonald, P.J. Pritchard, Introduction to Fluid Mechanics, Wiley, 7^th Ed., 2009.
F.M. White, Fluid Mechanics, Mc-Graw Hill, 6^th Ed., 2008.

Reference Books:

M. Denn, Process Fluid Mechanics, Prentice Hall, 1979.
V.L. Streeter, Fluid Mechanics, 5^th Ed., Mc-Graw Hill, 1971.
R.B. Bird, W.E. Stewart, E. N. Lightfoot, Transport Phenomena, 2^nd Ed., Wiley, 2006.
J. M. Coulson, J. F. Richardson, J. R. Backhurst and J. H. Harker, Chemical Engineering, Vol. 1, 5^th Ed., Elsevier, 2015.
W. L. McCabe, J. C. Smith, P. Harriott, Unit Operations of Chemical Engineering, 7^th Ed., Mc-Graw Hill, 2005.

CH2105

Course Number	CH2105
Course Credit	L-T-P-C : 3-0-0-3
Course Title	Chemical Process Calculations
Learning Mode	Classroom lectures and tutorials
Learning Objectives	To learn the fundamental concepts of material balance and their applications. To learn the fundamental concepts of energy balance and their applications. To learn the overall concepts of combined material and energy balance and their diverse applications.
Course Description	This course is mainly about learning the concepts of material balance and energy balance and their applications (individual or combined) with reference to different chemical engineering systems/processes.
Course Content	Steady-state and dynamic processes; Lumped and distributed processes; Single and multi-phase systems; Correlations for physical and transport properties; Equilibrium relations; Ideal gases and gaseous mixtures; Vapor pressure; Vapor liquid equilibrium; Various Thermodynamics cycle such as Rankine Cycle, Carnot Cycle; Otto Cycle; Brayton Cycle; Material balances: Non-reacting single-phase systems; Systems with recycle, bypass and purge; Processes involving vaporization and condensation; Intensive and extensive variables; Rate laws; Calculation of enthalpy change; Heat of reaction; Fuel calculations; Saturation humidity, humidity charts and their use; Energy balance calculations; Flow-sheeting; Degrees of freedom and its importance in flow-sheeting.
Learning Outcomes	Familiarize with different units. Analyse and comprehend steady-state and dynamic processes. Understand and calculate problems related to material balances. Understand and calculate problems related to energy balances. Understand and calculate problems related to combined material and energy balances.
Assessment Method	Assignments, Quiz, Mid-semester examination and End-semester examination

Text Books:

B. I. Bhatt; S. B. Thakore, Stoichiometry, McGraw Hill, 6^th Ed., 2021.
O. A. Hougen, K. M. Watson and R. A. Ragatz, Chemical Process Principles, CBS Publishers, Part-1, 2^nd Ed., 2004.
D. M. Himmelblau, Basic Principles and Calculations in Chemical Engineering, Prentice Hall of India, 8^th Ed., 2014.

Reference Books:

N. Chopey, Handbook of Chemical Engineering Calculations, Mc-Graw Hill, 3^rd Ed., 2004.
R. M. Felder and R. W. Rousseau, Elementary Principles of Chemical Processes, Wiley, 3^rd Ed., 2014.

HS21XX

HSS Elective-I

TOTAL

Structure and Function of Biomolecules

Sl. No.

Subject Code

SEMESTER IV

CH2201

Course Number	CH2201
Course Credit	L-T-P-C: 3-0-0-3
Course Title	Structure and Function of Biomolecules
Learning Mode	Offline
Learning Objectives	The aim is to impress upon the concept of Biomolecules and related metabolic processes in organisms; to lay down a strong foundation of biomolecules structure and their function.
Course Description	This course introduces molecular structure and interactions of biomolecules that help in functioning and organization of organisms including vital metabolic processes.
Course Outline	Module 1: Amino acids, Peptides and Proteins: structure, classification and function of amino acids, acid-base properties and Isoelectric point. Proteins: structure, classification and different protein functions, determination of primary structure of proteins, Ramachandran plot, enzyme as a special class of proteins, nomenclature and function, enzyme kinetics and enzyme inhibition. Module 2: Carbohydrates: Structure, properties, and reactions of mono- and disaccharides, carbohydrate conformers, function of different carbohydrates in our body, storage polysaccharides-linkage, variety, and uses. Module 3: Lipids: nomenclature, structure, properties and function of various biologically relevant lipids, membrane structure. Module 4: Nucleic acids: Building blocks, Structure, characteristics and functions of DNA/RNA.
Learning Outcome	Students will be able to 1. interpret molecular structure and interactions of essential biomolecules like proteins, nucleic acids, carbohydrates and lipids. 2. elucidate important metabolic pathways related to biomolecules. 3. explain landmark discoveries related to biochemistry important to modern day healthcare. 4. relate and interpret bioprocesses related to modern pharmaceutics and medicinal chemistry.
Assessment Method	Class test, assignment & quiz (20%), Mid sem examination (30%), End sem examination (50%).

Text Books:

Ratledge and B. Kristiansen, Basic Biotechnology, Cambridge University Press, 3^rd Edition, 2007.
Donald Voet and Judith Voet, Biochemistry by Wiley, 4^th Edition, 2010
L. Nelson; M. M. Cox, Lehninger Principle of Biochemistry, W. H. Freeman Co. Ltd, 8^th Edition, 2021.

CH2202

Introduction to Organometallics

Course Number	CH2202
Course Credit	L-T-P-C: 3-1-0-4
Course Title	Introduction to Organometallics
Learning Mode	Offline
Learning Objectives	To highlight the importance of organometallic chemistry that bridges organic and inorganic sub-disciplines in chemistry. To enthuse interest among students by discussing rich chemistry and important applications of transition-metal based organometallic molecules in catalysis and their relevance in industry.
Course Description	The course describes ligands, molecular models and reaction types relevant to describe organometallic chemistry of transition metals. Early and recent examples of transition-metal based catalysis of industrial importance in present context.
Course Outline	Module 1: s and p-block organometallic compounds Module 2: 18-Electron rule, organometallic complexes with ligands such as hydrides, alkyl, carbonyl, nitrosyl, olefin and phosphines. Module 3: Metal-carbene complexes and metallocenes. Fluxionality in organometallic complexes. Types of organometallic reactions. Module 4: Homogeneous catalysis including C-C coupling, metathesis and olefin oxidation, alkene isomerization, alkene hydrogenation, alkene hydroformylation, hydrocyanation of butadiene, alkene hydrosilylation and hydroboration Module 5: Heterogeneous catalysis including Fischer-Tropsch reaction and Ziegler-Natta polymerization.
Learning Outcome	Students will be able to 1. understand scientific reports describing use of transition metal based organometallics. 2. propose mechanism and identify intermediates for reactions catalyzed by transition metal based organometallics. 3. propose design and syntheses of new catalysts based on transition metals. 4. explain the application of transition metal based molecules as catalysts in organic transformation.
Assessment Method	Class test, assignment & quiz (20%), Mid sem examination (30%), End sem examination (50%)

Text books:

R. H. Crabtree, The Organometallic Chemistry of the Transition Metals, 7^th Edition, Wiley, 2019.
BD Gupta, Anil J. Elias, Basic Organometallic Chemistry: Concepts, Syntheses and Applications Paperback, Universities Press; 2^nd Edition, Reprint 2020.

Reference Book:

C. Housecroft, Alan G. Sharpe, Inorganic Chemistry, Pearson; 4^th Edition (September 4, 2012).

CH2203

Chemical Thermodynamics and Equilibrium

Course Number	CH2203
Course Credit	L-T-P-C: 3-1-0-4
Course Title	Chemical Thermodynamics and Equilibrium
Learning Mode	Offline
Learning Objectives	To develop the concept of classical thermodynamics, phase equilibria, solid solutions, and apply it to an existing, and emerging problem in basic science.
Course Description	This course demonstrates the concept of classical thermodynamics starting from basic laws, different thermodynamic parameters, phase equilibria, solid solutions etc. relevant for undergraduate students.
Course Outline	Module 1: Ideal gasses, real gasses, critical state; thermodynamic laws; reversible and irreversible processes. Module 2: Thermochemistry: Hess’s law, Kirchoff’s equation; Joule- Thompson experiment and co-efficient, Entropy; application of law of thermodynamics; Carnot cycle; Clausius inequality; equations of state; Gibbs and Helmholtz free energies; Maxwell equations and thermodynamic properties of pure substances; The thermodynamic description of mixtures, Colligative properties; chemical potential. Module 3: Chemical equilibria; equilibrium constant; Le Chatelier principle and its applications; Clapeyron equation and its applications; phase equilibria: Gibbs phase rule, one component systems and two component systems – simple eutectic, solid solutions; congruent melting and incongruent melting; Phase behavour of liquids and their application in chemical industry.
Learning Outcome	Students will be able to 1. understand the fundamentals of classical thermodynamics. 2. develop problem-solving ability in classical thermodynamics. 3. develop the concept of phase equilibria in one component and two component systems. 4. develop the concept of solid solutions. 5. apply the fundamental knowledge of classical thermodynamics to an existing and emerging problem in basic science.
Assessment Method	Class test, assignment & quiz (20%), Mid sem examination (30%), End sem examination (50%).

Text books:

W. Castellan, Physical Chemistry, 3^rd Edition, Addison Wesley Publishing Company,

1983.

A Textbook of Physical Chemistry, K. L. Kapoor, Vol: 1, 2, 6^th Edition, 2019, Vol: 3, 5^th

Edition, 2020, McGraw Hill.

Atkins’ Physical Chemistry, Peter Atkins,Julio de Paula,James Keeler, Oxford University

Press, 12^th Edition, 2022.

CH2204

Industrial Chemistry

Course Number	CH2204
Course Credit	L-T-P-C:3-0-0-3
Course Title	Industrial Chemistry
Learning Mode	Offline
Learning Objectives	To highlight the importance of selected inorganic reagents, inorganic materials and chemicals those are commercially important. Students will learn their classifications, composition, syntheses and environmental aspects of these materials.
Course Description	The course describes various classes of inorganic materials, their industrial syntheses, commercial applications and their impact on the environment.
Course Outline	Module 1: Inorganic reagents Hydrazine: Manufacturing of hydrazine, Raschig process, Urea process, Bayer process, H₂O₂ process; use of hydrazine as rocket fuel, in fuel cell. Module 2: Insecticides and Herbicides Definition and classification of Insecticides; Manufacturing of insecticides; environmental effects; definition and classification of herbicides, health effect. Module 3: Mineral Fertilizers Economic importance, manufacturing of N and P-containing fertilizers. Module 4: Construction Materials Lime, Quicklime, Slaked Lime; Cement, miscellaneous cement types, composition and manufacturing of cements. Module 5: Enamel Classification, Enameling, Coating processes, Storing of enamels. Module 6: Ceramics General information and classification, Physical and Chemical Processes related to manufacturing of clay ceramics, Metal and metalloid ceramic materials; Metallic hard materials and fibres.
Learning Outcome	Students will be able to 1. have a basic knowledge of propellants and rocket fuel. 2. have a basic knowledge of agrochemicals of commercial importance including inorganic based fertilizers. 3. identify construction materials in general and cement types in particular. 4. appreciate the types and importance of enamels and ceramics and their various applications.
Assessment Method	Class test, assignment & quiz (20%), Mid sem examination (30%), End sem examination (50%).

Text Books:

R. Mohrig, T. C. Morrill, C. N. Hammond and D.C. Neckers, Experimental Organic Chemistry, W.H. Freeman and Co. 1998.
Vogel's Textbook of Practical Organic Chemistry, B. S. Furniss, A. J. Hannaford, P. W. G. Smith and A. R. Tatchell, 5^th Edition, Pearson India; 2003.
Vogel's textbook of Quantitative Chemical Analysis, J. Mendham, R. C. Denney, J. D. Barnes and M. J. K. Thomas, 6^th Edition, Pearson Education, New Delhi, 2005.
N. K. Vishnoi, Advanced practical Organic Chemistry, 3^rd Edition, S. Chand Pvt. Ltd., 2010

XX22PQ

IDE-I

TOTAL

Macromolecular Science and Engineering

Sl. No.

Subject Code

SEMESTER V

CH3101

Course Number	CH3101
Course Credit	L-T-P-C: 3-1-0-4
Course Title	Macromolecular Science and Engineering
Learning Mode	Offline
Learning Objectives	• Basic understanding of polymers and their structure-property relation. • Design and synthesis of functional polymers via different methods. • Basis of properties of polymers and their characterizations. • Few examples of commercially available real life polymer applications and understanding of their synthesis and properties.
Course Description	The course provides an introduction to polymer science based on synthesis mechanisms, including outlined mechanisms of living polymerization, and properties of polymers and their origin. Characterization of polymeric properties and understanding of degradable polymers are included. Synthesis and properties of a number of commercially available polymers in our day-to-day life will be discussed.
Course Outline	Module 1: Basic Principles: Introduction and historical development, classification of polymers, nomenclature, number and weight average molecular weights, stereochemistry of polymers. Module 2: Different Polymerization techniques: Step growth and chain growth polymerizations. Examples and outlined mechanisms of controlled radical polymerization, anionic polymerization, ring opening polymerization, examples should include the preparation of block copolymers, star polymers and graft copolymers, emulsion polymerization and its uses in practical applications. Module 3: Polymer Conformation and Polymer solution: Three different models, Flory-Huggins theory (outlines and physical significances), polymer morphology: amorphous state and crystallinity. Module 4: Polymer properties and characterization: Thermal property: stability, glass-transition temperature, mechanical properties and rheology, polymer degradation. Polymer characterization by NMR (only polymer aspects), SEC, DSC and TGA. Module 5: Different class of commercially available polymers from day to day applications: specific discussion about synthesis and properties (as learned from module 1 to 4) of different class of commercially available polymers and their applications in different fields, for example vinylic polymers (such as polystyrenes and low and high density polyethylene), polyesters (such as PET, Ekonol), polyamides (such as Nylon, Kevlar), polycarbonates and polyethylene glycols.
Learning Outcome	At the end of the course the students should be able to 1. develop specific skills, competencies, and thought processes sufficient to support further study or work in this field of Polymer Chemistry. 2. learn various polymerization techniques, many of them are regularly used in industries. 3. various properties in polymers, including thermal and mechanical properties and polymer degradation and weathering. 4. characterization and analysis of different basic polymer properties.
Assessment Method	Class test, assignment & quiz (20%), Mid sem examination (30%), End sem examination (50%).

Text Books:

Fred W. Billmeyer, Jr., Textbook of Polymer Science, 3^rd Edition, Wiley, 2008

Manas Chanda, Salil K. Roy, Industrial Polymers, Specialty Polymers, and Their Applications, CRC Press, 2019.

Timothy P. Lodge, Paul C. Hiemenz, Polymer Chemistry, 3^rd Edition, CRC Press, 2020

Reference Books:

Malcolm P. Stevens, Polymer Chemistry: An Introduction, Oxford University Press, USA, 3^rdEdition, 1998.

Krzysztof Matyjaszewski, Yves Gnanou, Ludwik Leibler, Macromolecular Engineering: Precise Synthesis, Materials Properties, Applications, Wiley‐VCH Verlag GmbH & Co. KGaA, 2007.

R. J. Young, and P. A. Lovell, Introduction to Polymers, CRC Press, 3^rd Edition, 2011.

CH3102

Design and Applications of Nanomaterials

Course Number	CH3102
Course Credit	L-T-P-C: 2-1-0-3
Course Title	Design and Applications of Nanomaterials
Learning Mode	Offline
Learning Objectives	To impart foundational knowledge of Nanoscience and related fields, to make the students acquire an understanding of Nanoscience. To help them understand the broad outline of Nanoscience and Nanotechnology with experimental understanding of synthesis and characterization techniques.
Course Description	This course introduces the fundamentals of nano-scale science, engineering and technology, applications of nanostructured materials, synthetic routes, the main physical forces controlling size, shape and properties of nanomaterials. Laboratory experiments will cover well-established synthesis/fabrication methods with hands-on experience on standard characterization methods.
Course Outline	Module 1: Nanomaterials in daily life: Examples, and types of Nanomaterials: metal and metal oxides, semiconductor nanomaterial, carbon, polymeric, organic nanomaterials. Module 2: Nanomaterial properties: Optical and electronic properties. Module 3: Chemical Routes for Synthesis of Nanomaterials: Top down and bottom up approaches, Chemical precipitation; Sol-gel synthesis. Module 4: Applications of Nanomaterials.
Learning Outcome	Upon successful completion, students will have the knowledge and skills to 1. explain the fundamental principles of nanotechnology and their applications. 2. apply concepts to the nano-scale and non-continuum domain. 3. identify and compare state-of-the-art nanofabrication methods and perform a critical analysis of the research literature. 4. design processing conditions to engineer functional nanomaterials. 5. discuss, evaluate and perform state-of-the-art characterization methods for nanomaterials, and determine nanomaterial safety and handling methods required during characterization.
Assessment Method	Class test, assignment & quiz (20%), Mid sem examination (30%), End sem examination (50%).

Text Books:

M. Barrow, Physical Chemistry, 5^th Edition, Tata Mcgraw-Hill, 1992.
Laidler, Chemical Kinetics, 3^rd Edition, Pearson Education, 2004.
Atkins’ Physical Chemistry, Peter Atkins,Julio de Paula,James Keeler, Oxford University Press, 12^th Edition, 2022.

Reference Books:

W. Castellan, Physical Chemistry, 3^rd Edition, Narosa Publishing House, 1985.
J. Silbey and R. A. Alberty, Physical Chemistry, 3^rd Edition, John Wiley & Sons, 2002.
Engel and P. Reid, Physical Chemistry, 1^st Edition, Pearson Education, 2006.
Samuel Glasstone, An Introduction To Electrochemistry, East-West Press (Pvt.) Ltd., 2006.

Robert C. Fay Jill Kirsten Robinson, John E. McMurry, Chemistry, 8e Pearson Education, 2022.

CH3104

Techniques for Chemical Analysis

Course Number	CH3104
Course Credit	L-T-P-C:3-1-0-4
Course Title	Techniques for Chemical Analysis
Learning Mode	Offline
Learning Objectives	Impart concept on various purification and separation techniques and their applications in compound analysis, understanding different electrochemical methods and their application for various chemical analysis
Course Description	This course gives an introduction to analytical chemistry and an overview of important analytical methods and their range of application within detection of inorganic and organic compounds. Important analytical quantitative techniques from classical methods, electrochemical methods, spectrochemical/ spectrophotometric methods, mass spectrometry and separation techniques are reviewed. The course also includes steps and procedures in analytical chemistry, and evaluation/ interpretation of results. The course gives an overview of important use of selected classical and instrumental chemical quantitative analytical methods and a short introduction to their basic theory.
Course Outline	Module 1: UV-Visible Spectroscopy: General principles and instrumentation, analytical applications: qualitative and quantitative analysis of inorganic and organic compounds. Module 2: Infrared Spectroscopy: Instrumentation and application in chemistry. Vibrations of polyatomic molecules, group frequency and its application. Module 3: Nuclear Magnetic Resonance Spectroscopy: General principles, sensitivity of the method, instrumentation. Application in chemical analysis (with special reference to ¹H – NMR): basic definitions, shift reagents, off- resonance decoupling, multinuclear NMR. Module 4: Mass Spectrometry: Theory and principles, Instrumentation, Methods of ionization. Structure elucidation of inorganic and organic compounds. Module 4: Thermal Analysis: TGA, DTA and DSC and their applications in chemistry.
Learning Outcome	Student would be able to 1. explain quantitative methods of working, the theoretical principles and important applications of analytical techniques. 2. perform various techniques, selected instrumental methods within electroanalytical, spectrometric/spectrophotometric and mass spectrometry methods, and main components in such analytical instruments. 3. explain the data analysis to understand the unknown structure. 4. familiar with calculations in analytical chemistry and method evaluation, and perform statistical evaluation of results from classical and instrumental chemical experiments and analyses.
Assessment Method	Class test, assignment & quiz (20%), Mid sem examination (30%), End sem examination (50%).

Text books:

Principles of Medicinal Chemistry, W. O. Foye, 3^rd Edition, Lea & Febiger/Varghese Publishing House, Bombay, 1989.
Medicinal Chemistry, A. Burger, Vol. I-III, Wiley Interscience Publications, New York, 1995.
Lednicer, Strategies for Organic Drug Synthesis and Design, John Wiley & Sons Inc., New York, 2^nd Edition, 2008.
Strategies for organic drug synthesis and design, D. Lednicer, John Wiley & Sons, New York, 2009.
A. Williams and T. L. Lemke, V. F. Roche, S.W. Zito, Foye's Principles of Medicinal Chemistry, Lippincott Williams & Wilkins, Philadelphia, 2012

A. Kar, Medicinal Chemistry, New Age International Publishers, 2018.

CH3202

Environmental Science & Technology

Course Number	CH3202
Course Credit	L-T-P-C: 3-0-0-3
Course Title	Environmental Science & Technology
Learning Mode	Offline
Learning Objectives	The objective of the course is to understand our atmosphere in terms of its composition and the chemicals, chemical reactions that pollute air, water, and soil. The course objective is also to provide a basic understanding of removal pathways of contaminants from the environment and analytical techniques to determine the contamination level.
Course Description	The course is understanding of atmosphere, and pollutants that contaminate our environment, removal of contaminants and their chemical analysis
Course Outline	Module 1: Introduction: Atmospheric composition and behavior, principles of contaminant behavior in the environment. Module 2: Chemistry in Aqueous Media: Chemical and physical reactions in the water environment. Module 3: Major Contaminant Groups and Their Natural Pathways for Removal from Water, Soil: Groundwater and subsurface contamination, Soil profiles, Acid-base and ion exchange reactions in soils, Fertilizers, wastes and pollutants in soil. Module 4: Atmosphere and Atmospheric Chemistry: Inorganic and organic air pollutants, Sulfur dioxide sources and the sulfur cycle, Nitrogen oxides in the atmosphere, Smog forming reactions of organic compounds in the atmosphere, mechanisms of smog formation. Module 5: Nature and Importance of Chemical Analysis: Major categories of chemical analysis, Application of analytical chemistry to environmental chemical analysis.
Learning Outcome	After successful completion of the course, students will be able 1. to learn about the composition and behavior of atmosphere 2. to learn about the contaminants behavior in our environment 3. to learn about the chemical and physical reactions in aqueous media 4. to learn natural pathways for removal of contaminants 5. to learn inorganic and organic air pollutants and smog formation 6. to learn chemical analysis and its application in environmental chemical analysis
Assessment Method	Class test, assignment & quiz (20%), Mid sem examination (30%), End sem examination (50%).

Text Books:

S. E. Harnung, M. S. Johnson, Chemistry and the Environment, 1^st Edition, Cambridge University

Press,2012.

J. S. Gaffney, N. A. Marley, Chemistry of Environmental Systems: Fundamental Principles and

Analytical Methods, 1^st Edition, Wiley, 2019.

Reference Books:

S.E. Manahan, Fundamentals of Environmental and Toxicological Chemistry: Sustainable Science, 4^th

Edition, CRC Press, 2013.

M. K. Hill, Understanding Environmental Pollution, 4^th Edition, Cambridge University Press, 2020.

CH3203

Computational Chemistry

Course Number	CH3203
Course Credit	L-T-P-C:3-0-2-4
Course Title	Computational Chemistry
Learning Mode	Offline
Learning Objectives	The course addresses computer-based calculations within chemistry. The course intends to integrate theory with practical computation elements applied within the fields of environmental chemistry, protein chemistry and medicinal chemistry. The students are expected to acquire knowledge within quantum chemistry, molecular mechanics, bioinformatics, and the theoretical characterization of molecules, and applied methods for computation of the geometric and electronic structure of molecules.
Course Description	Central concepts for the computer-based application of organic molecules within quantum chemistry will be described and discussed. The focus within molecular mechanics is on describing and discussing the practical application of organic molecules, including proteins.
Course Outline	Module 1: Wave function of a particle in a box, harmonic oscillator, anharmonic oscillator. Module 2: Radial wave function of a hydrogen atom, atomic & hybridized orbitals, Wien's Law, ionization energy of hydrogen. Module 3: Time dependent perturbation theory: Integration of Schrodinger equation: 1D box, spherical box, simple harmonic oscillator, eigenvalues and eigenvectors. Module 4: SCF energies and dipole moment, calculation of auto-correlation function Fourier Transform and spectral applications.
Learning Outcome	After completing the course, students shall be able to 1. explain the most important principles for quantum chemical and molecular mechanical methods of computing the geometry and energy of molecules. 2. plan and apply computer-based calculations to determine the geometry, energies and electronic properties of molecules. 3. describe the theory behind methods of protein sequence comparisons and protein structure comparisons. 4. describe theoretical methods and plan to conduct computer-based calculations of chemical properties.
Assessment Method	Class test, assignment & quiz (20%), Mid sem examination (30%), End sem examination (50%).

Text books:

D. Athawale and Parul Mathur, Experimental Physical Chemistry, New Age International Publishers, 2001.
Viswanathan and P. S. Raghavan, Practical Physical Chemistry, Viva Books Private Ltd., 2005.
A. Day (Jr.) and A. L. Underwood, Quantitative Analysis, 6^th Edition, Prentice-Hall of India Pvt. Ltd., 2006.
P. Shoemaker, C. W. Garland and J. W. Nibler, Experiments in Physical Chemistry, 8^th Edition, McGraw- Hill International Ed., 2008.

J. M. Postma, J. L. Roberts (Jr.), Chemistry in the Laboratory, 8^th Edition, W.H. Freeman and Company, 2016.

CH32XX

Department Elective-I

TOTAL

Sl. No.	Subject Code	SEMESTER VII	L	T	P	C
1.	CH41XX	Departmental Elective – II	3	0	0	3
2.	CH41XX	Departmental Elective – III	3	0	0	3
3.	XX41PQ	IDE-III	3	0	0	3
4.	HS41XX	HSS Elective II	3	0	0	3
5.	CH4198	Summer Internship*	0	0	12	3
6.	CH4199	Project – I	0	0	12	6
TOTAL			12	0	24	21

* For specific cases of internship after 6^th Semester, the performance evaluation would be made on joining the VII^th Semester and graded accordingly in the VII^th Semester:

Note :

a) (i) Summer internship (*) period of at least 60 days’ (8 weeks) duration begins in the intervening vacation between semester VI and VII that may be done in industry / R&D / Academic Institutions including IIT Patna. The evaluation would comprise combined grading based on host supervisor evaluation, project internship report after plagiarism check and seminar presentation at the Department (DAPC to coordinate) with equal weightage of each of the three components stated herein.

a) (ii) Further, on return from internship, students will be evaluated for internship work through combined grading based on host supervisor evaluation, project internship report after plagiarism check, and presentation evaluation by the parent department with equal weightage of each component.
b) (i) In the VII^th semester, students can opt for a semester long internship on recommendation of the DAPC and approval of the Competent Authority.

b) (ii) On approval of semester long internship, at the maximum two courses (properly mapped/aligned syllabus) at par with institute electives may be opted from NPTEL and / or SWAYAM and the other two more should be done at the institute through course overloading in any other semester (either before or after the internship) and/or during following summer semester.
b) (iii) The candidates opting two courses from NPTEL and / or SWAYAM would be required to appear in the examination at the Institute as scheduled in the Academic Calendar.

Metal Ions in Chemical Biology

Department Electives - I

Department Electives - I

Sl. No.

Subject Code

Course Name

CH3206

Course Number	CH3206
Course Credit	L-T-P-C: 3-0-0-3
Course Title	Metal Ions in Chemical Biology
Learning Mode	Offline
Learning Objectives	To introduce this highly interdisciplinary subject and show how selected metal ions are important in the biological environment, highlight the role of the metal ions in catalysis of biochemical reactions and their importance in sustaining life-processes.
Course Description	The course describes the biological significance of various essential and trace metal including their storage, transport, and bio-mineralization. Students will learn about diseases associated with deficiency or toxicity of essential and trace metal ions. It also introduces the mechanistic course of action of various metalloproteins and enzymes under physiological conditions and their important roles in sustaining life.
Course Outline	Module 1: Historical development of Bioinorganic Chemistry and contributions of notable scientists and Nobel laureates, Role of metal ions in biological systems, alkali and alkaline earth cations in biological systems and ionophores. Module 2: Non-redox metalloenzymes such as Carboxypeptidases, Carbonic Anhydrase, and Alcohol Dehydrogenase. Module 3: Redox-proteins: Siderophores, Iron–Sulfur Proteins, Haem, non-haem and electron transfer proteins of iron. Copper proteins including Plastocyanin, Azurin, Superoxide Dismutase, and Hemocyanin. Module 4: Storage and transport of Zn, Mo, Co, Cr, V, and Ni; biomineralization of Iron, Active site structure and function/activity of xanthine oxidase, nitrogenase, vitamin B12 coenzyme, photosystem I and II.
Learning Outcome	Students will be able to 1. understand scientific reports describing inorganic aspects of proteins and enzymes. 2. understand structure-property relationship of metal-based proteins/enzymes and appreciate the importance of metal ions. 3. propose design and syntheses of new synthetic models that would mimic enzymes. 4. understand and explain the reaction pathways of various metal based enzymes in physiological systems 5. understand the causes of various diseases that are caused due to deficiency or biomagnification of metal ions.
Assessment Method	Class test, assignment & quiz (20%), Mid sem examination (30%), End sem examination (50%)

Suggested Readings:

Text Books:

Ochiai Ei-Ichiro, Bioinorganic Chemistry: A Survey, Elsevier India, 2012,
W. Kaim, B. Schwederski, A. Klein, Bioinorganic Chemistry - Inorganic Elements in the Chemistry of

Life: An Introduction and Guide, 2^nd Edition, Wiley, 2013.

Reference Book:

H. B. Gray, E. I. Stiefel, J. S. Valentine, I. Bertini, Biological Inorganic Chemistry: Structure and

Reactivity, 1^st Edition, University Science Book, 2006.

CH3207

Petroleum and Petrochemicals

Course Number	CH3207
Course Credit	L-T-P-C: 3-0-0-3
Course Title	Petroleum and Petrochemicals
Learning Mode	Offline
Learning Objectives	The objective of the course is to understand the basic chemistry pertaining to processing of petroleum products, thermal and catalytic cracking for formation of various useful chemicals, and synthesis process of petrochemical products. In addition, the aim of the course is to provide an understanding of various industrial problems and solutions related to petroleum and petrochemical industry
Course Description	The course is a understanding of various chemical processes related petroleum and petrochemical industry
Course Outline	Module 1: Origin: Formation and composition of petroleum Module 2: Petroleum processing: Fractionation, blending of gasoline, gasoline treatment, kerosene treatment, treatment of lubes, petroleum wax and purification Module 3: Thermal and catalytic processes: Thermal cracking, catalytic cracking, catalytic reforming, naphtha cracking, coking, hydrogen processes, alkylation, isomerization processes; polymer gasoline, asphalt, upgradation of heavy crudes; Module 4: Specialty products: Industrial gases, liquid paraffin, petroleum jelly. Module 5: Sources of Petrochemicals and Synthesis: Synthesis of methanol, formaldehyde, acetylene, synthetic gas, ethanol, ethylene, ethylene glycol, vinyl acetate, acrylic acid and acrylates, acrylonitrile, acetone, acetic acid, chloroprene, vinyl chloride, vinyl acetate, acrylonitrile, propylene, butadiene, butanes, isobutene, adipic acid, adiponitrile, benzene, toluene, xylene, phenol, styrene, phthalic acid, phthalic anhydride and their applications in chemical industry.
Learning Outcome	After successful completion of the course, students will be able 1. to learn about formation and composition of petroleum 2. to learn various techniques of petroleum products processing including the purification process. 3. to learn thermal and catalytic processes for preparation of various industrially useful chemicals. 4. to learn about synthesis of various petrochemicals 5. to learn about applications of petrochemical products in chemical industry
Assessment Method	Class test, assignment & quiz (20%), Mid sem examination (30%), End sem examination (50%).

Suggested Readings:

Text Books:

P. Wiseman, Petrochemicals, John Wiley & Sons, 1986.
I. D. Mall, Petrochemical Process Technology, 2^nd Edition, Laxmi Publications Private Limited, 2017.
B. K. B. Rao, Modern Petroleum Refining Processes, 6th Ed., Oxford & IBH Publishing Co. Pvt. Ltd.,

New Delhi, 2018.

Reference Books:

R. A. Meyers, Handbook of Petroleum Refining Processes, 4^th Edition, McGraw-Hill, 2016.

2. S. Raseev, Thermal and Catalytic Processes in Petroleum Refining, 1^st Edition, CRC Press, 2020.

Department Electives - II

Department Electives - II

Sl. No.

Subject Code

Course Name

CH4107

Drug Design and Development

Course Number	CH4107
Course Credit	L-T-P-C: 3-0-0-3
Course Title	Drug Design and Development
Learning Mode	Offline
Learning Objectives	Understand the fundamental principles of drug targets and their role in pharmacotherapy. Explain the concepts of absorption, distribution, metabolism, and excretion (ADME) in pharmacokinetics and their implications in drug development. Analyze different routes of administration and dosing strategies for various drugs. Differentiate between in vivo and in vitro drug testing methods and their applications in preclinical research. Identify natural and synthetic lead compounds in drug discovery and explain the process of combinatorial synthesis. Apply pharmacokinetics-based drug design principles to optimize drug efficacy and safety. Describe the principles of computer-aided drug design (CADD) including quantitative structure-activity relationship (QSAR) models. Assess the importance of toxicology, pharmacology, and drug metabolism studies in drug development. Understand the phases and design of clinical trials and the regulatory affairs involved in commercializing a pharmaceutical product. Comprehensive understanding of the drug development process from discovery to commercialization.
Course Description	This course provides a comprehensive overview of the multidisciplinary field of drug development, covering key concepts and methodologies from discovery to commercialization. Students will delve into the intricate processes involved in identifying drug targets, optimizing pharmacokinetics, conducting preclinical and clinical testing, and navigating regulatory affairs and commercialization strategies.
Course Outline	Module 1: Drug targets; Pharmacokinetics: ADME, administration and dosing; Drug testing: in vivo and in vitro. Module 2: Drug discovery with case studies: natural lead, synthetic lead, combinatorial synthesis. Module 3: Pharmacokinetics based drug design; Computer aided drug design: Principles of QSAR, 2D QSAR, 3D QSAR; Module 4: Chemical development, Patenting, Process development; Toxicology. Module 5: Pharmacology, Drug metabolism, Clinical trials, Commercialization: regulatory affairs, pipeline development, pharmaceutical market places, business opportunities.
Learning Outcome	Students will be able to gain a thorough understanding of the multidisciplinary aspects of drug development, including drug targets, pharmacokinetics, drug testing, discovery methods, and commercialization strategies. develop the ability to analyze and evaluate different drug development processes, including absorption, distribution, metabolism, and excretion (ADME), as well as in vivo and in vitro testing methods. apply concepts such as pharmacokinetics-based drug design, computer-aided drug design (CADD), and quantitative structure-activity relationship (QSAR) modeling to optimize drug efficacy and safety. integrate knowledge from various disciplines including chemistry, biology, pharmacology, toxicology, and business to understand the complex interplay involved in bringing a drug from discovery to commercialization.
Assessment Method	Class test, assignment & quiz (20%), Mid sem examination (30%), End sem examination (50%).

Suggested Readings:

Text Books:

G. Patrick, Instant Notes: Medicinal Chemistry, Viva Books Pvt. Ltd., 2002.
G. Thomas, Fundamentals of Medicinal Chemistry, John Wiley & Sons Ltd., 2006.

Reference Books:

G. Patrick, An Introduction to Medicinal Chemistry, Oxford University Press, 2001.
T. Nogrady, Medicinal Chemistry: A Biochemical Approach, Oxford University Press, 2004.

3. S. Pidgeon, Wiley handbook of Current and Emerging Drug Therapies, Vol. 4, Wiley Interscience, 2007.

CH4108

Dyes, Paints and Pigments

Course Number	CH4108
Course Credit	L-T-P-C:3-0-0-3
Course Title	Dyes, Paints and Pigments
Learning Mode	Offline
Learning Objectives	To impart knowledge regarding various types of paints, pigments and dyes in general. Specifically, the students will learn the composition of paints, pigments and dyes, their industrial syntheses and applications.
Course Description	The course describes various kinds of inorganic materials and organic molecules that are used as colouring agents. Basic requirements that are needed in materials/molecules for such application will be discussed. The syntheses and characterization of various paints, dyes and pigments will be discussed.
Course Outline	Module 1: Paints Compositions binders, extender, thinner and surface active agents; functions of the ingredients; paint formulations; importance of PVC, alkyds, epoxy and polyurethane resins. Module 2: Pigments Introduction – requirements of a pigment, typical inorganic pigments, general information and economic importance, white pigments, Titanium Dioxide pigments, manufacturing processes for TiO₂ pigments, applications for TiO₂ pigments, lithopone and zinc sulphide pigments, iron oxide pigments, Chromium(III) oxide pigments, magnetic pigments, manufacture of magnetic pigments. Module 3: Dyes Colour and chemical constitutions; classification; brightening agents; cyanine dyes; chemistry of colour developer – instant colour processes; synthesis and applications of Methyl orange, Congo red, Crystal violet, Malachite green, Phenolphthalein, Fluorescein, Alizarin and Indigo and Rhodamine B etc.
Learning Outcome	Students will be able to 1. have sound knowledge of polymers present in various commercial paints. 2. classify organic molecules in various categories of dyes based on the functional groups present. 3. identify inorganic compounds with applications as pigments. 4. be aware of the syntheses/manufacture of various paints, pigments and dyes that are of commercial importance.
Assessment Method	Class test, assignment & quiz (20%), Mid sem examination (30%), End sem examination (50%).

Text Books:

Dyes and Pigments, Samuel Delvin, Ivy Publishing House, 1^st Edition, 2006.

Industrial Organic Pigments, Martin U. Schmidt, Klaus Hunger and Thomas Heber, Wiley- VCH, 4^th Edition, 2018.

Department Electives - III

Department Electives - III

Sl. No.

Subject Code

Course Name

CH4109

Group Theory and Spectroscopy

Course Number	CH4109
Course Credit	L-T-P-C: 3-0-0-3
Course Title	Group Theory and Spectroscopy
Learning Mode	Offline
Learning Objectives	The student should be able to: Recognize symmetry elements in a molecule; State the point group a molecule belongs to. They will learn how to construct character tables and its applications. Students will be able to explain the different phenomena that take place due to the interaction of light with matter. Students will be able to get basic knowledge on molecular spectroscopy techniques such as rotational spectroscopy, vibrational spectroscopy, Raman spectroscopy, electronic spectroscopy. Fundamental theories behind these techniques, selection rules, and factors affecting the spectra will be covered in this course.
Course Description	This course introduces the concept of group, and different symmetry elements in a molecule, construction of character table and its applications. This course introduces different phenomena that take place due interaction of light with matter, fundamental theories, selection rules, factors that control spectral line width and line shape. This course describes the fundamental theories for different spectroscopic techniques such as rotational spectroscopy, vibrational spectroscopy, Raman spectroscopy, electronic spectroscopy.
Course Outline	Module 1: Group Theory: Definition of group, symmetry, point groups, representation of group, orthogonality theorem, irreducible representation, character table. Module 2: Spectroscopy: Electromagnetic radiation and its interaction with matter; Uncertainty principle: Natural line width and broadening. Module 3: Microwave: classification of molecules, selection rules, intensity of spectral lines, effect of isotopic substitution. Module 4: Infrared: Harmonic oscillator, selection rules, vibrational energy of diatomic molecules, zero-point energy, force constant and bond strength; anharmonicity, Morse potential energy diagram, vibration-rotation spectroscopy, P, Q, R, branches; Breakdown of Born-Oppenheimer approximation, vibration of polyatomic molecules; normal mode of vibration, overtone, hot bands. Module 5: Raman: Classical and quantum theories of Raman effect, pure rotational, vibrational and vibrational-rotational Raman spectra, selection rules, mutual exclusion principle; Resonance Raman. Module 6: Electronic Spectroscopy: Energy levels, Franck-Condon principle, electronic spectra of polyatomic molecules.
Learning Outcome	Students will be able to 1. categorize molecules on the basis of their symmetry properties, which allow them to predict many molecular properties. 2. describe factors involved in spectroscopic transition, such as transition probability, selection rules, spectral line width and line shape etc. 3. describe spectroscopy in microwave region, rotational spectra of rigid diatomic molecules, selection rules, non-rigid rotor. 4. study vibrating diatomic molecule, energy levels of a diatomic molecule, simple harmonic and anharmonic oscillator, selection rule. 5. understand the concepts of Raman spectroscopy, concept of polarizability, rotational and vibrational Raman Spectra. 6. understand the concepts of electronic spectroscopy.
Assessment Method	Class test, assignment & quiz (20%), Mid sem examination (30%), End sem examination (50%)

Suggested Readings:

Texts Books:
1. F.A. Cotton, Chemical Applications of Group Theory, 3^rd Edition, Wiley Interscience, 1990.
2. C. N. Banwell and E. M. McCash, Fundamentals of Molecular Spectroscopy, Tata McGraw Hill,
1994.
Reference books:
1. H. E. White, Introduction to Atomic Spectra, McGraw Hill, 1934.

2. G. M. Barrow, Introduction to Molecular Spectroscopy, McGraw Hill, 1962.
3. M. Tinkham, Group Theory and Quantum Mechanics, McGraw Hill, 1964.

CH4110

Application of Statistical Mechanics in Chemistry

Course Number	CH4110
Course Credit	L-T-P-C: 3-0-0-3
Course Title	Application of Statistical Mechanics in Chemistry
Learning Mode	Offline
Learning Objectives	An in-depth study of the Statistical mechanics and apply it to challenging problems in science.
Course Description	This course explores the fundamental principles of statistical mechanics with a focus on their application to chemistry, covering both physical and statistical aspects.
Course Outline	Module 1: Review of basics: Introduction and reviews of classical mechanics, quantum mechanics and thermodynamics; Module 2: Introduction of statistical mechanics: Concept of Microstates and macrostates; Liouville’s equation; Concept of ensemble: microcanonical, canonical, and grand canonical ensemble; Boltzmann distribution for distinguishable particles; The emergence of temperature from conditions for equilibrium; postulate for entropy; Module 3: Partition function for ideal gas: Canonical partition function: molecular partition function of non-interacting particles, translational, rotational, and vibrational partition functions for noninteracting particles; Absolute values of different thermodynamic quantities; Statistical mechanics approach of chemical equilibrium Module 3: Partition function for real gas: Derivation of canonical partition function for weakly interacting gas particles; derivation of the Virial equation of state and the second virial coefficient; Application for hard sphere and square well potential. Temperature dependence of the second virial coefficient. Module 4: Quantum statistics: Quantum statistics (Bose-Einstein and Femi-Dirac) for indistinguishable particles; Photon gas; Density of states for photons; Black body radiation; Debye frequency and specific heat of phonons, heat capacity of a Fermi gas, the classical limit from the quantum mechanical expression for partition function
Learning Outcome	Students will be able to 1. understand the fundamentals of statistical mechanics and its application in Chemistry. 2. develop problem-solving ability in statistical mechanics. 3. develop research aptitude in statistical mechanics related area such as Molecular Dynamics and Monte Carlo. 4. apply the fundamental knowledge in statistical thermodynaics to an existing and emerging problem, such as drug discovery, biophysical chemistry, material science and energy related research, where the knowledge is required.
Assessment Method	Class test, assignment & quiz (20%), Mid sem examination (30%), End sem examination (50%).

Text Books:

D. A. McQuarrie, Statistical Mechanics, University Science Books, 2000.
2. K. L. Kapoor, A Textbook of Physical Chemistry - Volume 5, 4^th Edition, McGraw Hill, 2020.

Reference Books:
1. K. Huang, Statistical Mechanics, Wiley, 2^nd Edition, 2008.

M. Tuckerman, Statistical Mechanics: Theory and Molecular Simulation, OUP Oxford, 2^nd Edition,
2010.

3. B. Bagchi, Statistical Mechanics for Chemistry and Materials Science, CRC Press, 1^st edition, 2018.

Department Electives - IV

Department Electives - IV

Sl. No.

Subject Code

Course Name

CH4207

Catalysis

Course Number	CH4207
Course Credit	L-T-P-C:3-0-0-3
Course Title	Catalysis
Learning Mode	Offline
Learning Objectives	To showcase the usefulness of catalysts to improve efficiency and yields of chemical reactions performed on a small scale as well as industrial scale. To teach the basics of catalysis, associated theories, synthesis and characterizations of commonly used catalysts.
Course Description	The course describes various types of catalysts that are known. This includes discrete inorganic complexes as well as organic molecules, enzymes and insoluble inorganic clusters and materials. Practical examples of catalysts used in small scale (laboratory) and large scale (industry) will be discussed.
Course Outline	Module 1: The basics of catalysis: Different types of catalysts. Module 2: Homogeneous catalysis: Preparation and characterization of transition metal based catalysts, selected reactions of commercial importance that use homogeneous catalysts. Module 3: Heterogeneous catalysis: Freundlich adsorption isotherm, Langmuir adsorption isotherm, determination of surface area of adsorbent, BET adsorption isotherm, thermodynamic treatment of adsorption, adsorption at the surface of a liquid. Industrial applications Module 4: Biocatalysis and Organocatalysis Design and synthesis of catalysts and their applications, practical examples of enzymatic catalysis.
Learning Outcome	Students will be able to 1. identify molecules or chemicals that can be used as catalysts. 2. understand requirements for a chemical to act as catalyst. 3. propose design and syntheses of new catalysts. 4. understand various theories related to enzyme catalysts 5. select an appropriate catalyst among several options for a given chemical transformation.
Assessment Method	Class test, assignment & quiz (20%), Mid sem examination (30%), End sem examination (50%).

Text Books:

Weitkamp, and L. Puppe, Catalysis and Zeolites: Fundamentals and Applications, Springer Verlag, 1999.
J. Carberry, Chemical and Catalytic Reaction Engineering, Dover, 2001.
Gadi Rothenberg, Catalysis: Concepts and Green Applications, 2^nd Edition, Wiley-VCH, 2017.

K. L. Kapoor, Text Book of Physical Chemistry, Vol 5, 4^th Edition, McGraw Hill, 2020.

CH4208

Colloids and Interface Chemistry

Course Number	CH4208
Course Credit	L-T-P-C: 3-0-0-3
Course Title	Colloids and Interface Chemistry
Learning Mode	Offline
Learning Objectives	The course aims to teach about properties of colloidal solutions, thermodynamics of colloidal solutions and few of their applications in practice.
Course Description	This course will cover a brief introduction and properties of colloidal solution, thermodynamics and stability of colloidal solution, light scattering principles and applications towards molecular weight determination. Also few examples of colloids from real life applications will be discussed and introduced.
Course Outline	Module 1: Colloidal state of matter. Properties of lyophillic and lyophobic colloidal solutions. Module 2: Thermodynamics of electrified interface, stability of colloidal solutions: Theory of Verwey and Overbeek, colloidal electrolytes, polyelectrolytes. Donnan membrane equilibria. Module 3: Determination of molecular weight of macromolecules. Micelles, reverse micelles. Surface energetics and adsorption from liquids. Emulsion, detergent, gels and foams. Module 4: Applications in detergents, personal‐care products, pharmaceuticals, nanotechnology, and food, textile, paint and petroleum industries.
Learning Outcome	After completion of the course, students will be able to: 1. Learn about basic properties of colloids. 2. Demonstrate knowledge about thermodynamics and stability of colloids. 3. Apply a concept to determine molecular weight of polymeric colloids via light scattering technique. 4. Apply the concept of colloids in real life applications and find relevance in industries.
Assessment Method	Class test, assignment & quiz (20%), Mid sem examination (30%), End sem examination (50%).

Textbooks:

C. Hiemenz and R. Rajagopalan, Principles of Colloid and Surface Chemistry, Marcel Dekker, New York, 1997.
Ghosh, Colloid and Interface Science, PHI Learning, New Delhi, 2009.

References:

Israelachvili, Intermolecular and Surface Forces, Academic Press, New York, 1992.
W. Adamson and A. P. Gast, Physical Chemistry of Surfaces, John Wiley & Sons, New York, 1997.
J. Hunter, Foundations of Colloid Science Oxford University Press, New York, 2005.
C. Berg, An Introduction to Interfaces and Colloids: The Bridge to Nanoscience, World Scientific, Singapore, 2010.

Department Electives - V

Department Electives - V

CH4209

Food Chemistry

Course Number	CH4209
Course Credit	L-T-P-C: 3-0-0-3
Course Title	Food Chemistry
Learning Mode	Offline
Learning Objectives	The course aims to lay a foundation for Material Chemistry. The course aims to nurture knowledge to understand fundamental concepts of Material science with their synthesis, characterization and applications.
Course Description	The course applies basic chemical principles to food systems and practical applications. Chemical/biochemical reactions of carbohydrates, lipids, proteins, and other constituents in fresh and processed foods are discussed with respect to food quality. Chemical processes that affect color, flavor, texture, nutrition, and safety of food. Important aspects of food and beverage industry including packaging are discussed.
Course Outline	Module 1:Introduction to food Chemistry: properties, reactions, qualitative and quantitative analysis of carbohydrates, lipids (oils, fats and emulsions) and proteins (meat and dairy) based food products; storage and processing of these food products and how these influence the quality and properties of the food, importance of water for stability and quality of foods. Module 2:Color, flavor, fragrance and preservatives: Chemistry of edible dyes, standards, and regulatory aspects, chemical components of different flavors and preservatives commonly used in the food industry, natural and artificial sweeteners, artificial flavor mimetic compounds including acids, alcohols, esters, ketones, aldehydes, and other flavor compounds; chemical compounds for fragrance and aroma in food and beverage industry, chemicals for preservation and ripening of fruits. Module 3:Microorganisms and toxins in food and beverage industry: role of microbes in dairy products and alcohol production; the use of microbes in flavor, aroma, texture, and digestibility; food spoilage and food poisoning, common chemical food toxins. Module 4: Chemistry of Food packaging: food packaging materials (plastics, biopolymers, glass, and metal) and their properties and applications; ethylene and Oxygen scavengers, toxicity issues, food packaging and circular economy Module 5:Vitamins and Nutraceuticals: Basic chemistry of vitamins and their function; emergence, importance and basic chemistry of nutraceuticals and their health benefits, natural sources and production of nutraceuticals
Learning Outcome	Upon successful completion, students will have the knowledge and skills to: 1. Demonstrate and apply knowledge of the core competencies in Food Chemistry and analysis. 2. Understand the chemistry involved in the properties and reactions of various foods and their components. 3. Understand and effectively apply the principles behind analytical techniques associated with food, components of commonly used chemicals used in food industry. 4. Understand and effectively apply food chemistry and analysis methods to intercept food quality, nutritional requirement and health benefits.
Assessment Method	20% Quiz and assignment, 30% Midsem and 50% End semester exam

Suggested reading

Fennema’s Food Chemistry, fourth edition, 2007, CRC Press
Food Science, B. Srilakhsmi, 3^rd Edition, New age International Publications

Food processing and preservation, S. Sivasankar, 2002, prentice Hall of India.

CH4210

Green and Sustainable Chemistry

Course Number	CH4210
Course Credit	L-T-P-C: 3-0-0-3
Course Title	Green and Sustainable Chemistry
Learning Mode	Offline
Learning Objectives	The course aims to lay a foundation of green and sustainable chemistry and its application in solving modern day-to-day problems. The course aims to nurture knowledge to understand fundamental concepts to applications of sustainable chemistry.
Course Description	New ideas and innovations are essential to meeting industry and society's growing needs for green chemistry and clean technology. This course aim is to provide key principles of green chemistry and the importance of clean and sustainable technology. The course will help to develop and enhance your transferable skills as well as those skills required for careers in a range of industries, such as chemical research techniques and how to apply them. Importance of protecting intellectual property and commercializing new inventions.
Course Outline	Module 1: Concepts of sustainable and green chemistry: Concept of sustainable and green chemistry: 12 principles of green chemistry: how to apply them in chemical synthesis: difference between sustainable and green chemistry. Module 2: Principles and systems thinking in green and sustainable Chemistry: Holistic-thinking: control of environmental impact of chemicals: alternative reaction media for green and sustainable chemistry: catalysis for green and sustainable chemistry. Module 3: Applications of green and sustainable chemistry: Clean synthesis: sustainable industrial technologies and processes: renewable resources. Module 4:Resources, recycling and circular economy: insights into the availability and specifics of mineral, biological and fossil resources: challenges of current and future use of these resources, their recycling and the establishment of a circular economy with respect to sustainability. Module 5: Case study for green and sustainable Chemistry: Intellectual Property Rights and Finance: Greener Products and Legislation including circular economy law and science.
Learning Outcome	After completion of the course, students will be able to: 1.learn about basic concept of Green and sustainable chemistry. 2.demonstrate knowledge about Green and sustainable chemistry. 3.apply the concept to differentiate and critically evaluate the importance of heterogeneous catalysis to green chemistry. 4.apply the concept of green and sustainable chemistry in real life applications and find relevance in modern industries.
Assessment Method	Class test, assignment & quiz (20%), Mid sem examination (30%), End sem examination (50%).

Suggested Readings:

Text Books:

Green Chemistry Metrics: Measuring and Monitoring Sustainable Processes A. Lapkin and D.

Constable, 2008.

Handbook of Green Chemistry, Green Processes, Designing Safer Chemicals P. Anastas and P.

Trevorrow, 2013.

Green Chemistry: An Introductory Text M. Lancaster, Royal Society of Chemistry, 3^rd Edition, 2016, e-

pub Print.

Reference Books:

Sustainable Catalysis (Green Chemistry Series) M. North, J.H. Clark, 2015.
Alternative Energy Sources for Green Chemistry (Green Chemistry Series) G. Stefanifis, A. Stankiewicz,

J.H. Clark, A. de la Hoz, J. Fan, R. Mato Chain, J. Santamaria, 2016.

3.Sustainable Solvents: Perspectives from Research, Business and International Policy (Green Chemistry

Series) J. H. Clark, A. Hunt, C. Topi, G. Paggiola and J. Sherwood, 2017.

Department Electives - VI

Department Electives - VI

CH4211

Material Chemistry

Course Number	CH4211
Course Credit	L-T-P-C: 3-0-0-3
Course Title	Material Chemistry
Learning Mode	Offline
Learning Objectives	The course aims to lay a foundation for Material Chemistry. The course aims to nurture knowledge to understand fundamental concepts of Material science with their synthesis, characterization and applications.
Course Description	This course introduces basic material chemistry in the macro and nanoscale, their types, properties and their applications in daily life.
Course Outline	Module 1: Introduction to Materials Chemistry: Materials for solid state devices: Rectifiers, transistors, capacitors - IV-V compounds - low -dimensional quantum structures, optical properties, Module 2: Nonlinear Optical Materials: Nonlinear optical effects, second and third order - molecular hyperpolarisability and second order electric susceptibility - materials for second and third harmonic generation. Module 3: Polymeric Materials: Molecular shape, structure and configuration - crystallinity – stress-strain behavior - thermal behavior - polymer types and their applications - conducting and ferroelectric polymers. Module 4: Liquids Crystals: Mesmorphic behavior - thermotropic and lyotropic phases - description of ordering in liquid crystals, the director field and order parameters - nematic and semectic mesophases, smectic -nematic transition and optical properties of liquid crystals. Module 5: Materials in micro and nanoscale: Top down and bottom up approach for construction, types: metallic, semiconductor, carbonaceous etc, analytical techniques to intercept those materials.
Learning Outcome	Upon successful completion, students will have the knowledge and skills to 1. explain the fundamental principles of Material science and technology and their applications 2. apply concepts to the nano-scale and non-continuum domain. 3. identify and compare material and methods to perform a critical analysis of the research literature. 4. design processing conditions to engineer functional materials. 5. distinguish and characterize material diversity, synthesis approaches and material safety issues.
Assessment Method	20% Quiz and assignment, 30% Mid sem and 50% End semester examination.

Suggested reading

Text Books:
1. Malcolm P. Stevens, Polymer Chemistry: An Introduction, Oxford University Press, USA, 3^rd Edition,

2. Robert J. Young, and Peter A. Lovell, Introduction to Polymers, CRC Press, 3^rd Edition, 2011.
3. W.D. Callister, D. G. Rethwisch Material Science and Engineering. An Introduction, Wiley, New York

10^th Edition (2018).

Reference Books:
1. N.W. Ashcroft, N.W. Mermin, Solid State Physics, Saunders College, Philadelphia (1976).

2. Paul C. Hiemenz, and Timothy P. Lodge, Polymer Chemistry, CRC Press, 2^nd Edition, 2007.

CH4212

Organic Semiconductors: Fundamentals to Applications

Course Number	CH4212
Course Credit	L-T-P-C: 3-0-0-3
Course Title	Organic Semiconductors: Fundamentals to Applications
Learning Mode	Offline
Learning Objectives	The course aims to lay a foundation for understanding Organic Semiconductors. It seeks to nurture a comprehensive knowledge of the fundamental concepts of Organic Semiconducting Materials, including their synthesis, characterization, and applications.
Course Description	This course offers an in-depth exploration of conjugated organic oligomers, dendrimers, and polymers, focusing on their structural types, synthesis methods, properties, and applications. The curriculum is designed to provide a comprehensive understanding of these materials, which play a crucial role in modern organic electronics and biomedicals.
Course Outline	Module 1:Introduction: Description of conjugated organic oligomers, dendrimers, and polymers. Types of structural polymers; polyacetylenes, polyphenylenevinylenes, polyphenyeleneethynylenes, polyfluorenes, polythiophenes, polyphenylenes, polyanilines, water soluble polymers. Linear and cross-conjugation mateials. Module 2:Synthesis: Synthetic methods for constructing of conjugated organic oligomers and polymers. C-C, C=C, C=C and C-heteroatom coupling reactions – historical context and latest developments. Representative examples. Mechanistic description. Benzenoid polycyclic aromatic hydrocarbons (PAHs)– synthesis, functionalization approaches and applications in organic electronics devices. Module 3:Properties: Electronic structure of organic semiconductors –molecular picture of conjugated organics. Thermal stability. Electrochemistry and energy level measurements. Absorption and Luminescence-Jablonski diagram. Excited state dynamics in organic semiconductors. Fluorescence quenching. Non-linear optical properties. Module 4: Applications: Organic Light-emitting diodes (OLEDs), solar cells–device architectures, Field-effect transistors, optical chemosensors for toxic anions and metals. Photocatalysis. Types of materials, characterization and theory of operation, bioimaging and photodyanamic therapy.
Learning Outcome	Upon successful completion, students will have the knowledge and skills to: 1. Attain a comprehensive understanding of the structural diversity of conjugated organic materials. 2. Gain knowledge of various synthetic methods for constructing organic semiconducting materials. 3. Understand the detailed characterization techniques for organic semiconducting materials. 4. Comprehend the molecular design and practical applications of organic materials in organic electronics and biomedical fields.
Assessment Method	20% Quiz and assignment, 30% Midsem and 50% End semester exam

Suggested reading

Principles of fluorescence spectroscopy by J. R. Lakowich, Thierd edition.
Organic electronics mateirals and devices, S. Ogawa, Springer, 2015
Smart Electronic Materials: Fundamentals and Applications”, Singh J, 2005, Cambridge University Press

“Carbon-Rich Compounds: From Molecules to Materials” by Haley, M.M. and Tykwinski, R.R. (Ed.), Wiley.

IDE (For students of B. Tech. other than Dept. of Chemistry)

Sl. No.

Course Code

Course Name

CH2206

IDE – 1: Green Science and Technology

Course Number	CH2206
Course Credit	L-T-P-C: 3-0-0-3
Course Title	IDE – 1: Green Science and Technology
Learning Mode	Offline
Learning Objectives	The course aims to nurture knowledge to understand fundamental concepts of sustainable or green chemistry in lab and industry.
Course Description	The course applies basic chemical principles to food systems and practical applications. Chemical/biochemical reactions of carbohydrates, lipids, proteins, and other constituents in fresh and processed foods are discussed with respect to food quality. Chemical processes that affect color, flavor, texture, nutrition, and safety of food. Important aspects of food and beverage industry including packaging are discussed.
Course Outline	Module 1:Principles and Concepts of Green Chemistry: Definition and twelve fundamental principles. Module 2:Waste: production, problems and prevention, sources of waste, cost of waste, waste minimization technique, waste treatment and recycling. Module 3:Greener reactions- catalysis and solvents: Classification of catalysts, heterogeneous catalysts heterogeneous catalysis, biocatalysis. Safer solvents, green solvents, water as solvents, solvent free conditions, ionic liquids, super critical solvents, fluorous biphase solvents Module 4: Alternative Energy Source: Energy efficient design, photochemical reactions, microwave assisted reactions, sonochemistry and electrochemistry. Module 5:Industrial Case Studies: Greening of acetic acid manufacture, Leather manufacture (tanning, fatliquoring), green dyeing, polymer, ecofriendly pesticides, paper and pulp industry, pharmaceutical industry. An integrated approach to green chemical industry.
Learning Outcome	Upon successful completion, students will have the knowledge and skills to: 1. Understand different parameters of green chemistry and sustainability. 2. Understand the basis how to designer greener reactions and processes. 3. Understand the basis of greener products/chemicals and materials. 4. Understand the application of green chemistry principles to buid safer industries.
Assessment Method	20% Quiz and assignment, 30% Midsem and 50% End semester exam

Textbooks:

K. Ahluwalia, Green Chemistry: Environmentally Benign Reactions, Ane Books India, New Delhi, 2006.
M. Srivastava, R. Sanghi, Chemistry for Green Environment, Narosa, New Delhi, 2005.

Reference:

T. Anastas and J.C. Warner, Green Chemistry, Theory and Practice Oxford, 2000. M. Doble and A. K. Kruthiventi, Green Chemistry and Engineering, Academic Press, Amsterdam, 2007.
Mike Lancaster, Green Chemistry: An Introductory Text, Royal Society of Chemistry, 2002.

R.E. Sanders, Chemical Process Safety: Learning from Case Histories, Butterworth Heinemann, Boston, 1999.

CH3106

IDE – II : Synthesis of Industrially Important Inorganic Molecules

Course Number	CH3106
Course Credit	L-T-P-C: 3-0-0-3
Course Title	IDE – II : Synthesis of Industrially Important Inorganic Molecules
Learning Mode	Offline
Learning Objectives	Impart concept on various synthesis of inorganic molecules which are industrially significant and understanding of different selected synthetic methods and their applications.
Course Description	This course gives an overview to synthetic inorganic methods and their application for the inorganic compounds. Important synthetic techniques are reviewed. The course gives an overview of important use of selected synthetic methods and a short introduction to their basic theory.
Course Outline	Module 1: Modern methods applied in the synthesis of inorganic, organometallic and polymer materials. Module 2: Handling of air and moisture sensitive compounds, dry box, glove bag, Schlenk line and vacuum line techniques. Module 3: Methods of purification of and handling of reactive industrial gases. Methods of purification of inorganic compounds and crystallization of solids for X-ray analysis. Module 4: General strategies, brief outline of theory and methodology used for the synthesis of inorganic/organometallic molecules to materials including macromolecules. Emphasis will be placed how to adopt appropriate synthetic routes to control shape and size of the final product, ranging from amorphous materials, porous solids, thin films, large single crystals, and special forms of nanomaterials. Module 5: A few examples of detailed synthesis will be highlighted in each category of materials.
Learning Outcome	Student would be able to 1.Get knowledge on synthetic methods, including sensitive methods. 2.Explain the theoretical principles and important applications of classical methods and the theoretical principles of selected methods. 3.Explain the theoretical theory and methodology of inorganic molecules/materials. 4. Suggest a suitable methodology for a specific purpose, and evaluate the synthesis using industrial important molecules and materials.
Assessment Method	Class test, assignment & quiz (20%), Mid Sem examination (30%), End Sem examination (50%).

CH4111

IDE – III : Analytical Chemistry

Course Number	CH4111
Course Credit	L-T-P-C: 3-0-0-3
Course Title	IDE – III : Analytical Chemistry
Learning Mode	Offline
Learning Objectives	Impart concept on various analytical methods and their applications in compound analysis, understanding of different selected analytical methods and their applications.
Course Description	This course gives an overview to analytical chemistry, analytical methods and their application for the detection of inorganic and organic compounds. Important analytical techniques are reviewed. The course gives an overview of important use of selected analytical methods and a short introduction to their basic theory.
Course Outline	Module 1: Introductory Topics: Development of Analytical Chemistry, Analytical Terms, Precision and Accuracy, Figures of Merit. Measurement Fundamentals: Signal-to-Noise Ratio, Origin of Instrument Noise, Quantifying Measurements and Extracting Information. Module 2: Atomic Spectroscopy: Principles, Flame Emission Spectroscopy, Atomic Absorption Spectroscopy, X-Ray Fluorescence. Module 3: Introduction to Chromatographic Separations: Classification, Chromatographic Parameters, Resolution, Band Broadening. Liquid Chromatography: HPLC Instrumentation, Adsorption Chromatography, Partition Chromatography. Gas Chromatography: Basic Description, Classification of GC Methods, Stationary Phase, Carrier Gas, Detectors. Module 4: Thermal & surface techniques: TGA, DSC, XPS, SEM, TEM.
Learning Outcome	Student would be able to 1. get knowledge on analytical chemistry, including basic analytical methods. 2. explain the theoretical principles and important applications of classical analytical methods Explain the theoretical principles of selected instrumental methods. 3. explain the theoretical principles of separation techniques in chromatography, and typical applications of chromatographic techniques. 4. suggest a suitable analytical method for a specific purpose, and evaluate the important sources of interferences and errors, and also suggest alternative analytical methods for quality assurance.
Assessment Method	Class test, assignment & quiz (20%), Mid sem examination (30%), End sem examination (50%).

Suggested Readings:

Text Books:

Skoog and Leary, "Principles of Instrumental Analysis"; 7^th Edition, 2020.

2. Douglas A. Skoog, Donald M. West, F. James Holler, Stanley R. Crouch, “Analytical Chemistry an Introduction”; 9^th edition, 2014

Artificial Intelligence and Data Science

Program Learning Objectives:	Program Learning Outcomes (PLO):
Program Goal 1: Fundamental Understanding: Establish a robust foundation in Artificial Intelligence (AI) and Data Science (DS) principles, theories, and methodologies.	Program Learning Outcome 1 (PLO-1): Students will acquire a deep understanding of the core concepts, algorithms, and tools used in AI, machine learning, deep learning, and data science. Program Learning Outcome 2 (PLO-2): Students will develop the ability to analyze and interpret complex data, using statistical and computational techniques to extract meaningful insights.
Program Goal 2: Basic Training for Research and Innovation: To equip students with the skills necessary to conduct cutting-edge research and innovate in the fields of AI and Data Science.	Program Learning Outcome 3 (PLO-3): Students will be able to innovate by developing new machine learning/ deep learning models, and systems in AI and DS, contributing to advancements in the field.
Program Goal 3: Technical Skill Proficiency: To enhance technical skills for developing AI and data-driven solutions for industry and academia.	Program Learning Outcome 4 (PLO-4): Students will demonstrate proficiency in programming, data management, and the use of AI and DS tools and frameworks in various fields including computer vision, natural language processing. Program Learning Outcome 5 (PLO-5): Students will be able to design and implement AI and DS solutions that are efficient, scalable, and reliable.
Program Goal 4: Communication and Collaboration: To develop communication and teamwork skills essential for professional success in AI and DS.	Program Learning Outcome 6 (PLO-6): Students will learn to effectively communicate AI and DS concepts, findings, and solutions to both technical and non-technical audiences.
Program Goal 5: Ethics and Social Responsibility: To understand the ethical, social, and environmental implications of AI and Data Science.	Program Learning Outcome 7 (PLO-7): Students will develop an awareness of ethical issues in AI and DS, such as data privacy, algorithmic bias, and the societal impacts of AI technologies. Program Learning Outcome 8 (PLO-8): Students will be able to apply ethical principles and responsible practices in the development and deployment of AI and DS solutions.

Sl. No.

Subject Code

SEMESTER I

MA1101

Course Number	MA1101
Course Credit (L-T-P-C)	3-1-0-4
Course Title	Calculus and Linear Algebra
Learning Mode	Lectures and Tutorials
Learning Objectives	To provide the essential knowledge of basic tools of Differential Calculus, Integral Calculus, Vector spaces and Matrix Algebra.
Course Description	This course provides a foundation for Calculus and Linear Algebra. Topics related to properties of single and two variable functions along with their applications will be discussed. In addition fundamentals of linear algebra and matrix theory with applications will also be discussed.
Course Content	Differential Calculus (12 Lectures): Limit and continuity of one variable function (including ε-δ definition). Limit, continuity and differentiability of functions of two variables, Tangent plane and normal, Change of variables, chain rule, Jacobians, Taylor’s Theorem for two variables, Extrema of functions of two or more variables, Lagrange’s method of undetermined multipliers. Integral Calculus (10 Lectures): Riemann integral for one variable functions, Double and Triple integrals, Change of order of integration. Change of variables, Applications of Multiple integrals such as surface area and volume. Vector Spaces (12 Lectures): Vector spaces (over the field of real numbers), subspaces, spanning set, linear independence, basis and dimension. Linear transformations, range and null space, rank-nullity theorem, matrix of a linear transformation. Matrix Algebra (8 Lectures): Elementary operations and their use in getting the rank, inverse of a matrix and solution of linear simultaneous equations, Orthogonal, symmetric, skew-symmetric, Hermitian, skew-Hermitian, normal and unitary matrices and their elementary properties, Eigenvalues and Eigenvectors of a matrix, Cayley-Hamilton theorem, Diagonalization of a matrix.
Learning Outcome	Students completing this course will be able to: 1. Understand various properties of functions such as limit, continuity and differentiability. 2. Learn about integrations in various dimension and their applications. 3. learn about the concept of basis and dimension of a vector space. 4. define Linear Transformations and compute the domain, range, kernel, rank, and nullity of a linear transformation. 5. compute the inverse of an invertible matrix. 6. solve the system of linear equations. 7. Apply linear algebra concepts to model, solve, and analyze real-world problems.
Assessment Method	Quiz /Assignment/ MSE / ESE

Textbooks:

Thomas, G. B., Hass, J., Heil, C. and Weir M. D., “Thomas’ Calculus”, 14th Ed., Pearson Education, 2018
Kreyszig, E., “Advanced Engineering Mathematics”, 10th Ed., Wiley India Pvt. Ltd, 2015

Reference Books:

Jain, R. K. and Iyenger, S. R. K., “Advanced Engineering Mathematics”, 5^th, Narosa Publishing House, 2017
Axler, S., “Linear Algebra Done Right”, 3rd Ed., Springer Nature, 2015

Strang, G., “Linear Algebra and Its Applications” 4th Ed., Cengage India Private Limited, 2005

4.0

CS1101

Course Number	CS1101
Course Credit (L-T-P-C)	3-0-3-4.5
Course Title	Foundations of Programming
Learning Mode	Offline
Learning Objectives	· To understand the fundamental concepts of programming · To develop the basic problem-solving skills by designing algorithms and implementing them. · To learn about various data types, control statements, functions, arrays, pointers, and file handling. · To achieve proficiency in debugging and testing a C program
Course Description	This introductory course provides a solid foundation in programming principles and techniques. Designed for students with little to no prior programming experience, it covers fundamental concepts such as variables, data types, control structures, functions, and basic data structures. Students will learn to write, debug, and execute programs using a high-level programming language. Emphasis is placed on developing problem-solving skills, logical thinking, and the ability to write clear and efficient code. By the end of the course, students will be equipped with the essential skills needed to pursue more advanced studies in computer science and software development.
Course Outline	Introduction and Programming basics, Expressions Control and Iterative statements, Functions, Arrays, Recursion vs. Iteration Pointers, 2D-Array with pointers, Structures, String, Dynamic memory allocation, File handling, Contemporary programming languages, and applications Practical component: Lab to be conducted on a 3-hour slot weekly. It will be conducted with the theory course so the topics for problems given in the lab are already initiated in the theory class.
Learning Outcome	· Understanding of Basic Syntax and Structure in C language · Proficiency in Data Types, Operators, and Control Structures · Function Implementation and learn to use them appropriately · Efficient Use of Arrays and Strings · Pointer Utilization · Ability to perform dynamic memory allocation and deallocation using malloc (), calloc (), realloc (), and free () functions. · Structured data management with structures and unions · Exposure of file Handling · Learning debugging and error Handling
Assessment Method	Internal (Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Reading

Knuth, Donald E. The art of computer programming, volume 4A: combinatorial algorithms, part 1. Pearson Education India, 2011.
P.J. Deitel and H.M. Deitel, C How To Program, Pearson Education (7th Edition)
Brian W. Kernighan and Dennis M. Ritchie, The C Programming Language, Prentice−Hall
A. Kelley and I. Pohl, A Book on C, Pearson Education (4th Edition)

K. N. King, C PROGRAMMING A Modern Approach, W. W. Norton & Company

4.5

PH1101/PH1201

Course Number	PH1101/PH1201
Course Credit (L-T-P-C)	3-1-3-5.5
Course Title	Physics
Learning Mode	Lectures and Tutorials
Learning Objectives	Complies with Program Goals 1 and 2
Course Description	This course deals with fundamentals in Classical mechanics, Waves and Oscillations and Quantum Mechanics. As a prerequisite, the mathematical preliminaries such as coordinate systems, vector calculus etc will be discussed in the beginning.
Course Outline	Orthogonal coordinate systems (Plane polar, Spherical, Cylindrical), concept of generalised coordinates, generalised velocity and phase space for a mechanical system, Introduction to vector operators, Gradient, divergence, curl and Laplacian in different co-ordinate systems. Central force problem and its applications. Rigid body rotation, vector nature of angular velocity, Finding the principal axes, Euler's equations; Gyroscopic motion and its application; Accelerated frame of reference, Fictitious forces. Potential energy and concept of equilibrium, Lennard-Jones and double-well potentials, Small oscillations, Harmonic oscillator, damped and forced oscillations, resonance and its different examples, oscillator states in phase space, coupled oscillations, normal modes, longitudinal and transverse waves, wave equation, plane waves, examples two- and three-dimensional waves. Michelson-Morley experiment, Lorentz transformation, Postulates of special theory of relativity, Time dilation and length contraction, Applications of special theory of relativity.
Learning Outcome	Complies with PLO 1a, 2a, 3a
Assessment Method	Quiz, Assignments and Exams

Suggested Readings:

Textbooks:

Engineering Mechanics, M. K. Harbola, 2^nd ed., Cengage, 2012
D. Kleppner and R. J. Kolenkow, An introduction to Mechanics, Tata McGraw-Hill, New Delhi, 2000.
I. G. Main, Oscillations and Waves
H. G. Pain, The Physics of Vibrations and Waves, 1968
Frank S. Crawford, Berkeley Physics Course Vol 3: Waves and Oscillations, McGraw Hill, 1966.

References:

R. P. Feynman, R. B. Leighton and M. Sands, The Feynman Lecture in Physics, Vol I, Narosa Publishing House, New Delhi, 2009.
David Morin, Introduction to Classical Mechanics, Cambridge University Press, NY, 2007.

3. P. C. Deshmukh, Foundations of Classical Mechanics, Cambridge University Press, 2019

5.5

CE1101/CE1201

Course code	CE1101/CE1201
Course Credit (L-T-P-C)	1-0-3-2.5
Course Title	Engineering Graphics
Learning Mode	Lectures and Practical
Learning Objectives	Complies with PLO-1a 1. The course on engineering drawing is designed to introduce the fundamentals of technical drawing as an important form of conveying information. 2. Apply principles of engineering visualization and projection theory to prepare engineering drawings, using conventional and modern drawing tools. 3. Practice drawing orthographic projections, isometric views, and sectional views, of simple and combined solids in different orientations.
Course Description	This course will introduce drawing as a tool to represent a complex three-dimensional object on two-dimensional paper through methods of projections. The course explains the use of different drafting tools and the importance of conventions for uniformity and standardization of the interpretation of the drawings.
Course Outline	Fundamental of engineering drawing, line types, dimensioning, and scales. Conic sections: ellipse, parabola, hyperbola; cycloidal curves. Principle of projection, method of projection, orthographic projection, plane of projection, first angle of projection, Projection of points, lines, planes and solids. Section of solids: Sectional views of simple solids- prism, pyramid, cylinder, cone, sphere; the true shape of the section. Methods of development, development of surfaces. Isometric projections: construction of isometric view of solids and combination of solids from orthographic projections. Introduction to AutoCad and solving isometric problems.
Learning Outcome	After attending this course, the following outcomes are expected: a) The student will understand the basic concepts of engineering drawing. b) The student will be able to use basic drafting tools, drawing instruments, and sheets. c) The student will be able to represent three-dimensional simple and combined solid objects on two-dimensional paper. d) The student will be able to visualize and interpret the orientation of simple and combine solid objects.
Assessment Method	Laboratory Assignments (30%), Mid-semester examination (25%) and End-semester examination (45%).

Suggested Readings:

Textbooks:

D. Bhatt, Engineering Drawing, Charotar Publishing House.
Agrawal & Agrawal, Engineering Drawing, McGraw Hill.
Jolhe, Engineering Drawing.

References:

Engineering Drawing and Design by David Madsen

2.5

EE1101/EE1201

Course Number	EE1101/EE1201
Course Credit (L-T-P-C)	3-0-3-4.5
Course Title	Electrical Sciences
Learning Mode	Lectures and Experiments
Learning Objectives	Complies with Program goals 1, 2 and 3
Course Description	The course is designed to meet the requirements of all B. Tech programmes. The course aims at giving an overview of the entire electrical engineering domain from the concepts of circuits, devices, digital systems and magnetic circuits.
Course Outline	Circuit Analysis Techniques, Circuit elements, Simple RL and RC Circuits, Kirchoff’s law, Nodal Analysis, Mesh Analysis, Linearity and Superposition, Source Transformations, Thevenin’s and Norton’s Theorems, Time Domain Response of RC, RL and RLC circuits, Sinusoidal Forcing Function, Phasor Relationship for R, L and C, Impedance and Admittance, Instantaneous power, Real, reactive power and power factor. Semiconductor Diode, Zener Diode, Rectifier Circuits, Clipper, Clamper, UJT, Bipolar Junction Transistors, MOSFET, Transistor Biasing, Transistor Small Signal Analysis, Transistor Amplifier and their types, Operational Amplifiers, Op-amp Equivalent Circuit, Practical Op-amp Circuits, Power Opamp, DC Offset, Constant Gain Multiplier, Voltage Summing, Voltage Buffer, Controlled Sources, Instrumentation Amplifier, Active Filters and Oscillators. Number Systems, Logic Gates, Boolean Theorem, Algebraic Simplification, K-map, Combinatorial Circuits, Encoder, Decoder, Combinatorial Circuit Design, Introduction to Sequential Circuits. Magnetic Circuits, Mutually Coupled Circuits, Transformers, Equivalent Circuit and Performance, Analysis of Three-Phase Circuits, Power measurement in three phase system, Electromechanical Energy Conversion, Introduction to Rotating Machines (DC and AC Machines). Laboratory: Experiments to verify Circuit Theorems; Experiments using diodes and bipolar junction transistor (BJT): design and analysis of half -wave and full-wave rectifiers, clipping and clamping circuits and Zener diode characteristics and its regulators, BJT characteristics (CE, CB and CC) and BJT amplifiers; Experiment on MOSFET characteristics (CS, CG, and CD), parameter extraction and amplifier; Experiments using operational amplifiers (op-amps): summing amplifier, comparator, precision rectifier, Astable and Monostable Multivibrators and oscillators; Experiments using logic gates: combinational circuits such as staircase switch, majority detector, equality detector, multiplexer and demultiplexer; Experiments using flip-flops: sequential circuits such as non-overlapping pulse generator, ripple counter, synchronous counter, pulse counter and numerical display; Power Measurement by two Wattmeter method; Open and Short Circuit Tests of Transformer.
Learning Outcomes	Complies with PLO 1a, 2a and 3a
Assessment Method	Quiz, Assignments and Exams

Texts/References:

K. Alexander, M. N. O. Sadiku, Fundamentals of Electric Circuits, 3rd Edition, McGraw-Hill, 2008.
H. Hayt and J. E. Kemmerly, Engineering Circuit Analysis, McGraw-Hill, 1993.
L. Boylestad and L. Nashelsky, Electronic Devices and Circuit Theory, 6th Edition, PHI, 2001.
M. Mano, M. D. Ciletti, Digital Design, 4th Edition, Pearson Education, 2008.
Floyd, Jain, Digital Fundamentals, 8th Edition, Pearson.
David V. Kerns, Jr. J. David Irwin, Essentials of Electrical and Computer Engineering, Pearson, 2004.
Donald A Neamen, Electronic Circuits; analysis and Design, 3rd Edition, Tata McGraw-Hill Publishing Company Limited.
Adel S. Sedra, Kenneth C. Smith, Microelectronic Circuits, 5th Edition, Oxford University Press, 2004.
E. Fitzgerald, C. Kingsley Jr., S. D. Umans, Electric Machinery, 6th Edition, Tata McGraw-Hill, 2003.
P. Kothari, I. J. Nagrath, Electric Machines, 3rd Edition, McGraw-Hill, 2004.

Del Toro, Vincent. "Principles of electrical engineering." (No Title) (1972).

4.5

HS1101

Course Number	HS1101
Course Credit (L-T-P-C)	2-0-1-2.5
Course Title	English for Professionals
Learning Mode	Offline
Learning Objectives	This course aims to help the students (a) attain proficiency in written English through the construction of grammatically correct sentences, utilization of subject-verb agreement principles, mastery of various tenses, and effective deployment of active and passive voice to ensure coherent and impactful written expression; (b) enhance oral communication skills by honing public speaking abilities, acquiring strategies to deliver persuasive presentations, and cultivating a polished telephone etiquette, enabling confident and articulate verbal communication; (c) foster active listening capabilities by recognizing different types of listening, and applying proven methods and strategies to improve active listening skills; (d) strengthen reading skills, including comprehension, interpretation, and critical analysis, to grasp diverse written materials and derive meaning from various types of texts encountered in academic and professional contexts; (e) develop adeptness in written communication for business purposes, encompassing the understanding of essential writing elements, mastery of appropriate writing styles thereby enhancing prospects for successful job interviews and subsequent professional endeavors.
Course Description	This academic course on communication skills aims to equip students with fluency in spoken and written English for effective expression in both academic and professional settings. By focusing on essential communication principles and providing practical experiences, students develop clarity, precision, and confidence in their communication. Through interactive discussions and exercises, students enhance critical thinking and adaptability in diverse contexts. Upon completion, students will excel in formal presentations, group discussions, and persuasive writing, enhancing their overall communication proficiency.
Course Outline	Unit I: Introduction to professional communication – LSRW - Phonetics and phonology Sounds in English Language – production and articulation – rhythm and intonation – connected speech - Basic Grammar and Advanced Vocabulary Sounds in English Language – production and articulation – rhythm and intonation – connected speech – persuading and negotiating – brevity and clarity in language. Unit II: Characteristics of Technical Communication: Types of communication and forms of communication - Formal and informal communication Verbal and non-Verbal Communication – Communication barriers and remedies Intercultural communication – neutral language Unit III: Comprehension and Composition – summarization, precis writing Business Letter Writing CV/ Resume – E-Communication Unit IV: Statement of Purpose, Writing Project Reports, Writing research proposal, writing abstracts, developing presentations, interviews – combating nervousness Tutorial: Listening Exercises, Speaking Practice (GDs, and Presentations), and Writing Practice Learning Outcome · Attain proficiency in written English, enabling the construction of grammatically correct sentences and coherent written expression through the use of appropriate grammar, tenses, and voice. · Enhance oral communication skills, including public speaking, persuasive presentation, and polished telephone etiquette, fostering confident and articulate verbal expression. · Cultivate active listening abilities, recognizing different listening types, overcoming obstacles, and employing strategies for attentive and effective communication. · Develop proficient written communication skills for business purposes, demonstrating understanding of essential writing elements, appropriate styles, and the creation of reports, notices, agendas, and minutes that effectively convey information.
Assessment Method	Class test + Quiz = 20%; Mid-semester = 25%; Assignment = 15%; End semester = 40%

Suggested Reading :

Balzotti, Jon. Technical Communication: A Design-Centric Approach. Routledge, 2022.
Kaul, Asha, Business Communication. PHI Learning Pvt. Ltd. 2009
Laplante, Phillip A. Technical Writing: A Practical Guide for Engineers, Scientists, and Nontechnical Professionals. CRC Press, 2018.
Lawson, Celeste, et al. Communication Skills for Business Professionals, Second Edition. CUP, 2019.
Sharon Gerson and Steven Gerson. Technical Writing: Process and Product (8th Edition), London: Longman, 2013
Rentz, Kathryn, Marie E. Flatley & Paula Lentz. Lesikar’s Business Communication Connecting in a Digital world, McGraw-Hill, Irwin.2012
Allan & Barbara Pease. The Definitive Book of Body Language, New York, Bantam,2004
Jones, Daniel. The Pronunciation of English, New Delhi, Universal Book Stall.2010
Savage, Alice. Effective Academic Writing. OUP. 2014

Swan and Alter. Oxford English grammar course. OUP. 201

2.5

TOTAL

23.5

Probability Theory and Ordinary Differential Equations

Sl. No.

Subject Code

SEMESTER II

MA1201

Course Number	MA1201
Course Credit (L-T-P-C)	3-1-0-4
Course Title	Probability Theory and Ordinary Differential Equations
Learning Mode	Lectures and Tutorials
Learning Objectives	To introduce the basic concepts of probability, statistics, and Differential equations.
Course Description	This course aims to cover basic concepts of probability, statistics and ordinary differential equations. In particular, popular distributions, random sampling, various estimators and hypothesis testing will be discussed. Students will also get exposure to the linear ordinary differential equations and their solution techniques.
Course Content	Probability (12 Lectures): Random variables and their probability distributions, Cumulative distribution functions, Expectation and Variance, probability inequalities, Binomial, Poisson, Geometric, negative binomial distributions, Uniform, Exponential, beta, Gamma, Normal and lognormal distributions. Statistics (10 Lectures): Random sampling, sampling distributions, Parameter estimation, Point estimation, unbiased estimators, maximum likelihood estimation, Confidence intervals for normal mean, Simple and composite hypothesis, Type I and Type II errors, Hypothesis testing for normal mean. Ordinary Differential Equations (20 Lectures): First order ordinary differential equations, exactness and integrating factors, Picard's iteration, Ordinary linear differential equations of n-th order, solutions of homogeneous and non-homogeneous equations (Method of variation of parameters). Systems of ordinary differential equations, Power series methods for solutions of ordinary differential equations. Legendre equation and Legendre polynomials, Bessel equation and Bessel functions.
Learning Outcome	Students will get exposure and understanding of: 1. Random variables and their probability distributions 2. Understand popular distributions and their properties 3. Sampling, estimation and hypothesis testing 4. Solution of ordinary differential equations 5. Solution of system of ordinary differential equations 6. Special functions arising as power series solutions of ordinary differential equations
Assessment Method	Quiz /Assignment/ MSE / ESE

Text Books:

Hogg, R. V., Mckean, J. and Craig, A. T., “Introduction to Mathematical Statistics”, 8th Ed., Pearson Education India, 2021
M. Ross “An introduction to Probability Models, Academic Press INC, 11th edition.
Miller, I. and Miller, M., “John E. Freund's Mathematical Statistics with Applications”, 8th Ed., Pearson Education India, 2013
L. Ross, Differential equations, 3rd Edition, Wiley, 1984

W. E. Boyce and R. C. Di Prima, Elementary Differential equations and Boundary Value Problems, 7th Edition, Wiley, 2001.

CS1201

Course Number	CS1201
Course Credit (L-T-P-C)	3-0-3-4.5
Course Title	Data Structure
Learning Mode	Offline
Learning Objectives	· Understand the principles and concepts of data structures and their importance in computer science. · Learn to implement various data structures and understand how different algorithms works. · Develop problem-solving skills by applying appropriate data structures to different computational problems. · Achieving proficiency in designing efficient algorithms.
Course Description	This course provides a comprehensive study of data structures and their applications in computer science. It focuses on the implementation, analysis, and use of various data structures such as arrays, linked lists, stacks, queues, trees, and graphs. Through theoretical concepts and practical programming exercises, this course aims to develop problem-solving and algorithmic thinking skills essential for advanced topics in computer science and software development.
Course Outline	· Introduction to Data Structure, · Time and space requirements, Asymptotic notations · Abstraction and Abstract data types · Linear Data Structure: stack, queue, list, and linked structure · Unfolding the recursion · Tree, Binary Tree, traversal · Search and Sorting, · Graph, traversal, MST, Shortest distance · Balanced Tree Practical component: Lab to be conducted on a 3-hour slot weekly. It will be conducted with the theory course so the topics for problems given in the lab are already initiated in the theory class.
Learning Outcome	· Understand Data Structure Fundamentals · Implement Basic Data Structures using a programming language · Analyse and Apply Algorithms · Design and Analyse Tree Structures · Understand the usage of graph and its related algorithms · Design and Implement Sorting and Searching Algorithms · Debug and Optimize Code
Assessment Method	Internal (Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Reading:

Alfred V. Aho, John E. Hopcroft, Jeffrey D. Ullman, Data Structures and Algorithms, Published by Addison-Wesley
Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest and Clifford Stein., Introduction to Algorithms,
Mark Allen Weiss, Data Structures and Algorithm Analysis in Java
Robert Sedgewick and Kevin Wayne, Algorithms

Narasimha Karumanchi, Data Structures and Algorithms Made Easy

4.5

CH1201/CH1101

Course Number	CH1201/CH1101
Course Credit	3-1-3-5.5
Course Title	Chemistry
Learning Mode	Offline
Learning Objectives	The course aims to lay a foundation for all three branches of chemistry, viz. Organic, Inorganic, and Physical Chemistry. The course aims to nurture knowledge to appreciate the interface of chemistry with other science and Engineering branches by combining theoretical concepts and experimental studies.
Course Description	This course introduces basic organic chemistry, inorganic chemistry and Physical chemistry to understand fundamental laws that governs various reactions, reaction rates, equilibrium, and their applications in daily life through relevant experimentation.
Course Outline	Module 1: Thermodynamics: The fundamental definition and concept, the zeroth and first law. Work, heat, energy and enthalpies. Second law: entropy, free energy and chemical potential. Change of Phase. Third law. Chemical equilibrium. Conductance of solutions, Kohlrausch’s law-ionic mobilities, Basic Electrochemistry. Module 2: Coordination chemistry: Crystal field theory and consequences color, magnetism, J.T distortion. Bioinorganic chemistry: Trace elements in biology, heme and non-heme oxygen carriers, haemoglobin and myoglobin; Organometallic chemistry. Module 3: Stereo and regio-chemistry of organic compounds, conformational analysis and conformers, Molecules devoid of point chirality (allenes and biphenyls); Significance of chirality in living systems, organic photochemistry, Modern techniques in structural elucidation of compounds (UV–Vis, IR, NMR). Module 4 (Lab Component): Experiments based on redox and complexometric titrations; synthesis and characterization of inorganic complexes and nanomaterials; synthesis and characterization of organic compounds; experiments based on chromatography; experiments based on pH and conductivity measurement; experiment related to chemical kinetics and spectroscopy.
Learning Outcome	Students will be able to 1. identify organic and inorganic molecules and relate them to daily life applications through experiments. 2. understand important hypothesis, laws and their derivations to intercept physical phenomenon of chemical reactions and apply them in hands-on experiments. 3. understand the importance of organic and inorganic molecules in our body and environment. 4. know important analytical techniques to intercept chemical entity. 5. approach organic and inorganic synthesis as a skillset for drug manufacturing, calculate limiting reagents and yields, use various analytical tools to characterize organic compounds, interpret and ascertain data related to Physical chemistry aspects and know laboratory safety measures, risk factors and scientific report writing skills.
Assessment Method	Theory: 20% Quiz and assignment, 30% Mid sem and 50% End semester exams for theory part (4 credits). Lab: 60% lab report, lab performance and assignment, 20% End semester exam for practical part, 20% viva/quiz (1.5 credits). Overall Weightage: Theory (70%), Lab (30%).

Suggested Reading:

Text books:

Vogel's Qualitative Inorganic Analysis, G. Svehla, 7^th Edition, Revised, Prentice Hall, 1996.
J. Elias, S. S. Manoharan and H. Raj, "Experiments in General Chemistry", Universities Press (India) Pvt. Ltd., 1997.

A. J. Elias, A Collection of Interesting General Chemistry Experiments, revised edition, Universities Press (India) Pvt. Ltd., 2007.

Albert Cotton, G. Wilkinson, C. A. Murillo, M. Bochmann, Advanced Inorganic Chemistry - 6^th Edition New Delhi: Wiley India, 2008.
Mukkanti, Practical Engineering Chemistry, B.S. Publications, Hyderabad, 2009.
Shriver and Atkins inorganic chemistry / Peter Atkins, Tina Overton, Jonathan Rourke, Mark Weller, Fraser Armstrong-5^th Edition – Oxford: UOP. 2012.
Atkins’ Physical Chemistry, Peter Atkins, Julio de Paula, James Keeler, Oxford University Press, 11^th Edition 2017.
L. Kapoor, A Textbook of Physical Chemistry, Vol: 1, 2 (6^th Edition, 2019), Vol: 3 (5^th Edition, 2020) MaGraw Hill.

G. R. Chatwal, S. K. Anand, Instrumental Methods of Chemical Analysis, 5^th Edition, Himalaya Publications, 2023.

5.5

ME1201/ME1101

Course Number	ME1201/ME1101
Course Credit (L-T-P-C)	0-0-3-1.5
Course Title	Mechanical Fabrication
Learning Mode	Fabrication work – hands on fabrication work in Workshop
Learning Objectives	Complies with PLOs 3-4. · This course aims to develop the concepts and skills of various mechanical fabrication methods. · Fabrication of metallic and non-metallic components, fabrication using bulk and sheet metals, subtractive and additive manufacturing methods, and assemble the parts
Course Description	This course is designed to fulfil the need of hand on experience about various approaches (conventional and CNC, subtractive and additive) of mechanical fabrication approaches. Prerequisite: NIL
Course Outline	The jobs for various shops should be planned such that they are the parts of an assembled item. The student groups will fabricate different parts in various shops which will involve some amount of their creativeness/input particularly in design and/or planning. Various components as required for the assembled part can be made using the following shops: Sheet Metal Working: Development, sheet cutting and fabrication of designated job using sheet metal (ferrous/nonferrous); Joining of required portions by soldering, in case part is desired to be made leak proof. Pattern Making and Foundry: Making of suitable pattern (wood); making of sand mould, melting of non-ferrous metal/alloy (Al or Al alloys), pouring, solidification. Observation/identification of various defects appeared on the component. Joining: Butt/lap/corner joint job fabrication as required of low carbon steel plates; weld quality inspection by dye-penetration test (non-destructive testing approach)of the component made. Demonstration of semi-automatic Gas Metal Arc welding (GMAW). Conventional machining: Operations on lathe and vertical milling to fabricate the required component. The fabrication of the component should cover various lathe operations like straight turning, facing, thread cutting, parting off etc., and operations using indexing mechanism on vertical milling. CNC centre: Fundamentals of CNC programming using G and M code; setting and operations of job using CNC lathe or milling, tool reference, work reference, tool offset, tool radius compensation to fabricate the component with a designed profile on Al/Al-alloy plate. 3D printing (Fused Filament Fabrication): (2 weeks) Create the model, select appropriate slicing and path for fabrication of a 3D job by layer deposition (additive manufacturing approach) using polymeric material. Demonstration on pattern fabrication using 3D printing.
Learning Outcome	· This course would enable the students to develop the concept of design, fabrication (subtractive and additive) for various engineering applications. Fabrication of components and assemble them. · The practical skill and hands on experience for various fabrication methods from bulk, sheet metal using conventional as well as CNC machines.
Assessment Method	Fabrication of components in each of the shops required for assembly of the given part; submission of reports for each shop, and quiz assessment.

Text and Reference books:

Hajra Choudhury, HazraChoudhary and Nirjhar Roy, 2007, Elements of Workshop Technology, vol. I,Mediapromoters and Publishers Pvt. Ltd.
W A J Chapman, Workshop Technology, 1998, Part -1, 1st South Asian Edition, Viva Book Pvt Ltd.
N. Rao, 2009, Manufacturing Technology, Vol.1, 3rd Ed., Tata McGraw Hill Publishing Company.

M.Adithan, B.S. Pabla, 2012, CNC machines, New Age International Publishers

1.5

ME1202/ME1102

Course Number	ME1202/ ME1102
Course Number	Engineering Mechanics
Course Credit (L-T-P-C)	3-1-0-4
Pre-requisites	Nil
Semester	Spring
Learning Mode	Lectures
Learning Objectives	Complies with PLOs 1, 4 · The objective of this first course in mechanics is to enable engineering students to analyze basic mechanics problems and apply vector-based approach to solve them.
Course Outline	1. Rigid body statics: Equivalent force system. Equations of equilibrium, Free body diagram, Reaction, Static indeterminacy. 2. Structures: 2D truss, Method of joints, Method of section. Beam, Frame, types of loading and supports, axial force, Bending moment, Shear force and Torque Diagrams for a member. 3. Friction: Dry friction (static and kinetic), wedge friction, disk friction (thrust bearing), belt friction, square threaded screw, journal bearings, Wheel friction, Rolling resistance. 4. Centroid and Moment of Inertia 5. Introduction to stress and strain: Definition of Stress, Normal and shear Stress. Relation between stress and strain, Cauchy formula. Stress in an axially loaded member and stress due to torsion in axisymmetric section
Learning Outcomes:	Following learning outcomes are expected after going through this course. · Learn and apply general mathematical and computer skills to solve basic mechanics problems. · Apply the vector-based approach to solve mechanics problems.
Assessment Method	Mid semester examination, End semester examination, Class test/Quiz, Tutorials

Reference Books:

Shames, Engineering Mechanics: Statics and dynamics, 4th Ed, PHI, 2002.
P. Beer and E. R. Johnston, Vector Mechanics for Engineers, Vol I - Statics, 3rd Ed, Tata McGraw Hill, 2000.
L. Meriam and L. G. Kraige, Engineering Mechanics, Vol I - Statics, 5th Ed, John Wiley, 2002.
P. Popov, Engineering Mechanics of Solids, 2nd Ed, PHI, 1998.

F. P. Beer and E. R. Johnston, J.T. Dewolf, and D.F. Mazurek, Mechanics of Materials, 6th Ed, McGraw Hill Education (India) Pvt. Ltd., 2012.

IK1201

Indian Knowledge System (IKS)

TOTAL

22.5

Digital Logic and Computer Organization

Sl. No.

Subject Code

SEMESTER III

CS2101

Algorithm

Course Number	CS2101
Course Credit (L-T-P-C)	3-0-3-4.5
Course Title	Algorithm
Learning Mode	Offline
Learning Objectives	This course aims to help the students (a) to understand and explain fundamental concepts of computational complexity, including time and space complexity, and analyses the efficiency of algorithms; (b) to apply various algorithm design paradigms such as divide-and-conquer, dynamic programming, greedy algorithms, and backtracking to solve computational problems; (c) to develop and implement common algorithms for tasks such as sorting, searching, and graph traversal, and utilize well-known algorithms like Dijkstra's and Kruskal's; (d) to utilize fundamental data structures, including arrays, linked lists, stacks, queues, trees, and graphs, selecting and implementing the most appropriate one for specific problems; and (e) to evaluate the performance and scalability of algorithms and data structures, conducting empirical analysis to understand their practical performance, and enhancing problem-solving skills through theoretical knowledge application in practical scenarios.
Course Description	The course introduces the basics of computational complexity analysis and various algorithm design paradigms. The goal is to provide students with solid foundations to deal with a wide variety of computational problems, and to provide a thorough knowledge of the most common algorithms and data structures.
Course Outline	Unit I Role of algorithms in computing and elementary data structures. Unit II Analysis framework: Asymptotic notations, Analysis & Master Theorem Unfolding of recursion: review of sorting and searching algorithms, Huffman Encoding, String matching, hashing, Trees, Subset sum Unit III Algorithm design paradigm: · Brute force algorithms- Exhaustive search · Greedy algorithms · Divide and conquer algorithms, Branch-and-bound · Backtracking · Dynamic programming: Matrix Chain Multiplication, 0/1 Knapsack problem Unit IV Graph based algorithm: MST, Shortest distance, colouring, Vertex cover, TSP Unit V Reducibility: P, NP, NP complete, and NP hard Unit VI Elements of Randomized and approximation Algorithms Practical component: Lab to be conducted on a 3-hour slot weekly. It will be conducted with the theory course so the topics for problems given in the lab are already initiated in the theory class.
Learning Outcome	· Describe how efficiency affects the practical usage of algorithms and data structures. · Identify different algorithmic techniques for running programs at scale. · Construct programs that apply computational concepts as a tool in other domains. · Discuss how computer science interacts with and affects the world.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Reading

H. Carmen, C. E. Leiserson, R. L. Rivest and C. Stein, Introduction to Algorithms, MIT Press, 2001.
Aho, J. E. HopcroŌ and J. D. Ullman, The Design and Analysis of Computer Algorithms, Addison-Wesley, 1974.

M. T. Goodrich and R. Tamassia, Algorithm Design: Foundations, Analysis and Internet Examples, John Wiley & Sons, 2001

4.5

CS2102

Course Number	CS2102
Course Credit (L-T-P-C)	3-0-3-4.5
Course Title	Digital Logic and Computer Organization
Learning Mode	Offline
Learning Objectives	This course targets to cover the different number systems, designing of combinational and sequential logic circuits. This course will also expose students to the basic architecture of processing, memory and i/o organization in a computer system.
Course Description	The course covers foundation of digital logic and Computer organization that including number systems, Boolean algebra, optimizing logic gates. Besides this it covers designing of different combinational and sequential circuits, computer organization
Course Outline	Number System and Codes; Combinational logic circuits: Sequential logic circuits; Finite State machines. Basic computer organization and design, Operational concepts, Instruction codes, Computer Registers, Computer Instructions Familiarization with assembly language programming; Execution of a complete instruction. Memory organization: concept of hierarchical memory organization I/O devices – Programmed Input/output -Interrupts – Direct Memory Access – Buses, I/O devices and processors. Practical component: Lab to be conducted on a 3-hour slot weekly. It will be conducted with the theory course so the topics for problems given in the lab are already initiated in the theory class.
Learning Outcome	The student will be able to: · Demonstrate an understanding of how data is represented within a computer system. · Appreciate understanding of the basic blocks, key terminology in digital logic and Computer organization · Demonstrate classic components of a computational system (i.e. input, output, memory, data path, control) and understanding their functionality.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Text Books:

Mano, M. Morris. Digital logic and computer design. Pearson Education India, 2017.
Harris, David, and Sarah Harris. Digital design and computer architecture. Morgan Kaufmann, 2010.
Moris Mano, “Computer Systems Architecture”, 4th Edition, Pearson/PHI,
Carl Hamacher, Zvonko Vranesic, Safwat Zaky, “Computer Organization”, 5th Edition, McGraw Hill.

William Stallings, “Computer Organization and Architecture”, 6th Edition, Pearson/PHI

4.5

CS2103

Artificial Intelligence Concepts

Course Number	CS2103
Course Credit (L-T-P-C)	2-0-2-3
Course Title	Artificial Intelligence Concepts
Learning Mode	Offline
Learning Objectives	This course aims to help the students (a) grasp the fundamental principles and subfields of Artificial Intelligence (AI) and Data Science.(b) Gain expertise in the stages of Data Science from data collection to model evaluation.(c) proficiency in applying supervised and unsupervised learning algorithms.(d) introduced to Deep Learning architectures and their applications.
Course Description	This course offers a comprehensive exploration of foundational principles and advanced techniques in Artificial Intelligence (AI), Data Science, Machine Learning (ML), and Deep Learning (DL). Students will delve into the ethical implications, applications, and future trends of AI, understanding its societal impacts and responsible deployment. The curriculum covers the evolution and stages of Data Science, emphasizing mastery of data collection, pre-processing, exploratory analytics, and rigorous model development and evaluation across various domains. In Machine Learning, students will gain proficiency in supervised and unsupervised learning algorithms, feature selection, dimensionality reduction, and a variety of classification and clustering techniques. Deep Learning concepts will be introduced, focusing on neural networks, Convolutional Neural Networks (CNNs) for image analysis, Recurrent Neural Networks (RNNs) for sequential data processing, attention mechanisms, and training Generative Adversarial Networks (GANs). Through theoretical lectures, practical exercises, and hands-on projects, students will acquire the skills necessary to apply these technologies effectively in solving real-world problems and advancing their careers in AI and Data Science.
Course Outline	Historical evolution of AI, Conceptualization of AI, related terms, and subfields with applications. Practical component: Lab to be conducted on a 2-hour slot weekly. It will be conducted with the theory course so the topics for problems given in the lab are already initiated in the theory class.
Learning Outcome	· Understand the basic concept of AI. · Analysis of Data using Data science and data Analytics. · Explore state-of-the-art techniques and applications in machine learning · Compare and contrast various multiple deep learning architectures
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Text Books:

Tom M. Mitchell, 2017.Machine Learning.
Andrew-ng. Lecture Series – Deep Learning.ai . (Stanford)
Relevant research articles.

Reference Books:

Grus, J., 2019. Data science from scratch: first principles with python

CS2104

Discrete Mathematics

Course Number

CS2104

Course Credit

(L-T-P-C)

3-0-0-3

Course Title

Discrete Mathematics

Learning Mode

Offline

Learning Objectives

The objective of the course is to introduce the fundamental concepts in discrete mathematics with emphasis on their applications to computer science.

Course Description

This course covers Fundamentals of logic (the laws of logic, rules of inferences, quantifiers, proofs of theorems), Fundamental principles of counting (permutations, combinations), set theory, relations and functions, graphs, shortest path and minimal spanning trees algorithms. Monoids and Groups.

Course Outline

· Logic and proofs

· Elementary set theory

· Relations and functions

· Recurrence relations

· Counting & Combinatorics

· Induction and Recursion

· Modular arithmetic

· Graph theory

· Elementary probability theory

Learning Outcomes

· Mathematical formalism of complex computer science problem and identifying their effective solutions.

· Improving critical thinking, and recognize valid, logical, mathematical arguments and construct valid arguments/proofs.

· Understanding the mathematical foundation behind cryptographic solutions in cryptology and others.

Assessment Method

Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Readings:

Discrete Mathematics and its Applications - Kenneth H. Rosen 7th Edition -Tata McGraw Hill, 2007
Elements of Discrete Mathematics, C. L Liu, McGraw-Hill Inc, 1985. Applied Combinatorics, Alan Tucker, 2007.
Concrete Mathematics, Ronald Graham, Donald Knuth, and Oren Patashnik, 2nd Edition - Pearson Education Publishers - 1996.

Combinatorics: Topics, Techniques, Algorithms by Peter J. Cameron, Cambridge University Press, 1994 (reprinted 1996).

CS2105

Optimization Techniques

Course Number	CS2105
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Optimization Techniques
Learning Mode	Offline
Learning Objectives	· To gain a thorough understanding principles of linear programming including problem formulation, geometric interpretations, and graphical solutions. · To explore advanced methods such as the Simplex algorithm, Big M method, and Revised Simplex method for optimizing linear programming problems. · To understand duality theory and sensitivity analysis in linear programming, and apply them to real-world scenarios like transportation and assignment problems. · To learn integer programming techniques like Branch and Bound and the Gomory cutting plane method for solving integer and mixed integer problems. To understand game theory concepts such as saddle points, matrix games, and strategies, and apply optimization methods to solve game-theoretic problems effectively.
Course Description	This course provides an exploration of essential methods for solving complex problems across various domains, including operations research, engineering, economics, and artificial intelligence. Beginning with foundational concepts in linear programming, students will delve into problem formulation, geometric interpretations, and graphical solutions, progressing to advanced techniques such as the Simplex algorithm, Big M method, and Revised Simplex method. Duality theory in linear programming is extensively covered, alongside integer programming techniques like Branch and Bound and the Gomory cutting plane method for both integer and mixed integer problems. The course also explores game theory applications, focusing on matrix games and two-person zero-sum games, utilizing graphical and simplex methods to derive optimal solutions. Additionally, students will gain insights into optimization techniques tailored for artificial intelligence and machine learning applications, preparing them to tackle real-world optimization challenges effectively.
Course Outline	Linear programming problem (LLP): Introduction and problem formulation, Concepts from Geometry, Geometrical aspects of LPP, Graphical solutions, Linear programming in standard form, Simplex, Big M and Two Phase Methods, Revised simplex method, Special cases of LPP. Duality theory: Dual simplex method, Sensitivity analysis of LP problems, Transportation, Assignment, and Traveling Salesman problems. Integer programming problems: Branch and bound method, Gomory cutting plane method for all integers and for mixed integer LPP. Theory of games: Saddle point, Linear programming formulation of matrix games, Two-person zero-sum games with and without saddle-points, Pure and mixed strategies, Graphical method of solution of a game, Solution of a game by simplex method. Basics of optimization techniques for artificial intelligence and machine learning
Learning Outcome	Upon successful completion of this course, students will: · Demonstrate proficiency in formulating and solving linear programming problems using advanced methods like the Simplex algorithm and its variants. · Apply duality theory and sensitivity analysis to analyze and optimize solutions in linear programming applications, including transportation and assignment problems. · Utilize integer programming techniques, such as Branch and Bound and Gomory cutting plane methods, to solve integer and mixed integer linear programming problems effectively. · Apply game theory concepts to analyze and solve matrix games using linear programming formulations, employing graphical and simplex methods for optimal strategy determination. · Apply optimization techniques relevant to artificial intelligence and machine learning applications, demonstrating the ability to optimize models
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Reading

Hamdy A. Taha, Operations Research: An Introduction, 10^th edition, PHI, New Delhi (2019).
S. Hillier, G.J. Lieberman, Introduction to Operations Research, 10^thedition, McGraw Hill (2017).
Ravindran, D.T. Phillips, J.J. Solberg, Operations Research, John Wiley and Sons, New York (2005).
S. Bazaraa, J.J. Jarvis and H.D. Sherali, Linear Programming and Network Flows, 3rd Edition, Wiley (2004).

D.G. Luenberger, Linear and Nonlinear Programming, 2nd Edition, Kluwer (2003).

HS21XX

HSS Elective - I

TOTAL

Formal Language and Automata Theory

Sl. No.

Subject Code

SEMESTER IV

CS2201

Course Number	CS2201
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Formal Language and Automata Theory
Learning Mode	Offline
Learning Objectives	This course will introduce Learners about the basic mathematical models of computation, problems that can be solved by computers and problems that are computationally hard. It also introduces basic computation models, their properties and the necessary mathematical techniques to prove more advanced attributes of these models. The learners will be able to express computer science problems as mathematical statements and formulate proofs.
Course Description	This course is designed to cover computability and computational complexity theory. Topics include regular and context-free languages, decidable and undecidable problems, reducibility, time and space measures on computation.
Course Outline	Introduction: Alphabet, languages and grammars, productions and derivation, Chomsky hierarchy of languages. Regular languages and finite automata: Regular expressions and languages, deterministic finite automata (DFA) and equivalence with regular expressions, nondeterministic finite automata (NFA) and equivalence with DFA, regular grammars and equivalence with finite automata, properties of regular languages, pumping lemma for regular languages, minimization of finite automata. Context-free languages and pushdown automata: Context-free grammars (CFG) and languages (CFL), Chomsky and Greibach normal forms, nondeterministic pushdown automata (PDA) and equivalence with CFG, parse trees, ambiguity in CFG, pumping lemma for context-free languages, deterministic pushdown automata, closure properties of CFLs. Context-sensitive languages: Context-sensitive grammars (CSG) and languages, linear bounded automata and equivalence with CSG. Turing machines: The basic model for Turing machines (TM), Turing-recognizable (recursively enumerable) and Turing-decidable (recursive) languages and their closure properties, variants of Turing machines, nondeterministic TMs and equivalence with deterministic TMs, unrestricted grammars and equivalence with Turing machines, TMs as enumerators. Undecidability: Church-Turing thesis, universal Turing machine, the universal and diagonalization languages, reduction between languages and Rice’s theorem, undecidable problems about languages; Complexity theory: time and space complexity, Classes P, NP, NP-complete.
Learning Outcomes	The student will be able to: · Gain proficiency with mathematical tools and formal methods · Understand various mathematical models of computation and formal languages · Understand Turing machines, decidable languages, and undecidable languages · Design and analyze Turing machines, their capabilities and limitations · Understand the basics of complexity theory, complexity classes and possible unsolved problems in theoretical computer science
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Reading:

J. E. Hopcroft, R. Motwani and J. D. Ullman, Introduction to Automata Theory, Languages and Computation, Pearson Education India (3rd edition).
2. K. L. P. Mishra, N. Chandrasekaran, Theory of Computer Science: Automata, Languages and Computation, PHI Learning Pvt. Ltd. (3rd edition).
3. D. I. A. Cohen, Introduction to Computer Theory, John Wiley & Sons, 1997.
4. J. C. Martin, Introduction to Languages and the Theory of Computation, Tata McGraw-Hill (3rd Ed.).
5. H. R. Lewis and C. H. Papadimitriou, Elements of the Theory of Computation, Prentice Hall, 1997.
6. Garey, D.S., Johnson, G., Computers and Intractability: A Guide to the Theory of NP- Completeness, Freeman, New York, 1979

7. M. Sipser, Introduction to the Theory of Computation, Thomson, 2004

CS2202

Data Analytics and Visualization

Course Number	CS2202
Course Credit (L-T-P-C)	3-0-2-4
Course Title	Database and Warehousing
Learning Mode	Offline
Learning Objectives	· Understand the fundamental principles of database systems and data warehousing. · Learn to design, implement, and manage databases using relational database management systems (RDBMS). · Explore the concepts and techniques of data warehousing and data mining. · Develop skills in SQL for querying and managing databases. · Analyze and optimize database performance and ensure data integrity and security.
Course Description	This course provides an in-depth exploration of database systems and data warehousing, covering essential concepts, technologies, and techniques. Students will learn about the design and implementation of relational databases, including data modeling, normalization, and SQL. The course will also introduce data warehousing concepts, focusing on data extraction, transformation, and loading (ETL), as well as data mining techniques. Through practical exercises and projects, students will gain hands-on experience in working with databases and data warehouses, preparing them for real-world applications.
Course Outline	1. Introduction to Databases, Overview of database systems, Types of databases and database models, Database architecture and components 2. Data Modeling, Entity-Relationship (ER) modeling, Relational model and schema design, Normalization and denormalization 3. Structured Query Language (SQL), Basic SQL queries (SELECT, INSERT, UPDATE, DELETE), Advanced SQL (joins, subqueries, indexing) , SQL functions and stored procedures 4. Database Design and Implementation, Database design principles, Creating and managing databases using RDBMS, Data integrity and constraints 5. Database Management and Administration, Database backup and recovery, User management and security, Performance tuning and optimization 6. Introduction to Data Warehousing, Concepts and architecture of data warehousing, Data warehousing vs. databases, Data modeling for data warehousing 7. ETL Processes, Data extraction, transformation, and loading (ETL), ETL tools and techniques, Data cleaning and integration 8. Data Mining and Analytics, Introduction to data mining, Data mining techniques and algorithms, Applications of data mining 9. Advanced Topics in Data Warehousing, Big data and data warehousing, Cloud-based data warehousing solutions, Data governance and data quality management Practical component: Lab to be conducted on a 2-hour slot weekly. It will be conducted with the theory course so the topics for problems given in the lab are already initiated in the theory class.
Learning Outcome	The student will be able to: · Demonstrate a thorough understanding of database and data warehousing principles. · Design, implement, and manage relational databases using RDBMS. · Write efficient SQL queries for data manipulation and retrieval. · Implement data warehousing solutions, including ETL processes and data mining techniques. · Analyze and optimize the performance of databases and data warehouses, ensuring data integrity and security.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Textbooks:

"Database System Concepts" (7th Edition) by Abraham Silberschatz, Henry F. Korth, and S. Sudarshan
"Fundamentals of Database Systems" (7th Edition) by Ramez Elmasri and Shamkant B. Navathe
"Data Warehousing: The Ultimate Guide to Building a Data Warehouse for Business Intelligence" (1st Edition) by Erik Thomsen
"The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling" (3rd Edition) by Ralph Kimball and Margy Ross

5. "SQL: The Complete Reference" (3rd Edition) by James R. Groff and Paul N. Weinberg

CS2203

Artificial Intelligence

Course Number	CS2203
Course Credit (L-T-P-C)	3-0-3-4.5
Course Title	Artificial Intelligence
Learning Mode	Offline
Learning Objectives	· To understand the core concepts and principles of Artificial Intelligence and intelligent agents. · To learn and apply uninformed and informed search strategies to solve complex problems. · To formulate and solve constraint satisfaction problems and engage in adversarial search. · To represent knowledge using propositional and first-order logic and perform inference and planning. · To utilize various learning techniques and understand their applications in different AI domains.
Course Description	This course provides a comprehensive introduction to the fundamental concepts and techniques of Artificial Intelligence (AI). Students will learn about the design and implementation of intelligent agents, various search strategies, constraint satisfaction problems, knowledge representation, and reasoning. Additionally, the course covers learning techniques and their practical applications, preparing students to apply AI principles in real-world scenarios. The lab component allows students to implement these concepts, reinforcing theoretical knowledge through hands-on experience.
Course Outline	Introduction: Definition and scope of Artificial Intelligence, background and evolution, intelligent agents and environment Problem Solving: Solving problems by searching, uninformed and informed search Uninformed search: Breadth-first search (BFS), Depth-first search (DFS), Uniform-cost search (UCS) Informed search: Heuristic function design and evaluation, A* search Local search: Hill climbing Adversarial search: Min-max, alpha-beta pruning Constraint Satisfaction Problem (CSP): definition and examples of CSPs Knowledge Representation and Reasoning: Propositional Logic, First Order Logic Introduction to Learning Techniques: Bayesian, decision tree, etc. Some applications of AI Practical component: Lab to be conducted on a 3-hour slot weekly. It will be conducted with the theory course so the topics for problems given in the lab are already initiated in the theory class.
Learning Outcome	By the end of this course, students will be able to: · Understand the core concepts and principles of Artificial Intelligence and intelligent agents. · Apply uninformed and informed search strategies to solve complex problems. · Formulate and solve constraint satisfaction problems and engage in adversarial search. · Represent knowledge using propositional and first-order logic and perform inference and planning. · Utilize various learning techniques and understand AI applications in different domains.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Reading

Russell, S. J., & Norvig, P. (2016). Artificial intelligence: A modern approach. Pearson.

Poole, D. L., & Mackworth, A. K. (2010). Artificial Intelligence: foundations of computational agents. Cambridge University Press.

Hastie, T., Tibshirani, R., Friedman, J. H., & Friedman, J. H. (2009). The elements of statistical learning: data mining, inference, and prediction (Vol. 2, pp. 1-758). New York: Springer.

4.5

CS2204

IT Workshop

Course Number	CS2204
Course Credit (L-T-P-C)	0-2-2-3
Course Title	IT Workshop
Learning Mode	Offline
Learning Objectives	· To understand the basics of shell scripting and its applications in automating tasks. · To learn the fundamentals of Android programming and app development. · To gain practical experience in writing scripts and developing Android applications. · To develop problem-solving skills through scripting and programming exercises. · To explore the integration of shell scripts within Android environments.
Course Description	This undergraduate course provides a foundational understanding of both shell scripting and Android programming. Students will start by learning the essential concepts of shell scripting, including syntax, commands, and script writing techniques to automate tasks in Unix/Linux environments. The course then transitions into Android programming, covering the basics of Java/Kotlin, Android Studio, and app development. By combining these two areas, the course aims to equip students with a versatile skill set that is highly valuable in the tech industry. Through a series of lectures, hands-on labs, and projects, students will gain the knowledge and experience needed to create efficient scripts and functional Android applications.
Course Outline	1. Introduction to Shell Scripting: Overview of Unix/Linux systems, Basic shell commands and utilities, Writing and executing simple shell scripts 2. Advanced Shell Scripting: Control structures (loops, conditionals) , Functions and arrays in shell scripting , Script debugging and error handling 3. Practical Shell Scripting: Automating tasks and processes, File manipulation and text processing, Networking and system administration scripts 4. Introduction to Android Programming: Overview of Android OS and development environment, Setting up Android Studio and creating a basic app, Introduction to Java/Kotlin for Android development 5. Building Android Applications: User interface design and XML layouts, Activity lifecycle and event handling, Using intents and data passing between activities 6. Advanced Android Features: Working with databases and content providers, Networking and web services in Android, Integrating shell scripts within Android apps 7. Project Development and Deployment: Developing a complete Android app project, Testing and debugging Android applications Lab to be conducted on a 2-hour slot weekly.
Learning Outcome	· Write and execute shell scripts to automate various tasks in Unix/Linux environments. · Understand and apply advanced shell scripting techniques for more complex automation. · Develop Android applications using Java/Kotlin and Android Studio. · Design and implement user interfaces for Android apps. · Integrate shell scripting functionalities within Android applications.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Reading

"Learning the bash Shell: Unix Shell Programming" by Cameron Newham, 3rd Edition.
"Shell Scripting: How to Automate Command Line Tasks Using Bash Scripting and Shell Programming" by Jason Cannon, 1st Edition.
"Android Programming: The Big Nerd Ranch Guide" by Bill Phillips, Chris Stewart, and Kristin Marsicano, 4th Edition.
"Head First Android Development: A Brain-Friendly Guide" by Dawn Griffiths and David Griffiths, 2nd Edition.

5. "Kotlin for Android Developers: Learn Kotlin the Easy Way While Developing an Android App" by Antonio Leiva, 1st Edition.

CS2205

Course Number	CS2205
Course Credit (L-T-P-C)	3-0-3-4.5
Course Title	Data Analytics and Visualization
Learning Mode	Offline
Learning Objectives	This course aims to help the students · Develop a thorough understanding of each stage of the data analytics lifecycle, from data discovery and preparation to modeling, evaluation, and operationalization. · Gain expertise in advanced statistical methods such as simple and multiple linear regression, logistic regression for classification tasks, and time series analysis using ARMA and ARIMA models. · Acquire skills in text analytics, including text mining techniques to extract meaningful patterns and sentiment analysis to understand subjective information from textual data. · Develop proficiency in data visualization using popular libraries and tools such as Matplotlib, Seaborn, Pandas, and NumPy in both R and Python.
Course Description	These comprehensive data analytics course equips students with a robust skill set essential for navigating the entire data lifecycle from discovery to operationalization. Students will master advanced statistical techniques such as simple and multiple linear regression, logistic regression, and time series analysis using ARMA and ARIMA models. Additionally, they will develop proficiency in text analytics methods including text mining and sentiment analysis to derive insights from unstructured data. Practical expertise in data visualization using tools like Matplotlib, Seaborn, Pandas, and NumPy in R and Python will enable students to create compelling visualizations that effectively communicate complex data findings. By the course's conclusion, students will be well-prepared to apply these skills in real-world scenarios, driving data-driven decisions and innovations across diverse industries.
Course Outline	Introduction to Data analytics, Background and Overview of Data Analytics Lifecycle Project -Discovery, Data Preparation, Model Planning, Model Building, Communicate Results, Operationalize. Exploratory Data Analysis, Extraction Transformation and Loading, Data Exploration versus presentation. Visualization tools and techniques Practical component: Lab to be conducted on a 3-hour slot weekly. It will be conducted with the theory course so the topics for problems given in the lab are already initiated in the theory class.
Learning Outcomes	· Understand and Apply the Data Analytics Lifecycle. · Apply the Regression model on the data set and · Analyze the time series data and text. · Utilize R and Python for data visualization
Assessment Method	Assignments, Quizzes, Mid-semester examination and End-semester examination.

Text Books:

H. S. Fogler, Elements of Chemical Reaction Engineering, Prentice Hall, 4^th Ed., 2008.
O. Levenspiel, Chemical Reaction Engineering, Wiley Eastern, 3^rd Ed., 2003.

Reference Books:

J. M. Smith, Chemical Engineering Kinetics, McGraw Hill, 3^rd Ed., 1980.

2. L. D. Schmidt, The Engineering of Chemical Reactions, Oxford University Press, 1998.

4.5

XX22PQ

IDE-I

TOTAL

Natural Language Processing

Sl. No.

Subject Code

SEMESTER V

CS3101

Operating System

Course Number	CS3101
Course Credit (L-T-P-C)	3-0-3-4.5
Course Title	Operating System
Learning Mode	Offline
Learning Objectives	This course provides an in-depth understanding of the fundamental concepts, principles, and mechanisms of operating systems. Topics include process management, memory management, file systems, concurrency, and scheduling.
Course Description	This course comprehensively introduces the fundamental concepts and principles underlying operating systems. Key topics include definitions of operating systems, the concept of a process, inter-process communication mechanisms, and multi-threading concepts. The course also addresses critical issues such as deadlock, discussing the necessary conditions for its occurrence and strategies for avoidance and prevention. In the realm of memory management, students will learn about both contiguous and non-contiguous allocation, paging concepts, and page table architecture. Further, the virtual memory concept will be explored, focusing on demand paging, replacement algorithms, and the phenomenon of thrashing. The course also includes a detailed study of file systems and disk management. By the end of this course, students will have a robust understanding of the essential components and functions of operating systems, preparing them for advanced studies and practical applications in the field of computer science.
Course Outline	Basics of Operating System: Definition and objectives of operating systems Types of operating systems: Batch, Time-sharing, Real-time, Distributed Systems Concept of process: Process control block, State transition, Scheduling algorithms, context switching, Process synchronization and inter-process communication Threads: Popular thread libraries, thread synchronization, multi-therading concepts Deadlock: necessary conditions, avoidance and prevention Memory management: Contiguous and non-contiguous allocation, Physical and logical addresses, Paging, different Page Table architectures, Virtual Memory: demand paging, replacement algorithms, thrashing. File systems: file operations, organization, mounting, sharing, File system implementation Disk management: disk structure, disk scheduling, disk management Practical component: Lab to be conducted on a 3-hour slot weekly. It will be conducted with the theory course so the topics for problems given in the lab are already initiated in the theory class.
Learning Outcome	· Understand the basic principles and functionalities of operating systems. · Analyse and evaluate different operating system components and their interactions. · Apply operating system concepts to solve real-world problems. · Develop an appreciation for the role of operating systems in modern computing environments.
Learning Outcome	· Understand the basic principles and functionalities of operating systems. · Analyse and evaluate different operating system components and their interactions. · Apply operating system concepts to solve real-world problems. · Develop an appreciation for the role of operating systems in modern computing environments.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested readings:

A. Silberschatz, P. B. Galvin and G. Gagne, Operating System Concepts, 7^th Ed, John Wiley and Sons, 2004.
M. Singhal and N. Shivratri, Advanced Concepts in Operating Systems, McGraw Hill, 1994.

3. David A Patterson and John L Hennessy, Computer Organisation and Design: The Hardware/Software Interface, Morgan Kaufmann, 1994. ISBN 1-55860-281-X.

4.5

CS3102

Computer Network

Course Number	CS3102
Course Credit (L-T-P-C)	3-0-3-4.5
Course Title	Computer Network
Learning Mode	Offline
Learning Objectives	The primary objectives of this course are to provide students with a solid foundation in computer networking principles and to prepare them for real-world networking challenges. Students will learn about network architectures, protocols, and technologies, and develop the skills necessary to design, implement, and manage networks. By the end of the course, students will be proficient in understanding network layers, configuring network devices, and troubleshooting network issues.
Course Description	This course provides an in-depth study of computer networks, covering essential concepts and technologies that form the backbone of modern communication systems. Students will learn about network topologies, protocols, hardware, and software that enable data transmission across networks. The course will also delve into advanced topics such as network security, wireless networking, and network management. Through practical exercises and projects, students will apply theoretical knowledge to real-world networking scenarios.
Course Outline	Introduction to computer networks and layered architecture, network applications, web architecture. Application Layer: HTTP, email protocols, DNS, and peer-to-peer applications. Transport layer: TCP, UDP, SCTP, and congestion control. Network layer: IP addressing, routing, and protocols like IPv4 and IPv6. link layer: LAN, error detection, MAC protocols. Physical Layer: Basics of data communication, transmission media and topology Future trends in networking: SDN, NFV Practical component: Lab to be conducted on a 3-hour slot weekly. It will be conducted with the theory course so the topics for problems given in the lab are already initiated in the theory class.
Learning Outcome	· Demonstrate an understanding of the core concepts and principles of computer networks. · Design and configure various types of network topologies and protocols. · Implement and manage network services and applications. · Identify and mitigate network security threats. · Analyze network performance and troubleshoot issues effectively.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Text Books:

"Computer Networking: A Top-Down Approach" (7th Edition) by James F. Kurose and Keith W. Ross
"Data Communications and Networking" (5th Edition) by Behrouz A. Forouzan
"Computer Networks" (5th Edition) by Andrew S. Tanenbaum and David J. Wetherall
"Network+ Guide to Networks" (8th Edition) by Jill West, Tamara Dean, and Jean Andrews

5. "TCP/IP Illustrated, Volume 1: The Protocols" (2nd Edition) by Kevin R. Fall and W. Richard Stevens

4.5

CS3103

Machine Learning

Course Number	CS3103
Course Credit (L-T-P-C)	3-0-3-4.5
Course Title	Machine Learning
Learning Mode	Offline
Learning Objectives	This course aims to help the students (a) to understand the fundamental concepts of machine learning; (b) to develop the basic problem-solving skills by implementing the basic machine learning algorithms; (c) to learn about various paradigms of machine learning and various approaches under different paradigms; and (d) to achieve proficiency in designing some real-life project using machine learning.
Course Description	This course provides a comprehensive introduction to the field of Machine Learning (ML), covering fundamental concepts, techniques, and applications. It is designed to give students a solid foundation in understanding how machines learn from data and make decisions. Through a combination of theoretical insights and practical applications, students will explore various aspects of machine learning, including supervised and unsupervised learning, generalization, regression, classification, clustering, data reduction, and ensemble learning.
Course Outline	1.Understanding of Machine Learning: Definition, Tasks (Classification, Regression, Prediction, and Clustering), Supervised and unsupervised machine learning. 2.Learning to Generalization: Bias-Variance Trade-off, Overfitting vs. Underfitting, Regularization 3.Regression (single & multivariate, linear and nonlinear, Logistic Regression 4.Classification: (kNN, Bayes classifier, decision tree, random forest, Support vector Machines) 5.Unsupervised Learning: K-Means & variants, Hierarchical techniques 6.Data Reduction and Ensemble Learning Practical component: Lab to be conducted on a 3-hour slot weekly. It will be conducted with the theory course so the topics for problems given in the lab are already initiated in the theory class.
Learning Outcome	· Understanding of fundamental concepts of ML · Understanding different types of ML tasks: Classification, Regression, and Clustering · Understanding of various algorithms under different paradigms of ML: supervised, unsupervised, semi-supervised. · Capable of conducting some real-life projects using machine learning algorithms
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Reading

Mitchell. Machine Learning. McGraw-Hill, 1997.
Duda, Richard O., and Peter E. Hart. Pattern classification. John Wiley & Sons, 2006.
Pattern recognition and machine learning by Christopher Bishop, Springer Verlag, 2006.
Machine Learning in Action by Peter Harrington
Probability, Random Variables and Stochastic processes by Papoulis and Pillai, 4th Edition, Tata McGraw Hill Edition.
Linear Algebra and Its Applications by Gilbert Strand. Thompson Books.
Data Mining: Concepts and Techniques by Jiawei Han, Micheline Kamber, Morgan Kaufmann Publishers.

A. K. Jain and R. C. Dubes. Algorithms for Clustering Data. Prentice Hall, 1988

4.5

CS3105

Course Number	CS3105
Course Credit (L-T-P-C)	3-0-3-4.5
Course Title	Natural Language Processing
Learning Mode	Offline
Learning Objectives	The objectives of this course are to provide students with a comprehensive understanding of natural language processing (NLP) techniques and their applications. Students will learn the fundamentals of text processing, word vector representations, and fundamentals of language models. The course aims to equip students with the skills to implement and evaluate various NLP tasks, such as part-of-speech tagging, named entity recognition, sentiment analysis, question answering, opinion mining, and machine translation. Additionally, students will explore advanced topics like language generation, summarization, and machine learning-based language processing methods. By the end of the course, students will be prepared to apply NLP techniques to real-world problems and contribute to the development of intelligent language-based systems.
Course Description	This course offers an in-depth exploration of natural language processing (NLP), covering both foundational and advanced topics. Students will begin with an introduction to the scope and applications of NLP, followed by essential text processing techniques. The course will delve into word vector representations, including word2vec and GloVe. Key NLP tasks such as part-of-speech tagging, named entity recognition, opinion mining, sentence classification, machine translation, question answering, language generation, and summarization will be covered. Emphasis will be placed on both rule-based and machine learning-based approaches to language processing. The course is designed to provide practical experience and theoretical knowledge, preparing students for advanced study or professional work in the field of NLP.
Course Outline	Introduction and Basic Text Processing, Spelling Correction, Language Modeling, Advanced smoothing for language modelling, POS tagging, Named Entity Recognition; Models for Sequential tagging-MaxEnt, CRF; Syntax-Constituency Parsing, Dependency Parsing. Dependency Parsing, Distributional Semantics, Lexical Semantics, Topic Models; Entity Linking, Information Extraction, Text Summarization, Text Classification, Coreference Resolution. Sentiment Analysis and Opinion Mining Simple Word Vector representations: word2vec, GloVe, Word Representations in Vector Space, Advanced word vector representations for language models, Machine Translation, Question Answering, Natural Language Generation. Practical component: Lab to be conducted on a 3-hour slot weekly. It will be conducted with the theory course so the topics for problems given in the lab are already initiated in the theory class. a) Use the NLTK and spaCy toolkit for NLP Programming. b) Analyze various corpora for developing programs. c) Develop various pre-processing techniques for a given corpus. d) Develop programming logic using NLTK functions. e) Build applications using various NLP techniques for a given corpus.
Learning Outcome	By the end of this course, students will be able to: · Explain the fundamental concepts and scope of natural language processing. · Describe foundational text processing techniques. · Discuss word vector representations like word2vec and GloVe to NLP tasks. · Interpret part-of-speech tagging and named entity recognition with proficiency. · Explain language models and perform opinion mining. · Execute sentence classification, machine translation, and question answering tasks. · Generate and summarize language using various NLP techniques. · Execute machine learning methods for various NLP applications. · Analyze and evaluate the performance of different NLP models and techniques.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Reading:

Daniel Jurafsky and James H. Martin, "Speech and Language Processing," 3rd Edition, Prentice Hall, 2020.
Christopher D. Manning, Hinrich Schütze, "Foundations of Statistical Natural Language Processing," 1st Edition, MIT Press, 1999.
Jacob Eisenstein, "Introduction to Natural Language Processing," 1st Edition, MIT Press, 2019.
Yoav Goldberg, "Neural Network Methods for Natural Language Processing," 1st Edition, Morgan & Claypool Publishers, 2017.

Steven Bird, Ewan Klein, and Edward Loper, "Natural Language Processing with Python," 1st Edition, O'Reilly Media, 2009.

4.5

XX31PQ

IDE-II

TOTAL

Sl. No.

Subject Code

SEMESTER VI

CS3201

Cyber Security

Course Number	CS3201
Course Credit (L-T-P-C)	3-0-2-4
Course Title	Cyber Security
Learning Mode	offline
Learning Objectives	To understand the basic concepts of cyber-attacks, legal issues and countermeasures.
Course Description	The course covers cyber-attacks, legal issues and countermeasures various aspects of cybersecurity, including basic principles, legal considerations, risk assessment, and security management. The course covers essential topics such as cybercrime, phishing attacks, cryptography basics, authentication mechanisms, and authorization protocols. Additionally, it delves into specific areas of vulnerability assessment and mitigation, focusing on secure programming practices and identifying threats to networks.
Course Outline	Introduction to cybersecurity: Basic concepts, cybercrime, legal issues, risk analysis and security management, phishing attack. Crypto basics, Authentication and authorization, Kerberos, PKI Vulnerabilities and Countermeasure: Vulnerabilities in code, Secure programming. Threats to network, network defense, social network security issues and countermeasures, email security Cyber system security: Hardware security, mobile security. Practical component: Lab to be conducted on a 2-hour slot weekly. It will be conducted with the theory course so the topics for problems given in the lab are already initiated in the theory class.
Learning Outcome	After completion of this course a student will have: · Understanding the legal aspects, risk and vulnerabilities in cyberspace. · Understanding the concepts of different attacks and their countermeasures in cyberspace.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Readings:

Nina Godbole and Sunit Belapure, Cyber Security, Wiley India

CS3202

Course Number	CS3202
Course Credit (L-T-P-C)	3-0-3-4.5
Course Title	Deep Learning
Pre-requisite	offline
Learning Mode	This course aims to provide an introductory overview of deep learning and its application varied domains. The course will provide basic understanding of neural networks, mathematical description of it and finally applications of it in multiple domains. A few open source tools will be demonstrated during the course to provide hands-on experience.
Learning Objectives	This course will provide an overview of neural networks and hands-on experience for the same.
Course Description	Introduction: Introduction to bigdata problem, overview of linear algebra Feature engineering: Basics of machine learning (linear regression, classification) Neural network: Deep feed forward network, cost function, activation functions, overfitting, underfitting, Universal approximation theorem Gradient based learning: Gradient Descent, Stochastic Gradient Descent, Backpropagation Regularization: L2, L1, L\infinity, drop-out, early stopping, data augmentation, etc. Optimization: Multivariable taylor series, momentum, adaptive learning rate, ADAM, Nesterov Accelerated Gradient (NAG), AdaGrad, etc. Convolutional Neural Network (CNN): Theory and its application in computer vision Recurrent Neural Network (RNN): Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU) and their applications in natural language processing Advanced topics: Autoencoder, Transformer, Deep reinforcement learning Practical component: Lab to be conducted on a 3-hour slot weekly. It will be conducted with the theory course so the topics for problems given in the lab are already initiated in the theory class.
Course Content	· Basic understanding of deep learning and neural networks · Problem modeling skill · Usage of different open source tools / libraries Analysis of large volume of data
Learning Outcome	Knowledge on various forms of stresses in pressure vessels and their relation. Mechanical designing of different parts/components used in heat exchangers or in separation units such as nuts/bolts, flanges, heads, shell, etc. Consideration and elementary sizing calculation on tall, horizontal/vertical vessels, and their constructional supports.
Assessment Method	Assignments, Quiz, Mid-semester examination and End-semester examination.

Suggested Reading:

Ian Goodfellow, Yoshua Bengio and Aaron Courville, “Deep Learning”, Book in preparation for MIT Press, 2016.
Reference books:
Jerome H. Friedman, Robert Tibshirani, and Trevor Hastie, “The elements of statistical learning”, Springer Series in Statistics, 2009.
Charu C Aggarwal, “Neural Networks and Deep Learning”, Springer.
Aston Zhang, Zachary C. Lipton, Mu Li, Alexander J. Smola, "Dive into Deep Learning"
Iddo Drori, "The Science of Deep Learning", Cambridge University Press
Simon O. Haykin, "Neural Networks and Learning Machines", Pearson Education India
Richard S. Sutton, Andrew G. Barto, "Reinforcement Learning: An Introduction", MIT Press
M. Bishop, H. Bishop, "Deep Learning: Foundations and Concepts", Springer, 2022

Simon J. D. Prince, "Understanding Deep Learning", MIT Press 2023

4.5

CS3204

Computer Vision

Course Number	CS3204
Course Credit (L-T-P-C)	3-0-3-4.5
Course Title	Computer Vision
Learning Mode	Offline
Learning Objectives	This course aims to provide an introductory overview of computer vision techniques. The course will provide both traditional methodologies and advanced techniques for image analysis.
Course Description	The course will cover different aspects of image formation and the basis of imaging techniques. It will start with the mathematical foundations required for understanding imaging and the various computer vision techniques employed for imaging. After that it will cover various image analysis techniques and their applicability, usage, etc.
Course Outline	Basis of Imaging: Formation, Capture and Representation. Image filter, convolution, geometric transforms, reconstruction. Segmentation, Enhancement, Restoration, Detection. Illumination and Color models. Practical component: Lab to be conducted on a 3-hour slot weekly. It will be conducted with the theory course so the topics for problems given in the lab are already initiated in the theory class.
Learning Outcome	Course training via lectures & coding sessions enable with. · Basic understanding of image formation and analysis · Various techniques related to image manipulation and restoration. · Practical applications and usage of imaging techniques.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Readings:

Szeliski, Richard. Computer vision: algorithms and applications. Springer Nature, 2022.
Forsyth, David A., and Jean Ponce. Computer vision: a modern approach. prentice hall professional technical reference, 2002.

3. Hartley, Richard, and Andrew Zisserman. Multiple view geometry in computer vision. Cambridge university press, 2003.

4.5

CS3299

Capstone Project

CS32XX

DE-I (AI ELECTIVES LIST)

TOTAL

Sl. No.	Subject Code	SEMESTER VII	L	T	P	C
1.	CS41XX	DE-II (AI ELECTIVES LIST)	3	0	0	3
2.	CS41XX	DE-III (AI ELECTIVES LIST)	3	0	0	3
3.	XX41PQ	IDE - III	3	0	0	3
4.	HS41XX	HSS Elective - II	3	0	0	3
5.	CS4198	Summer Internship*/ Summer Project	0	0	12	3
6.	CS4199	Project – I	0	0	12	6
TOTAL			12	0	24	21

* For specific cases of internship after 6^th Semester, the performance evaluation would be made on joining the VII^th Semester and graded accordingly in the VII^th Semester:

Note :

a) (i) Summer internship (*) period of at least 60 days’ (8 weeks) duration begins in the intervening vacation between semester VI and VII that may be done in industry / R&D / Academic Institutions including IIT Patna. The evaluation would comprise combined grading based on host supervisor evaluation, project internship report after plagiarism check and seminar presentation at the Department (DAPC to coordinate) with equal weightage of each of the three components stated herein.

a) (ii) Further, on return from internship, students will be evaluated for internship work through combined grading based on host supervisor evaluation, project internship report after plagiarism check, and presentation evaluation by the parent department with equal weightage of each component.

b) (i) In the VII^th semester, students can opt for a semester long internship on recommendation of the DAPC and approval of the Competent Authority.

b) (ii) On approval of semester long internship, at the maximum two courses (properly mapped/aligned syllabus) at par with institute electives may be opted from NPTEL and / or SWAYAM and the other two more should be done at the institute through course overloading in any other semester (either before or after the internship) and/or during following summer semester.

b) (iii) The candidates opting two courses from NPTEL and / or SWAYAM would be required to appear in the examination at the Institute as scheduled in the Academic Calendar.

Object-Oriented Programming

Department Elective - I

Sl. No.

Course Code

Course Name

CS3205

Course Number	CS3205
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Object-Oriented Programming
Learning Mode	Offline
Learning Objectives	The primary objectives of this course are to introduce students to the principles and practices of object-oriented programming (OOP) and to equip them with the skills necessary to design and implement software using OOP techniques. Students will learn about core OOP concepts such as classes, objects, inheritance, polymorphism, encapsulation, and abstraction. They will also develop proficiency in using an object-oriented programming language such as Java or Python.
Course Description	This course provides a comprehensive introduction to the fundamental concepts and methodologies of OOP. The course covers essential topics such as class and object design, inheritance, polymorphism, and encapsulation, and explores advanced concepts including exception handling, file I/O, and graphical user interfaces (GUIs). Through a series of practical exercises and projects, students will gain hands-on experience in writing clean, efficient, and maintainable code. The course emphasizes best practices and design patterns that are critical for developing robust software applications.
Course Outline	1. Introduction to Object-Oriented Programming, Overview of programming paradigms, Key concepts of OOP: classes, objects, and methods, Benefits of OOP 2. Classes and Objects, Defining and creating classes, Constructors and destructors, Object lifecycle and memory management 3. Encapsulation and Data Hiding, Access modifiers (public, private, protected), Getters and setters, Maintaining data integrity 4. Inheritance and Polymorphism, Base and derived classes, Method overriding and overloading, Dynamic binding and polymorphic behavior 5. Abstraction and Interfaces, Abstract classes and methods, Interface implementation, Multiple inheritance in OOP 6. Object-Oriented Design Principles, SOLID principles, Design patterns (e.g., Singleton, Factory, Observer), UML diagrams for OOP design 7. Exception Handling and File I/O, Error detection and handling, using exceptions to manage errors, File input/output operations 8. Advanced OOP Concepts, Generic programming and templates, Reflection and metadata, Multithreading in OOP.
Learning Outcome	Upon successful completion of this course, students will be able to: · Understand and apply the core principles of object-oriented programming. · Design and implement software solutions using object-oriented techniques. · Develop and debug programs in an object-oriented programming language. · Utilize advanced OOP features such as inheritance, polymorphism, and interfaces effectively. · Write clean, maintainable, and efficient code following best practices and design patterns. · Create basic graphical user interfaces and handle events in GUI applications. · Apply OOP concepts in various real-world scenarios and software development projects.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Reading

"Object-Oriented Analysis and Design with Applications" by Grady Booch
Data Structures and Algorithm Analysis in C++ Hardcover, by Mark A. Weiss, Jun 2013, Publisher: PHI; 4 editions, ISBN-10: 013284737X ISBN-13: 978-0132847377.
Algorithms in C++: Fundamentals, Data Structures, Sorting, Searching, Parts 1-4, 3rd Edition (Paperback), Pearson India, ISBN-10 8131713059, 2009, ISBN-13 9788131713051.
"Thinking in C++" by Bruce Eckel
"C++ Primer" by Stanley B. Lippman, Josée Lajoie, and Barbara E. Moo
"Head First Object-Oriented Analysis and Design" by Brett McLaughlin, Gary Pollice, and David West

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CS3206

Agile Computing

Course Number	CS3206
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Agile Computing
Learning Mode	Offline
Learning Objectives	· To gain a thorough understanding of agile computing principles, methodologies, and their application in software development. · To learn to effectively apply agile practices such as Scrum, Kanban, and Extreme Programming (XP) to enhance project visibility, collaboration, and adaptability. · To develop skills in managing and leading agile teams, utilizing agile project management tools for planning and tracking projects. · To acquire knowledge of metrics and performance measurement techniques to analyze and optimize agile processes. · To apply agile principles to cultivate a culture of continuous improvement and innovation within organizations.
Course Description	This course provides a comprehensive exploration of agile computing, focusing on its principles, methodologies, and practical applications in software development. Students will delve into popular agile frameworks like Scrum, Kanban, and Extreme Programming (XP), learning how these methodologies enhance project management, collaboration, and responsiveness to change. Topics include agile estimation, planning, testing, quality assurance, and scaling agile practices. The course also covers agile leadership, metrics for performance measurement, and fostering an agile culture of continuous improvement and innovation.
Course Outline	Introduction to Agile Computing and scope Overview of popular agile methodologies like Scrum, Kanban, and Extreme Programming (XP), Scrum roles, artifacts, and events, Lean and Kanban Principles, Extreme Programming (XP): est-driven development (TDD) and pair programming, Agile Estimation and Planning, Agile Testing and Quality Assurance, Scaling Agile, Agile Leadership and Culture, Agile Metrics and Performance Measurement, Applications of agile computing
Learning Outcome	By the end of this course, students will be able to: · Understand the principles and philosophy of agile computing. · Apply various agile methodologies and practices to software development projects. · Effectively manage and lead agile teams. · Use agile project management tools to plan, track, and deliver projects. · Analyze and optimize agile processes using metrics and performance measurement techniques. · Apply agile principles to foster a culture of continuous improvement and innovation within organizations
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Reading

"Agile Estimating and Planning" by Mike Cohn, 2006
"Agile Testing: A Practical Guide for Testers and Agile Teams" by Lisa Crispin, Janet Gregory, 2009
"Scrum: The Art of Doing Twice the Work in Half the Time" by Jeff Sutherland, 2014
"Kanban: Successful Evolutionary Change for Your Technology Business" by David J. Anderson, 2010
"Extreme Programming Explained: Embrace Change" by Kent Beck, 2004

"Lean Software Development: An Agile Toolkit" by Mary Poppendieck, Tom Poppendieck, 2003

CS3207

Software Engineering

Course Number	CS3207
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Software Engineering
Learning Mode	Offline
Learning Objectives	This course aims to help the students · with a comprehensive understanding of the fundamental principles and concepts of software engineering; · with the software development life cycle (SDLC) and various software process models; · in using modern software engineering tools and techniques for efficient software development; and · understanding of quality assurance practices and the importance of software project documentation.
Course Description	This comprehensive course provides an in-depth understanding of the principles and practices of software engineering. Students will explore the software development lifecycle, including requirements analysis, design, implementation, testing, and maintenance. Emphasis is placed on methodologies such as Agile, Waterfall, and DevOps. Key topics include software project management, version control, software architecture, design patterns, and quality assurance. Through hands-on projects and case studies, students will gain practical experience in developing reliable, scalable, and maintainable software systems. This course prepares students for real-world challenges in software engineering, equipping them with the skills necessary for successful careers in the tech industry.
Course Outline	Software life cycle- important steps and effort distribution. Aspects of estimation and scheduling. Software evaluation techniques-modular design- coupling and cohesion, Software and complexity measures. Issues in software reliability. System Analysis- Requirement analysis. Specification languages. Feasibility analysis. File and data structure design, Systems analysis tools. Software design methodologies- Data flow and Data Structure oriented design strategies. Software development, coding, verification, and integration. Issues in project management-team structure, scheduling, software quality assurance.
Learning Outcome	Demonstrate a clear understanding of the fundamental concepts and methodologies in software engineering. Apply software engineering principles and techniques to design, develop, test, and maintain software systems. Use modern software engineering tools and environments effectively in software development tasks. Plan and manage software projects, including tasks such as requirements analysis, project scheduling, risk management, and quality assurance. Produce and maintain comprehensive documentation for all phases of the software development process. Work effectively as part of a software development team, demonstrating strong collaboration and communication skills.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Reading:

Design Patterns: Elements of Reusable Object-Oriented Software" by Erich Gamma, Richard Helm, Ralph Johnson, and John Vlissides (The Gang of Four)
"Software Architecture in Practice" by Len Bass, Paul Clements, and Rick Kazman
"Software Requirements" by Karl E. Wiegers and Joy Beatty
"Software Engineering: A Practitioner's Approach" by Roger S. Pressman and Bruce R. Maxim

Fundamentals of Software Engineering, Fifth Edition, Rajiv Mall

CS3208

Bayesian Data Analysis

Course Number	CS3208
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Bayesian Data Analysis
Learning Mode	Offline
Learning Objectives	The learning objectives of the course include comprehending the fundamental concepts of Bayesian statistics, such as likelihood and priors, and applying these to develop various models, including single-parameter, multi-parameter, and hierarchical models. Additionally, techniques for validating these models will be covered. Students will also learn the programming skills necessary to computationally implement these models for different real-world problems.
Course Description	The primary goal of this course is to introduce Bayesian approaches for data analysis and apply these techniques to various real-world problems. Although the focus will be on issues pertinent to computer science, the skills acquired are broadly applicable across several disciplines related to machine learning. The lectures will cover the fundamental theory behind Bayesian statistical inference. Additionally, the course will introduce programming languages like R and Stan, which are well-suited for implementing these Bayesian concepts.
Course Outline	Basics of Probability and Inference, Single Parameter Models, Multiparameter models, Programming Bayesian models using R, Bayesian Computation Techniques, Markov-chain Monte Carlo simulations, Programming Stan with R, Efficient Markov chain simulation techniques, Hierarchical models, Model checking, Model Evaluation, Case studies
Learning Outcome	On successful completion of this course students will be able to: · Assess the fundamental philosophical differences between Bayesian probability and traditional frequentist approaches. · Construct flexible Bayesian models using likelihood and prior functions. · Implement Markov Chain Monte Carlo (MCMC) algorithms in R and Stan for inference in small to medium-sized problems. · Develop Bayesian machine learning algorithms capable of inference in high-dimensional problems.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested readings:

Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, and Donald B. Rubin, Bayesian Data Analysis, Third Edition, CRC Press

John K. Kruschke, Doing Bayesian Data Analysis, A Tutorial with R, JAGS, and Stan, Second Edition, Academic Press

CS3209

Data Mining

Course Number	CS3209
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Data Mining
Learning Mode	Offline
Learning Objectives	This course aims to help the students (a) Understand the fundamental concepts and techniques of data mining. (b) Gain proficiency in data preprocessing, including data cleaning, transformation, and reduction. (c) Apply various data mining algorithms for classification, clustering, association, and anomaly detection. (d) To achieve proficiency in designing some real-life projects using data mining techniques.
Course Description	This comprehensive course on data mining aims to equip students with the knowledge and skills required to extract meaningful insights from large datasets. By focusing on core concepts and providing practical experiences, students will learn to apply various data mining techniques and tools effectively. Through a combination of lectures and real-world projects, students will explore topics such as classification, clustering, association rule mining, and anomaly detection. Upon completion, students will be adept at transforming raw data into actionable knowledge, enabling them to solve complex problems and make data-driven decisions in academic and professional settings.
Course Outline	Fundamentals of data warehousing, architectures, schemas, OLAP technology, and data cube processing. Data preprocessing, integration, transformation, reduction, and basics of data mining techniques. Association rule mining, algorithms (Apriori, FP-Growth), and latest trends in association rule mining. Data classification and clustering techniques, algorithms, prediction methods, and outlier analysis. Introduction to web, spatial and temporal text mining, security, privacy, and ethical issues.
Learning Outcome	· Mastery of fundamental concepts and techniques in data mining. · Proficiency in various data mining algorithms. · Comprehensive understanding of essential data mining tasks such as association rule mining, clustering, and classification. · Ability to apply data mining techniques to real-world projects.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Reading:

Arun K. Pujari “Data Mining Technique” University Press
Han, Kamber, “Data Mining Concepts & Techniques”,
M. Kaufman., P.Ponnian, “Data Warehousing Fundamentals”, JohnWiley.
M.H.Dunham, “Data Mining Introductory & Advanced Topics”, Pearson Education.
Ralph Kimball, “The Data Warehouse Lifecycle Tool Kit”, John Wiley.

E.G. Mallach, “The Decision Support & Data Warehouse Systems”, TMH

CS3210

Information Retrieval

Course Number	CS3210
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Information Retrieval
Learning Mode	Offline
Learning Objectives	The potential learning objectives of the course includes understanding the fundamental concepts and theories of information retrieval, including indexing, querying, and relevance ranking. Furthermore, the students will gain proficiency in utilizing various retrieval models, such as boolean, vector space, and probabilistic models. They would learn about the challenges and techniques involved in processing natural language for information retrieval purposes. The students would be familiarized with the architecture and components of modern search engines and recommendation systems.
Course Description	This course focuses on Information Retrieval (IR), which involves extracting pertinent data from extensive document sets. IR finds utility in various realms such as proprietary retrieval systems, the World Wide Web, Digital Libraries, and commercial recommendation platforms. The course aims to acquaint students with the theoretical foundations of IR with several real world applications and examples.
Course Outline	Introduction: concepts and terminology of information retrieval systems, Information Retrieval Vs Information Extraction Indexing: inverted files, encoding, Zipf's Law, compression, boolean queries Fundamental IR models: Boolean, Vector Space, probabilistic, TFIDF, Okapi, language modeling, latent semantic indexing, query processing and refinement techniques Performance Evaluation: precision, recall, F-measure; Classification: Rocchio, Naive Bayes, k-nearest neighbors, support vector machine Clustering: partitioning methods, k-means clustering, hierarchical Introduction to advanced topics: search, relevance feedback, ranking, query expansion.
Learning Outcome	Course training via lectures & tutorial sessions to · Understand the fundamental concepts and theories of information retrieval, including indexing, querying, and relevance ranking. · Gain proficiency in utilizing various retrieval models, such as boolean, vector space, and probabilistic models. · Learn about the challenges and techniques involved in processing natural language for information retrieval purposes. · Acquire knowledge of evaluation metrics and methodologies used to assess the performance of information retrieval systems. · Familiarize with the architecture and components of modern search engines and recommendation systems. · Analyze case studies and real-world applications of information retrieval in diverse domains, including web search, digital libraries, and e-commerce.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested readings:

Christopher D. Manning, Prabhakar Raghavan and Hinrich Schtze, Introduction to Information Retrieval, Cambridge University Press. 2008.
Ricardo Baeza-Yates and Berthier Ribeiro-Neto, Modern Information Retrieval, Addison Wesley, 1st edition, 1999.
Soumen Chakrabarti, Mining the Web, Morgan-Kaufmann Publishers, 2002.

Bing Liu, Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data, Springer, Corr. 2nd printing edition, 2009.

Principles of Programming Languages

Department Elective - II

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Course Code

Course Name

CS4101

Pattern Recognition

Course Number	CS4101
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Pattern Recognition
Learning Mode	Offline
Learning Objectives	This course aims to help the students (a) Understand the fundamental principles and techniques of pattern recognition, including classification and clustering methods. (b) To develop basic problem-solving skills by implementing the basic pattern recognition algorithms. (c) To gain proficiency in feature extraction, selection, and dimensionality reduction to enhance pattern recognition performance. (d) Apply pattern recognition algorithms to practical applications in image processing, speech recognition, and data mining.
Course Description	This course on pattern recognition aims to equip students with the theoretical foundations and practical skills necessary to identify and analyze patterns in data. By focusing on essential principles, students will develop the ability to implement and evaluate various pattern recognition algorithms. Students will enhance their understanding of machine learning, statistical methods, and data preprocessing techniques through interactive lectures, exercises, and projects. Upon completion, students will be proficient in designing and applying pattern recognition systems for applications such as image processing, speech recognition, and data mining, thereby enhancing their analytical and problem-solving capabilities in diverse domains.
Course Outline	Introduction to pattern recognition, key concepts, learning types, approaches, decision boundaries, and distance metrics. Pattern extraction and preprocessing, pattern classification and algorithms Different paradigms and representations for pattern clustering techniques and validation. Feature extraction and selection methods, problem statements, and relevant algorithms (branch and bound, sequential selection). Recent advances in pattern recognition, including structural pattern recognition, neuro-fuzzy techniques, and real-life applications.
Learning Outcome	· Mastery of fundamental concepts in pattern recognition. · In-depth understanding of various algorithms across different pattern recognition paradigms. · Comprehensive knowledge of theoretical aspects of feature selection, feature extraction, and projection techniques. · Ability to apply pattern recognition algorithms to real-world projects
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Reading:

Pattern recognition and machine learning by Christopher Bishop, Springer Verlag, 2006.
Trevor Hastie, Robert Tibshirani , Jerome Friedman. The elements of Statistical Learning. Springer Verlag (2009).
Fundamentals of Pattern Recognition and Machine Learning by Ulisses Braga-Neto. Springer Cham (2020)
Probability, Random Variables and Stochastic processes by Papoulis and Pillai, 4th Edition, Tata McGraw Hill Edition.
Linear Algebra and Its Applications by Gilbert Strand. Thompson Books.
Data Mining: Concepts and Techniques by Jiawei Han, Micheline Kamber, Morgan Kaufmann Publishers.

A. K. Jain and R. C. Dubes. Algorithms for Clustering Data. Prentice Hall, 1988

CS4102

Course Number	CS4102
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Principles of Programming Languages
Learning Mode	Offline
Learning Objectives	To make students understand the existence of different programming language paradigms (i.e., logic, functional, procedural, object-oriented), their specific features, and to choose an appropriate language for a given application. To make students capable to learn new languages easily and to make clear and efficient use of any given language.
Course Description	The objective of this course is to study the design and implementation of programming languages from a foundational perspective.
Course Outline	Introduction: History of Programming Languages; Evolution of the Major Programming Languages; Art of Programming Language Design; Properties and Success of Programming Languages. Programming Language-Paradigms: Imperative (e.g. C, Pascal, Fortran); Functional (e.g. LISP, HASKELL, OCaml); Object Oriented (e.g. JAVA, C++, Scala); Logic-based (e.g. Prolog); Multiparadigm programming languages (e.g. Python, C++11). Programming Language Concepts: Values and Data Types; Block Structure; Scope, Binding and Lifetime of Variables; Static vs. Dynamic Typing; Static vs. Dynamic Scoping; Memory Management; Procedural Abstraction; Data Abstraction; Concurrency; etc. Case Study: Defining Syntax and Semantics of IMP (a simple WHILE-language) and COOL (Classroom Object Oriented Language).
Learning Outcome	· Understand a variety of concepts underpinning modern programming languages. · Understand the concepts and terms used to describe languages that support the imperative, functional, object-oriented, and logic programming paradigms. · Critically evaluate what paradigm and language are best suited for a new problem. · Solve problems using the functional paradigm. · Solve problems using the object-oriented paradigm. · Solve problems using the logic programming paradigm. · Understand how to design and implement your own (domain-specific) language.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Readings:

Michael L. Scott, “Programming Language Pragmatics”, Morgan Kaufmann, 3rd Edition.
Harold Abelson, Gerald Jay Sussman, Julie Sussman, “Structure and Interpretation of Computer Programs”, MIT Press, 2nd Edition.
Ravi Sethi, K.V. Vishwanatha,“Programming Languages: Concepts and Constructs”, 2/e, Pearson Education, 2007.
W. Pratt and M.V. Zelkowitz, “Programming Languages – Design and Implementation”, Prentice-Hall.
Robert W. Sebesta, “Concepts of Programming Languages”, Addison-Wesley.
A. Watt, “Programming Language Design Concepts”, John Wiley & Sons.
Kenneth C. Louden and Kennath A. Lambert, “Programming Languages: Principles and Practice”, Cengage Learning.

Recent Research Papers relevant to the course.

CS4103

Social Networks

Course Number	CS4103
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Social Networks
Learning Mode	Offline
Learning Objectives	The major objectives of the course would be to make the students understand the basic concepts of social network, understand the fundamental concepts in analyzing the large-scale data that are derived from social networks, implement mining algorithms for social networks, and perform mining on large social networks and illustrate the results.
Course Description	This course delves into the analysis of data within social networks, emphasizing efficient strategies for managing large-scale networks. It presents fundamental theoretical findings in social network mining alongside practical exercises addressing critical topics within the field.
Course Outline	Introduction to social networks. Illustration of various social network mining tasks with real-world examples. Data characteristics unique to these settings and potential biases due to them. Social Networks as Graphs. Random graph models/ graph generators (Erdos-Renyi, power law, preferential attachment, small world, stochastic block models, Kronecker graphs), degree distributions. Models of evolving networks. Node based metrics, ranking algorithms (Pagerank). Graph visualisation. Social network exploration/ processing: Graph kernels, graph classification, clustering of social-network graphs, centrality measures, community detection and mining, degeneracy (outlier detection and centrality), partitioning of graphs. Information Diffusion in Social Networks: Information diffusion in graphs - Cascading behavior, spreading, epidemics, heterogeneous social network mining, influence maximization, outbreak detection; Opinion analysis on social networks - Contagion, opinion formation, coordination and cooperation. Dynamic social networks, Link prediction, Social learning on networks.
Learning Outcome	By completing the course, the students will be able to: • Understand the basic concepts of social networks • Understand the fundamental concepts in analyzing the large-scale data that are derived from social networks • Implement mining algorithms for social networks • Perform mining on large social networks and illustrate the results.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested readings:

David Easley and Jon Kleinberg, Networks, crowds, and markets, Cambridge University Press, 2010.

Jure Leskovec, Anand Rajaraman and Jeffrey David Ullman, Mining of massive datasets, Cambridge University Press, 2014.

CS4104

Multimedia Systems

Course Number	CS4104
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Multimedia Systems
Learning Mode	Offline
Learning Objectives	The main objective of this course is to provide students with a comprehensive understanding of multimedia systems. Students will learn about the various components and technologies involved in multimedia systems, including audio, video, and image processing. They will explore the principles of multimedia compression, storage, and retrieval, as well as the techniques used for multimedia communication and networking. By the end of the course, students will have a solid theoretical foundation in multimedia systems and will be able to apply this knowledge to solve real-world problems in the field.
Course Description	The course begins with an introduction to multimedia systems, covering the basics of multimedia data representation and the different types of multimedia data. This is followed by a detailed study of multimedia compression techniques, including lossless and lossy compression methods for text, images, audio, and video. The course then explores multimedia storage and retrieval, discussing the different storage media and retrieval techniques used for multimedia data. Next, students will learn about multimedia communication and networking, including the protocols and architectures used for multimedia transmission over networks. The course concludes with a discussion of advanced topics in multimedia systems, such as quality of service, synchronization, and security.
Course Outline	Introduction to Multimedia Systems Understanding multimedia data types: Text, images, audio, and video. Multimedia Data Representation: Pixel-based representation for images, waveform representation for audio, and frame-based representation for video. Compression techniques for multimedia data: Lossy and lossless compression algorithms. Multimedia Storage and Retrieval Multimedia Networking and Streaming Multimedia Synchronization and Interactivity: Timecodes, timestamps, and synchronization protocols, Hypermedia, and interactive multimedia applications. Multimedia applications and trends: virtual and augmented reality
Learning Outcome	By the end of this course, students will be able to: · Understand the fundamental concepts and components of multimedia systems. · Analyse and evaluate different multimedia data types and their representation techniques. · Design and implement multimedia storage, retrieval, and streaming solutions. · Evaluate multimedia networking protocols and techniques for efficient multimedia transmission. · Implement multimedia synchronization and interactivity features in multimedia applications. · Explore real-world applications of multimedia systems and identify future trends in multimedia technology
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested readings:

Multimedia Systems: Algorithms, Standards, and Industry Practices" by Parag Havaldar, Gerard Medioni
"Multimedia Computing: Algorithms, Systems, and Applications" by Ralf Steinmetz, Klara Nahrstedt

"Introduction to Multimedia Systems" by Sugata Mitra, Tamalika Chaira

CS4105

Nature Inspired Algorithms

Course Number	CS4105
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Nature Inspired Algorithms
Learning Mode	Offline
Learning Objectives	· To develop a comprehensive understanding of the principles and motivation behind nature-inspired algorithms. · To gain the ability to design, implement, and evaluate various nature-inspired meta-heuristic algorithms. · To apply nature-inspired algorithms to solve complex optimization and search problems across different domains. · To explore and understand advanced techniques such as hybrid and memetic algorithms. · To stay updated with recent trends and emerging algorithms in the field.
Course Description	This course offers an in-depth exploration of nature-inspired algorithms, focusing on their principles, motivation, and practical applications. Students will learn to design, implement, and evaluate various nature-inspired meta-heuristic algorithms, such as genetic algorithms, ant colony optimization, and bee colony optimization. The course also covers advanced techniques including hybrid and memetic algorithms, as well as recent trends like Cuckoo Search, Firefly algorithm, Bat algorithm, and Dolphin echolocation. By the end of the course, students will be equipped to apply these algorithms to solve complex optimization and search problems across different domains.
Course Outline	Introduction and Motivation of Nature Inspired Algorithms Meta-Heuristic Learning: Basics and characteristics of meta-heuristic algorithms, Exploration vs. exploitation strategies Ant Colony Optimization (ACO): Biological inspiration and principles of ACO, Variants and applications in routing, scheduling, and optimization Artificial Bee Colony (ABC) Algorithm: Bee foraging behavior and communication mechanisms, Structure, working, and applications of ABC Hybrid and Memetic Algorithms: Combining multiple algorithms and their advantages, Concept and implementation of memetic algorithms Swarm Intelligence: Principles and examples of swarm intelligence, Particle Swarm Optimization (PSO) and its applications Recent Trends in Nature Inspired Algorithms: Cuckoo Search and Firefly Algorithm: inspiration, principles, and applications, Bat Algorithm and Dolphin Echolocation Algorithm: biological basis, design, and use cases Applications of Nature Inspired Algorithms: Engineering and real-world applications
Learning Outcome	By the end of this course, students will be able to: · Understand and articulate the motivation, principles, and core concepts of nature-inspired algorithms. · Design, implement, and optimize meta-heuristic algorithms such as genetic algorithms, ant colony optimization, and bee colony optimization. · Implement and experiment with advanced algorithms like hybrid and memetic algorithms, and swarm intelligence techniques. · Design and utilize recent algorithms such as Cuckoo Search, Firefly algorithm, Bat algorithm, and Dolphin echolocation for various applications. · Apply these algorithms to real-world problems in diverse domains, demonstrating their practical utility.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Reading

Yang, X. S. (2020). Nature-inspired optimization algorithms. Academic Press.
Balamurugan, S., Jain, A., Sharma, S., Goyal, D., Duggal, S., & Sharma, S. (Eds.). (2021). Nature-Inspired Algorithms and Applications. John Wiley & Sons.

Yang, X. S. (2023). Nature-Inspired Algorithms in Optimization: Introduction, Hybridization, and Insights. In Benchmarks and Hybrid

Computational Data Analysis

Department Elective - III

Sl. No.

Course Code

Course Name

CS4106

Graph Machine Learning

Course Number	CS4106
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Graph Machine Learning
Learning Mode	Offline
Learning Objectives	Several real world systems can be represented as a network of entities that are connected to each other through some relations. Often the number of entities is immensely large, thus forming a very large network. Typical examples of such large networks include network of entities in knowledge graphs, co-occurrence graph of the keywords in natural languages, interaction graph of users in social networks, protein-protein interaction graphs and the network of routers in Internet to name a few. Study of these networks is often needed for relational learning tasks, as well as for developing frameworks for representing the intrinsic structure of the data. This course will mainly deal with both the traditional as well as current state of the art machine learning techniques to be applied on Graphs for different downstream tasks.
Course Description	The course will provide knowledge on the representation and statistical descriptions of large networks, along with traditional machine learning and deep learning techniques applied on graphs. Several use cases of Graph Machine Learning across different domains including Natural Language Processing, Social Network Analysis and Computational Biology would be studied.
Course Outline	Introduction and background knowledge of graphs; Network analysis metrics like paths, components, degree distribution, clustering, degree correlations, centrality etc., social network analysis methods; Spectral Analysis of Graphs and its applicability to graph partitioning and community detection; Overview of machine learning applications on graphs; Shallow embedding and deep Learning techniques for generating node and graph representations – Graph Neural Networks, Graph Attention Networks Random Networks; Graph Evolution, Generative models for graphs
Learning Outcome	Course training via lectures & tutorial sessions to · Represent and analyze the structure of graphs · Discover recurring and significant patterns of interconnections in your data with network motifs and community structure. · Gain Knowledge on traditional machine learning techniques applied on graphs · Leverage graph-structured data to make better predictions using graph neural networks · Understand the problems in dealing with large graphs for machine learning tasks and learn how to improvise.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested readings:

E.J. Newman, Networks - An introduction , Oxford Univ Press, 2010.
Yao Ma and Jilian Tang, Deep Learning on Graphs, Cambridge University Press, 2021

Goyal, Palash and Emilio Ferrara. “Graph embedding techniques, applications, and performance: A survey.” Knowl.-Based Syst. 151 (2018): 78-94.

CS4107

Bioinformatics

Course Number	CS4107
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Bioinformatics
Learning Mode	Offline
Learning Objectives	This course aims to help the students (a) Gain a thorough understanding of fundamental concepts in bioinformatics. (b) Develop problem-solving skills by implementing basic algorithms tailored for bioinformatics applications. (c) Explore various paradigms and approaches in bioinformatics as applied to biological data, such as sequence alignment, clustering, and classification. (d) Achieve proficiency in designing and implementing real-life bioinformatics projects that integrate deep learning techniques for data analysis and interpretation.
Course Description	This interdisciplinary course on bioinformatics aims to equip students with the knowledge and skills necessary to analyze and interpret biological data using computational tools and techniques. By focusing on fundamental concepts and providing hands-on experiences, students will learn to manage and analyze large-scale biological datasets. Through a combination of lectures, practical lab sessions, and collaborative projects, students will explore topics such as sequence alignment, gene expression analysis, protein structure prediction, and biological databases. Upon completion, students will be proficient in utilizing bioinformatics software and algorithms to address complex biological questions, preparing them for careers in research, biotechnology, and related fields.
Course Outline	Overview of biological databases: Protein Data Bank, SCOP, genome databases, and Cambridge Structural Database. Introduction to protein structures and biophysical methods for structure determination. Protein structure analysis, visualization techniques, and molecular modelling. Mining techniques using protein sequences and structures, including short sequence alignments and multiple sequence alignments. Phylogenetic analysis, genome context-based methods, and RNA/transcriptome analysis techniques. Mass spectrometry applications in proteome and metabolome analysis. Protein docking, dynamics simulation, and algorithms for handling big biological data challenges. Applications of Bioinformatics.
Learning Outcome	· Mastery of fundamental principles and techniques in bioinformatics, including sequence analysis, structural biology, and genomic data interpretation. · Proficiency in applying pattern recognition algorithms to solve biological data problems, such as sequence alignment, clustering, and classification. · Ability to critically analyze and interpret bioinformatics data using computational tools and techniques. · Understanding of the interdisciplinary nature of bioinformatics and its applications in biological research and medicine.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Reading

Mount, D.W., Bioinformatics: Sequence and Genome Analysis, Cold. Spring Harbor Laboratory Press, 2001.
Protein Bioinformatics: From Sequence to Function by M. Michael Gromiha Academic Press, 2010
Bioinformatics: A Practical Guide to the Analysis of Genes and Proteins 4th Edition, by Andreas D. Baxevanis (Editor), Gary D. Bader (Editor), David S. Wishart (Editor), WILEY

C. Branden and J. Tooze (eds) Introduction to Protein Structure, Garland, 1991

CS4108

Time Series Analysis

Course Number	CS4108
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Time Series Analysis
Learning Mode	Offline
Learning Objectives	· The course is designed to provide basic understanding time series analysis. · Develop Skills in statistical time series Analysis. · To learn variety of modeling techniques that can be used for time series analysis. · Gain proficiency in forecasting and anomaly detection methods · Apply the basic machine learning for time series analysis
Course Description	Using a set of fundamental techniques and broadly explains how time series analysis work at various levels of abstraction. The course introduces time series analysis with focus on applications
Course Outline	Basics of inferential and descriptive statistics: Population vs Sample; Measures of Central tendency, Measures of Variability, probability density functions, properties, mathematical expectation, hypothesis testing, ANOVA. Mathematical models for analysing time series data: Time Series Modelling, autoregressive integrated moving average (ARIMA), Exponential smoothing in time series analysis, process and the Box-Jenkins methodology. Outlier Analysis for Time Series, Multivariate Time Series Models and State-space Models, Forecasting Methods and Application Examples. Transfer Function Model Building. Imputation techniques, Point forecast and confidence intervals. Machine Learning Approaches for Time Series, Probabilistic Neural Networks, Different methods of estimation and inferences of modern dynamic stochastic general equilibrium models: simulated method of moments.
Learning Outcome	The student will be able to: · Appreciate understanding of the time series analysis, key terminology, and current industry trends in time series modeling · Evaluate time series model performance. · Create real-time applications, including anomaly detection and predictive maintenance
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Readings:

Palit, Ajoy K., and Dobrivoje Popovic. Computational intelligence in time series forecasting: theory and engineering applications. Springer Science & Business Media, 2006.
Box, George EP, Gwilym M. Jenkins, Gregory C. Reinsel, and Greta M. Ljung. Time series analysis: forecasting and control. John Wiley & Sons, 2015.
Brockwell, Peter J., Richard A. Davis, and Matthew V. Calder. Introduction to time series and forecasting. Vol. 2. New York: springer, 2002.
Pollock, David Stephen Geoffrey, Richard C. Green, and Truong Nguyen, eds. Handbook of time series analysis, signal processing, and dynamics. Elsevier, 1999.

Shumway, Robert H., and David S. Stoffer. Time series analysis and its applications: with R examples. Springer, 2017.

CS4109

Course Number	CS4109
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Computational Data Analysis
Learning Mode	Offline
Learning Objective	In this subject, the students will be trained with the knowledge of various computational techniques required for multi-dimensional data analysis such that they are able to apply these techniques in practice through programming, modeling etc.
Course Description	Modern day data is vast and diverse owing to their different acquisition systems and medium. This course aims to give an in-depth view to different data generation/acquisition mechanisms over diverse domains and the challenges incurred. It will discuss the role of computational data analysis techniques to understand and mathematically model data formation process. It will also teach them about the various data processing techniques required to manipulate and operate data to suit various objectives.
Course Outline	Understanding multi-dimensional data formation from physical acquisition devices with example cases in Remote Sensing, Geoscience, Medical sciences. Drawbacks and challenges in data acquisition, Necessity for computational modelling and analysis of data. Mathematical models for data formation and analysis, Probability models, Linear inverse optimization models, L1-L2 Regularizers, Minimizers, Cascade Modelling, Multiscale Modelling, Machine Learning models. Data Interpretation: Handling missing/corrupted data, Handling outliers, Imputation techniques, Interpolation techniques, Curve based approximation, non-convex optimization, sparse regularizers, Non-convex minimizers, Machine learning based. Data compression: Necessity, Applications, Lossless compression techniques, Lossy compression techniques, JPEG compression, Machine learning based. Statistical Models, Data preprocessing techniques in Machine learning, Signal processing techniques for multi-dimensional data, Application in various domains.
Learning Outcome	After completion of course, students will be able to · Understand data formation/generation process and the role of computational techniques in analyzing those data. · Apply the Mathematical principles behind computational techniques for data analysis. · Understand the utilities of statistical models and ML models in data analysis.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Readings:

Signal Processing: A Mathematical Approach, Charles L. Byrne, Second Edition, Chapman & Hall, 2014.
Digital Functions and Data Reconstruction: Digital-Discrete Methods, Li M Chen, Springer, 2013.
Machine Learning with Neural Networks: An Introduction for Scientists and Engineers, Bernhard Mehlig, Cambridge University Press, 2021
Signal Processing and Machine Learning with Applications, Michael M. Richter, Sheuli Paul, Veton Këpuska, Marius Silaghi, Springer Cham, 2022

Data Compression: The Complete Reference, David Solomon, 4^th Edition, Springer, 2007

CS4110

Blockchain Technology

Course Number	CS4110
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Blockchain Technology
Learning Mode	Offline
Learning Objectives	This course will introduce the fundamentals of the blockchain technology. It will highlight the use of blockchain technology in different applications and the learners will be able to develop decentralized applications.
Course Description	This course provides an introductory background of this revolutionary technology, followed by an interesting case study on bitcoin to demonstrate how the technology works. Following this, we would introduce Ethereum and Hyperledger. In addition, the course includes a number of hands-on sessions where we introduce basic blockchain tools and techniques, such as geth, ganache, remix, metamask, truffle, hyperledger, and real case studies.
Course Outline	Introduction and History; Blockchain Foundations; Generic elements of a blockchain; Features of blockchain; Types of blockchain; Applications of blockchain technology; Cryptocurrency and bitcoin basics; Introduction to Ethereum/Hyperledger and Programming; Privacy, Safety and Security Issues in blockchain; Some ongoing research topics.
Learning Outcome	· Gain proficiency in blockchain technology. · Understanding of how bitcoin/ethereum/hyperledger work. · Hands-on experience with various blockchain platforms, tools and techniques.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Readings:

Arvind Narayanan, Joseph Bonneau, Edward Felten, Andrew Miller, Steven Goldfeder, Bitcoin and Cryptocurrency Technologies – A Comprehensive Introduction, Princeton University Press, 2016.
Roger Wattenhofer, The Science of the Blockchain, Inverted Forest Publishing, First Edition, 2016.

Recent Research Papers relevant to the course.

CS4111

Evolutionary Computing

Course Number	CS4111
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Evolutionary Computing
Learning Mode	Offline
Learning Objectives	· To grasp the overview, principles, and different types of evolutionary computation methods. · To learn the fundamentals, representation, genetic operators (selection, crossover, mutation), and applications of BGA (Binary-Coded Genetic Algorithm) through simulations. · To Understand the introduction, differences from BGA, genetic operators for real-coded genes, and applications of RGA (Real-Coded Genetic Algorithm) through simulations. · To gain knowledge of the principles, applications, coding, simulations, and performance analysis of PSO (Particle Swarm Optimization). · To understand the fundamentals, applications, and implementations of DE (Differential Evolution).
Course Description	This course introduces students to evolutionary computing, focusing on principles, algorithms, and applications in optimization and search problems. Students will learn to implement various evolutionary algorithms using programming languages, explore advanced topics in evolutionary computing research, and apply these techniques to solve real-world optimization problems across diverse domains.
Course Outline	Introduction to Evolutionary Computation: Overview, principles, and types of evolutionary computation. Binary-Coded Genetic Algorithm (BGA): Fundamentals, representation, and applications of BGA. Operators and Simulations of BGA: Genetic operators (selection, crossover, mutation) and BGA simulations. Real-Coded Genetic Algorithm (RGA): Introduction, differences from BGA, and applications of RGA, Genetic operators for real-coded genes and RGA simulations. Particle Swarm Optimization (PSO): Introduction, principles, and applications of PSO, Simulations and Algorithmic Implementation of PSO, Coding, simulations, and performance analysis of PSO. Differential Evolution (DE): Fundamentals, applications, and implementations of DE.
Learning Outcome	At the end of the course, students will have achieved the following learning objectives. · Demonstrate a comprehensive understanding of evolutionary computing principles and algorithms. · Design and implement evolutionary algorithms to solve optimization problems. · Evaluate the performance of evolutionary algorithms using appropriate metrics and benchmarks. · Apply evolutionary computing techniques to various domains, such as engineering design, scheduling, and data mining. · Critically analyze and compare different evolutionary algorithms and their variants. · Communicate effectively about evolutionary computing concepts, methods, and applications.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Reading

Dan Simon, Evolutionary Optimization Algorithms, John Wiley & Sons, 1st Edition, 2013
Carlos A. Coello Coello, Evolutionary Algorithms for Solving Multi-Objective Problems, Springer, 2nd Edition, 2007

Thomas Bäck, David B. Fogel, and Zbigniew Michalewicz, Handbook of Evolutionary Computation, Oxford University Press, 1st Edition, 1997

Statistical Machine Learning

Department Elective - IV

Sl. No.

Course Code

Course Name

CS4201

Multivariate Analysis

Course number	CS4201
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Multivariate Analysis
Learning Mode	offline
Learning Objectives	Multivariate analysis is about handling vector valued data. In ordinary regression modeling we are used to a vector valued predictor. But a vector valued response variable brings new issues. Sometimes we can handle a k dimensional response by treating it as k unrelated 1 dimensional problems. But often that approach will fail to find the key structure. Sometimes we are forced to study the data as an inherently k dimensional thing. It can also pay to reduce the dimension k, sometimes to 3 or 2 where plotting is available, sometimes to k=1 where ordinary methods can then be applied. Also, some of the methods are useful for exploratory work and not just for modeling responses.
Course Description	This course will provide an overview of different statistical methods applied in data science.
Course Outline	1. Multivariate Normal Distribution Theory: Joint, marginal, and conditional distribution; distributions of linear functions and quadratic forms of multivariate normal random variables 2. Correlation Analysis, Linear Regression, and Predication: Simple correlation, partial correlation, multiple correlation, linear regression equation, best prediction function and best linear predication function 3. Sampling Distributions: Sampling distributions for the mean vector and for the various correlation coefficients, partitioning of sum of squares, Hotelling's T2 distribution, the Wishart distribution 4. Introduction to Multivariate Probability Inequalities via Dependence and Heterogeneity 5. Estimation of Parameter Vectors via applications of the results on the topics in (3) and (4) above, especially for elliptical and rectangular confidence regions 6. Hypotheses Testing for Parameter Vectors 7. Multivariate Discriminant Analysis and Classification Theory, with Specific Applications to Medicine and Pattern Recognition
Learning Outcome	· Basic understanding of multivariate analysis · Problem modeling skill considering uncertainty
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Reading:

Multivariate Statistical Methods: a Primer" by B.F.J. Manly.
"Modern Applied Statistics with S" by Venables and Ripley.

CS4202

Generative AI

Course Number	CS4202
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Generative AI
Learning Mode	Offline
Learning Objectives	· To provide a comprehensive understanding of advanced AI concepts with a focus on generative AI. · To design and implement various generative models such as GANs, VAEs, and Diffusion Models. · To explore the architecture and applications of Generative Pre-trained Transformers (GPT). · To design application-specific architectures for prompt engineering and multimodal generative AI. · To analyze and address ethical considerations in the development and deployment of generative AI models. · To conduct independent research and projects involving advanced generative AI techniques.
Course Description	This course provides an in-depth exploration of advanced artificial intelligence (AI) concepts, with a specific focus on generative AI (GenAI). Students will delve into advanced generative models, including Generative Adversarial Networks (GANs), Variational AutoEncoders (VAEs), Diffusion Models, and Generative Pre-trained Transformers (GPT). The course also covers the application of these models across various domains, the design of application-specific architectures for prompt engineering, and multimodal generative AI. Additionally, ethical considerations surrounding the use of generative AI will be discussed. By the end of the course, students will have the knowledge and skills to design, implement, and evaluate advanced generative AI models and understand their ethical implications.
Course Outline	Introduction to Generative AI (GenAI): Overview of GenAI, historical context and scope. Generative Adversarial Networks (GAN) and Deep Convolutional GAN (DCGAN): Understanding the architecture of GANs, Training dynamics and loss functions in GANs, Implementation and applications of DCGANs, Challenges and solutions in training GANs. Advanced Variational AutoEncoders (VAE): Fundamentals of VAEs and their architectures, Latent space representation and sampling techniques, Advanced VAE variants and their improvements, Applications of VAEs in image and data generation. Basics of Diffusion Models and Attention Mechanisms in Generative Models: Introduction to diffusion models and their principles, Understanding the role of attention mechanisms in generative models, Implementation of attention-based generative models, Case studies and applications of diffusion models. Generative Pre-trained Transformers (GPT) Basics: Overview of transformer architecture, Understanding the training and functioning of GPT models, Applications of GPT models in text generation and NLP, Fine-tuning and optimizing GPT for specific tasks. Application-Specific Architecture for Prompt Engineering and Multimodality: Designing and optimizing prompt engineering techniques, Exploring multimodal generative models, Integrating text, image, and audio in generative models, Case studies of application-specific generative architectures. Ethical Considerations in Generative AI: Understanding the ethical implications of Generative AI, Addressing bias, fairness, and accountability in generative models, Privacy concerns and data security in Generative AI.
Learning Outcome	By the end of this course, students will be able to: · Understand the foundational concepts and the latest advancements in artificial intelligence and generative AI. · Design and implement Generative Adversarial Networks (GANs) and their advanced variants, such as DCGAN. · Develop and apply advanced Variational AutoEncoders (VAEs) for generative tasks. · Grasp the basics of Diffusion Models and the role of attention mechanisms in enhancing generative models. · Understand the architecture and functioning of Generative Pre-trained Transformers (GPT) and their applications. · Create application-specific architectures for prompt engineering and explore the integration of multimodal generative AI techniques. · Analyze and address ethical considerations and challenges in the development and deployment of generative AI models. · Conduct independent research and projects involving advanced generative AI techniques, demonstrating a comprehensive understanding of both theoretical and practical aspects.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Reading

Foster, D. (2022). Generative deep learning: Teaching Machines to Paint, Write, Compose, and Play. O'Reilly Media, Inc.
Valle, R. (2019). Hands-On Generative Adversarial Networks with Keras: Your guide to implementing next-generation generative adversarial networks. Packt Publishing Ltd.

Research Papers and Articles from Journals such as JMLR, IEEE Transactions on Neural Networks and Learning Systems, etc., and Conference Proceedings from NeurIPS, ICML, and CVPR,etc.

CS4203

Course Number	CS4203
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Statistical Machine Learning
Learning Mode	Offline
Learning Objectives	The learning objectives of the course includes understanding the basic concepts of machine learning, and classic algorithms such as Support Vector Machines and Neural Networks, Deep Learning. The students would be able to explain the basic principles and theory of machine learning, that would guide to invent their own algorithms.
Course Description	This is an introductory course on statistical machine learning which presents an overview of many fundamental concepts, popular techniques, and algorithms in statistical machine learning. It covers basic topics such as dimensionality reduction, linear classification and regression as well as more recent topics such as ensemble learning/boosting, support vector machines, kernel methods and manifold learning. This course will provide the students the basic ideas and intuition behind modern statistical machine learning methods. After studying this course, students will understand how, why, and when machine learning works on practical problems.
Course Outline	Statistical Theory: Maximum likelihood, Bayes, minimax, parametric versus nonparametric methods, Bayesian versus Non-Bayesian approaches, classification, regression, density estimation. Convexity and Optimization: Convexity, conjugate functions, unconstrained and constrained optimization, KKT conditions. Parametric Methods: Linear regression, model selection, generalized linear models, mixture models, classification, graphical models, structured prediction, hidden Markov models Sparsity: High dimensional data and the role of sparsity, techniques for handling sparsity. Nonparametric Methods: Nonparametric regression and density estimation, nonparametric classification, clustering and dimension reduction, manifold methods, spectral methods, the bootstrap and subsampling, nonparametric Bayes. Other Learning Methods: Semi-supervised learning, reinforcement learning, minimum description length, online learning, the PAC model, active learning
Learning Outcome	On successful completion of this course students will be able to: • Explain the basic concepts of machine learning, and classic algorithms such as Support Vector Machines and Neural Networks, Deep Learning. • Explain the basic principles and theory of machine learning, which may guide students to invent their own algorithms in future. • Ability to program the algorithms in the course. • Ability to do mathematical derivation of the machine learning algorithms.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested readings:

Chris Bishop, Pattern Recognition and Machine Learning, Springer, Information Science and Statistics Series, 2006.
Trevor Hastie, Robert Tibshirani, Jerome Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer Texts in Statistics, SpringerVerlag, New York, 2001.

Larry Wasserman, All of Statistics: A Concise Course in Statistical Inference, Springer Texts in Statistics, Springer-Verlag, New York, 2004.

CS4204

Text Mining

Course Number	CS4204
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Text Mining
Learning Mode	Offline
Learning Objectives	· To understand the fundamental principles and key concepts in text mining. · To gain the ability to collect and preprocess text data, including cleaning and integration. · To master text preprocessing techniques such as tokenization, stemming, stopword removal, and normalization. · To learn the construction and utilization of knowledge graphs for relationship extraction. · To implement frequent pattern mining and association rules using algorithms like apriori. · To extract features using methods like Bag-of-Words, TF-IDF, and word embeddings. · To apply clustering and classification techniques to text data. · To utilize text mining techniques in practical applications, such as sentiment analysis.
Course Description	This course provides a comprehensive understanding of the fundamental principles and techniques used in text mining. Students will learn the entire process from data collection and preprocessing to advanced techniques for mining patterns and analyzing text. The course covers practical applications, such as sentiment analysis, equipping students with the skills needed to extract meaningful insights from large datasets and text corpora. By the end of the course, students will be adept at employing text mining techniques to solve real-world problems.
Course Outline	Text mining introduction: Overview, motivation, challenges and opportunities, Data Collection and Pre-processing: Techniques for collecting data from various sources Data cleaning and integration: Handling noise, missing values, and inconsistent formats in text data Text preprocessing: tokenization, stemming, stopword removal, and normalization Knowledge graph construction: Basics of graph construction and relationship extraction Basic concepts of frequent patterns, association rules, mining frequent patterns: apriori algorithm. Feature extraction, Bag-of-Words, TF-IDF, word embeddings Clustering and classifying text data Some applications: sentiment analysis, etc.
Learning Outcome	By the end of this course, students will be able to: · Grasp key concepts, motivation, and challenges in text mining. · Collect and preprocess data, including cleaning and integration. · Perform text preprocessing tasks like tokenization, stemming, stopword removal, and normalization. · Construct and utilize knowledge graphs for relationship extraction. · Implement frequent pattern mining and association rules using the apriori algorithm. · Extract features using Bag-of-Words, TF-IDF, and word embeddings. · Apply clustering and classification to text data. · Use data mining and text analytics techniques in applications such as sentiment analysis.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Reading:

Srivastava, A. N., & Sahami, M. (Eds.). (2009). Text mining: Classification, clustering, and applications. CRC press.
Jiawei, H., & Micheline, K. (2006). Data mining: concepts and techniques. Morgan kaufmann.

Witten, I. H., Frank, E., Hall, M. A., Pal, C. J., & Data, M. (2005, June). Practical machine learning tools and techniques. In Data mining (Vol. 2, No. 4, pp. 403-413). Amsterdam, The Netherlands: Elsevier.

Department Elective - V

Department Elective - V

Sl. No.

Course Code

Course Name

CS4205

Cloud Computing

Course Number	CS4205
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Cloud Computing
Learning Mode	Offline
Learning Objectives	This course aims to help the students (a) define and explain the fundamental concepts and principles of cloud computing; (b) identify and describe various cloud computing service models (IaaS, PaaS, SaaS) and deployment models (public, private, hybrid, community); (c) understand the underlying technologies and infrastructure used in cloud computing, including virtualization, containers, and software-defined networking; (d) evaluate the benefits and challenges of adopting cloud computing for businesses and organizations; (e) design and implement cloud-based solutions for common use cases, such as web hosting, data storage, and application development; and (f) analyze security, privacy, and compliance considerations in cloud computing environments.
Course Description	This course provides a comprehensive overview of cloud computing, covering its fundamental concepts, architecture, and deployment models. Students will explore the various service models, including Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS), and understand the benefits and challenges associated with each. The course delves into cloud storage, computing resources, and virtualization technologies, offering hands-on experience with leading cloud platforms such as AWS, Azure, and Google Cloud. Security, compliance, and cost management in cloud environments are also addressed, equipping students with the skills to design, deploy, and manage cloud-based solutions effectively.
Course Outline	Introduction to Cloud Computing- Overview of cloud computing and its key principles, Fundamentals of distributed systems: Models and architectures. Cloud Storage and Virtualization- Understanding cloud storage technologies: Key-value stores, NoSQL databases, Virtualization techniques for resource abstraction and management Distributed Algorithms in Cloud Computing- Fault tolerance and consensus algorithms: PAXOS, leader election, Time ordering and distributed mutual exclusion. Industry Systems and Cloud Platforms- Overview of industry-standard cloud platforms: Apache Spark, Apache Zookeeper, HBase, Introduction to containerization technologies: Docker, Kubernetes Advanced Topics in Cloud Computing- Big data processing in the cloud: MapReduce, Apache Cassandra, Emerging trends in cloud computing: Edge computing, serverless architectures
Learning Outcome	· Define and explain the key concepts and components of cloud computing, including virtualization, elasticity, and on-demand provisioning. · Evaluate different cloud computing service models and deployment models, and select appropriate options for specific use cases and requirements. · Demonstrate proficiency in deploying and managing cloud-based resources using popular cloud platforms (e.g., AWS, Azure, Google Cloud). · Analyze the economic factors and cost considerations associated with cloud computing, including pricing models and Total Cost of Ownership (TCO) calculations. · Design and implement scalable and resilient cloud architectures using best practices and design patterns. · Assess security risks and implement appropriate security controls to protect cloud-based assets and data.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Reading:

Distributed and Cloud Computing From Parallel Processing to the Internet of Things; Kai Hwang, Jack Dongarra, Geoffrey Fox Publisher: Morgan Kaufmann, Elsevier, 2013.
Cloud Computing: Principles and Paradigms; Rajkumar Buyya, James Broberg, and Andrzej M. Goscinski Publisher: Wiley, 2011.
Distributed Algorithms Nancy Lynch Publisher: Morgan Kaufmann, Elsevier, 1996.
Cloud Computing Bible Barrie Sosinsky Publisher: Wiley, 2011.

Cloud Computing: Principles, Systems and Applications, Nikos Antonopoulos, Lee Gillam Publisher: Springer, 2012.

CS4206

Quantum Computing

Course Number	CS4206
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Quantum Computing
Learning Mode	Offline
Learning Objectives	This course aims to help the students (a) comprehend the foundational principles of quantum mechanics that underpin quantum computing.(b) proficiency in designing and analyzing quantum circuits.(c) explore and understand advanced quantum algorithms used in quantum computing.(d) quantum computing principles to solve computational problems and simulate quantum systems.
Course Description	Explore the foundational principles and transformative potential of quantum mechanics and quantum computing in this comprehensive course. Students will delve into quantum mechanics, covering concepts like superposition, entanglement, and quantum measurement, and their application to quantum computing. Through lectures, practical sessions, and case studies, participants will master quantum circuit design, analyze advanced quantum algorithms such as Grover's and Shor's algorithms, and apply these principles to solve real-world computational problems. By the end of the course, students will possess the theoretical understanding and practical skills needed to contribute to the rapidly advancing field of quantum computing across diverse industries.
Course Outline	States, Wavefunction, Orthogonality and Orthonormality of Wave function, Superposition Quantum Circuits: Single-qubit gates, Multiple qubit gates, Design of quantum circuits, Dirac Notations, Measurements, Bloch Sphere Entanglement, Bell State, Teleportation, Q-Sphere, Data Structures for Quantum Computing, Quantum Annealing Quantum Algorithms: Grover’s Search Algorithm, Shor’s Factoring Algorithm, Quantum Amplitude Estimation, Quantum Phase Estimation, Quantum Fourier Transform
Learning Outcome	Understand Fundamental Quantum Mechanics Principles. Develop Skills in Quantum Circuit Computing and Analysis. Explore Advanced Quantum Computing Concepts. Gain proficiency in Master Quantum Algorithms.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Textbooks:

Nielsen, M.A. and Chuang, I.L., 2010. Quantum computation and quantum information.
Pittenger, A.O., 2012. An introduction to quantum computing algorithms (Vol. 19).
Relevant research articles.

Reference books:

Bernhardt, C., 2019. Quantum computing for everyone.

CS4207

Drone Data Processing

Course Number	CS4207
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Drone Data Processing
Learning Mode	Offline
Learning Objectives	This course aims to help the students (a) gain foundational knowledge of unmanned aerial systems (UAS), including their history, components, and classifications; (b) comprehend the various elements that make up a drone system, such as the air vehicle, communication data links, command and control elements, payloads, and launch/recovery systems; (c) acquire the ability to design and plan drone missions, including studying area maps, designing flight routes, and calibrating sensors; (d) learn the principles and practices of photogrammetry and geographic information systems (GIS) for processing and analyzing drone-collected data; (e) understand the importance of data quality, accuracy standards, error estimation, and strategies for achieving high-precision geospatial data.
Course Description	This course offers an in-depth exploration of Unmanned Aerial Systems (UAS) and drone operations, providing a comprehensive understanding of their history, types, and technological advancements. Students will learn about the various categories and missions of drones, the design and communication systems essential for drone functionality, and the roles and responsibilities in UAS operations. The course covers the fundamentals of geospatial data, photogrammetry, and GIS, emphasizing map accuracy and mission planning.
Course Outline	Introduction to Unmanned Aerial Systems (UAS). Types, Categories, and Missions of Drones. Drone Design and Communication Systems. Concepts of Operations (CONOP) and Risk Assessment Geospatial Data and Photogrammetry. Drone Mission Planning and Control. Route Planning and Operational Fundamentals. Regulatory Requirements and Guidelines. Applications and Challenges in Drone Operations
Learning Outcome	· Identify and categorize various types of unmanned aerial systems and their specific missions. · Create comprehensive mission plans, including route design, sensor selection, and calibration, ensuring optimal data collection. · Utilize photogrammetric methods and GIS tools to process and analyze drone-collected data, producing accurate geospatial products. · Assess data accuracy and quality, understand and apply mapping standards, and manage errors in measurements effectively. · Apply drone technology in diverse fields such as agriculture, construction, environmental monitoring, and disaster response.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Reading:

Barnhart, R., Michael, M., Marshall, D., and Shappee, E. ed. 2016. Introduction to Unmanned Aircraft Systems, 2nd edition. Boca Raton. CRC Press.
Fahlstrom, P. and Gleason, T. 2012. Introduction to UAV Systems. 4th edition. United Kingdom. John Wiley & Sons Ltd.
Wolf, P., DeWitt, B., and Wilkinson, B. 2014. Elements of Photogrammetry with Applications in GIS, 4th edition. McGraw-Hil
Introduction to UAV Systems, Paul G. Fahlstrom and Thomas J. Gleason
Drone Technology in Architecture, Engineering, and Construction, Daniel Tal and Jon Altschuld

UAV or Drones for Remote Sensing Applications, edited by Felipe Gonzalez Toro and Antonios Tsourdos

CS4208

Edge Computing

Course Number	CS4208
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Edge Computing
Learning Mode	Offline
Learning Objectives	This course aims to help the students (a) to define edge computing and its role in modern computing paradigms; (b) to understand the principles and benefits of moving computation closer to the data source; (c) to identify various edge computing architectures, including fog computing and mobile edge computing; (d) to analyze and compare edge computing frameworks and platforms; (e) design and implement edge computing solutions to address latency, bandwidth, and privacy concerns; (f) to evaluate the impact of edge computing on traditional cloud computing models and network infrastructures; (g) to discuss emerging trends and challenges in edge computing, such as security, interoperability, and resource management; and (h) to apply edge computing principles and techniques to real-world scenarios and use cases.
Course Description	This course provides a comprehensive overview of edge computing, starting with the limitations of cloud computing in supporting low latency and round trip time (RTT), and the subsequent innovation waves leading to edge computing. Students will delve into edge computing architectures and their applications, including 5G slicing and self-driving cars. Key concepts of distributed systems such as time ordering, clock synchronization, and distributed snapshots will be explored within the context of edge computing. The course also introduces edge data centers, lightweight edge clouds, and services provided by various service providers. Practical knowledge of Docker containers and Kubernetes in edge computing, along with the design of edge storage systems like key-value stores, will be covered. Additionally, students will learn about MQTT and Kafka for creating end-to-end edge pipelines and edge analytics topologies for M2M and WSN networks. The course concludes with use cases of machine learning for edge sensor data, including predictive maintenance, image classification, and deep learning on-device inference to support latency-sensitive applications.
Course Outline	Introduction to Cloud and its limitations to support low latency and Round Trip Time (RTT). From Cloud to Edge computing: Waves of innovation. Introduction to Edge Computing Architectures. Edge Computing to support User Applications (5G-Slicing, self-driving cars and more) Concepts of distributed systems in edge computing such as time ordering and clock synchronization, distributed snapshot, etc. Introduction to Edge Data Center, Lightweight Edge Clouds and its services provided by different service providers. Introduction to docker container and Kubernetes in edge computing. Design of edge storage systems like key-value stores. Introduction to MQTT and Kafka for end-to-end edge pipeline. Edge analytics topologies for M2M and WSN network (MQTT) Use cases of machine learning for edge sensor data in predictive maintenance, image classifier and self-driving cars. Deep Learning On-Device inference at the edge to support latency-based application
Learning Outcome	· Define and explain the concept of edge computing and its significance in distributed computing architectures. · Analyze the advantages and limitations of edge computing compared to traditional centralized and cloud-based approaches. · Identify and describe different edge computing architectures, such as hierarchical, decentralized, and hybrid models. · Evaluate edge computing platforms and tools for their suitability in various application domains. · Design and implement edge computing solutions that leverage distributed computing principles to improve performance, reliability, and efficiency. · Analyze the impact of edge computing on network traffic, data privacy, and regulatory compliance. · Critically assess the security implications of deploying edge computing systems and propose mitigation strategies. · Collaborate in teams to develop and present case studies or projects demonstrating the practical application of edge computing concepts and techniques.
Assessment ethod	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Reading:

Fog and Edge Computing: Principles and Paradigms, Rajkumar Buyya (Editor), Satish Narayana Srirama (Editor), Wiley, 2019
Cloud Computing: Principles and Paradigms, Editors: Rajkumar Buyya, James Broberg, Andrzej M. Goscinski, Wiley, 2011
Cloud and Distributed Computing: Algorithms and Systems, Rajiv Misra, Yashwant Patel, Wiley 2020.

Journal papers as references.

CS4209

Wireless Networks

Course Number	CS4209
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Wireless Networks
Learning Mode	Offline
Learning Objectives	In this subject, the students will be trained with the knowledge of 802.11 wireless networks, including protocol knowledge and the associated security vulnerabilities.
Course Description	In the consumer, industrial, and military sectors, 802.11-based wireless access networks have been widely used due to their convenience. This application, however, is reliant on the unstated assumptions of availability and anonymity. The management and media access protocols of 802.11 may be particularly vulnerable to malicious denial-of-service (DoS) and various security attacks. This course analyzes these 802.11-specific attacks, including their applicability, effectiveness, and proposed low-cost implementation improvements to mitigate the underlying vulnerabilities.
Course Outline	Overview of 802.11 networks, 802.11 MAC Layer, Wireless LAN physical components. Wireless LAN topologies and technologies - 802.11 a/b/g/n/ac features. Configure and install wireless adapters, access points. 802.11 architecture (access points, SSID, channels, beacons, scanning, association), Hidden terminal problem, RTS/CTS, 802.11 CSMA-CA protocol. Wireless communication technology: FHSS, DSSS, CDMA etc. Physical Layer, MAC Layer, MAC Management, Power Management. Multiple access protocols: ALOHA, Carrier sense multiple access protocols, collision free protocols. 802.11 Frame Structure & WLAN services-association, disassociation, re-association, distribution, integration, authentication, de-authentication and data delivery services. Security Features of 802.11: WEP, WPA1, and WPA2, PSK Authentication, TKIP Encryption and AES-CCMP Encryption.
Learning Outcome	On successful completion of the course, students should be able to: · Understand the fundamentals of 802.11 wireless networks · Describe the WLAN services-association, disassociation, re-association, distribution, integration, authentication, de authentication and data delivery services · Comprehend the vulnerabilities associated with 802.11 protocol.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Text Books and References:

"Wireless Communications: Principles and Practice" by Theodore S. Rappaport (2nd Edition)
"802.11 Wireless Networks: The Definitive Guide" by Matthew S. Gast (2nd Edition)
"Wireless Communications & Networks" by William Stallings (2nd Edition)
"Wireless Communications: Principles and Practice" by Andreas F. Molisch (2nd Edition)
"Fundamentals of Wireless Communication" by David Tse and Pramod Viswanath (1st Edition)
"Next Generation Wireless LANs: 802.11n and 802.11ac" by Eldad Perahia and Robert Stacey (2nd Edition)
"Wireless Networking: Understanding Internetworking Challenges" by Anurag Kumar, D. Manjunath, and Joy Kuri 1st Edition)

8. "Wireless Communications: Principles and Practice" by Kaveh Pahlavan and Prashant Krishnamurthy (1st Edition)

Department Elective - VI

Department Elective - VI

Sl. No.

Course Code

Course Name

CS4210

Computer Security

Course Number	CS4210
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Computer Security
Learning Mode	Offline
Learning Objectives	To have a clear understanding of security and privacy issues in various aspects of computing, including: Programs, Operating systems & Networks
Course Description	The course covers. security and privacy issues in various aspects of computing, including: Programs, Operating systems, Networks, Web Applications
Course Outline	Introduction to Computer Security and Privacy: security and privacy; types of threats and attacks; methods of defense Program Security: nonmalicious program errors; vulnerabilities in code, Secure programs; malicious code; Malware detection Operating System Security: Methods of protection; access control; user authentication Network Security: Network threats; firewalls, intrusion detection systems Application Security and Privacy: Basics of cryptography; security and privacy for Internet applications, IPSEC, TLS
Learning Outcome	After completion of this course a student will have · Understanding of security issues in computing at program, , · Understand the operations of different malware · The ability to analysis Malwares · Ability to analyse the security of Operating system and Networks
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Readings:

Security in Computing, Charles P. Pfleeger and Shari Lawrence Pfleeger, 4^th edition or later Prentice-Hall, 2007

Computer Security: Principles and Practice, Dr. William Stallings and Lawrie Brown, Pearson

CS4211

Cryptography

Course Number	CS4211
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Cryptography
Learning Mode	physical
Learning Objectives	To have a clear understanding of design and analysis of different cryptographic primitives
Course Description	The course covers design and analysis of different cryptographic primitives including Symmetric and asymmetric key cryptography
Course Outline	Mathematical Background: Modular Arithmetic, Finite Fields, The Group Law, Elliptic Curves over Finite Fields , Projective Coordinates. Symmetric Encryption: Shift Cipher, Substitution Cipher, Permutation Cipher, Stream Cipher Basics, Linear Feedback Shift Registers, RC4; Block Ciphers: DES, AES, and Different modes of Block ciphers. Key Management, Secret Key Distribution. Hash Functions and Message Authentication Codes: SHA, MD5, HMAC. Public Key Encryption: RSA, ElGamal Encryption, Rabin Encryption, Elliptic curve based encryption. Digital Signatures: RSA based, DSA, ECDSA. Public key based infra structure. Key Exchange: Diffie–Hellman Key Exchange, Authenticated Key Agreement
Learning Outcome	After completion of this course a student will have · Understanding of modular arithmetic and Finite fields, · Understanding and analysis of symmetric key cryptography DES, AES · Understanding and analysis of Hash function, MAC function, · Understanding and analysis of asymmetric key cryptography · Understanding and analysis of key agreement protocols
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Readings:

Mao, Modern Cryptography: Theory and Practice. Pearson Education
Hand book of applied cryptography by A. Menezes, CRC press
Doug Stinson, Cryptography: Theory and Practice, Chapman and Hall/CRC,

CS4212

Big Data Analytics

Course Number	CS4212
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Big Data Analytics
Learning Mode	Offline
Learning Objectives	The objective of this course is to provide students (a) with a comprehensive understanding of Big Data analytics, covering the challenges, applications, and technologies involved in managing and analyzing large-scale data; (b) about the Big Data stack, various Big Data platforms (such as Apache Spark, HDFS, and YARN), and the MapReduce programming model; (c) knowledge of Big Data storage platforms, streaming platforms, and machine learning algorithms in Spark, including an introduction to deep learning for Big Data; (d) information about Big Data applications in graph processing.
Course Description	This comprehensive course provides an in-depth overview of big data and its significant impact across various industries. Students will explore the foundational characteristics of big data, including Volume, Velocity, Variety, Veracity, and Value, and understand the distinctions between big data and traditional data.
Course Outline	Introduction to Big Data: Overview of big data and its characteristics (Volume, Velocity, Variety, Veracity, Value), Big data vs. traditional data, Introduction to big data technologies and tools, Applications of big data in various industries. Big Data Architecture, Components of big data architecture, Distributed computing and storage, Introduction to Hadoop ecosystem (HDFS, YARN, MapReduce), Overview of other big data platforms (Spark, Flink, Storm) Data Ingestion and Storage, Data ingestion techniques and tools (Flume, Kafka, Sqoop), NoSQL databases (HBase, Cassandra, MongoDB), Data warehousing solutions (Hive, HBase), Real-time data processing. Data Processing with Hadoop, Hadoop Distributed File System (HDFS), MapReduce programming model, Writing and executing MapReduce jobs, Data processing workflows with Apache Pig. Data Processing with Apache Spark, Introduction to Apache Spark, Spark Core and RDDs (Resilient Distributed Datasets), Spark SQL and DataFrames, Spark Streaming for real-time data processing Data Analysis and Visualization, Exploratory Data Analysis (EDA) techniques, Data visualization tools (Tableau, Power BI, D3.js), Creating dashboards and reports, Visualizing big data with Python (Matplotlib, Seaborn). Applying machine learning algorithms to big data (classification, regression, clustering), MLlib: Spark’s machine learning library, Time-series analysis and forecasting, Text mining and sentiment analysis, Graph analytics with big data, Recommender systems Overview of cloud platforms for big data (AWS, Azure, Google Cloud), Cloud-based big data services and tools, Deploying big data applications in the cloud, Scalability and performance optimization. Security and Privacy in Big Data, Data privacy and security challenges in big data, Data anonymization and encryption techniques, Regulatory and compliance considerations (GDPR, CCPA), Best practices for securing big data, Real-world big data applications in healthcare, finance, marketing, and IoT.
Learning Outcome	· Comprehend the introduction, challenges, and applications of Big Data. · Understand the components and distribution packages of the Big Data stack. · Work with Apache Spark, HDFS, YARN, and implement the MapReduce programming model. · Manage Big Data Storage · Apply Machine Learning in Big Data · Explore Big Data Applications in Graph Processing · Understand and utilize Pregel, Giraph, and Spark GraphX for graph processing.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Reading

Bart Baesens, Analytics in a Big Data World: The Essential Guide to Data Science and its Applications, Wiley, 2014
Dirk Deroos et al., Hadoop for Dummies, Dreamtech Press, 2014.
Chuck Lam, Hadoop in Action, December, 2010
Mining of Massive Datasets. Leskovec, Rajaraman, Ullman, Cambridge University Press
Data Mining: Practical Machine learning tools and techniques, by I.H. Witten and E. Frank

Erik Brynjolfsson et al., The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies, W. W. Norton & Company, 2014

CS4213

Computer Forensics

Course Number	CS4213
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Computer Forensics
Learning Mode	Offline/online
Learning Objectives	This course aims to: · impart principles and techniques for digital forensics investigation · make aware of various digital forensics tools · guide one how to perform forensics procedures to ensure court admissibility of evidence, as well as the legal and ethical implications
Course Description	Digital forensics involves the investigation of computer-related crimes with the goal of obtaining evidence to be presented in a court of law. In this course, students will learn the principles and techniques for digital forensics investigation and the spectrum of available computer forensics tools. One will learn about core forensics procedures to ensure court admissibility of evidence, as well as the legal and ethical implications. One will learn how to perform a forensic investigation on both Unix/Linux and Windows systems with different file systems. One will also be guided through forensic procedures and review and analyze forensics reports. Although the course does not have any lab components but students may have to work out some assignments/case project works related to data analysis and data recovery, data acquisition, recovering graphics file, validation of a forensic image file, etc.
Course Outline	Digital Forensics Fundamentals: Overview, Preparation for Digital Forensics, Conducting Investigation, Understanding Forensics Lab requirements, Cyber Laws Data Acquisition: Understanding the storage formats, Determining acquisition method, Use of acquisition tools, Validating data acquisition Processing crime and incident scenes: Identifying digital evidence, preparing for a search, Seizing and storing Digital Evidence Working with Windows and Linux File Systems: Understanding File Systems, Exploring Microsoft File Structure, Examining NTFS Disks, Windows Registry, Virtual Machine, File structure in Ext4, Some Forensics Tools: Software Tools, Hardware Tool, Validating and Testing Forensics Software, Password protection, Password Recovery Tools Recovering Graphics Files: Recognizing Graphics File, Understanding Data Compression, Identifying Unknown File Formats, Understanding Copyright Issues with Graphics Digital Forensics Analysis and Validation: Determining what data to collect and analyze, Validating Forensics Data, Addressing Data Hiding Techniques, Forensics handwriting and signature analysis Overview Email and Social Media Investigations, Mobile Device Forensics, Cloud Forensics, Memory Forensics
Learning Outcome	Upon successful completion of this course, the students will: · be able to perform forensics analysis using digital evidence · gain exposure on analyzing the performance of various forensics tools · obtain more in depth knowledge on various file system related artifacts
Assessment Methods	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Readings:

Amelia Phillips, Bill Nelson, Christopher Steuart - “Guide to Computer Forensics and Investigations”, 6^th Editon, Cengage
Darren Hayes: Practical Guide to Digital Forensics Investigations, Pearson
Michael K. Robinson: Digital Forensics: Hands-on Activities in Digital Forensics, Createspace Independent Pub; Workbook edition
Gerard Johnsen, Digital Forensics and Incident Response: Incident response tools and techniques for effective cyber threat response, 3rd Edition, 2022
William Oettinger, Learn Computer Forensics: Your one-stop guide to searching, analyzing, acquiring, and securing digital evidence, 2nd Edition, 2022
Thomas J. Holt, Adam M. Bossler, Kathryn C. Seigfried-Spellar, Cybercrime and Digital Forensics: An Introduction, 3rd Edition, 2022

IDE from AI&DS (Available to students other than Dept. of CSE)

IDE

Semester

Course Code

Course Name

IDE-I

Semester-4

CS2207

Introduction to Data Science

Course Number	CS2207 (IDE-1)
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Introduction to Data Science
Learning Mode	Offline
Learning Objectives	This course aims to help the students (a) to understand the fundamental concepts and principles of data science. (b) to provide an understanding of the data science process, including data collection, cleaning, analysis, and interpretation (c) to develop understanding in statistical and machine learning techniques for data analysis (d) to conduct exploratory data analysis (EDA) and to create predictive models (e) in developing of problem-solving skills using data science methodologies (f) to develop skills in visualizing data and creating compelling data stories (g) to highlight the importance of ethical decision-making in data science projects endeavors.
Course Description	This academic course on Introduction to Data Science aims to introduce methods for data collection and cleaning and finally inferring insightful information from the data and presenting that to audience in meaningful way. Major thrust is given on data processing and model preparation for some insightful information. Upon completion, students will excel in data handling, raising meaningful question for insights and come with model/ statistical test for acquiring the insight. Finally, a number of data representation methods are used to present the result in meaningful way.
Course Outline	Unit I Introduction to the data science and Python. Unit II Exploratory Data Analysis and the Data Science Process - Basic tools (Pandas, ScikitLearn, NumPy, Matplotlib, etc.). Unit III Python Programming for Statistics: Probability, Random Variable, Probability Distribution, central limit theorem Unit IV Inferential Statistics: population and sample, Point estimation, Interval estimation, hypothesis testing Unit V Supervised Learning- Linear Regression, k-Nearest Neighbors (kNN), Naïve Bayes, Decision Trees Unit VI Unsupervised Learning- k-means, DBSCAN, GMM, Principal Component Analysis
Learning Outcome	· A clear understanding of the core concepts and methodologies in data science. · Knowledge regarding programming languages (e.g., Python) and data manipulation libraries (e.g., pandas, NumPy) to clean, process, and analyze data. · Knowledge regarding exploratory data analysis (EDA) and capability to create predictive models using appropriate data science tools and techniques. · Drawing data-driven insights and recommendations from data. · Create visualizations and reports that convey findings in a compelling and understandable manner.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Reading:

Probability and Statistics for Engineers and Scientist by Ronald E. Walpole, Raymond H. Myers, Sharon L. Myers, Keying E. Ye, Pearson, 9th Edition
An Introduction to Statistical Learning with Applications in R by Gareth James Daniela Witten, Travor Hastie, Robert Tibshirani, Springer
Machine Learning by Tom Mitchel, McGraw Hill Education
Cathy O'Neil and Rachel Schutt. Doing Data Science, Straight Talk From The Frontline O'Reilly. 2014
Anil K. Jain, Richard C. Dubes, Algorithms for clustering data, Prentice Hall Advanced Reference Series: Computer Science, (2008)
Rajeev Motwani and Prabhakar Raghavan, Python for Rusers a data science approach, Wiley, Year: 2018

John D. Kelleher, Brendan Tierney, Data Science, The MIT Press, 2018

IDE-II

Semester-5

CS3106

Computer Graphics

Course Number	CS3106 (IDE-2)
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Computer Graphics
Learning Mode	Offline
Learning Objective	The objective of the course is to provide a conceptual and theoretical understanding of the organization and functioning of a computer graphics rendering pipeline.
Course Description	Computer Graphics comprises of a pipeline of technologies that play an important role in developing computer vision and image processing technologies with wide applications in the field of Artificial Intelligence (AI).
Course Outline	Graphics imaging pipeline, Rasterization, Display devices, CRT displays, Random scan display, Raster scan display, Raster Scan Basics. 2D transformations, 3D transformations, Vanishing points, Viewing Transformation. Coding sessions in class using C++, Python. Digital Differential Algorithms, Bresenham’s algorithms, polygon filling, Windowing and Clipping, problems of aliasing. Coding sessions in class using C++, Python. Graph based models, B-REP model, Constructive Solid Geometry (CSG), Octree based representation, Quadtree based representation. Parametric representation of curves, parametric cubic curves, Bezier curves, continuity of curves, modeling of surfaces. Hidden Surface Removal, Back face removal, Z-Buffer Algorithm, Scan-line algorithm for VSD, algorithm, BSP trees. Coding sessions in class using C++, Python.
Learning Outcome	· This course will teach the fundamentals of imaging graphics through which you will be able to develop various imaging applications. · This course also accompanies coding, using Python or C++ or Java and OpenGL, of every algorithm/technology that will be taught giving a first hand experience of imaging app development and how it works.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Readings:

Shirley, M. Ashikhmin and S. Marschner, Fundamentals of Computer Graphics, 3rd Edition, CRC Press, 2009.
Angel and D. Shreiner, Interactive Computer Graphics, A top-down approach with OpenGL, 6th Edition, Addison Wesley, 2012.
D. Foley, A. van Dam, S. Feiner, and J. F. Hughes, Computer Graphics: Principles and Practice, 2nd Ed, Addison-Wesley, 1996.

D. F. Rogers and J. A. Adams, Mathematical Elements for Computer Graphics, 2nd Edition, McGraw-Hill International Edition, 1990.

IDE-III

Semester-7

CS4113

Data Analysis and Visualization

Course Number	CS4113 (IDE-3)
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Data Analysis and Visualization
Learning Mode	Offline
Learning Objectives	· To understand the fundamental concepts and principles of data analysis. · To acquire skills in data collection, cleaning, and preparation for analysis. · To learn statistical techniques and methods for analyzing data. · To gain proficiency in using software tools for data analysis, such as Python, R, and Excel. · To develop the ability to create meaningful and effective data visualizations. · To interpret and communicate data findings clearly and accurately. · To apply data analysis and visualization techniques to real-world problems.
Course Description	This course provides a comprehensive introduction to data analysis and visualization techniques. Students will learn how to gather, clean, and analyze data using various tools and methodologies. The course covers statistical analysis, data manipulation, and visualization best practices. Through hands-on projects and real-world examples, students will develop the skills necessary to transform data into actionable insights and effectively communicate their findings using visualizations.
Course Outline	Introduction to Data Analysis and Visualization: Overview of Data Analysis and Visualization, Importance of Data in Decision Making, Data Preprocessing Tasks, Some Mathematical Preliminaries Introduction to various tools: Python, R, Tableau, etc. Exploratory Data Analysis programming: Descriptive Statistics, Data Cleaning and Handling Missing Values, Data Visualization with ggplot2, Correlation and Covariance, Data Distribution and Outliers, Introduction to Statistical Modeling programming: Linear Regression: Concepts and Implementation, Multiple Linear Regression Analysis, Supervised Data Analysis programming: Introduction of Supervised Analysis Techniques, Various Classifier Models- Logistic Regression, Naïve Bayes Classifier, LDA, KNN, SVM, Decision Trees. etc. Evaluation Parameters, Practice and Analysis using R Unsupervised Data Analysis programming: Introduction of Unsupervised Analysis, Various Clustering Strategies- K-Means, DBSCAN, Hierarchical. Evaluation Strategies, Practice and Analysis using R Real-world applications and case studies, industry-specific use cases, mini project
Learning Outcome	By the end of this course, students will be able to: · Apply various data analysis and visualization techniques using various tools. · Perform data preprocessing, including cleaning, handling missing values, and transforming data. · Conduct exploratory data analysis and create informative visualizations. · Implement and interpret statistical models and supervised learning techniques. · Execute unsupervised learning techniques and evaluate their effectiveness. · Apply learned techniques to real-world scenarios through case studies and projects, demonstrating their practical utility.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Readings:

Data Analytics & Visualization, Jack A. Hyman et al, April 2024
An Introduction to Statistical Learning with Application in R by Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, 2^nd Edition, Springer
Applied Predictive Modeling by Max Kuhn and Kjell Johnson, 2^nd Edition, Springer, ISBN: 978-1461468486
Visual Analytics with Tableau by Alexander Loth, ISBN: 978-1119560203
Introduction to Data Mining, by Pang-Ning Tan, Michael Steinbach, Anuj Karpatne, Vipin Kumar, 2nd Edition, Pearson.
Machine Learning with R by Brett Lantz, Packt Publishing
Practical Data Science with R by Nina Zumel, John Mount, Manning Publication, ISBN- 978-1617291562
The Art of R Programming by Norman Matloff, No Starch Press, ISBN: 9781593273842
R in a Nuttshell- A Desktop Quick Reference by Joseph Adler, Shroff/O'Reilly, ISBN: 978-9350239209
Hands-On Machine Learning with R by Brad Boehmke and Brandon Greenwell, CRC Press, 978-1138495685
Mastering Tableau 2023 by Marleen Meier, Packt Publishing; 4th ed. Edition, ISB: 978-1803233765

Minor in AI&DS (List of Courses)

Minor in AI&DS (List of Courses)

Course Code

Course Name

Minor-1

CS2103

Artificial Intelligence Concepts

Course Number	CS2103
Course Credit (L-T-P-C)	2-0-2-3
Course Title	Artificial Intelligence Concepts
Learning Mode	Offline
Learning Objectives	This course aims to help the students (a) grasp the fundamental principles and subfields of Artificial Intelligence (AI) and Data Science.(b) Gain expertise in the stages of Data Science from data collection to model evaluation.(c) proficiency in applying supervised and unsupervised learning algorithms.(d) introduced to Deep Learning architectures and their applications.
Course Description	This course offers a comprehensive exploration of foundational principles and advanced techniques in Artificial Intelligence (AI), Data Science, Machine Learning (ML), and Deep Learning (DL). Students will delve into the ethical implications, applications, and future trends of AI, understanding its societal impacts and responsible deployment. The curriculum covers the evolution and stages of Data Science, emphasizing mastery of data collection, pre-processing, exploratory analytics, and rigorous model development and evaluation across various domains. In Machine Learning, students will gain proficiency in supervised and unsupervised learning algorithms, feature selection, dimensionality reduction, and a variety of classification and clustering techniques. Deep Learning concepts will be introduced, focusing on neural networks, Convolutional Neural Networks (CNNs) for image analysis, Recurrent Neural Networks (RNNs) for sequential data processing, attention mechanisms, and training Generative Adversarial Networks (GANs). Through theoretical lectures, practical exercises, and hands-on projects, students will acquire the skills necessary to apply these technologies effectively in solving real-world problems and advancing their careers in AI and Data Science.
Course Outline	Historical evolution of AI, Conceptualization of AI, related terms, and subfields with applications. Practical component: Lab to be conducted on a 2-hour slot weekly. It will be conducted with the theory course so the topics for problems given in the lab are already initiated in the theory class.
Learning Outcome	· Understand the basic concept of AI. · Analysis of Data using Data science and data Analytics. · Explore state-of-the-art techniques and applications in machine learning · Compare and contrast various multiple deep learning architectures
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Text Books:

Tom M. Mitchell, 2017.Machine Learning.
Andrew-ng. Lecture Series – Deep Learning.ai . (Stanford)
Relevant research articles.

Reference Books:

Grus, J., 2019. Data science from scratch: first principles with python

Minor-2

CS2202

Course Number	CS2202
Course Credit (L-T-P-C)	3-0-2-4
Course Title	Database and Warehousing
Learning Mode	Offline
Learning Objectives	· Understand the fundamental principles of database systems and data warehousing. · Learn to design, implement, and manage databases using relational database management systems (RDBMS). · Explore the concepts and techniques of data warehousing and data mining. · Develop skills in SQL for querying and managing databases. · Analyze and optimize database performance and ensure data integrity and security.
Course Description	This course provides an in-depth exploration of database systems and data warehousing, covering essential concepts, technologies, and techniques. Students will learn about the design and implementation of relational databases, including data modeling, normalization, and SQL. The course will also introduce data warehousing concepts, focusing on data extraction, transformation, and loading (ETL), as well as data mining techniques. Through practical exercises and projects, students will gain hands-on experience in working with databases and data warehouses, preparing them for real-world applications.
Course Outline	1. Introduction to Databases, Overview of database systems, Types of databases and database models, Database architecture and components 2. Data Modeling, Entity-Relationship (ER) modeling, Relational model and schema design, Normalization and denormalization 3. Structured Query Language (SQL), Basic SQL queries (SELECT, INSERT, UPDATE, DELETE), Advanced SQL (joins, subqueries, indexing) , SQL functions and stored procedures 4. Database Design and Implementation, Database design principles, Creating and managing databases using RDBMS, Data integrity and constraints 5. Database Management and Administration, Database backup and recovery, User management and security, Performance tuning and optimization 6. Introduction to Data Warehousing, Concepts and architecture of data warehousing, Data warehousing vs. databases, Data modeling for data warehousing 7. ETL Processes, Data extraction, transformation, and loading (ETL), ETL tools and techniques, Data cleaning and integration 8. Data Mining and Analytics, Introduction to data mining, Data mining techniques and algorithms, Applications of data mining 9. Advanced Topics in Data Warehousing, Big data and data warehousing, Cloud-based data warehousing solutions, Data governance and data quality management Practical component: Lab to be conducted on a 2-hour slot weekly. It will be conducted with the theory course so the topics for problems given in the lab are already initiated in the theory class.
Learning Outcome	The student will be able to: · Demonstrate a thorough understanding of database and data warehousing principles. · Design, implement, and manage relational databases using RDBMS. · Write efficient SQL queries for data manipulation and retrieval. · Implement data warehousing solutions, including ETL processes and data mining techniques. · Analyze and optimize the performance of databases and data warehouses, ensuring data integrity and security.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Textbooks:

"Database System Concepts" (7th Edition) by Abraham Silberschatz, Henry F. Korth, and S. Sudarshan
"Fundamentals of Database Systems" (7th Edition) by Ramez Elmasri and Shamkant B. Navathe
"Data Warehousing: The Ultimate Guide to Building a Data Warehouse for Business Intelligence" (1st Edition) by Erik Thomsen
"The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling" (3rd Edition) by Ralph Kimball and Margy Ross

5. "SQL: The Complete Reference" (3rd Edition) by James R. Groff and Paul N. Weinberg

Minor-3

CS3103

Machine Learning

Course Number	CS3103
Course Credit (L-T-P-C)	3-0-3-4.5
Course Title	Machine Learning
Learning Mode	Offline
Learning Objectives	This course aims to help the students (a) to understand the fundamental concepts of machine learning; (b) to develop the basic problem-solving skills by implementing the basic machine learning algorithms; (c) to learn about various paradigms of machine learning and various approaches under different paradigms; and (d) to achieve proficiency in designing some real-life project using machine learning.
Course Description	This course provides a comprehensive introduction to the field of Machine Learning (ML), covering fundamental concepts, techniques, and applications. It is designed to give students a solid foundation in understanding how machines learn from data and make decisions. Through a combination of theoretical insights and practical applications, students will explore various aspects of machine learning, including supervised and unsupervised learning, generalization, regression, classification, clustering, data reduction, and ensemble learning.
Course Outline	1.Understanding of Machine Learning: Definition, Tasks (Classification, Regression, Prediction, and Clustering), Supervised and unsupervised machine learning. 2.Learning to Generalization: Bias-Variance Trade-off, Overfitting vs. Underfitting, Regularization 3.Regression (single & multivariate, linear and nonlinear, Logistic Regression 4.Classification: (kNN, Bayes classifier, decision tree, random forest, Support vector Machines) 5.Unsupervised Learning: K-Means & variants, Hierarchical techniques 6.Data Reduction and Ensemble Learning Practical component: Lab to be conducted on a 3-hour slot weekly. It will be conducted with the theory course so the topics for problems given in the lab are already initiated in the theory class.
Learning Outcome	· Understanding of fundamental concepts of ML · Understanding different types of ML tasks: Classification, Regression, and Clustering · Understanding of various algorithms under different paradigms of ML: supervised, unsupervised, semi-supervised. · Capable of conducting some real-life projects using machine learning algorithms
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Reading

Mitchell. Machine Learning. McGraw-Hill, 1997.
Duda, Richard O., and Peter E. Hart. Pattern classification. John Wiley & Sons, 2006.
Pattern recognition and machine learning by Christopher Bishop, Springer Verlag, 2006.
Machine Learning in Action by Peter Harrington
Probability, Random Variables and Stochastic processes by Papoulis and Pillai, 4th Edition, Tata McGraw Hill Edition.
Linear Algebra and Its Applications by Gilbert Strand. Thompson Books.
Data Mining: Concepts and Techniques by Jiawei Han, Micheline Kamber, Morgan Kaufmann Publishers.

A. K. Jain and R. C. Dubes. Algorithms for Clustering Data. Prentice Hall, 1988

4.5

Minor-4

CS3202

Computer Science and Engineering

Course Number	CS3202
Course Credit (L-T-P-C)	3-0-3-4.5
Course Title	Deep Learning
Pre-requisite	offline
Learning Mode	This course aims to provide an introductory overview of deep learning and its application varied domains. The course will provide basic understanding of neural networks, mathematical description of it and finally applications of it in multiple domains. A few open source tools will be demonstrated during the course to provide hands-on experience.
Learning Objectives	This course will provide an overview of neural networks and hands-on experience for the same.
Course Description	Introduction: Introduction to bigdata problem, overview of linear algebra Feature engineering: Basics of machine learning (linear regression, classification) Neural network: Deep feed forward network, cost function, activation functions, overfitting, underfitting, Universal approximation theorem Gradient based learning: Gradient Descent, Stochastic Gradient Descent, Backpropagation Regularization: L2, L1, L\infinity, drop-out, early stopping, data augmentation, etc. Optimization: Multivariable taylor series, momentum, adaptive learning rate, ADAM, Nesterov Accelerated Gradient (NAG), AdaGrad, etc. Convolutional Neural Network (CNN): Theory and its application in computer vision Recurrent Neural Network (RNN): Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU) and their applications in natural language processing Advanced topics: Autoencoder, Transformer, Deep reinforcement learning Practical component: Lab to be conducted on a 3-hour slot weekly. It will be conducted with the theory course so the topics for problems given in the lab are already initiated in the theory class.
Course Content	· Basic understanding of deep learning and neural networks · Problem modeling skill · Usage of different open source tools / libraries Analysis of large volume of data
Learning Outcome	Knowledge on various forms of stresses in pressure vessels and their relation. Mechanical designing of different parts/components used in heat exchangers or in separation units such as nuts/bolts, flanges, heads, shell, etc. Consideration and elementary sizing calculation on tall, horizontal/vertical vessels, and their constructional supports.
Assessment Method	Assignments, Quiz, Mid-semester examination and End-semester examination.

Suggested Reading:

Ian Goodfellow, Yoshua Bengio and Aaron Courville, “Deep Learning”, Book in preparation for MIT Press, 2016.
Reference books:
Jerome H. Friedman, Robert Tibshirani, and Trevor Hastie, “The elements of statistical learning”, Springer Series in Statistics, 2009.
Charu C Aggarwal, “Neural Networks and Deep Learning”, Springer.
Aston Zhang, Zachary C. Lipton, Mu Li, Alexander J. Smola, "Dive into Deep Learning"
Iddo Drori, "The Science of Deep Learning", Cambridge University Press
Simon O. Haykin, "Neural Networks and Learning Machines", Pearson Education India
Richard S. Sutton, Andrew G. Barto, "Reinforcement Learning: An Introduction", MIT Press
M. Bishop, H. Bishop, "Deep Learning: Foundations and Concepts", Springer, 2022

Simon J. D. Prince, "Understanding Deep Learning", MIT Press 2023

4.5

Total Credits

Program Learning Objectives:	Program Learning Outcomes (PLO):
Program Goal 1: Fundamental Understanding: To impart knowledge and proficiency in an advanced level of theoretical and practical aspects in the major fields of Computer Science and Engineering.	Program Learning Outcome 1: PLO-1: Students will acquire and demonstrate a comprehensive understanding of core concepts in computing principles, data structure, algorithms, programming languages. Program Learning Outcome 2: PLO-2: Students will be able to understand problems computationally, design efficient algorithms, and implement software solutions.
Program Goal 2: Basic Training for Research and Industry: To provide quality training for conducting fundamental and advanced research in Computer Science and Engineering and software development.	Program Learning Outcome 3: PLO-3: Students will develop the ability to apply the scientific method to computer science problems, including formulating hypotheses, designing experiments, and analyzing results. Program Learning Outcome 4: PLO-4: Students will demonstrate proficiency in software development, including the use of modern programming environments, operating systems, computer networks, version control, and collaborative development practices.
Program Goal 3: Skill Enhancement: To focus on skill enhancement in system development and security.	Program Learning Outcome 5: PLO-5: Students will be able to design, implement, and manage complex systems, computer architecture, networking, ensuring quality, and security.
Program Goal 4: Communication Skill: To develop various communication skills such as reading, listening, speaking, etc. This will help in expressing ideas and views clearly and effectively.	Program Learning Outcome 6: PLO-6: Students will develop the ability to communicate technical information effectively through written reports, oral presentations, and collaborative projects.
Program Goal 5: Social Awareness: To understand societal issues related to computer science and allied areas and develop methods and means to abate and create awareness in society.	Program Learning Outcome 7: PLO-7: Students will develop an awareness of ethical, social, and environmental issues related to computing, applying responsible practices in their professional activities. Program Learning Outcome 8: PLO-8: Students will learn to work effectively in teams, demonstrating leadership, collaboration, and project management skills.

Sl. No.

Subject Code

SEMESTER I

MA1101

Course Number	MA1101
Course Credit (L-T-P-C)	3-1-0-4
Course Title	Calculus and Linear Algebra
Learning Mode	Lectures and Tutorials
Learning Objectives	To provide the essential knowledge of basic tools of Differential Calculus, Integral Calculus, Vector spaces and Matrix Algebra.
Course Description	This course provides a foundation for Calculus and Linear Algebra. Topics related to properties of single and two variable functions along with their applications will be discussed. In addition fundamentals of linear algebra and matrix theory with applications will also be discussed.
Course Content	Differential Calculus (12 Lectures): Limit and continuity of one variable function (including ε-δ definition). Limit, continuity and differentiability of functions of two variables, Tangent plane and normal, Change of variables, chain rule, Jacobians, Taylor’s Theorem for two variables, Extrema of functions of two or more variables, Lagrange’s method of undetermined multipliers. Integral Calculus (10 Lectures): Riemann integral for one variable functions, Double and Triple integrals, Change of order of integration. Change of variables, Applications of Multiple integrals such as surface area and volume. Vector Spaces (12 Lectures): Vector spaces (over the field of real numbers), subspaces, spanning set, linear independence, basis and dimension. Linear transformations, range and null space, rank-nullity theorem, matrix of a linear transformation. Matrix Algebra (8 Lectures): Elementary operations and their use in getting the rank, inverse of a matrix and solution of linear simultaneous equations, Orthogonal, symmetric, skew-symmetric, Hermitian, skew-Hermitian, normal and unitary matrices and their elementary properties, Eigenvalues and Eigenvectors of a matrix, Cayley-Hamilton theorem, Diagonalization of a matrix.
Learning Outcome	Students completing this course will be able to: 1. Understand various properties of functions such as limit, continuity and differentiability. 2. Learn about integrations in various dimension and their applications. 3. learn about the concept of basis and dimension of a vector space. 4. define Linear Transformations and compute the domain, range, kernel, rank, and nullity of a linear transformation. 5. compute the inverse of an invertible matrix. 6. solve the system of linear equations. 7. Apply linear algebra concepts to model, solve, and analyze real-world problems.
Assessment Method	Quiz /Assignment/ MSE / ESE

Textbooks:

Thomas, G. B., Hass, J., Heil, C. and Weir M. D., “Thomas’ Calculus”, 14th Ed., Pearson Education, 2018
Kreyszig, E., “Advanced Engineering Mathematics”, 10th Ed., Wiley India Pvt. Ltd, 2015

Reference Books:

Jain, R. K. and Iyenger, S. R. K., “Advanced Engineering Mathematics”, 5^th, Narosa Publishing House, 2017
Axler, S., “Linear Algebra Done Right”, 3rd Ed., Springer Nature, 2015

Strang, G., “Linear Algebra and Its Applications” 4th Ed., Cengage India Private Limited, 2005

4.0

CS1101

Course Number	CS1101
Course Credit	3-0-3-4.5
Course Title	Foundations of Programming
Learning Mode	Offline
Learning Objectives	· To understand the fundamental concepts of programming · To develop the basic problem-solving skills by designing algorithms and implementing them. · To learn about various data types, control statements, functions, arrays, pointers, and file handling. · To achieve proficiency in debugging and testing a C program
Course Description	This introductory course provides a solid foundation in programming principles and techniques. Designed for students with little to no prior programming experience, it covers fundamental concepts such as variables, data types, control structures, functions, and basic data structures. Students will learn to write, debug, and execute programs using a high-level programming language. Emphasis is placed on developing problem-solving skills, logical thinking, and the ability to write clear and efficient code. By the end of the course, students will be equipped with the essential skills needed to pursue more advanced studies in computer science and software development.
Course Outline	Introduction and Programming basics, Expressions Control and Iterative statements, Functions, Arrays, Recursion vs. Iteration Pointers, 2D-Array with pointers, Structures, String, Dynamic memory allocation, File handling, Contemporary programming languages, and applications Practical component: Lab to be conducted on a 3-hour slot weekly. It will be conducted with the theory course so the topics for problems given in the lab are already initiated in the theory class.
Learning Outcome	· Understanding of Basic Syntax and Structure in C language · Proficiency in Data Types, Operators, and Control Structures · Function Implementation and learn to use them appropriately · Efficient Use of Arrays and Strings · Pointer Utilization · Ability to perform dynamic memory allocation and deallocation using malloc (), calloc (), realloc (), and free () functions. · Structured data management with structures and unions · Exposure of file Handling · Learning debugging and error Handling
Assessment Method	Internal (Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Reading

Knuth, Donald E. The art of computer programming, volume 4A: combinatorial algorithms, part 1. Pearson Education India, 2011.
P.J. Deitel and H.M. Deitel, C How To Program, Pearson Education (7th Edition)
Brian W. Kernighan and Dennis M. Ritchie, The C Programming Language, Prentice−Hall
A. Kelley and I. Pohl, A Book on C, Pearson Education (4th Edition)

K. N. King, C PROGRAMMING A Modern Approach, W. W. Norton & Company

4.5

PH1101/PH1201

Course Number	PH1101/PH1201
Course Credit	3-1-3-5.5
Course Title	Physics
Learning Mode	Lectures and Tutorials
Learning Objectives	Complies with Program Goals 1 and 2
Course Description	This course deals with fundamentals in Classical mechanics, Waves and Oscillations and Quantum Mechanics. As a prerequisite, the mathematical preliminaries such as coordinate systems, vector calculus etc will be discussed in the beginning.
Course Outline	Orthogonal coordinate systems (Plane polar, Spherical, Cylindrical), concept of generalised coordinates, generalised velocity and phase space for a mechanical system, Introduction to vector operators, Gradient, divergence, curl and Laplacian in different co-ordinate systems. Central force problem and its applications. Rigid body rotation, vector nature of angular velocity, Finding the principal axes, Euler's equations; Gyroscopic motion and its application; Accelerated frame of reference, Fictitious forces. Potential energy and concept of equilibrium, Lennard-Jones and double-well potentials, Small oscillations, Harmonic oscillator, damped and forced oscillations, resonance and its different examples, oscillator states in phase space, coupled oscillations, normal modes, longitudinal and transverse waves, wave equation, plane waves, examples two- and three-dimensional waves. Michelson-Morley experiment, Lorentz transformation, Postulates of special theory of relativity, Time dilation and length contraction, Applications of special theory of relativity.
Learning Outcome	Complies with PLO 1a, 2a, 3a
Assessment Method	Quiz, Assignments and Exams

Suggested Readings:

Textbooks:

Engineering Mechanics, M. K. Harbola, 2^nd ed., Cengage, 2012
D. Kleppner and R. J. Kolenkow, An introduction to Mechanics, Tata McGraw-Hill, New Delhi, 2000.
I. G. Main, Oscillations and Waves
H. G. Pain, The Physics of Vibrations and Waves, 1968
Frank S. Crawford, Berkeley Physics Course Vol 3: Waves and Oscillations, McGraw Hill, 1966.

References:

R. P. Feynman, R. B. Leighton and M. Sands, The Feynman Lecture in Physics, Vol I, Narosa Publishing House, New Delhi, 2009.
David Morin, Introduction to Classical Mechanics, Cambridge University Press, NY, 2007.

3. P. C. Deshmukh, Foundations of Classical Mechanics, Cambridge University Press, 2019

5.5

CE1101/CE1201

Course code	CE1101/CE1201
Course Credit (L-T-P-C)	1-0-3-2.5
Course Title	Engineering Graphics
Learning Mode	Lectures and Practical
Learning Objectives	Complies with PLO-1a 1. The course on engineering drawing is designed to introduce the fundamentals of technical drawing as an important form of conveying information. 2. Apply principles of engineering visualization and projection theory to prepare engineering drawings, using conventional and modern drawing tools. 3. Practice drawing orthographic projections, isometric views, and sectional views, of simple and combined solids in different orientations.
Course Description	This course will introduce drawing as a tool to represent a complex three-dimensional object on two-dimensional paper through methods of projections. The course explains the use of different drafting tools and the importance of conventions for uniformity and standardization of the interpretation of the drawings.
Course Outline	Fundamental of engineering drawing, line types, dimensioning, and scales. Conic sections: ellipse, parabola, hyperbola; cycloidal curves. Principle of projection, method of projection, orthographic projection, plane of projection, first angle of projection, Projection of points, lines, planes and solids. Section of solids: Sectional views of simple solids- prism, pyramid, cylinder, cone, sphere; the true shape of the section. Methods of development, development of surfaces. Isometric projections: construction of isometric view of solids and combination of solids from orthographic projections. Introduction to AutoCad and solving isometric problems.
Learning Outcome	After attending this course, the following outcomes are expected: a) The student will understand the basic concepts of engineering drawing. b) The student will be able to use basic drafting tools, drawing instruments, and sheets. c) The student will be able to represent three-dimensional simple and combined solid objects on two-dimensional paper. d) The student will be able to visualize and interpret the orientation of simple and combine solid objects.
Assessment Method	Laboratory Assignments (30%), Mid-semester examination (25%) and End-semester examination (45%).

Suggested Readings:

Textbooks:

D. Bhatt, Engineering Drawing, Charotar Publishing House.
Agrawal & Agrawal, Engineering Drawing, McGraw Hill.
Jolhe, Engineering Drawing.

References:

Engineering Drawing and Design by David Madsen

2.5

EE1101/EE1201

Course Number	EE1101/EE1201
Course Credit	3-0-3-4.5
Course Title	Electrical Sciences
Learning Mode	Lectures and Experiments
Learning Objectives	Complies with Program goals 1, 2 and 3
Course Description	The course is designed to meet the requirements of all B. Tech programmes. The course aims at giving an overview of the entire electrical engineering domain from the concepts of circuits, devices, digital systems and magnetic circuits.
Course Outline	Circuit Analysis Techniques, Circuit elements, Simple RL and RC Circuits, Kirchoff’s law, Nodal Analysis, Mesh Analysis, Linearity and Superposition, Source Transformations, Thevenin’s and Norton’s Theorems, Time Domain Response of RC, RL and RLC circuits, Sinusoidal Forcing Function, Phasor Relationship for R, L and C, Impedance and Admittance, Instantaneous power, Real, reactive power and power factor. Semiconductor Diode, Zener Diode, Rectifier Circuits, Clipper, Clamper, UJT, Bipolar Junction Transistors, MOSFET, Transistor Biasing, Transistor Small Signal Analysis, Transistor Amplifier and their types, Operational Amplifiers, Op-amp Equivalent Circuit, Practical Op-amp Circuits, Power Opamp, DC Offset, Constant Gain Multiplier, Voltage Summing, Voltage Buffer, Controlled Sources, Instrumentation Amplifier, Active Filters and Oscillators. Number Systems, Logic Gates, Boolean Theorem, Algebraic Simplification, K-map, Combinatorial Circuits, Encoder, Decoder, Combinatorial Circuit Design, Introduction to Sequential Circuits. Magnetic Circuits, Mutually Coupled Circuits, Transformers, Equivalent Circuit and Performance, Analysis of Three-Phase Circuits, Power measurement in three phase system, Electromechanical Energy Conversion, Introduction to Rotating Machines (DC and AC Machines). Laboratory: Experiments to verify Circuit Theorems; Experiments using diodes and bipolar junction transistor (BJT): design and analysis of half -wave and full-wave rectifiers, clipping and clamping circuits and Zener diode characteristics and its regulators, BJT characteristics (CE, CB and CC) and BJT amplifiers; Experiment on MOSFET characteristics (CS, CG, and CD), parameter extraction and amplifier; Experiments using operational amplifiers (op-amps): summing amplifier, comparator, precision rectifier, Astable and Monostable Multivibrators and oscillators; Experiments using logic gates: combinational circuits such as staircase switch, majority detector, equality detector, multiplexer and demultiplexer; Experiments using flip-flops: sequential circuits such as non-overlapping pulse generator, ripple counter, synchronous counter, pulse counter and numerical display; Power Measurement by two Wattmeter method; Open and Short Circuit Tests of Transformer.
Learning Outcomes	Complies with PLO 1a, 2a and 3a
Assessment Method	Quiz, Assignments and Exams

Texts/References

K. Alexander, M. N. O. Sadiku, Fundamentals of Electric Circuits, 3rd Edition, McGraw-Hill, 2008.
H. Hayt and J. E. Kemmerly, Engineering Circuit Analysis, McGraw-Hill, 1993.
L. Boylestad and L. Nashelsky, Electronic Devices and Circuit Theory, 6th Edition, PHI, 2001.
M. Mano, M. D. Ciletti, Digital Design, 4th Edition, Pearson Education, 2008.
Floyd, Jain, Digital Fundamentals, 8th Edition, Pearson.
David V. Kerns, Jr. J. David Irwin, Essentials of Electrical and Computer Engineering, Pearson, 2004.
Donald A Neamen, Electronic Circuits; analysis and Design, 3rd Edition, Tata McGraw-Hill Publishing Company Limited.
Adel S. Sedra, Kenneth C. Smith, Microelectronic Circuits, 5th Edition, Oxford University Press, 2004.
E. Fitzgerald, C. Kingsley Jr., S. D. Umans, Electric Machinery, 6th Edition, Tata McGraw-Hill, 2003.
P. Kothari, I. J. Nagrath, Electric Machines, 3rd Edition, McGraw-Hill, 2004.

Del Toro, Vincent. "Principles of electrical engineering." (No Title) (1972).

4.5

HS1101

Course Number	HS1101
Course Credit	L-T-P-W: 2-0-1-2.5
Course Title	English for Professionals
Learning Mode	Offline
Learning Objectives	This course aims to help the students (a) attain proficiency in written English through the construction of grammatically correct sentences, utilization of subject-verb agreement principles, mastery of various tenses, and effective deployment of active and passive voice to ensure coherent and impactful written expression; (b) enhance oral communication skills by honing public speaking abilities, acquiring strategies to deliver persuasive presentations, and cultivating a polished telephone etiquette, enabling confident and articulate verbal communication; (c) foster active listening capabilities by recognizing different types of listening, and applying proven methods and strategies to improve active listening skills; (d) strengthen reading skills, including comprehension, interpretation, and critical analysis, to grasp diverse written materials and derive meaning from various types of texts encountered in academic and professional contexts; (e) develop adeptness in written communication for business purposes, encompassing the understanding of essential writing elements, mastery of appropriate writing styles thereby enhancing prospects for successful job interviews and subsequent professional endeavors.
Course Description	This academic course on communication skills aims to equip students with fluency in spoken and written English for effective expression in both academic and professional settings. By focusing on essential communication principles and providing practical experiences, students develop clarity, precision, and confidence in their communication. Through interactive discussions and exercises, students enhance critical thinking and adaptability in diverse contexts. Upon completion, students will excel in formal presentations, group discussions, and persuasive writing, enhancing their overall communication proficiency.
Course Outline	Unit I: Introduction to professional communication – LSRW - Phonetics and phonology Sounds in English Language – production and articulation – rhythm and intonation – connected speech - Basic Grammar and Advanced Vocabulary Sounds in English Language – production and articulation – rhythm and intonation – connected speech – persuading and negotiating – brevity and clarity in language. Unit II: Characteristics of Technical Communication: Types of communication and forms of communication - Formal and informal communication Verbal and non-Verbal Communication – Communication barriers and remedies Intercultural communication – neutral language Unit III: Comprehension and Composition – summarization, precis writing Business Letter Writing CV/ Resume – E-Communication Unit IV: Statement of Purpose, Writing Project Reports, Writing research proposal, writing abstracts, developing presentations, interviews – combating nervousness Tutorial: Listening Exercises, Speaking Practice (GDs, and Presentations), and Writing Practice Learning Outcome · Attain proficiency in written English, enabling the construction of grammatically correct sentences and coherent written expression through the use of appropriate grammar, tenses, and voice. · Enhance oral communication skills, including public speaking, persuasive presentation, and polished telephone etiquette, fostering confident and articulate verbal expression. · Cultivate active listening abilities, recognizing different listening types, overcoming obstacles, and employing strategies for attentive and effective communication. · Develop proficient written communication skills for business purposes, demonstrating understanding of essential writing elements, appropriate styles, and the creation of reports, notices, agendas, and minutes that effectively convey information.
Assessment Method	Class test + Quiz = 20%; Mid-semester = 25%; Assignment = 15%; End semester = 40%

Suggested Reading

Balzotti, Jon. Technical Communication: A Design-Centric Approach. Routledge, 2022.
Kaul, Asha, Business Communication. PHI Learning Pvt. Ltd. 2009
Laplante, Phillip A. Technical Writing: A Practical Guide for Engineers, Scientists, and Nontechnical Professionals. CRC Press, 2018.
Lawson, Celeste, et al. Communication Skills for Business Professionals, Second Edition. CUP, 2019.
Sharon Gerson and Steven Gerson. Technical Writing: Process and Product (8th Edition), London: Longman, 2013
Rentz, Kathryn, Marie E. Flatley & Paula Lentz. Lesikar’s Business Communication Connecting in a Digital world, McGraw-Hill, Irwin.2012
Allan & Barbara Pease. The Definitive Book of Body Language, New York, Bantam,2004
Jones, Daniel. The Pronunciation of English, New Delhi, Universal Book Stall.2010
Savage, Alice. Effective Academic Writing. OUP. 2014

Swan and Alter. Oxford English grammar course. OUP. 201

2.5

TOTAL

23.5

Probability Theory and Ordinary Differential Equations

Sl. No.

Subject Code

SEMESTER II

MA1201

Course Number	MA1201
Course Credit (L-T-P-C)	3-1-0-4
Course Title	Probability Theory and Ordinary Differential Equations
Learning Mode	Lectures and Tutorials
Learning Objectives	To introduce the basic concepts of probability, statistics, and Differential equations.
Course Description	This course aims to cover basic concepts of probability, statistics and ordinary differential equations. In particular, popular distributions, random sampling, various estimators and hypothesis testing will be discussed. Students will also get exposure to the linear ordinary differential equations and their solution techniques.
Course Content	Probability (12 Lectures): Random variables and their probability distributions, Cumulative distribution functions, Expectation and Variance, probability inequalities, Binomial, Poisson, Geometric, negative binomial distributions, Uniform, Exponential, beta, Gamma, Normal and lognormal distributions. Statistics (10 Lectures): Random sampling, sampling distributions, Parameter estimation, Point estimation, unbiased estimators, maximum likelihood estimation, Confidence intervals for normal mean, Simple and composite hypothesis, Type I and Type II errors, Hypothesis testing for normal mean. Ordinary Differential Equations (20 Lectures): First order ordinary differential equations, exactness and integrating factors, Picard's iteration, Ordinary linear differential equations of n-th order, solutions of homogeneous and non-homogeneous equations (Method of variation of parameters). Systems of ordinary differential equations, Power series methods for solutions of ordinary differential equations. Legendre equation and Legendre polynomials, Bessel equation and Bessel functions.
Learning Outcome	Students will get exposure and understanding of: 1. Random variables and their probability distributions 2. Understand popular distributions and their properties 3. Sampling, estimation and hypothesis testing 4. Solution of ordinary differential equations 5. Solution of system of ordinary differential equations 6. Special functions arising as power series solutions of ordinary differential equations
Assessment Method	Quiz /Assignment/ MSE / ESE

Text Books:

Hogg, R. V., Mckean, J. and Craig, A. T., “Introduction to Mathematical Statistics”, 8th Ed., Pearson Education India, 2021
M. Ross “An introduction to Probability Models, Academic Press INC, 11th edition.
Miller, I. and Miller, M., “John E. Freund's Mathematical Statistics with Applications”, 8th Ed., Pearson Education India, 2013
L. Ross, Differential equations, 3rd Edition, Wiley, 1984

W. E. Boyce and R. C. Di Prima, Elementary Differential equations and Boundary Value Problems, 7th Edition, Wiley, 2001.

CS1201

Course Number	CS1201
Course Credit	3-0-3-4.5
Course Title	Data Structure
Learning Mode	Offline
Learning Objectives	· Understand the principles and concepts of data structures and their importance in computer science. · Learn to implement various data structures and understand how different algorithms works. · Develop problem-solving skills by applying appropriate data structures to different computational problems. · Achieving proficiency in designing efficient algorithms.
Course Description	This course provides a comprehensive study of data structures and their applications in computer science. It focuses on the implementation, analysis, and use of various data structures such as arrays, linked lists, stacks, queues, trees, and graphs. Through theoretical concepts and practical programming exercises, this course aims to develop problem-solving and algorithmic thinking skills essential for advanced topics in computer science and software development.
Course Outline	· Introduction to Data Structure, · Time and space requirements, Asymptotic notations · Abstraction and Abstract data types · Linear Data Structure: stack, queue, list, and linked structure · Unfolding the recursion · Tree, Binary Tree, traversal · Search and Sorting, · Graph, traversal, MST, Shortest distance · Balanced Tree Practical component: Lab to be conducted on a 3-hour slot weekly. It will be conducted with the theory course so the topics for problems given in the lab are already initiated in the theory class.
Learning Outcome	· Understand Data Structure Fundamentals · Implement Basic Data Structures using a programming language · Analyse and Apply Algorithms · Design and Analyse Tree Structures · Understand the usage of graph and its related algorithms · Design and Implement Sorting and Searching Algorithms · Debug and Optimize Code
Assessment Method	Internal (Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Reading:

Alfred V. Aho, John E. Hopcroft, Jeffrey D. Ullman, Data Structures and Algorithms, Published by Addison-Wesley
Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest and Clifford Stein., Introduction to Algorithms,
Mark Allen Weiss, Data Structures and Algorithm Analysis in Java
Robert Sedgewick and Kevin Wayne, Algorithms

Narasimha Karumanchi, Data Structures and Algorithms Made Easy

4.5

CH1201/CH1101

Course Number	CH1201/CH1101
Course Credit	3-1-3-5.5
Course Title	Chemistry
Learning Mode	Offline
Learning Objectives	The course aims to lay a foundation for all three branches of chemistry, viz. Organic, Inorganic, and Physical Chemistry. The course aims to nurture knowledge to appreciate the interface of chemistry with other science and Engineering branches by combining theoretical concepts and experimental studies.
Course Description	This course introduces basic organic chemistry, inorganic chemistry and Physical chemistry to understand fundamental laws that governs various reactions, reaction rates, equilibrium, and their applications in daily life through relevant experimentation.
Course Outline	Module 1: Thermodynamics: The fundamental definition and concept, the zeroth and first law. Work, heat, energy and enthalpies. Second law: entropy, free energy and chemical potential. Change of Phase. Third law. Chemical equilibrium. Conductance of solutions, Kohlrausch’s law-ionic mobilities, Basic Electrochemistry. Module 2: Coordination chemistry: Crystal field theory and consequences color, magnetism, J.T distortion. Bioinorganic chemistry: Trace elements in biology, heme and non-heme oxygen carriers, haemoglobin and myoglobin; Organometallic chemistry. Module 3: Stereo and regio-chemistry of organic compounds, conformational analysis and conformers, Molecules devoid of point chirality (allenes and biphenyls); Significance of chirality in living systems, organic photochemistry, Modern techniques in structural elucidation of compounds (UV–Vis, IR, NMR). Module 4 (Lab Component): Experiments based on redox and complexometric titrations; synthesis and characterization of inorganic complexes and nanomaterials; synthesis and characterization of organic compounds; experiments based on chromatography; experiments based on pH and conductivity measurement; experiment related to chemical kinetics and spectroscopy.
Learning Outcome	Students will be able to 1. identify organic and inorganic molecules and relate them to daily life applications through experiments. 2. understand important hypothesis, laws and their derivations to intercept physical phenomenon of chemical reactions and apply them in hands-on experiments. 3. understand the importance of organic and inorganic molecules in our body and environment. 4. know important analytical techniques to intercept chemical entity. 5. approach organic and inorganic synthesis as a skillset for drug manufacturing, calculate limiting reagents and yields, use various analytical tools to characterize organic compounds, interpret and ascertain data related to Physical chemistry aspects and know laboratory safety measures, risk factors and scientific report writing skills.
Assessment Method	Theory: 20% Quiz and assignment, 30% Mid sem and 50% End semester exams for theory part (4 credits). Lab: 60% lab report, lab performance and assignment, 20% End semester exam for practical part, 20% viva/quiz (1.5 credits). Overall Weightage: Theory (70%), Lab (30%).

Suggested Reading:

Text books:

Vogel's Qualitative Inorganic Analysis, G. Svehla, 7^th Edition, Revised, Prentice Hall, 1996.
J. Elias, S. S. Manoharan and H. Raj, "Experiments in General Chemistry", Universities Press (India) Pvt. Ltd., 1997.

A. J. Elias, A Collection of Interesting General Chemistry Experiments, revised edition, Universities Press (India) Pvt. Ltd., 2007.

Albert Cotton, G. Wilkinson, C. A. Murillo, M. Bochmann, Advanced Inorganic Chemistry - 6^th Edition New Delhi: Wiley India, 2008.
Mukkanti, Practical Engineering Chemistry, B.S. Publications, Hyderabad, 2009.
Shriver and Atkins inorganic chemistry / Peter Atkins, Tina Overton, Jonathan Rourke, Mark Weller, Fraser Armstrong-5^th Edition – Oxford: UOP. 2012.
Atkins’ Physical Chemistry, Peter Atkins, Julio de Paula, James Keeler, Oxford University Press, 11^th Edition 2017.
L. Kapoor, A Textbook of Physical Chemistry, Vol: 1, 2 (6^th Edition, 2019), Vol: 3 (5^th Edition, 2020) MaGraw Hill.

G. R. Chatwal, S. K. Anand, Instrumental Methods of Chemical Analysis, 5^th Edition, Himalaya Publications, 2023.

5.5

ME1201/ME1101

Course Number	ME1201/ME1101
Course Credit (L-T-P-C)	0-0-3-1.5
Course Title	Mechanical Fabrication
Learning Mode	Fabrication work – hands on fabrication work in Workshop
Learning Objectives	Complies with PLOs 3-4. · This course aims to develop the concepts and skills of various mechanical fabrication methods. · Fabrication of metallic and non-metallic components, fabrication using bulk and sheet metals, subtractive and additive manufacturing methods, and assemble the parts
Course Description	This course is designed to fulfil the need of hand on experience about various approaches (conventional and CNC, subtractive and additive) of mechanical fabrication approaches. Prerequisite: NIL
Course Outline	The jobs for various shops should be planned such that they are the parts of an assembled item. The student groups will fabricate different parts in various shops which will involve some amount of their creativeness/input particularly in design and/or planning. Various components as required for the assembled part can be made using the following shops: Sheet Metal Working: Development, sheet cutting and fabrication of designated job using sheet metal (ferrous/nonferrous); Joining of required portions by soldering, in case part is desired to be made leak proof. Pattern Making and Foundry: Making of suitable pattern (wood); making of sand mould, melting of non-ferrous metal/alloy (Al or Al alloys), pouring, solidification. Observation/identification of various defects appeared on the component. Joining: Butt/lap/corner joint job fabrication as required of low carbon steel plates; weld quality inspection by dye-penetration test (non-destructive testing approach)of the component made. Demonstration of semi-automatic Gas Metal Arc welding (GMAW). Conventional machining: Operations on lathe and vertical milling to fabricate the required component. The fabrication of the component should cover various lathe operations like straight turning, facing, thread cutting, parting off etc., and operations using indexing mechanism on vertical milling. CNC centre: Fundamentals of CNC programming using G and M code; setting and operations of job using CNC lathe or milling, tool reference, work reference, tool offset, tool radius compensation to fabricate the component with a designed profile on Al/Al-alloy plate. 3D printing (Fused Filament Fabrication): (2 weeks) Create the model, select appropriate slicing and path for fabrication of a 3D job by layer deposition (additive manufacturing approach) using polymeric material. Demonstration on pattern fabrication using 3D printing.
Learning Outcome	· This course would enable the students to develop the concept of design, fabrication (subtractive and additive) for various engineering applications. Fabrication of components and assemble them. · The practical skill and hands on experience for various fabrication methods from bulk, sheet metal using conventional as well as CNC machines.
Assessment Method	Fabrication of components in each of the shops required for assembly of the given part; submission of reports for each shop, and quiz assessment.

Text and Reference books:

Hajra Choudhury, HazraChoudhary and Nirjhar Roy, 2007, Elements of Workshop Technology, vol. I,Mediapromoters and Publishers Pvt. Ltd.
W A J Chapman, Workshop Technology, 1998, Part -1, 1st South Asian Edition, Viva Book Pvt Ltd.
N. Rao, 2009, Manufacturing Technology, Vol.1, 3rd Ed., Tata McGraw Hill Publishing Company.

M.Adithan, B.S. Pabla, 2012, CNC machines, New Age International Publishers

1.5

ME1202/ME1102

Course Number	ME1202/ ME1102
Course Number	Engineering Mechanics
Course Credit (L-T-P-C)	3-1-0-4
Pre-requisites	Nil
Semester	Spring
Learning Mode	Lectures
Learning Objectives	Complies with PLOs 1, 4 · The objective of this first course in mechanics is to enable engineering students to analyze basic mechanics problems and apply vector-based approach to solve them.
Course Outline	1. Rigid body statics: Equivalent force system. Equations of equilibrium, Free body diagram, Reaction, Static indeterminacy. 2. Structures: 2D truss, Method of joints, Method of section. Beam, Frame, types of loading and supports, axial force, Bending moment, Shear force and Torque Diagrams for a member. 3. Friction: Dry friction (static and kinetic), wedge friction, disk friction (thrust bearing), belt friction, square threaded screw, journal bearings, Wheel friction, Rolling resistance. 4. Centroid and Moment of Inertia 5. Introduction to stress and strain: Definition of Stress, Normal and shear Stress. Relation between stress and strain, Cauchy formula. 6. Stress in an axially loaded member and stress due to torsion in axisymmetric section
Learning Outcomes:	Following learning outcomes are expected after going through this course. · Learn and apply general mathematical and computer skills to solve basic mechanics problems. · Apply the vector-based approach to solve mechanics problems.
Assessment Method	Mid semester examination, End semester examination, Class test/Quiz, Tutorials

Reference Books

Shames, Engineering Mechanics: Statics and dynamics, 4th Ed, PHI, 2002.
P. Beer and E. R. Johnston, Vector Mechanics for Engineers, Vol I - Statics, 3rd Ed, Tata McGraw Hill, 2000.
L. Meriam and L. G. Kraige, Engineering Mechanics, Vol I - Statics, 5th Ed, John Wiley, 2002.
P. Popov, Engineering Mechanics of Solids, 2nd Ed, PHI, 1998.

F. P. Beer and E. R. Johnston, J.T. Dewolf, and D.F. Mazurek, Mechanics of Materials, 6th Ed, McGraw Hill Education (India) Pvt. Ltd., 2012.

IK1201

Indian Knowledge System (IKS)

TOTAL

22.5

Digital Logic and Computer Organization

Sl. No.

Subject Code

SEMESTER III

CS2101

Algorithm

Course Number	CS2101
Course Credit (L-T-P-C)	3-0-3-4.5
Course Title	Algorithm
Learning Mode	Offline
Learning Objectives	This course aims to help the students (a) to understand and explain fundamental concepts of computational complexity, including time and space complexity, and analyses the efficiency of algorithms; (b) to apply various algorithm design paradigms such as divide-and-conquer, dynamic programming, greedy algorithms, and backtracking to solve computational problems; (c) to develop and implement common algorithms for tasks such as sorting, searching, and graph traversal, and utilize well-known algorithms like Dijkstra's and Kruskal's; (d) to utilize fundamental data structures, including arrays, linked lists, stacks, queues, trees, and graphs, selecting and implementing the most appropriate one for specific problems; and (e) to evaluate the performance and scalability of algorithms and data structures, conducting empirical analysis to understand their practical performance, and enhancing problem-solving skills through theoretical knowledge application in practical scenarios.
Course Description	The course introduces the basics of computational complexity analysis and various algorithm design paradigms. The goal is to provide students with solid foundations to deal with a wide variety of computational problems, and to provide a thorough knowledge of the most common algorithms and data structures.
Course Outline	Unit I Role of algorithms in computing and elementary data structures. Unit II Analysis framework: Asymptotic notations, Analysis & Master Theorem Unfolding of recursion: review of sorting and searching algorithms, Huffman Encoding, String matching, hashing, Trees, Subset sum Unit III Algorithm design paradigm: · Brute force algorithms- Exhaustive search · Greedy algorithms · Divide and conquer algorithms, Branch-and-bound · Backtracking · Dynamic programming: Matrix Chain Multiplication, 0/1 Knapsack problem Unit IV Graph based algorithm: MST, Shortest distance, colouring, Vertex cover, TSP Unit V Reducibility: P, NP, NP complete, and NP hard Unit VI Elements of Randomized and approximation Algorithms Practical component: Lab to be conducted on a 3-hour slot weekly. It will be conducted with the theory course so the topics for problems given in the lab are already initiated in the theory class.
Learning Outcome	· Describe how efficiency affects the practical usage of algorithms and data structures. · Identify different algorithmic techniques for running programs at scale. · Construct programs that apply computational concepts as a tool in other domains. · Discuss how computer science interacts with and affects the world.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Reading

H. Carmen, C. E. Leiserson, R. L. Rivest and C. Stein, Introduction to Algorithms, MIT Press, 2001.
Aho, J. E. HopcroŌ and J. D. Ullman, The Design and Analysis of Computer Algorithms, Addison-Wesley, 1974.

M. T. Goodrich and R. Tamassia, Algorithm Design: Foundations, Analysis and Internet Examples, John Wiley & Sons, 2001

4.5

CS2102

Course Number	CS2102
Course Credit (L-T-P-C)	3-0-3-4.5
Course Title	Digital Logic and Computer Organization
Learning Mode	Offline
Learning Objectives	This course targets to cover the different number systems, designing of combinational and sequential logic circuits. This course will also expose students to the basic architecture of processing, memory and i/o organization in a computer system.
Course Description	The course covers foundation of digital logic and Computer organization that including number systems, Boolean algebra, optimizing logic gates. Besides this it covers designing of different combinational and sequential circuits, computer organization
Course Outline	Number System and Codes; Combinational logic circuits: Sequential logic circuits; Finite State machines. Basic computer organization and design, Operational concepts, Instruction codes, Computer Registers, Computer Instructions Familiarization with assembly language programming; Execution of a complete instruction. Memory organization: concept of hierarchical memory organization I/O devices – Programmed Input/output -Interrupts – Direct Memory Access – Buses, I/O devices and processors. Practical component: Lab to be conducted on a 3-hour slot weekly. It will be conducted with the theory course so the topics for problems given in the lab are already initiated in the theory class.
Learning Outcome	The student will be able to: · Demonstrate an understanding of how data is represented within a computer system. · Appreciate understanding of the basic blocks, key terminology in digital logic and Computer organization · Demonstrate classic components of a computational system (i.e. input, output, memory, data path, control) and understanding their functionality.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Text Books:

Mano, M. Morris. Digital logic and computer design. Pearson Education India, 2017.
Harris, David, and Sarah Harris. Digital design and computer architecture. Morgan Kaufmann, 2010.
Moris Mano, “Computer Systems Architecture”, 4th Edition, Pearson/PHI,
Carl Hamacher, Zvonko Vranesic, Safwat Zaky, “Computer Organization”, 5th Edition, McGraw Hill.

William Stallings, “Computer Organization and Architecture”, 6th Edition, Pearson/PHI

4.5

CS2103

Artificial Intelligence Concepts

Course Number	CS2103
Course Credit (L-T-P-C)	2-0-2-3
Course Title	Artificial Intelligence Concepts
Learning Mode	Offline
Learning Objectives	This course aims to help the students (a) grasp the fundamental principles and subfields of Artificial Intelligence (AI) and Data Science.(b) Gain expertise in the stages of Data Science from data collection to model evaluation.(c) proficiency in applying supervised and unsupervised learning algorithms.(d) introduced to Deep Learning architectures and their applications.
Course Description	This course offers a comprehensive exploration of foundational principles and advanced techniques in Artificial Intelligence (AI), Data Science, Machine Learning (ML), and Deep Learning (DL). Students will delve into the ethical implications, applications, and future trends of AI, understanding its societal impacts and responsible deployment. The curriculum covers the evolution and stages of Data Science, emphasizing mastery of data collection, pre-processing, exploratory analytics, and rigorous model development and evaluation across various domains. In Machine Learning, students will gain proficiency in supervised and unsupervised learning algorithms, feature selection, dimensionality reduction, and a variety of classification and clustering techniques. Deep Learning concepts will be introduced, focusing on neural networks, Convolutional Neural Networks (CNNs) for image analysis, Recurrent Neural Networks (RNNs) for sequential data processing, attention mechanisms, and training Generative Adversarial Networks (GANs). Through theoretical lectures, practical exercises, and hands-on projects, students will acquire the skills necessary to apply these technologies effectively in solving real-world problems and advancing their careers in AI and Data Science.
Course Outline	Historical evolution of AI, Conceptualization of AI, related terms, and subfields with applications. Practical component: Lab to be conducted on a 2-hour slot weekly. It will be conducted with the theory course so the topics for problems given in the lab are already initiated in the theory class.
Learning Outcome	· Understand the basic concept of AI. · Analysis of Data using Data science and data Analytics. · Explore state-of-the-art techniques and applications in machine learning · Compare and contrast various multiple deep learning architectures
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Text Books:

Tom M. Mitchell, 2017.Machine Learning.
Andrew-ng. Lecture Series – Deep Learning.ai . (Stanford)
Relevant research articles.

Reference Books:

Grus, J., 2019. Data science from scratch: first principles with python

CS2104

Discrete Mathematics

Course Number

CS2104

Course Credit

(L-T-P-C)

3-0-0-3

Course Title

Discrete Mathematics

Learning Mode

Offline

Learning Objectives

The objective of the course is to introduce the fundamental concepts in discrete mathematics with emphasis on their applications to computer science.

Course Description

Course Outline

· Logic and proofs

· Elementary set theory

· Relations and functions

· Recurrence relations

· Counting & Combinatorics

· Induction and Recursion

· Modular arithmetic

· Graph theory

· Elementary probability theory

Learning Outcomes

· Mathematical formalism of complex computer science problem and identifying their effective solutions.

· Improving critical thinking, and recognize valid, logical, mathematical arguments and construct valid arguments/proofs.

· Understanding the mathematical foundation behind cryptographic solutions in cryptology and others.

Assessment Method

Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Readings:

Discrete Mathematics and its Applications - Kenneth H. Rosen 7th Edition -Tata McGraw Hill, 2007
Elements of Discrete Mathematics, C. L Liu, McGraw-Hill Inc, 1985. Applied Combinatorics, Alan Tucker, 2007.
Concrete Mathematics, Ronald Graham, Donald Knuth, and Oren Patashnik, 2nd Edition - Pearson Education Publishers - 1996.
Combinatorics: Topics, Techniques, Algorithms by Peter J. Cameron, Cambridge University Press, 1994 (reprinted 1996).

CS2105

Optimization Techniques

Course Number	CS2105
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Optimization Techniques
Learning Mode	Offline
Learning Objectives	· To gain a thorough understanding principles of linear programming including problem formulation, geometric interpretations, and graphical solutions. · To explore advanced methods such as the Simplex algorithm, Big M method, and Revised Simplex method for optimizing linear programming problems. · To understand duality theory and sensitivity analysis in linear programming, and apply them to real-world scenarios like transportation and assignment problems. · To learn integer programming techniques like Branch and Bound and the Gomory cutting plane method for solving integer and mixed integer problems. To understand game theory concepts such as saddle points, matrix games, and strategies, and apply optimization methods to solve game-theoretic problems effectively.
Course Description	This course provides an exploration of essential methods for solving complex problems across various domains, including operations research, engineering, economics, and artificial intelligence. Beginning with foundational concepts in linear programming, students will delve into problem formulation, geometric interpretations, and graphical solutions, progressing to advanced techniques such as the Simplex algorithm, Big M method, and Revised Simplex method. Duality theory in linear programming is extensively covered, alongside integer programming techniques like Branch and Bound and the Gomory cutting plane method for both integer and mixed integer problems. The course also explores game theory applications, focusing on matrix games and two-person zero-sum games, utilizing graphical and simplex methods to derive optimal solutions. Additionally, students will gain insights into optimization techniques tailored for artificial intelligence and machine learning applications, preparing them to tackle real-world optimization challenges effectively.
Course Outline	Linear programming problem (LLP): Introduction and problem formulation, Concepts from Geometry, Geometrical aspects of LPP, Graphical solutions, Linear programming in standard form, Simplex, Big M and Two Phase Methods, Revised simplex method, Special cases of LPP. Duality theory: Dual simplex method, Sensitivity analysis of LP problems, Transportation, Assignment, and Traveling Salesman problems. Integer programming problems: Branch and bound method, Gomory cutting plane method for all integers and for mixed integer LPP. Theory of games: Saddle point, Linear programming formulation of matrix games, Two-person zero-sum games with and without saddle-points, Pure and mixed strategies, Graphical method of solution of a game, Solution of a game by simplex method. Basics of optimization techniques for artificial intelligence and machine learning
Learning Outcome	Upon successful completion of this course, students will: · Demonstrate proficiency in formulating and solving linear programming problems using advanced methods like the Simplex algorithm and its variants. · Apply duality theory and sensitivity analysis to analyze and optimize solutions in linear programming applications, including transportation and assignment problems. · Utilize integer programming techniques, such as Branch and Bound and Gomory cutting plane methods, to solve integer and mixed integer linear programming problems effectively. · Apply game theory concepts to analyze and solve matrix games using linear programming formulations, employing graphical and simplex methods for optimal strategy determination. · Apply optimization techniques relevant to artificial intelligence and machine learning applications, demonstrating the ability to optimize models
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Reading

Hamdy A. Taha, Operations Research: An Introduction, 10^th edition, PHI, New Delhi (2019).
S. Hillier, G.J. Lieberman, Introduction to Operations Research, 10^thedition, McGraw Hill (2017).
Ravindran, D.T. Phillips, J.J. Solberg, Operations Research, John Wiley and Sons, New York (2005).
S. Bazaraa, J.J. Jarvis and H.D. Sherali, Linear Programming and Network Flows, 3rd Edition, Wiley (2004).

D.G. Luenberger, Linear and Nonlinear Programming, 2nd Edition, Kluwer (2003).

HS21XX

HSS Elective I

TOTAL

Formal Language and Automata Theory

Sl. No.

Subject Code

SEMESTER IV

CS2201

Course Number	CS2201
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Formal Language and Automata Theory
Learning Mode	Offline
Learning Objectives	This course will introduce Learners about the basic mathematical models of computation, problems that can be solved by computers and problems that are computationally hard. It also introduces basic computation models, their properties and the necessary mathematical techniques to prove more advanced attributes of these models. The learners will be able to express computer science problems as mathematical statements and formulate proofs.
Course Description	This course is designed to cover computability and computational complexity theory. Topics include regular and context-free languages, decidable and undecidable problems, reducibility, time and space measures on computation.
Course Outline	Introduction: Alphabet, languages and grammars, productions and derivation, Chomsky hierarchy of languages. Regular languages and finite automata: Regular expressions and languages, deterministic finite automata (DFA) and equivalence with regular expressions, nondeterministic finite automata (NFA) and equivalence with DFA, regular grammars and equivalence with finite automata, properties of regular languages, pumping lemma for regular languages, minimization of finite automata. Context-free languages and pushdown automata: Context-free grammars (CFG) and languages (CFL), Chomsky and Greibach normal forms, nondeterministic pushdown automata (PDA) and equivalence with CFG, parse trees, ambiguity in CFG, pumping lemma for context-free languages, deterministic pushdown automata, closure properties of CFLs. Context-sensitive languages: Context-sensitive grammars (CSG) and languages, linear bounded automata and equivalence with CSG. Turing machines: The basic model for Turing machines (TM), Turing-recognizable (recursively enumerable) and Turing-decidable (recursive) languages and their closure properties, variants of Turing machines, nondeterministic TMs and equivalence with deterministic TMs, unrestricted grammars and equivalence with Turing machines, TMs as enumerators. Undecidability: Church-Turing thesis, universal Turing machine, the universal and diagonalization languages, reduction between languages and Rice’s theorem, undecidable problems about languages; Complexity theory: time and space complexity, Classes P, NP, NP-complete.
Learning Outcomes	The student will be able to: · Gain proficiency with mathematical tools and formal methods · Understand various mathematical models of computation and formal languages · Understand Turing machines, decidable languages, and undecidable languages · Design and analyze Turing machines, their capabilities and limitations · Understand the basics of complexity theory, complexity classes and possible unsolved problems in theoretical computer science
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Reading

J. E. Hopcroft, R. Motwani and J. D. Ullman, Introduction to Automata Theory, Languages and Computation, Pearson Education India (3rd edition).
2. K. L. P. Mishra, N. Chandrasekaran, Theory of Computer Science: Automata, Languages and Computation, PHI Learning Pvt. Ltd. (3rd edition).
3. D. I. A. Cohen, Introduction to Computer Theory, John Wiley & Sons, 1997.
4. J. C. Martin, Introduction to Languages and the Theory of Computation, Tata McGraw-Hill (3rd Ed.).
5. H. R. Lewis and C. H. Papadimitriou, Elements of the Theory of Computation, Prentice Hall, 1997.
6. Garey, D.S., Johnson, G., Computers and Intractability: A Guide to the Theory of NP- Completeness, Freeman, New York, 1979

7. M. Sipser, Introduction to the Theory of Computation, Thomson, 2004

CS2202

Course Number	CS2202
Course Credit (L-T-P-C)	3-0-2-4
Course Title	Database and Warehousing
Learning Mode	Offline
Learning Objectives	· Understand the fundamental principles of database systems and data warehousing. · Learn to design, implement, and manage databases using relational database management systems (RDBMS). · Explore the concepts and techniques of data warehousing and data mining. · Develop skills in SQL for querying and managing databases. · Analyze and optimize database performance and ensure data integrity and security.
Course Description	This course provides an in-depth exploration of database systems and data warehousing, covering essential concepts, technologies, and techniques. Students will learn about the design and implementation of relational databases, including data modeling, normalization, and SQL. The course will also introduce data warehousing concepts, focusing on data extraction, transformation, and loading (ETL), as well as data mining techniques. Through practical exercises and projects, students will gain hands-on experience in working with databases and data warehouses, preparing them for real-world applications.
Course Outline	1. Introduction to Databases, Overview of database systems, Types of databases and database models, Database architecture and components 2. Data Modeling, Entity-Relationship (ER) modeling, Relational model and schema design, Normalization and denormalization 3. Structured Query Language (SQL), Basic SQL queries (SELECT, INSERT, UPDATE, DELETE), Advanced SQL (joins, subqueries, indexing) , SQL functions and stored procedures 4. Database Design and Implementation, Database design principles, Creating and managing databases using RDBMS, Data integrity and constraints 5. Database Management and Administration, Database backup and recovery, User management and security, Performance tuning and optimization 6. Introduction to Data Warehousing, Concepts and architecture of data warehousing, Data warehousing vs. databases, Data modeling for data warehousing 7. ETL Processes, Data extraction, transformation, and loading (ETL), ETL tools and techniques, Data cleaning and integration 8. Data Mining and Analytics, Introduction to data mining, Data mining techniques and algorithms, Applications of data mining 9. Advanced Topics in Data Warehousing, Big data and data warehousing, Cloud-based data warehousing solutions, Data governance and data quality management Practical component: Lab to be conducted on a 2-hour slot weekly. It will be conducted with the theory course so the topics for problems given in the lab are already initiated in the theory class.
Learning Outcome	The student will be able to: · Demonstrate a thorough understanding of database and data warehousing principles. · Design, implement, and manage relational databases using RDBMS. · Write efficient SQL queries for data manipulation and retrieval. · Implement data warehousing solutions, including ETL processes and data mining techniques. · Analyze and optimize the performance of databases and data warehouses, ensuring data integrity and security.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Textbooks

"Database System Concepts" (7th Edition) by Abraham Silberschatz, Henry F. Korth, and S. Sudarshan
"Fundamentals of Database Systems" (7th Edition) by Ramez Elmasri and Shamkant B. Navathe
"Data Warehousing: The Ultimate Guide to Building a Data Warehouse for Business Intelligence" (1st Edition) by Erik Thomsen
"The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling" (3rd Edition) by Ralph Kimball and Margy Ross

5. "SQL: The Complete Reference" (3rd Edition) by James R. Groff and Paul N. Weinberg

CS2203

Artificial Intelligence

Course Number	CS2203
Course Credit (L-T-P-C)	3-0-3-4.5
Course Title	Artificial Intelligence
Learning Mode	Offline
Learning Objectives	· To understand the core concepts and principles of Artificial Intelligence and intelligent agents. · To learn and apply uninformed and informed search strategies to solve complex problems. · To formulate and solve constraint satisfaction problems and engage in adversarial search. · To represent knowledge using propositional and first-order logic and perform inference and planning. · To utilize various learning techniques and understand their applications in different AI domains.
Course Description	This course provides a comprehensive introduction to the fundamental concepts and techniques of Artificial Intelligence (AI). Students will learn about the design and implementation of intelligent agents, various search strategies, constraint satisfaction problems, knowledge representation, and reasoning. Additionally, the course covers learning techniques and their practical applications, preparing students to apply AI principles in real-world scenarios. The lab component allows students to implement these concepts, reinforcing theoretical knowledge through hands-on experience.
Course Outline	Introduction: Definition and scope of Artificial Intelligence, background and evolution, intelligent agents and environment Problem Solving: Solving problems by searching, uninformed and informed search Uninformed search: Breadth-first search (BFS), Depth-first search (DFS), Uniform-cost search (UCS) Informed search: Heuristic function design and evaluation, A* search Local search: Hill climbing Adversarial search: Min-max, alpha-beta pruning Constraint Satisfaction Problem (CSP): definition and examples of CSPs Knowledge Representation and Reasoning: Propositional Logic, First Order Logic Introduction to Learning Techniques: Bayesian, decision tree, etc. Some applications of AI Practical component: Lab to be conducted on a 3-hour slot weekly. It will be conducted with the theory course so the topics for problems given in the lab are already initiated in the theory class.
Learning Outcome	By the end of this course, students will be able to: · Understand the core concepts and principles of Artificial Intelligence and intelligent agents. · Apply uninformed and informed search strategies to solve complex problems. · Formulate and solve constraint satisfaction problems and engage in adversarial search. · Represent knowledge using propositional and first-order logic and perform inference and planning. · Utilize various learning techniques and understand AI applications in different domains.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Reading

Russell, S. J., & Norvig, P. (2016). Artificial intelligence: A modern approach. Pearson.

Poole, D. L., & Mackworth, A. K. (2010). Artificial Intelligence: foundations of computational agents. Cambridge University Press.

Hastie, T., Tibshirani, R., Friedman, J. H., & Friedman, J. H. (2009). The elements of statistical learning: data mining, inference, and prediction (Vol. 2, pp. 1-758). New York: Springer.

4.5

CS2204

IT Workshop

Course Number	CS2204
Course Credit (L-T-P-C)	0-2-2-3
Course Title	IT Workshop
Learning Mode	Offline
Learning Objectives	· To understand the basics of shell scripting and its applications in automating tasks. · To learn the fundamentals of Android programming and app development. · To gain practical experience in writing scripts and developing Android applications. · To develop problem-solving skills through scripting and programming exercises. · To explore the integration of shell scripts within Android environments.
Course Description	This undergraduate course provides a foundational understanding of both shell scripting and Android programming. Students will start by learning the essential concepts of shell scripting, including syntax, commands, and script writing techniques to automate tasks in Unix/Linux environments. The course then transitions into Android programming, covering the basics of Java/Kotlin, Android Studio, and app development. By combining these two areas, the course aims to equip students with a versatile skill set that is highly valuable in the tech industry. Through a series of lectures, hands-on labs, and projects, students will gain the knowledge and experience needed to create efficient scripts and functional Android applications.
Course Outline	1. Introduction to Shell Scripting: Overview of Unix/Linux systems, Basic shell commands and utilities, Writing and executing simple shell scripts 2. Advanced Shell Scripting: Control structures (loops, conditionals) , Functions and arrays in shell scripting , Script debugging and error handling 3. Practical Shell Scripting: Automating tasks and processes, File manipulation and text processing, Networking and system administration scripts 4. Introduction to Android Programming: Overview of Android OS and development environment, Setting up Android Studio and creating a basic app, Introduction to Java/Kotlin for Android development 5. Building Android Applications: User interface design and XML layouts, Activity lifecycle and event handling, Using intents and data passing between activities 6. Advanced Android Features: Working with databases and content providers, Networking and web services in Android, Integrating shell scripts within Android apps 7. Project Development and Deployment: Developing a complete Android app project, Testing and debugging Android applications Lab to be conducted on a 2-hour slot weekly.
Learning Outcome	· Write and execute shell scripts to automate various tasks in Unix/Linux environments. · Understand and apply advanced shell scripting techniques for more complex automation. · Develop Android applications using Java/Kotlin and Android Studio. · Design and implement user interfaces for Android apps. · Integrate shell scripting functionalities within Android applications.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Reading

"Learning the bash Shell: Unix Shell Programming" by Cameron Newham, 3rd Edition.
"Shell Scripting: How to Automate Command Line Tasks Using Bash Scripting and Shell Programming" by Jason Cannon, 1st Edition.
"Android Programming: The Big Nerd Ranch Guide" by Bill Phillips, Chris Stewart, and Kristin Marsicano, 4th Edition.
"Head First Android Development: A Brain-Friendly Guide" by Dawn Griffiths and David Griffiths, 2nd Edition.

5. "Kotlin for Android Developers: Learn Kotlin the Easy Way While Developing an Android App" by Antonio Leiva, 1st Edition.

CS2206

Computer Architecture

Course Number	CS2206
Course Credit (L-T-P-C)	3-0-3-4.5
Course Title	Computer Architecture
Learning Mode	Offline
Learning Objectives	The course is designed to provide basic understanding of structure, and function of various building blocks of computer system. Students will be able to design various functional units and components of computers and to identify the elements of modern instructions sets and their impact on processor design including memory hierarchy
Course Description	Using a set of fundamental techniques and technologies, the computer systems theme broadly explains how computing platforms work at various levels of abstraction, including both software and hardware. The course introduces computer architecture with focus on bridging the gap between high-level programming languages and the hardware (e.g., micro-processors) on which associated programs execute.
Course Outline	Computer types, RISC, CISCs, Structure of basic computer components. CPU Design Assembly language programming processor (MIPS); CPU control unit design: hardwired and micro-programmed design approaches, design of single Cycle MIPS, Multi cycle MIPS processor; Pipelining: Basic concepts of pipelining, throughput and speedup, pipeline hazards; Superscalar Architecture, Memory organization: cache memory, cache size vs block size, mapping functions, replacement algorithms, write policy; Peripheral devices and their characteristics: Input-output subsystems, Super Scalar Architectures, Super Pipelined Architecture, VLIW Architecture, SPARC and ARM processors. Practical component: The objective to this course is give hands on experience on computer architecture. It will provide an overview of understanding of the basic building blocks such Arithmetic Logic Blocks, register file, and memory. It also focuses on exploring various computer architecture simulators which include Simulation of Data Path and Control of CPUs and assembly language programming.
Learning Outcome	The student will be able to: · Demonstrate an understanding of how data is represented within a computer system. · Appreciate understanding of the basic blocks, key terminology, and current industry trends in computer architecture. · Demonstrate classic components of a computational system (i.e. input, output, memory, data path, control) and understanding their functionality. · Understand the processor (CPU) subsystem. · Employ concepts of the memory subsystem and hierarchy
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term.

Text books:

Moris Mano, “Computer Systems Architecture”, 4th Edition, Pearson/PHI, ISBN:10:0131755633
Carl Hamacher, Zvonko Vranesic, Safwat Zaky, “Computer Organization”, 5th Edition, McGraw Hill.
John L. Hennessy and David A. Patterson, “Computer Architecture a quantitative approach”, 4th Edition Elsevier, ISBN:10:0123704901

William Stallings, “Computer Organization and Architecture”, 6th Edition, Pearson/PHI, ISBN:10:0-13-609704-9

4.5

XX22PQ

IDE - I

TOTAL

Sl. No.

Subject Code

SEMESTER V

CS3101

Operating System

Course Number	CS3101
Course Credit (L-T-P-C)	3-0-3-4.5
Course Title	Operating System
Learning Mode	Offline
Learning Objectives	This course provides an in-depth understanding of the fundamental concepts, principles, and mechanisms of operating systems. Topics include process management, memory management, file systems, concurrency, and scheduling.
Course Description	This course comprehensively introduces the fundamental concepts and principles underlying operating systems. Key topics include definitions of operating systems, the concept of a process, inter-process communication mechanisms, and multi-threading concepts. The course also addresses critical issues such as deadlock, discussing the necessary conditions for its occurrence and strategies for avoidance and prevention. In the realm of memory management, students will learn about both contiguous and non-contiguous allocation, paging concepts, and page table architecture. Further, the virtual memory concept will be explored, focusing on demand paging, replacement algorithms, and the phenomenon of thrashing. The course also includes a detailed study of file systems and disk management. By the end of this course, students will have a robust understanding of the essential components and functions of operating systems, preparing them for advanced studies and practical applications in the field of computer science.
Course Outline	Basics of Operating System: Definition and objectives of operating systems Types of operating systems: Batch, Time-sharing, Real-time, Distributed Systems Concept of process: Process control block, State transition, Scheduling algorithms, context switching, Process synchronization and inter-process communication Threads: Popular thread libraries, thread synchronization, multi-therading concepts Deadlock: necessary conditions, avoidance and prevention Memory management: Contiguous and non-contiguous allocation, Physical and logical addresses, Paging, different Page Table architectures, Virtual Memory: demand paging, replacement algorithms, thrashing. File systems: file operations, organization, mounting, sharing, File system implementation Disk management: disk structure, disk scheduling, disk management Practical component: Lab to be conducted on a 3-hour slot weekly. It will be conducted with the theory course so the topics for problems given in the lab are already initiated in the theory class.
Learning Outcome	· Understand the basic principles and functionalities of operating systems. · Analyse and evaluate different operating system components and their interactions. · Apply operating system concepts to solve real-world problems. · Develop an appreciation for the role of operating systems in modern computing environments.
Learning Outcome	· Understand the basic principles and functionalities of operating systems. · Analyse and evaluate different operating system components and their interactions. · Apply operating system concepts to solve real-world problems. · Develop an appreciation for the role of operating systems in modern computing environments.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested readings:

A. Silberschatz, P. B. Galvin and G. Gagne, Operating System Concepts, 7^th Ed, John Wiley and Sons, 2004.
M. Singhal and N. Shivratri, Advanced Concepts in Operating Systems, McGraw Hill, 1994.

3. David A Patterson and John L Hennessy, Computer Organisation and Design: The Hardware/Software Interface, Morgan Kaufmann, 1994. ISBN 1-55860-281-X.

4.5

CS3102

Computer Network

Course Number	CS3102
Course Credit (L-T-P-C)	3-0-3-4.5
Course Title	Computer Network
Learning Mode	Offline
Learning Objectives	The primary objectives of this course are to provide students with a solid foundation in computer networking principles and to prepare them for real-world networking challenges. Students will learn about network architectures, protocols, and technologies, and develop the skills necessary to design, implement, and manage networks. By the end of the course, students will be proficient in understanding network layers, configuring network devices, and troubleshooting network issues.
Course Description	This course provides an in-depth study of computer networks, covering essential concepts and technologies that form the backbone of modern communication systems. Students will learn about network topologies, protocols, hardware, and software that enable data transmission across networks. The course will also delve into advanced topics such as network security, wireless networking, and network management. Through practical exercises and projects, students will apply theoretical knowledge to real-world networking scenarios.
Course Outline	Introduction to computer networks and layered architecture, network applications, web architecture. Application Layer: HTTP, email protocols, DNS, and peer-to-peer applications. Transport layer: TCP, UDP, SCTP, and congestion control. Network layer: IP addressing, routing, and protocols like IPv4 and IPv6. link layer: LAN, error detection, MAC protocols. Physical Layer: Basics of data communication, transmission media and topology Future trends in networking: SDN, NFV Practical component: Lab to be conducted on a 3-hour slot weekly. It will be conducted with the theory course so the topics for problems given in the lab are already initiated in the theory class.
Learning Outcome	· Demonstrate an understanding of the core concepts and principles of computer networks. · Design and configure various types of network topologies and protocols. · Implement and manage network services and applications. · Identify and mitigate network security threats. · Analyze network performance and troubleshoot issues effectively.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Text Books:

"Computer Networking: A Top-Down Approach" (7th Edition) by James F. Kurose and Keith W. Ross
"Data Communications and Networking" (5th Edition) by Behrouz A. Forouzan
"Computer Networks" (5th Edition) by Andrew S. Tanenbaum and David J. Wetherall
"Network+ Guide to Networks" (8th Edition) by Jill West, Tamara Dean, and Jean Andrews

5. "TCP/IP Illustrated, Volume 1: The Protocols" (2nd Edition) by Kevin R. Fall and W. Richard Stevens

4.5

CS3103

Machine Learning

Course Number	CS3103
Course Credit (L-T-P-C)	3-0-3-4.5
Course Title	Machine Learning
Learning Mode	Offline
Learning Objectives	This course aims to help the students (a) to understand the fundamental concepts of machine learning; (b) to develop the basic problem-solving skills by implementing the basic machine learning algorithms; (c) to learn about various paradigms of machine learning and various approaches under different paradigms; and (d) to achieve proficiency in designing some real-life project using machine learning.
Course Description	This course provides a comprehensive introduction to the field of Machine Learning (ML), covering fundamental concepts, techniques, and applications. It is designed to give students a solid foundation in understanding how machines learn from data and make decisions. Through a combination of theoretical insights and practical applications, students will explore various aspects of machine learning, including supervised and unsupervised learning, generalization, regression, classification, clustering, data reduction, and ensemble learning.
Course Outline	1.Understanding of Machine Learning: Definition, Tasks (Classification, Regression, Prediction, and Clustering), Supervised and unsupervised machine learning. 2.Learning to Generalization: Bias-Variance Trade-off, Overfitting vs. Underfitting, Regularization 3.Regression (single & multivariate, linear and nonlinear, Logistic Regression 4.Classification: (kNN, Bayes classifier, decision tree, random forest, Support vector Machines) 5.Unsupervised Learning: K-Means & variants, Hierarchical techniques 6.Data Reduction and Ensemble Learning Practical component: Lab to be conducted on a 3-hour slot weekly. It will be conducted with the theory course so the topics for problems given in the lab are already initiated in the theory class.
Learning Outcome	· Understanding of fundamental concepts of ML · Understanding different types of ML tasks: Classification, Regression, and Clustering · Understanding of various algorithms under different paradigms of ML: supervised, unsupervised, semi-supervised. · Capable of conducting some real-life projects using machine learning algorithms
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Reading

Mitchell. Machine Learning. McGraw-Hill, 1997.
Duda, Richard O., and Peter E. Hart. Pattern classification. John Wiley & Sons, 2006.
Pattern recognition and machine learning by Christopher Bishop, Springer Verlag, 2006.
Machine Learning in Action by Peter Harrington
Probability, Random Variables and Stochastic processes by Papoulis and Pillai, 4th Edition, Tata McGraw Hill Edition.
Linear Algebra and Its Applications by Gilbert Strand. Thompson Books.
Data Mining: Concepts and Techniques by Jiawei Han, Micheline Kamber, Morgan Kaufmann Publishers.

A. K. Jain and R. C. Dubes. Algorithms for Clustering Data. Prentice Hall, 1988

4.5

CS3104

Compiler

Course Number

CS3104

Course Credit

(L-T-P-C)

3-0-3-4.5

Course Title

Compiler

Learning Mode

Offline

Learning Objectives

The objective of the course is to introduce basic theory underlying different components and phases of a compiler, including parsing, code generation, optimization, etc. The students will also learn how to use various tools that are used for building modern compilers.

Course Description

This course is designed to cover various phases of compilers. Topics include regular languages for lexical analysis, context free languages for syntactic analysis, SDD/SDT for semantic analysis, IR code generation, and code optimization.

Course Outline

Introduction to Compilers: The role of language translation in programming; Interpreters Vs. Compilers, Language translation phases, Machine-dependent and machine-independent aspects of translation
Lexical Analysis: Regular expressions, Finite automata, Conflict Resolution
Syntax Analysis: Context-free grammars, Ambiguous grammers, Top-down parsing (LL parsing), Bottom-up parsing (LR parsing), CYK parser, Conflict resolution
Semantic Analysis: Formal Language Semantics, Symbol tables, Type checking, Attribute grammars
Intermediate Code Generation: Various Intermediate Representations (IR) of code (e.g., Abstract syntax trees (AST), Single Assignment (SA), Three-address code, etc.), Translation schemes
Optimization Techniques: Various optimization scopes (Constant folding, constant propagation, invariant motion, etc.), Data-flow analysis (Liveness, available expression, very busy expression, reaching definition)
Code Generation: Instruction selection, Code generation for different architectures; Code generation tools

Lab Component: Hands-on experience with various parsers, such as ANTLR, Lex, Flex, Yacc, Bison, etc.

Learning Outcome

The student will gain proficiency in both theory Parsing, Code generation, optimization) and practical (Lexer, Parser) aspects. They will be able to construct a compiler that converts from a non-trivial high level language to machine code.

Assessment Method

Internal(Quiz/Assignment/Project), Mid-Term, End-Term.

Suggested Readings:

J. E. Hopcroft, R. Motwani and J. D. Ullman, Introduction to Automata Theory, Languages and Computation, Pearson Education India (3rd edition).
2. K. L. P. Mishra, N. Chandrasekaran, Theory of Computer Science: Automata, Languages and Computation, PHI Learning Pvt. Ltd. (3rd edition).
3. D. I. A. Cohen, Introduction to Computer Theory, John Wiley & Sons, 1997.
4. J. C. Martin, Introduction to Languages and the Theory of Computation, Tata McGraw-Hill (3rd Ed.).
5. H. R. Lewis and C. H. Papadimitriou, Elements of the Theory of Computation, Prentice Hall, 1997.
6. Garey, D.S., Johnson, G., Computers and Intractability: A Guide to the Theory of NP- Completeness, Freeman, New York, 1979
M. Sipser, Introduction to the Theory of Computation, Thomson, 2004

4.5

XX31PQ

IDE - II

TOTAL

Sl. No.

Subject Code

SEMESTER VI

CS3201

Cyber Security

Course Number	CS3201
Course Credit (L-T-P-C)	3-0-2-4
Course Title	Cyber Security
Learning Mode	offline
Learning Objectives	To understand the basic concepts of cyber-attacks, legal issues and countermeasures.
Course Description	The course covers cyber-attacks, legal issues and countermeasures various aspects of cybersecurity, including basic principles, legal considerations, risk assessment, and security management. The course covers essential topics such as cybercrime, phishing attacks, cryptography basics, authentication mechanisms, and authorization protocols. Additionally, it delves into specific areas of vulnerability assessment and mitigation, focusing on secure programming practices and identifying threats to networks.
Course Outline	Introduction to cybersecurity: Basic concepts, cybercrime, legal issues, risk analysis and security management, phishing attack. Crypto basics, Authentication and authorization, Kerberos, PKI Vulnerabilities and Countermeasure: Vulnerabilities in code, Secure programming. Threats to network, network defense, social network security issues and countermeasures, email security Cyber system security: Hardware security, mobile security. Practical component: Lab to be conducted on a 2-hour slot weekly. It will be conducted with the theory course so the topics for problems given in the lab are already initiated in the theory class.
Learning Outcome	After completion of this course a student will have: · Understanding the legal aspects, risk and vulnerabilities in cyberspace. · Understanding the concepts of different attacks and their countermeasures in cyberspace.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Readings:

Nina Godbole and Sunit Belapure, Cyber Security, Wiley India

CS3202

Course Number	CS3202
Course Credit (L-T-P-C)	3-0-3-4.5
Course Title	Deep Learning
Pre-requisite	offline
Learning Mode	This course aims to provide an introductory overview of deep learning and its application varied domains. The course will provide basic understanding of neural networks, mathematical description of it and finally applications of it in multiple domains. A few open source tools will be demonstrated during the course to provide hands-on experience.
Learning Objectives	This course will provide an overview of neural networks and hands-on experience for the same.
Course Description	Introduction: Introduction to bigdata problem, overview of linear algebra Feature engineering: Basics of machine learning (linear regression, classification) Neural network: Deep feed forward network, cost function, activation functions, overfitting, underfitting, Universal approximation theorem Gradient based learning: Gradient Descent, Stochastic Gradient Descent, Backpropagation Regularization: L2, L1, L\infinity, drop-out, early stopping, data augmentation, etc. Optimization: Multivariable taylor series, momentum, adaptive learning rate, ADAM, Nesterov Accelerated Gradient (NAG), AdaGrad, etc. Convolutional Neural Network (CNN): Theory and its application in computer vision Recurrent Neural Network (RNN): Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU) and their applications in natural language processing Advanced topics: Autoencoder, Transformer, Deep reinforcement learning Practical component: Lab to be conducted on a 3-hour slot weekly. It will be conducted with the theory course so the topics for problems given in the lab are already initiated in the theory class.
Course Content	· Basic understanding of deep learning and neural networks · Problem modeling skill · Usage of different open source tools / libraries Analysis of large volume of data
Learning Outcome	Knowledge on various forms of stresses in pressure vessels and their relation. Mechanical designing of different parts/components used in heat exchangers or in separation units such as nuts/bolts, flanges, heads, shell, etc. Consideration and elementary sizing calculation on tall, horizontal/vertical vessels, and their constructional supports.
Assessment Method	Assignments, Quiz, Mid-semester examination and End-semester examination.

Suggested Reading:

Ian Goodfellow, Yoshua Bengio and Aaron Courville, “Deep Learning”, Book in preparation for MIT Press, 2016.
Reference books:
Jerome H. Friedman, Robert Tibshirani, and Trevor Hastie, “The elements of statistical learning”, Springer Series in Statistics, 2009.
Charu C Aggarwal, “Neural Networks and Deep Learning”, Springer.
Aston Zhang, Zachary C. Lipton, Mu Li, Alexander J. Smola, "Dive into Deep Learning"
Iddo Drori, "The Science of Deep Learning", Cambridge University Press
Simon O. Haykin, "Neural Networks and Learning Machines", Pearson Education India
Richard S. Sutton, Andrew G. Barto, "Reinforcement Learning: An Introduction", MIT Press
M. Bishop, H. Bishop, "Deep Learning: Foundations and Concepts", Springer, 2022

Simon J. D. Prince, "Understanding Deep Learning", MIT Press 2023

4.5

CS3203

Internet of Things

Course Number	CS3203
Course Credit (L-T-P-C)	3-0-3-4.5
Course Title	Internet of Things
Learning Mode	Offline
Learning Objectives	· The layout of the course follows various popular IoT courses being followed by various universities globally · This course will also provide a brief overview of the very basics of networking, the precursor technologies of IoT and the emergence of IoT so that the students are not abruptly faced with complex ideas and terminologies from the onset of this course. · This course includes various building blocks, which are essential for developing an IoT platform · After describing different computing technologies in IoT, a few use cases are described. This part of the course will help a student to understand the use of different components, described in the previous lectures, in real life. · Conduct tutorial classes, in which the practical knowledge of IoT implementation will be provided to the students. Additionally, two projects will be provided to the students to learn the hands-on in IoT
Course Description	This undergraduate course provides a foundational understanding of Internet of Things (IoT). Students will start by learning the essential concepts of IoT, including sensors, actuators, connectivity and communications protocols. This course provides a comprehensive overview of the principles, technologies, and applications of IoT. Through hands-on projects and real-world case studies, students will learn how to create innovative IoT applications in various domains such as smart homes, healthcare, industrial automation, and smart cities.
Course Outline	Introduction to IoT: Predecessors of IoT, Emergence of IoT Sensors, Actuators, and Processors: Sensors, Actuators and their types, IoT Processing Topologies and types IoT Communications Technologies: IEEE 802.15.4, ZigBee, Wireless HART, RFID, NFC, Z-Wave, LoRa, Sigfox, NB-IoT, Wi-Fi IoT Communication Protocols: Introduction to Constrained Environment, Infrastructure Protocols, Discovery Protocols, Data Protocols Cloud and Fog Computing for IoT: Virtualization, Cloud models, Service-Level Agreement in Cloud, Basics of Fog computing, Fog Nodes IoT Applications: Agricultural IoT, Vehicular IoT, Healthcare IoT, Paradigm, Challenges, and the Future of IoT Lab outline: - Working with IoT Processor boards: Node MCU and Arduino, Raspberry-Pi - Sensors and actuators integration - Working with IoT Communication module: Integration and data communication - Implementing IoT communication protocols: Infrastructure, discovery, and data protocols - IoT-based alert generation systems: Temperature, Humidity, and Air quality monitoring systems, - Implementation of cloud and fog computing for IoT - Developments of Smart home, smart irrigation, and smart parking systems using cloud and fog computing Lab to be conducted on a 3-hour slot weekly.
Learning Outcome	A student will be capable of applying the various computing technologies and designing the system for implementing an IoT platform. A student will be capable of applying ideas and knowledge in developing IoT systems. A student will know primary focus areas in IoT, including interoperability, Cloud Computing, Fog, and Edge computing. A student will be capable of developing IoT projects for different applications The tutorial classes of the course will provide an opportunity to the student for learning IoT hands-on, and working in the projects
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term.

Suggested Reading

Peter Waher, Learning Internet of Things, Packt Publishing Ltd., UK, 2015

Michael Miller, Internet of Things The: How Smart TVs Sm: How Smart TVs, Smart Cars, Smart Homes, and Smart Cities Are Changing the World, Pearson Education, Inc, USA, 2015

Sudip Misra, Anandarup Mukherjee, and Arijit Roy. Introduction to IoT. Cambridge: Cambridge University Press, (2021) doi:10.1017/9781108913560

Cuno Pfister, Getting Started with the Internet of Things, O' Reilly Media, Inc, Gravenstein Highway North, Sebastopol, CA, 2011

Adrian McEwen and Hakim Cassimally, Designing the Internet of Things, John Wiley and Sons, Ltd, West Sussex, UK, 2014

4.5

CS32XX

DE-I (CS ELECTIVES LIST)

CS3299

Capstone Project

TOTAL

Minor - IV

Sl. No.	Subject Code	SEMESTER VII	L	T	P	C
1.	CS41XX	DE-II (CS ELECTIVES LIST)	3	0	0	3
2.	CS41XX	DE-III (CS ELECTIVES LIST)	3	0	0	3
3.	XX41PQ	IDE-III	3	0	0	3
4.	HS41XX	HSS Elective II	3	0	0	3
5.	CS4198	Summer Internship*/ Summer Project	0	0	12	3
6.	CS4199	Project – I	0	0	12	6
TOTAL			12	0	24	21

* For specific cases of internship after 6^th Semester, the performance evaluation would be made on joining the VII^th Semester and graded accordingly in the VII^th Semester:

Note :

a) (i) Summer internship (*) period of at least 60 days’ (8 weeks) duration begins in the intervening vacation between semester VI and VII that may be done in industry / R&D / Academic Institutions including IIT Patna. The evaluation would comprise combined grading based on host supervisor evaluation, project internship report after plagiarism check and seminar presentation at the Department (DAPC to coordinate) with equal weightage of each of the three components stated herein.

a) (ii) Further, on return from internship, students will be evaluated for internship work through combined grading based on host supervisor evaluation, project internship report after plagiarism check, and presentation evaluation by the parent department with equal weightage of each component.

b) (i) In the VII^th semester, students can opt for a semester long internship on recommendation of the DAPC and approval of the Competent Authority.

b) (ii) On approval of semester long internship, at the maximum two courses (properly mapped/aligned syllabus) at par with institute electives may be opted from NPTEL and / or SWAYAM and the other two more should be done at the institute through course overloading in any other semester (either before or after the internship) and/or during following summer semester.

b) (iii) The candidates opting two courses from NPTEL and / or SWAYAM would be required to appear in the examination at the Institute as scheduled in the Academic Calendar.

Object-Oriented Programming

Department Elective - I

Sl. No.

Course Code

Course Name

CS3205

Course Number	CS3205
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Object-Oriented Programming
Learning Mode	Offline
Learning Objectives	The primary objectives of this course are to introduce students to the principles and practices of object-oriented programming (OOP) and to equip them with the skills necessary to design and implement software using OOP techniques. Students will learn about core OOP concepts such as classes, objects, inheritance, polymorphism, encapsulation, and abstraction. They will also develop proficiency in using an object-oriented programming language such as Java or Python.
Course Description	This course provides a comprehensive introduction to the fundamental concepts and methodologies of OOP. The course covers essential topics such as class and object design, inheritance, polymorphism, and encapsulation, and explores advanced concepts including exception handling, file I/O, and graphical user interfaces (GUIs). Through a series of practical exercises and projects, students will gain hands-on experience in writing clean, efficient, and maintainable code. The course emphasizes best practices and design patterns that are critical for developing robust software applications.
Course Outline	1. Introduction to Object-Oriented Programming, Overview of programming paradigms, Key concepts of OOP: classes, objects, and methods, Benefits of OOP 2. Classes and Objects, Defining and creating classes, Constructors and destructors, Object lifecycle and memory management 3. Encapsulation and Data Hiding, Access modifiers (public, private, protected), Getters and setters, Maintaining data integrity 4. Inheritance and Polymorphism, Base and derived classes, Method overriding and overloading, Dynamic binding and polymorphic behavior 5. Abstraction and Interfaces, Abstract classes and methods, Interface implementation, Multiple inheritance in OOP 6. Object-Oriented Design Principles, SOLID principles, Design patterns (e.g., Singleton, Factory, Observer), UML diagrams for OOP design 7. Exception Handling and File I/O, Error detection and handling, using exceptions to manage errors, File input/output operations 8. Advanced OOP Concepts, Generic programming and templates, Reflection and metadata, Multithreading in OOP.
Learning Outcome	Upon successful completion of this course, students will be able to: · Understand and apply the core principles of object-oriented programming. · Design and implement software solutions using object-oriented techniques. · Develop and debug programs in an object-oriented programming language. · Utilize advanced OOP features such as inheritance, polymorphism, and interfaces effectively. · Write clean, maintainable, and efficient code following best practices and design patterns. · Create basic graphical user interfaces and handle events in GUI applications. · Apply OOP concepts in various real-world scenarios and software development projects.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Reading

"Object-Oriented Analysis and Design with Applications" by Grady Booch
Data Structures and Algorithm Analysis in C++ Hardcover, by Mark A. Weiss, Jun 2013, Publisher: PHI; 4 editions, ISBN-10: 013284737X ISBN-13: 978-0132847377.
Algorithms in C++: Fundamentals, Data Structures, Sorting, Searching, Parts 1-4, 3rd Edition (Paperback), Pearson India, ISBN-10 8131713059, 2009, ISBN-13 9788131713051.
"Thinking in C++" by Bruce Eckel
"C++ Primer" by Stanley B. Lippman, Josée Lajoie, and Barbara E. Moo

"Head First Object-Oriented Analysis and Design" by Brett McLaughlin, Gary Pollice, and David West

CS3206

Agile Computing

Course Number	CS3206
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Agile Computing
Learning Mode	Offline
Learning Objectives	· To gain a thorough understanding of agile computing principles, methodologies, and their application in software development. · To learn to effectively apply agile practices such as Scrum, Kanban, and Extreme Programming (XP) to enhance project visibility, collaboration, and adaptability. · To develop skills in managing and leading agile teams, utilizing agile project management tools for planning and tracking projects. · To acquire knowledge of metrics and performance measurement techniques to analyze and optimize agile processes. · To apply agile principles to cultivate a culture of continuous improvement and innovation within organizations.
Course Description	This course provides a comprehensive exploration of agile computing, focusing on its principles, methodologies, and practical applications in software development. Students will delve into popular agile frameworks like Scrum, Kanban, and Extreme Programming (XP), learning how these methodologies enhance project management, collaboration, and responsiveness to change. Topics include agile estimation, planning, testing, quality assurance, and scaling agile practices. The course also covers agile leadership, metrics for performance measurement, and fostering an agile culture of continuous improvement and innovation.
Course Outline	Introduction to Agile Computing and scope Overview of popular agile methodologies like Scrum, Kanban, and Extreme Programming (XP), Scrum roles, artifacts, and events, Lean and Kanban Principles, Extreme Programming (XP): est-driven development (TDD) and pair programming, Agile Estimation and Planning, Agile Testing and Quality Assurance, Scaling Agile, Agile Leadership and Culture, Agile Metrics and Performance Measurement, Applications of agile computing
Learning Outcome	By the end of this course, students will be able to: · Understand the principles and philosophy of agile computing. · Apply various agile methodologies and practices to software development projects. · Effectively manage and lead agile teams. · Use agile project management tools to plan, track, and deliver projects. · Analyze and optimize agile processes using metrics and performance measurement techniques. · Apply agile principles to foster a culture of continuous improvement and innovation within organizations
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Reading

"Agile Estimating and Planning" by Mike Cohn, 2006
"Agile Testing: A Practical Guide for Testers and Agile Teams" by Lisa Crispin, Janet Gregory, 2009
"Scrum: The Art of Doing Twice the Work in Half the Time" by Jeff Sutherland, 2014
"Kanban: Successful Evolutionary Change for Your Technology Business" by David J. Anderson, 2010
"Extreme Programming Explained: Embrace Change" by Kent Beck, 2004

"Lean Software Development: An Agile Toolkit" by Mary Poppendieck, Tom Poppendieck, 2003

CS3207

Software Engineering

Course Number	CS3207
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Software Engineering
Learning Mode	Offline
Learning Objectives	This course aims to help the students · with a comprehensive understanding of the fundamental principles and concepts of software engineering; · with the software development life cycle (SDLC) and various software process models; · in using modern software engineering tools and techniques for efficient software development; and · understanding of quality assurance practices and the importance of software project documentation.
Course Description	This comprehensive course provides an in-depth understanding of the principles and practices of software engineering. Students will explore the software development lifecycle, including requirements analysis, design, implementation, testing, and maintenance. Emphasis is placed on methodologies such as Agile, Waterfall, and DevOps. Key topics include software project management, version control, software architecture, design patterns, and quality assurance. Through hands-on projects and case studies, students will gain practical experience in developing reliable, scalable, and maintainable software systems. This course prepares students for real-world challenges in software engineering, equipping them with the skills necessary for successful careers in the tech industry.
Course Outline	Software life cycle- important steps and effort distribution. Aspects of estimation and scheduling. Software evaluation techniques-modular design- coupling and cohesion, Software and complexity measures. Issues in software reliability. System Analysis- Requirement analysis. Specification languages. Feasibility analysis. File and data structure design, Systems analysis tools. Software design methodologies- Data flow and Data Structure oriented design strategies. Software development, coding, verification, and integration. Issues in project management-team structure, scheduling, software quality assurance.
Learning Outcome	Demonstrate a clear understanding of the fundamental concepts and methodologies in software engineering. Apply software engineering principles and techniques to design, develop, test, and maintain software systems. Use modern software engineering tools and environments effectively in software development tasks. Plan and manage software projects, including tasks such as requirements analysis, project scheduling, risk management, and quality assurance. Produce and maintain comprehensive documentation for all phases of the software development process. Work effectively as part of a software development team, demonstrating strong collaboration and communication skills.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Reading:

Design Patterns: Elements of Reusable Object-Oriented Software" by Erich Gamma, Richard Helm, Ralph Johnson, and John Vlissides (The Gang of Four)
"Software Architecture in Practice" by Len Bass, Paul Clements, and Rick Kazman
"Software Requirements" by Karl E. Wiegers and Joy Beatty
"Software Engineering: A Practitioner's Approach" by Roger S. Pressman and Bruce R. Maxim

Fundamentals of Software Engineering, Fifth Edition, Rajiv Mall

CS3208

Bayesian Data Analysis

Course Number	CS3208
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Bayesian Data Analysis
Learning Mode	Offline
Learning Objectives	The learning objectives of the course include comprehending the fundamental concepts of Bayesian statistics, such as likelihood and priors, and applying these to develop various models, including single-parameter, multi-parameter, and hierarchical models. Additionally, techniques for validating these models will be covered. Students will also learn the programming skills necessary to computationally implement these models for different real-world problems.
Course Description	The primary goal of this course is to introduce Bayesian approaches for data analysis and apply these techniques to various real-world problems. Although the focus will be on issues pertinent to computer science, the skills acquired are broadly applicable across several disciplines related to machine learning. The lectures will cover the fundamental theory behind Bayesian statistical inference. Additionally, the course will introduce programming languages like R and Stan, which are well-suited for implementing these Bayesian concepts.
Course Outline	Basics of Probability and Inference, Single Parameter Models, Multiparameter models, Programming Bayesian models using R, Bayesian Computation Techniques, Markov-chain Monte Carlo simulations, Programming Stan with R, Efficient Markov chain simulation techniques, Hierarchical models, Model checking, Model Evaluation, Case studies
Learning Outcome	On successful completion of this course students will be able to: · Assess the fundamental philosophical differences between Bayesian probability and traditional frequentist approaches. · Construct flexible Bayesian models using likelihood and prior functions. · Implement Markov Chain Monte Carlo (MCMC) algorithms in R and Stan for inference in small to medium-sized problems. · Develop Bayesian machine learning algorithms capable of inference in high-dimensional problems.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested readings:

Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, and Donald B. Rubin, Bayesian Data Analysis, Third Edition, CRC Press

John K. Kruschke, Doing Bayesian Data Analysis, A Tutorial with R, JAGS, and Stan, Second Edition, Academic Press

CS3209

Data Mining

Course Number	CS3209
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Data Mining
Learning Mode	Offline
Learning Objectives	This course aims to help the students (a) Understand the fundamental concepts and techniques of data mining. (b) Gain proficiency in data preprocessing, including data cleaning, transformation, and reduction. (c) Apply various data mining algorithms for classification, clustering, association, and anomaly detection. (d) To achieve proficiency in designing some real-life projects using data mining techniques.
Course Description	This comprehensive course on data mining aims to equip students with the knowledge and skills required to extract meaningful insights from large datasets. By focusing on core concepts and providing practical experiences, students will learn to apply various data mining techniques and tools effectively. Through a combination of lectures and real-world projects, students will explore topics such as classification, clustering, association rule mining, and anomaly detection. Upon completion, students will be adept at transforming raw data into actionable knowledge, enabling them to solve complex problems and make data-driven decisions in academic and professional settings.
Course Outline	Fundamentals of data warehousing, architectures, schemas, OLAP technology, and data cube processing. Data preprocessing, integration, transformation, reduction, and basics of data mining techniques. Association rule mining, algorithms (Apriori, FP-Growth), and latest trends in association rule mining. Data classification and clustering techniques, algorithms, prediction methods, and outlier analysis. Introduction to web, spatial and temporal text mining, security, privacy, and ethical issues.
Learning Outcome	· Mastery of fundamental concepts and techniques in data mining. · Proficiency in various data mining algorithms. · Comprehensive understanding of essential data mining tasks such as association rule mining, clustering, and classification. · Ability to apply data mining techniques to real-world projects.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Reading:

Arun K. Pujari “Data Mining Technique” University Press
Han, Kamber, “Data Mining Concepts & Techniques”,
M. Kaufman., P.Ponnian, “Data Warehousing Fundamentals”, JohnWiley.
M.H.Dunham, “Data Mining Introductory & Advanced Topics”, Pearson Education.
Ralph Kimball, “The Data Warehouse Lifecycle Tool Kit”, John Wiley.

E.G. Mallach, “The Decision Support & Data Warehouse Systems”, TMH

CS3210

Information Retrieval

Course Number	CS3210
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Information Retrieval
Learning Mode	Offline
Learning Objectives	The potential learning objectives of the course includes understanding the fundamental concepts and theories of information retrieval, including indexing, querying, and relevance ranking. Furthermore, the students will gain proficiency in utilizing various retrieval models, such as boolean, vector space, and probabilistic models. They would learn about the challenges and techniques involved in processing natural language for information retrieval purposes. The students would be familiarized with the architecture and components of modern search engines and recommendation systems.
Course Description	This course focuses on Information Retrieval (IR), which involves extracting pertinent data from extensive document sets. IR finds utility in various realms such as proprietary retrieval systems, the World Wide Web, Digital Libraries, and commercial recommendation platforms. The course aims to acquaint students with the theoretical foundations of IR with several real world applications and examples.
Course Outline	Introduction: concepts and terminology of information retrieval systems, Information Retrieval Vs Information Extraction Indexing: inverted files, encoding, Zipf's Law, compression, boolean queries Fundamental IR models: Boolean, Vector Space, probabilistic, TFIDF, Okapi, language modeling, latent semantic indexing, query processing and refinement techniques Performance Evaluation: precision, recall, F-measure; Classification: Rocchio, Naive Bayes, k-nearest neighbors, support vector machine Clustering: partitioning methods, k-means clustering, hierarchical Introduction to advanced topics: search, relevance feedback, ranking, query expansion.
Learning Outcome	Course training via lectures & tutorial sessions to · Understand the fundamental concepts and theories of information retrieval, including indexing, querying, and relevance ranking. · Gain proficiency in utilizing various retrieval models, such as boolean, vector space, and probabilistic models. · Learn about the challenges and techniques involved in processing natural language for information retrieval purposes. · Acquire knowledge of evaluation metrics and methodologies used to assess the performance of information retrieval systems. · Familiarize with the architecture and components of modern search engines and recommendation systems. · Analyze case studies and real-world applications of information retrieval in diverse domains, including web search, digital libraries, and e-commerce.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested readings:

Christopher D. Manning, Prabhakar Raghavan and Hinrich Schtze, Introduction to Information Retrieval, Cambridge University Press. 2008.
Ricardo Baeza-Yates and Berthier Ribeiro-Neto, Modern Information Retrieval, Addison Wesley, 1st edition, 1999.
Soumen Chakrabarti, Mining the Web, Morgan-Kaufmann Publishers, 2002.

Bing Liu, Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data, Springer, Corr. 2nd printing edition, 2009.

Principles of Programming Languages

Department Elective - II

Sl. No.

Course Code

Course Name

CS4101

Pattern Recognition

Course Number	CS4101
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Pattern Recognition
Learning Mode	Offline
Learning Objectives	This course aims to help the students (a) Understand the fundamental principles and techniques of pattern recognition, including classification and clustering methods. (b) To develop basic problem-solving skills by implementing the basic pattern recognition algorithms. (c) To gain proficiency in feature extraction, selection, and dimensionality reduction to enhance pattern recognition performance. (d) Apply pattern recognition algorithms to practical applications in image processing, speech recognition, and data mining.
Course Description	This course on pattern recognition aims to equip students with the theoretical foundations and practical skills necessary to identify and analyze patterns in data. By focusing on essential principles, students will develop the ability to implement and evaluate various pattern recognition algorithms. Students will enhance their understanding of machine learning, statistical methods, and data preprocessing techniques through interactive lectures, exercises, and projects. Upon completion, students will be proficient in designing and applying pattern recognition systems for applications such as image processing, speech recognition, and data mining, thereby enhancing their analytical and problem-solving capabilities in diverse domains.
Course Outline	Introduction to pattern recognition, key concepts, learning types, approaches, decision boundaries, and distance metrics. Pattern extraction and preprocessing, pattern classification and algorithms Different paradigms and representations for pattern clustering techniques and validation. Feature extraction and selection methods, problem statements, and relevant algorithms (branch and bound, sequential selection). Recent advances in pattern recognition, including structural pattern recognition, neuro-fuzzy techniques, and real-life applications.
Learning Outcome	· Mastery of fundamental concepts in pattern recognition. · In-depth understanding of various algorithms across different pattern recognition paradigms. · Comprehensive knowledge of theoretical aspects of feature selection, feature extraction, and projection techniques. · Ability to apply pattern recognition algorithms to real-world projects
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Reading:

Pattern recognition and machine learning by Christopher Bishop, Springer Verlag, 2006.
Trevor Hastie, Robert Tibshirani , Jerome Friedman. The elements of Statistical Learning. Springer Verlag (2009).
Fundamentals of Pattern Recognition and Machine Learning by Ulisses Braga-Neto. Springer Cham (2020)
Probability, Random Variables and Stochastic processes by Papoulis and Pillai, 4th Edition, Tata McGraw Hill Edition.
Linear Algebra and Its Applications by Gilbert Strand. Thompson Books.
Data Mining: Concepts and Techniques by Jiawei Han, Micheline Kamber, Morgan Kaufmann Publishers.

A. K. Jain and R. C. Dubes. Algorithms for Clustering Data. Prentice Hall, 1988

CS4102

Course Number	CS4102
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Principles of Programming Languages
Learning Mode	Offline
Learning Objectives	To make students understand the existence of different programming language paradigms (i.e., logic, functional, procedural, object-oriented), their specific features, and to choose an appropriate language for a given application. To make students capable to learn new languages easily and to make clear and efficient use of any given language.
Course Description	The objective of this course is to study the design and implementation of programming languages from a foundational perspective.
Course Outline	Introduction: History of Programming Languages; Evolution of the Major Programming Languages; Art of Programming Language Design; Properties and Success of Programming Languages. Programming Language-Paradigms: Imperative (e.g. C, Pascal, Fortran); Functional (e.g. LISP, HASKELL, OCaml); Object Oriented (e.g. JAVA, C++, Scala); Logic-based (e.g. Prolog); Multiparadigm programming languages (e.g. Python, C++11). Programming Language Concepts: Values and Data Types; Block Structure; Scope, Binding and Lifetime of Variables; Static vs. Dynamic Typing; Static vs. Dynamic Scoping; Memory Management; Procedural Abstraction; Data Abstraction; Concurrency; etc. Case Study: Defining Syntax and Semantics of IMP (a simple WHILE-language) and COOL (Classroom Object Oriented Language).
Learning Outcome	· Understand a variety of concepts underpinning modern programming languages. · Understand the concepts and terms used to describe languages that support the imperative, functional, object-oriented, and logic programming paradigms. · Critically evaluate what paradigm and language are best suited for a new problem. · Solve problems using the functional paradigm. · Solve problems using the object-oriented paradigm. · Solve problems using the logic programming paradigm. · Understand how to design and implement your own (domain-specific) language.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Readings:

Michael L. Scott, “Programming Language Pragmatics”, Morgan Kaufmann, 3rd Edition.
Harold Abelson, Gerald Jay Sussman, Julie Sussman, “Structure and Interpretation of Computer Programs”, MIT Press, 2nd Edition.
Ravi Sethi, K.V. Vishwanatha,“Programming Languages: Concepts and Constructs”, 2/e, Pearson Education, 2007.
W. Pratt and M.V. Zelkowitz, “Programming Languages – Design and Implementation”, Prentice-Hall.
Robert W. Sebesta, “Concepts of Programming Languages”, Addison-Wesley.
A. Watt, “Programming Language Design Concepts”, John Wiley & Sons.
Kenneth C. Louden and Kennath A. Lambert, “Programming Languages: Principles and Practice”, Cengage Learning.

Recent Research Papers relevant to the course.

CS4103

Social Networks

Course Number	CS4103
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Social Networks
Learning Mode	Offline
Learning Objectives	The major objectives of the course would be to make the students understand the basic concepts of social network, understand the fundamental concepts in analyzing the large-scale data that are derived from social networks, implement mining algorithms for social networks, and perform mining on large social networks and illustrate the results.
Course Description	This course delves into the analysis of data within social networks, emphasizing efficient strategies for managing large-scale networks. It presents fundamental theoretical findings in social network mining alongside practical exercises addressing critical topics within the field.
Course Outline	Introduction to social networks. Illustration of various social network mining tasks with real-world examples. Data characteristics unique to these settings and potential biases due to them. Social Networks as Graphs. Random graph models/ graph generators (Erdos-Renyi, power law, preferential attachment, small world, stochastic block models, Kronecker graphs), degree distributions. Models of evolving networks. Node based metrics, ranking algorithms (Pagerank). Graph visualisation. Social network exploration/ processing: Graph kernels, graph classification, clustering of social-network graphs, centrality measures, community detection and mining, degeneracy (outlier detection and centrality), partitioning of graphs. Information Diffusion in Social Networks: Information diffusion in graphs - Cascading behavior, spreading, epidemics, heterogeneous social network mining, influence maximization, outbreak detection; Opinion analysis on social networks - Contagion, opinion formation, coordination and cooperation. Dynamic social networks, Link prediction, Social learning on networks.
Learning Outcome	By completing the course, the students will be able to: • Understand the basic concepts of social networks • Understand the fundamental concepts in analyzing the large-scale data that are derived from social networks • Implement mining algorithms for social networks • Perform mining on large social networks and illustrate the results.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested readings:

David Easley and Jon Kleinberg, Networks, crowds, and markets, Cambridge University Press, 2010.

Jure Leskovec, Anand Rajaraman and Jeffrey David Ullman, Mining of massive datasets, Cambridge University Press, 2014.

CS4104

Multimedia Systems

Course Number	CS4104
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Multimedia Systems
Learning Mode	Offline
Learning Objectives	The main objective of this course is to provide students with a comprehensive understanding of multimedia systems. Students will learn about the various components and technologies involved in multimedia systems, including audio, video, and image processing. They will explore the principles of multimedia compression, storage, and retrieval, as well as the techniques used for multimedia communication and networking. By the end of the course, students will have a solid theoretical foundation in multimedia systems and will be able to apply this knowledge to solve real-world problems in the field.
Course Description	The course begins with an introduction to multimedia systems, covering the basics of multimedia data representation and the different types of multimedia data. This is followed by a detailed study of multimedia compression techniques, including lossless and lossy compression methods for text, images, audio, and video. The course then explores multimedia storage and retrieval, discussing the different storage media and retrieval techniques used for multimedia data. Next, students will learn about multimedia communication and networking, including the protocols and architectures used for multimedia transmission over networks. The course concludes with a discussion of advanced topics in multimedia systems, such as quality of service, synchronization, and security.
Course Outline	Introduction to Multimedia Systems Understanding multimedia data types: Text, images, audio, and video. Multimedia Data Representation: Pixel-based representation for images, waveform representation for audio, and frame-based representation for video. Compression techniques for multimedia data: Lossy and lossless compression algorithms. Multimedia Storage and Retrieval Multimedia Networking and Streaming Multimedia Synchronization and Interactivity: Timecodes, timestamps, and synchronization protocols, Hypermedia, and interactive multimedia applications. Multimedia applications and trends: virtual and augmented reality
Learning Outcome	By the end of this course, students will be able to: · Understand the fundamental concepts and components of multimedia systems. · Analyse and evaluate different multimedia data types and their representation techniques. · Design and implement multimedia storage, retrieval, and streaming solutions. · Evaluate multimedia networking protocols and techniques for efficient multimedia transmission. · Implement multimedia synchronization and interactivity features in multimedia applications. · Explore real-world applications of multimedia systems and identify future trends in multimedia technology
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested readings:

Multimedia Systems: Algorithms, Standards, and Industry Practices" by Parag Havaldar, Gerard Medioni
"Multimedia Computing: Algorithms, Systems, and Applications" by Ralf Steinmetz, Klara Nahrstedt

"Introduction to Multimedia Systems" by Sugata Mitra, Tamalika Chaira

CS4105

Program Analysis and Verification

Course Number	CS4105
Course Credit	3-0-0-3
Course Title	Program Analysis and Verification
Learning Mode	Offline
Learning Objectives	This course will focus on static and dynamic program analysis techniques that can be used to perform various software engineering tasks. The students will learn the concepts behind the techniques and will apply their learning to develop analyses using the state-of-art tools.
Course Description	This course covers both foundations and practical aspects of the automated analysis of programs. It covers how to represent source codes in appropriate forms, enabling one to apply tools and techniques to extract relevant information about the code and to verify them.
Course Outline	Introduction: Program Analysis and Verification; Abstraction Vs. Approximation; Precision-Efficiency Dilemma. Control and Data Flow: Control-flow analysis; Data-flow analysis; Inter-procedural Analysis. Dependency Analysis: Program dependence graphs; Program slicing; Pointer and Alias analysis. Verification and Validation: Verification and validation strategies; Different testing methodologies; Introduction to formal verification approaches.
Learning Outcome	By taking this course, students will gain an understanding of the concepts and theories that underlie these analysis techniques. Students will also learn to design, implement, and leverage program analysis techniques to solve new verification problems.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term.

Suggested Readings:

Flemming Nielson, Hanne R. Nielson, Chris Hankin. Principles of Program Analysis, Springer, 1999.
Edsger Wybe Dijkstra. A Discipline of Programming. Prentice Hall PTR, Upper Saddle River, NJ, USA, 1997.
David Gries. The Science of Programming. Springer-Verlag New York, Inc., Secaucus, NJ, USA, 1987.
S. S. Muchnick and N. D. Jones, editors. Program Flow Analysis: Theory and Applications. Prentice-Hall: Englewood Cliffs, NJ, 1981.
Cem Kaner, Hung Q. Nguyen, and Jack L. Falk. Testing Computer Software. John Wiley & Sons, Inc., New York, NY, USA, 1993.
Recent Research Papers relevant to the course.

Computational Data Analysis

Department Elective - III

Sl. No.

Course Code

Course Name

CS4106

Graph Machine Learning

Course Number	CS4106
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Graph Machine Learning
Learning Mode	Offline
Learning Objectives	Several real world systems can be represented as a network of entities that are connected to each other through some relations. Often the number of entities is immensely large, thus forming a very large network. Typical examples of such large networks include network of entities in knowledge graphs, co-occurrence graph of the keywords in natural languages, interaction graph of users in social networks, protein-protein interaction graphs and the network of routers in Internet to name a few. Study of these networks is often needed for relational learning tasks, as well as for developing frameworks for representing the intrinsic structure of the data. This course will mainly deal with both the traditional as well as current state of the art machine learning techniques to be applied on Graphs for different downstream tasks.
Course Description	The course will provide knowledge on the representation and statistical descriptions of large networks, along with traditional machine learning and deep learning techniques applied on graphs. Several use cases of Graph Machine Learning across different domains including Natural Language Processing, Social Network Analysis and Computational Biology would be studied.
Course Outline	Introduction and background knowledge of graphs; Network analysis metrics like paths, components, degree distribution, clustering, degree correlations, centrality etc., social network analysis methods; Spectral Analysis of Graphs and its applicability to graph partitioning and community detection; Overview of machine learning applications on graphs; Shallow embedding and deep Learning techniques for generating node and graph representations – Graph Neural Networks, Graph Attention Networks Random Networks; Graph Evolution, Generative models for graphs
Learning Outcome	Course training via lectures & tutorial sessions to · Represent and analyze the structure of graphs · Discover recurring and significant patterns of interconnections in your data with network motifs and community structure. · Gain Knowledge on traditional machine learning techniques applied on graphs · Leverage graph-structured data to make better predictions using graph neural networks · Understand the problems in dealing with large graphs for machine learning tasks and learn how to improvise.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested readings:

E.J. Newman, Networks - An introduction , Oxford Univ Press, 2010.
Yao Ma and Jilian Tang, Deep Learning on Graphs, Cambridge University Press, 2021
Goyal, Palash and Emilio Ferrara. “Graph embedding techniques, applications, and performance: A survey.” -Based Syst.151 (2018): 78-94.

CS4107

Bioinformatics

Course Number	CS4107
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Bioinformatics
Learning Mode	Offline
Learning Objectives	This course aims to help the students (a) Gain a thorough understanding of fundamental concepts in bioinformatics. (b) Develop problem-solving skills by implementing basic algorithms tailored for bioinformatics applications. (c) Explore various paradigms and approaches in bioinformatics as applied to biological data, such as sequence alignment, clustering, and classification. (d) Achieve proficiency in designing and implementing real-life bioinformatics projects that integrate deep learning techniques for data analysis and interpretation.
Course Description	This interdisciplinary course on bioinformatics aims to equip students with the knowledge and skills necessary to analyze and interpret biological data using computational tools and techniques. By focusing on fundamental concepts and providing hands-on experiences, students will learn to manage and analyze large-scale biological datasets. Through a combination of lectures, practical lab sessions, and collaborative projects, students will explore topics such as sequence alignment, gene expression analysis, protein structure prediction, and biological databases. Upon completion, students will be proficient in utilizing bioinformatics software and algorithms to address complex biological questions, preparing them for careers in research, biotechnology, and related fields.
Course Outline	Overview of biological databases: Protein Data Bank, SCOP, genome databases, and Cambridge Structural Database. Introduction to protein structures and biophysical methods for structure determination. Protein structure analysis, visualization techniques, and molecular modelling. Mining techniques using protein sequences and structures, including short sequence alignments and multiple sequence alignments. Phylogenetic analysis, genome context-based methods, and RNA/transcriptome analysis techniques. Mass spectrometry applications in proteome and metabolome analysis. Protein docking, dynamics simulation, and algorithms for handling big biological data challenges. Applications of Bioinformatics.
Learning Outcome	· Mastery of fundamental principles and techniques in bioinformatics, including sequence analysis, structural biology, and genomic data interpretation. · Proficiency in applying pattern recognition algorithms to solve biological data problems, such as sequence alignment, clustering, and classification. · Ability to critically analyze and interpret bioinformatics data using computational tools and techniques. · Understanding of the interdisciplinary nature of bioinformatics and its applications in biological research and medicine.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Reading

Mount, D.W., Bioinformatics: Sequence and Genome Analysis, Cold. Spring Harbor Laboratory Press, 2001.
Protein Bioinformatics: From Sequence to Function by M. Michael Gromiha Academic Press, 2010
Bioinformatics: A Practical Guide to the Analysis of Genes and Proteins 4th Edition, by Andreas D. Baxevanis (Editor), Gary D. Bader (Editor), David S. Wishart (Editor), WILEY
C. Branden and J. Tooze (eds) Introduction to Protein Structure, Garland, 1991

CS4108

Time Series Analysis

Course Number	CS4108
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Time Series Analysis
Learning Mode	Offline
Learning Objectives	· The course is designed to provide basic understanding time series analysis. · Develop Skills in statistical time series Analysis. · To learn variety of modeling techniques that can be used for time series analysis. · Gain proficiency in forecasting and anomaly detection methods · Apply the basic machine learning for time series analysis
Course Description	Using a set of fundamental techniques and broadly explains how time series analysis work at various levels of abstraction. The course introduces time series analysis with focus on applications
Course Outline	Basics of inferential and descriptive statistics: Population vs Sample; Measures of Central tendency, Measures of Variability, probability density functions, properties, mathematical expectation, hypothesis testing, ANOVA. Mathematical models for analysing time series data: Time Series Modelling, autoregressive integrated moving average (ARIMA), Exponential smoothing in time series analysis, process and the Box-Jenkins methodology. Outlier Analysis for Time Series, Multivariate Time Series Models and State-space Models, Forecasting Methods and Application Examples. Transfer Function Model Building. Imputation techniques, Point forecast and confidence intervals. Machine Learning Approaches for Time Series, Probabilistic Neural Networks, Different methods of estimation and inferences of modern dynamic stochastic general equilibrium models: simulated method of moments.
Learning Outcome	The student will be able to: · Appreciate understanding of the time series analysis, key terminology, and current industry trends in time series modeling · Evaluate time series model performance. · Create real-time applications, including anomaly detection and predictive maintenance
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Readings:

Palit, Ajoy K., and Dobrivoje Popovic. Computational intelligence in time series forecasting: theory and engineering applications. Springer Science & Business Media, 2006.
Box, George EP, Gwilym M. Jenkins, Gregory C. Reinsel, and Greta M. Ljung. Time series analysis: forecasting and control. John Wiley & Sons, 2015.
Brockwell, Peter J., Richard A. Davis, and Matthew V. Calder. Introduction to time series and forecasting. Vol. 2. New York: springer, 2002.
Pollock, David Stephen Geoffrey, Richard C. Green, and Truong Nguyen, eds. Handbook of time series analysis, signal processing, and dynamics. Elsevier, 1999.
Shumway, Robert H., and David S. Stoffer. Time series analysis and its applications: with R examples. Springer, 2017.

CS4109

Course Number	CS4109
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Computational Data Analysis
Learning Mode	Offline
Learning Objective	In this subject, the students will be trained with the knowledge of various computational techniques required for multi-dimensional data analysis such that they are able to apply these techniques in practice through programming, modeling etc.
Course Description	Modern day data is vast and diverse owing to their different acquisition systems and medium. This course aims to give an in-depth view to different data generation/acquisition mechanisms over diverse domains and the challenges incurred. It will discuss the role of computational data analysis techniques to understand and mathematically model data formation process. It will also teach them about the various data processing techniques required to manipulate and operate data to suit various objectives.
Course Outline	Understanding multi-dimensional data formation from physical acquisition devices with example cases in Remote Sensing, Geoscience, Medical sciences. Drawbacks and challenges in data acquisition, Necessity for computational modelling and analysis of data. Mathematical models for data formation and analysis, Probability models, Linear inverse optimization models, L1-L2 Regularizers, Minimizers, Cascade Modelling, Multiscale Modelling, Machine Learning models. Data Interpretation: Handling missing/corrupted data, Handling outliers, Imputation techniques, Interpolation techniques, Curve based approximation, non-convex optimization, sparse regularizers, Non-convex minimizers, Machine learning based. Data compression: Necessity, Applications, Lossless compression techniques, Lossy compression techniques, JPEG compression, Machine learning based. Statistical Models, Data preprocessing techniques in Machine learning, Signal processing techniques for multi-dimensional data, Application in various domains.
Learning Outcome	After completion of course, students will be able to · Understand data formation/generation process and the role of computational techniques in analyzing those data. · Apply the Mathematical principles behind computational techniques for data analysis. · Understand the utilities of statistical models and ML models in data analysis.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Readings:

Signal Processing: A Mathematical Approach, Charles L. Byrne, Second Edition, Chapman & Hall, 2014.
Digital Functions and Data Reconstruction: Digital-Discrete Methods, Li M Chen, Springer, 2013.
Machine Learning with Neural Networks: An Introduction for Scientists and Engineers, Bernhard Mehlig, Cambridge University Press, 2021
Signal Processing and Machine Learning with Applications, Michael M. Richter, Sheuli Paul, Veton Këpuska, Marius Silaghi, Springer Cham, 2022
Data Compression: The Complete Reference, David Solomon, 4^th Edition, Springer, 2007

CS4110

Blockchain Technology

Course Number	CS4110
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Blockchain Technology
Learning Mode	Offline
Learning Objectives	This course will introduce the fundamentals of the blockchain technology. It will highlight the use of blockchain technology in different applications and the learners will be able to develop decentralized applications.
Course Description	This course provides an introductory background of this revolutionary technology, followed by an interesting case study on bitcoin to demonstrate how the technology works. Following this, we would introduce Ethereum and Hyperledger. In addition, the course includes a number of hands-on sessions where we introduce basic blockchain tools and techniques, such as geth, ganache, remix, metamask, truffle, hyperledger, and real case studies.
Course Outline	Introduction and History; Blockchain Foundations; Generic elements of a blockchain; Features of blockchain; Types of blockchain; Applications of blockchain technology; Cryptocurrency and bitcoin basics; Introduction to Ethereum/Hyperledger and Programming; Privacy, Safety and Security Issues in blockchain; Some ongoing research topics.
Learning Outcome	· Gain proficiency in blockchain technology. · Understanding of how bitcoin/ethereum/hyperledger work. · Hands-on experience with various blockchain platforms, tools and techniques.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Readings:

Arvind Narayanan, Joseph Bonneau, Edward Felten, Andrew Miller, Steven Goldfeder, Bitcoin and Cryptocurrency Technologies – A Comprehensive Introduction, Princeton University Press, 2016.
Roger Wattenhofer, The Science of the Blockchain, Inverted Forest Publishing, First Edition, 2016.
Recent Research Papers relevant to the course.

CS4112

Graph Theory

Course Number	CS4112
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Graph Theory
Learning Mode	Offline
Learning Objectives	This course aims to help the students (a) to provide students with a comprehensive understanding of advanced topics in graph theory; (b) to analyze and solve complex problems involving graphs, understand and apply various graph algorithms, and explore the theoretical underpinnings of graph theory; and (c) develop critical thinking and analytical skills, enabling them to approach and solve real-world problems using graph theory concepts.
Course Description	This course delves into the advanced aspects of graph theory, building on foundational knowledge to explore more complex and abstract concepts. Students will study advanced topics such as graph coloring, planarity, network flows, and extremal graph theory. The course will also cover specialized graph classes, advanced algorithms, and their applications in various fields such as computer science, biology, and social sciences. Through a blend of theoretical exploration and practical application, students will gain a deep understanding of how graph theory can be used to model and solve intricate problems.
Course Outline	Introduction and Review of Basic Concepts- Review of fundamental graph theory concepts (graphs, subgraphs, isomorphism, paths, cycles), Types of graphs (simple, multigraphs, weighted graphs, directed and undirected graphs), Graph representation (adjacency matrix, adjacency list, incidence matrix) Graph Connectivity- Connectivity and components, Menger's Theorem, Network flow and cuts, Applications in network design and reliability, Vertex coloring, Edge coloring, Chromatic number and chromatic polynomial, Applications in scheduling and register allocation Euler's formula for planar graphs, Kuratowski's and Wagner's theorems, Graph embedding, Applications in geographic mapping and circuit design, Graph minors and the Minor Theorem, Well-quasi-ordering and its implications, Applications in algorithm design and complexity theory Shortest path algorithms (Dijkstra's, Bellman-Ford, Floyd-Warshall), Maximum flow algorithms (Ford-Fulkerson, Edmonds-Karp, Push-Relabel), Matching algorithms (Bipartite matching, Hungarian algorithm, Stable matching), Basic models of random graphs (Erdős–Rényi model), Properties of random graphs (connectivity, diameter, phase transition), Applications in network theory and epidemiology Eigenvalues and eigenvectors of graphs, Laplacian matrix and its properties, Cheeger’s inequality and applications, Applications in clustering and data analysis, Small-world networks, Scale-free networks, Community detection algorithms, Applications in social networks, biological networks, and information networks Graph theory in computational biology (protein-protein interaction networks, metabolic networks), Graph theory in computer networks (routing, fault tolerance), Graph theory in machine learning (graph neural networks, data mining)
Learning Outcome	· Understand and articulate advanced concepts in graph theory. · Analyze and solve complex problems involving graphs. · Apply various graph algorithms to practical problems. · Understand and implement graph coloring techniques and their applications. · Analyze the properties of planar graphs and utilize graph drawing algorithms. · Apply network flow algorithms to solve problems in various fields. · Understand and apply principles of extremal graph theory, including Turán's theorem and Ramsey theory. · Identify and work with specialized graph classes such as bipartite, perfect, and chordal graphs. · Develop and implement advanced graph algorithms, including shortest path, matching, and covering algorithms. · Apply graph theory concepts to real-world scenarios in computer science, biology, and social sciences. · Design and conduct practical projects that demonstrate the application of advanced graph theory concepts.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term.

Suggested Reading

Diestel, R. (2017). Graph Theory (5th ed.). Springer.
Bondy, J. A., & Murty, U. S. R. (2008). Graph Theory (1st ed.). Springer.
West, D. B. (2001). Introduction to Graph Theory (2nd ed.). Prentice Hall.
Gross, J. L., & Yellen, J. (2005). Graph Theory and Its Applications (2nd ed.). CRC Press.

Chartrand, G., & Zhang, P. (2012). A First Course in Graph Theory (1st ed.). Dover Publications.

Statistical Machine Learning

Department Elective - IV

Sl. No.

Course Code

Course Name

CS4201

Multivariate Analysis

Course number	CS4201
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Multivariate Analysis
Learning Mode	offline
Learning Objectives	Multivariate analysis is about handling vector valued data. In ordinary regression modeling we are used to a vector valued predictor. But a vector valued response variable brings new issues. Sometimes we can handle a k dimensional response by treating it as k unrelated 1 dimensional problems. But often that approach will fail to find the key structure. Sometimes we are forced to study the data as an inherently k dimensional thing. It can also pay to reduce the dimension k, sometimes to 3 or 2 where plotting is available, sometimes to k=1 where ordinary methods can then be applied. Also, some of the methods are useful for exploratory work and not just for modeling responses.
Course Description	This course will provide an overview of different statistical methods applied in data science.
Course Outline	1. Multivariate Normal Distribution Theory: Joint, marginal, and conditional distribution; distributions of linear functions and quadratic forms of multivariate normal random variables 2. Correlation Analysis, Linear Regression, and Predication: Simple correlation, partial correlation, multiple correlation, linear regression equation, best prediction function and best linear predication function 3. Sampling Distributions: Sampling distributions for the mean vector and for the various correlation coefficients, partitioning of sum of squares, Hotelling's T2 distribution, the Wishart distribution 4. Introduction to Multivariate Probability Inequalities via Dependence and Heterogeneity 5. Estimation of Parameter Vectors via applications of the results on the topics in (3) and (4) above, especially for elliptical and rectangular confidence regions 6. Hypotheses Testing for Parameter Vectors 7. Multivariate Discriminant Analysis and Classification Theory, with Specific Applications to Medicine and Pattern Recognition
Learning Outcome	· Basic understanding of multivariate analysis · Problem modeling skill considering uncertainty
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Reading:

Multivariate Statistical Methods: a Primer" by B.F.J. Manly.

"Modern Applied Statistics with S" by Venables and Ripley.

CS4202

Generative AI

Course Number	CS4202
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Generative AI
Learning Mode	Offline
Learning Objectives	· To provide a comprehensive understanding of advanced AI concepts with a focus on generative AI. · To design and implement various generative models such as GANs, VAEs, and Diffusion Models. · To explore the architecture and applications of Generative Pre-trained Transformers (GPT). · To design application-specific architectures for prompt engineering and multimodal generative AI. · To analyze and address ethical considerations in the development and deployment of generative AI models. · To conduct independent research and projects involving advanced generative AI techniques.
Course Description	This course provides an in-depth exploration of advanced artificial intelligence (AI) concepts, with a specific focus on generative AI (GenAI). Students will delve into advanced generative models, including Generative Adversarial Networks (GANs), Variational AutoEncoders (VAEs), Diffusion Models, and Generative Pre-trained Transformers (GPT). The course also covers the application of these models across various domains, the design of application-specific architectures for prompt engineering, and multimodal generative AI. Additionally, ethical considerations surrounding the use of generative AI will be discussed. By the end of the course, students will have the knowledge and skills to design, implement, and evaluate advanced generative AI models and understand their ethical implications.
Course Outline	Introduction to Generative AI (GenAI): Overview of GenAI, historical context and scope. Generative Adversarial Networks (GAN) and Deep Convolutional GAN (DCGAN): Understanding the architecture of GANs, Training dynamics and loss functions in GANs, Implementation and applications of DCGANs, Challenges and solutions in training GANs. Advanced Variational AutoEncoders (VAE): Fundamentals of VAEs and their architectures, Latent space representation and sampling techniques, Advanced VAE variants and their improvements, Applications of VAEs in image and data generation. Basics of Diffusion Models and Attention Mechanisms in Generative Models: Introduction to diffusion models and their principles, Understanding the role of attention mechanisms in generative models, Implementation of attention-based generative models, Case studies and applications of diffusion models. Generative Pre-trained Transformers (GPT) Basics: Overview of transformer architecture, Understanding the training and functioning of GPT models, Applications of GPT models in text generation and NLP, Fine-tuning and optimizing GPT for specific tasks. Application-Specific Architecture for Prompt Engineering and Multimodality: Designing and optimizing prompt engineering techniques, Exploring multimodal generative models, Integrating text, image, and audio in generative models, Case studies of application-specific generative architectures. Ethical Considerations in Generative AI: Understanding the ethical implications of Generative AI, Addressing bias, fairness, and accountability in generative models, Privacy concerns and data security in Generative AI.
Learning Outcome	By the end of this course, students will be able to: · Understand the foundational concepts and the latest advancements in artificial intelligence and generative AI. · Design and implement Generative Adversarial Networks (GANs) and their advanced variants, such as DCGAN. · Develop and apply advanced Variational AutoEncoders (VAEs) for generative tasks. · Grasp the basics of Diffusion Models and the role of attention mechanisms in enhancing generative models. · Understand the architecture and functioning of Generative Pre-trained Transformers (GPT) and their applications. · Create application-specific architectures for prompt engineering and explore the integration of multimodal generative AI techniques. · Analyze and address ethical considerations and challenges in the development and deployment of generative AI models. · Conduct independent research and projects involving advanced generative AI techniques, demonstrating a comprehensive understanding of both theoretical and practical aspects.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Reading

Foster, D. (2022). Generative deep learning: Teaching Machines to Paint, Write, Compose, and Play. O'Reilly Media, Inc.
Valle, R. (2019). Hands-On Generative Adversarial Networks with Keras: Your guide to implementing next-generation generative adversarial networks. Packt Publishing Ltd.
Research Papers and Articles from Journals such as JMLR, IEEE Transactions on Neural Networks and Learning Systems, etc., and Conference Proceedings from NeurIPS, ICML, and CVPR,etc.

CS4203

Course Number	CS4203
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Statistical Machine Learning
Learning Mode	Offline
Learning Objectives	The learning objectives of the course includes understanding the basic concepts of machine learning, and classic algorithms such as Support Vector Machines and Neural Networks, Deep Learning. The students would be able to explain the basic principles and theory of machine learning, that would guide to invent their own algorithms.
Course Description	This is an introductory course on statistical machine learning which presents an overview of many fundamental concepts, popular techniques, and algorithms in statistical machine learning. It covers basic topics such as dimensionality reduction, linear classification and regression as well as more recent topics such as ensemble learning/boosting, support vector machines, kernel methods and manifold learning. This course will provide the students the basic ideas and intuition behind modern statistical machine learning methods. After studying this course, students will understand how, why, and when machine learning works on practical problems.
Course Outline	Statistical Theory: Maximum likelihood, Bayes, minimax, parametric versus nonparametric methods, Bayesian versus Non-Bayesian approaches, classification, regression, density estimation. Convexity and Optimization: Convexity, conjugate functions, unconstrained and constrained optimization, KKT conditions. Parametric Methods: Linear regression, model selection, generalized linear models, mixture models, classification, graphical models, structured prediction, hidden Markov models Sparsity: High dimensional data and the role of sparsity, techniques for handling sparsity. Nonparametric Methods: Nonparametric regression and density estimation, nonparametric classification, clustering and dimension reduction, manifold methods, spectral methods, the bootstrap and subsampling, nonparametric Bayes. Other Learning Methods: Semi-supervised learning, reinforcement learning, minimum description length, online learning, the PAC model, active learning
Learning Outcome	On successful completion of this course students will be able to: • Explain the basic concepts of machine learning, and classic algorithms such as Support Vector Machines and Neural Networks, Deep Learning. • Explain the basic principles and theory of machine learning, which may guide students to invent their own algorithms in future. • Ability to program the algorithms in the course. • Ability to do mathematical derivation of the machine learning algorithms.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested readings:

Chris Bishop, Pattern Recognition and Machine Learning, Springer, Information Science and Statistics Series, 2006.
Trevor Hastie, Robert Tibshirani, Jerome Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer Texts in Statistics, SpringerVerlag, New York, 2001.

Larry Wasserman, All of Statistics: A Concise Course in Statistical Inference, Springer Texts in Statistics, Springer-Verlag, New York, 2004.

CS4204

Text Mining

Course Number	CS4204
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Text Mining
Learning Mode	Offline
Learning Objectives	· To understand the fundamental principles and key concepts in text mining. · To gain the ability to collect and preprocess text data, including cleaning and integration. · To master text preprocessing techniques such as tokenization, stemming, stopword removal, and normalization. · To learn the construction and utilization of knowledge graphs for relationship extraction. · To implement frequent pattern mining and association rules using algorithms like apriori. · To extract features using methods like Bag-of-Words, TF-IDF, and word embeddings. · To apply clustering and classification techniques to text data. · To utilize text mining techniques in practical applications, such as sentiment analysis.
Course Description	This course provides a comprehensive understanding of the fundamental principles and techniques used in text mining. Students will learn the entire process from data collection and preprocessing to advanced techniques for mining patterns and analyzing text. The course covers practical applications, such as sentiment analysis, equipping students with the skills needed to extract meaningful insights from large datasets and text corpora. By the end of the course, students will be adept at employing text mining techniques to solve real-world problems.
Course Outline	Text mining introduction: Overview, motivation, challenges and opportunities, Data Collection and Pre-processing: Techniques for collecting data from various sources Data cleaning and integration: Handling noise, missing values, and inconsistent formats in text data Text preprocessing: tokenization, stemming, stopword removal, and normalization Knowledge graph construction: Basics of graph construction and relationship extraction Basic concepts of frequent patterns, association rules, mining frequent patterns: apriori algorithm. Feature extraction, Bag-of-Words, TF-IDF, word embeddings Clustering and classifying text data Some applications: sentiment analysis, etc.
Learning Outcome	By the end of this course, students will be able to: · Grasp key concepts, motivation, and challenges in text mining. · Collect and preprocess data, including cleaning and integration. · Perform text preprocessing tasks like tokenization, stemming, stopword removal, and normalization. · Construct and utilize knowledge graphs for relationship extraction. · Implement frequent pattern mining and association rules using the apriori algorithm. · Extract features using Bag-of-Words, TF-IDF, and word embeddings. · Apply clustering and classification to text data. · Use data mining and text analytics techniques in applications such as sentiment analysis.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Reading

Srivastava, A. N., & Sahami, M. (Eds.). (2009). Text mining: Classification, clustering, and applications. CRC press.
Jiawei, H., & Micheline, K. (2006). Data mining: concepts and techniques. Morgan kaufmann.
Witten, I. H., Frank, E., Hall, M. A., Pal, C. J., & Data, M. (2005, June). Practical machine learning tools and techniques. In Data mining (Vol. 2, No. 4, pp. 403-413). Amsterdam, The Netherlands: Elsevier.

CS4214

Combinatorial Optimization

Course number	CS4214
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Combinatorial Optimization
Learning Mode	offline
Learning Objectives	Introduces the use of combinatorial and algorithmic techniques for optimization problems. Emphasis on algorithms with provable correctness and efficiency. Illustrates the use of these techniques for several real-world applications.
Course Description	This course will provide an overview of different methods of solving computationally intractable problems.
Course Outline	Linear and Integer Programs: Overview of modeling problems in linear and integer programming model Geometry of Polyhedra: Feasible region of LPs and polyhedra, Convexity, Extreme points, Faces and facets, tools for analysis Solving linear programs: Possible outcomes (infeasibility, unboundedness, and optimality) and their certificates, bases and canonical forms, the Simplex method Duality: Weak duality, strong duality, complementary slackness, Farkas’ Lemma Combinatorial Optimization: Primal-dual method for exact and approximation algorithms, Shortest paths, Minimum cost perfect matchings, Max-Flow Min-Cut Theorem, Totally Unimodular Matrices Additional topics: Interior-point methods, Randomized/Online algorithms for LPs, Integer Programs, Convex Optimization, Matroids, T-joins, Applications to Game Theory.
Learning Outcome	· Basic understanding of combinatorial optimization · Problem modeling skill · Solving computationally intractable problems
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term.

Suggested Reading:

Papadimitriou, C. H., and K. Steiglitz. Combinatorial Optimization.
Lovasz, Laszlo. Matching Theory.
Ahuja, R., T. Magnanti, and J. Orlin. Network Flows.
Schrijver, A. Theory of Linear and Integer Programming.
Chvatal, V. Linear Programming.
Bertsimas, D., and J. Tsitsiklis. Linear Optimization.

Cook, W. J., W. H. Cunningham, W. R. Pulleyblank, and A. Schrijver. Combinatorial Optimization.

Department Elective - V

Department Elective - V

Sl. No.

Course Code

Course Name

CS4205

Cloud Computing

Course Number	CS4205
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Cloud Computing
Learning Mode	Offline
Learning Objectives	This course aims to help the students (a) define and explain the fundamental concepts and principles of cloud computing; (b) identify and describe various cloud computing service models (IaaS, PaaS, SaaS) and deployment models (public, private, hybrid, community); (c) understand the underlying technologies and infrastructure used in cloud computing, including virtualization, containers, and software-defined networking; (d) evaluate the benefits and challenges of adopting cloud computing for businesses and organizations; (e) design and implement cloud-based solutions for common use cases, such as web hosting, data storage, and application development; and (f) analyze security, privacy, and compliance considerations in cloud computing environments.
Course Description	This course provides a comprehensive overview of cloud computing, covering its fundamental concepts, architecture, and deployment models. Students will explore the various service models, including Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS), and understand the benefits and challenges associated with each. The course delves into cloud storage, computing resources, and virtualization technologies, offering hands-on experience with leading cloud platforms such as AWS, Azure, and Google Cloud. Security, compliance, and cost management in cloud environments are also addressed, equipping students with the skills to design, deploy, and manage cloud-based solutions effectively.
Course Outline	Introduction to Cloud Computing- Overview of cloud computing and its key principles, Fundamentals of distributed systems: Models and architectures. Cloud Storage and Virtualization- Understanding cloud storage technologies: Key-value stores, NoSQL databases, Virtualization techniques for resource abstraction and management Distributed Algorithms in Cloud Computing- Fault tolerance and consensus algorithms: PAXOS, leader election, Time ordering and distributed mutual exclusion. Industry Systems and Cloud Platforms- Overview of industry-standard cloud platforms: Apache Spark, Apache Zookeeper, HBase, Introduction to containerization technologies: Docker, Kubernetes Advanced Topics in Cloud Computing- Big data processing in the cloud: MapReduce, Apache Cassandra, Emerging trends in cloud computing: Edge computing, serverless architectures
Learning Outcome	· Define and explain the key concepts and components of cloud computing, including virtualization, elasticity, and on-demand provisioning. · Evaluate different cloud computing service models and deployment models, and select appropriate options for specific use cases and requirements. · Demonstrate proficiency in deploying and managing cloud-based resources using popular cloud platforms (e.g., AWS, Azure, Google Cloud). · Analyze the economic factors and cost considerations associated with cloud computing, including pricing models and Total Cost of Ownership (TCO) calculations. · Design and implement scalable and resilient cloud architectures using best practices and design patterns. · Assess security risks and implement appropriate security controls to protect cloud-based assets and data.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Reading

Distributed and Cloud Computing From Parallel Processing to the Internet of Things; Kai Hwang, Jack Dongarra, Geoffrey Fox Publisher: Morgan Kaufmann, Elsevier, 2013.
Cloud Computing: Principles and Paradigms; Rajkumar Buyya, James Broberg, and Andrzej M. Goscinski Publisher: Wiley, 2011.
Distributed Algorithms Nancy Lynch Publisher: Morgan Kaufmann, Elsevier, 1996.
Cloud Computing Bible Barrie Sosinsky Publisher: Wiley, 2011.

Cloud Computing: Principles, Systems and Applications, Nikos Antonopoulos, Lee Gillam Publisher: Springer, 2012.

CS4206

Quantum Computing

Course Number	CS4206
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Quantum Computing
Learning Mode	Offline
Learning Objectives	This course aims to help the students (a) comprehend the foundational principles of quantum mechanics that underpin quantum computing.(b) proficiency in designing and analyzing quantum circuits.(c) explore and understand advanced quantum algorithms used in quantum computing.(d) quantum computing principles to solve computational problems and simulate quantum systems.
Course Description	Explore the foundational principles and transformative potential of quantum mechanics and quantum computing in this comprehensive course. Students will delve into quantum mechanics, covering concepts like superposition, entanglement, and quantum measurement, and their application to quantum computing. Through lectures, practical sessions, and case studies, participants will master quantum circuit design, analyze advanced quantum algorithms such as Grover's and Shor's algorithms, and apply these principles to solve real-world computational problems. By the end of the course, students will possess the theoretical understanding and practical skills needed to contribute to the rapidly advancing field of quantum computing across diverse industries.
Course Outline	States, Wavefunction, Orthogonality and Orthonormality of Wave function, Superposition Quantum Circuits: Single-qubit gates, Multiple qubit gates, Design of quantum circuits, Dirac Notations, Measurements, Bloch Sphere Entanglement, Bell State, Teleportation, Q-Sphere, Data Structures for Quantum Computing, Quantum Annealing Quantum Algorithms: Grover’s Search Algorithm, Shor’s Factoring Algorithm, Quantum Amplitude Estimation, Quantum Phase Estimation, Quantum Fourier Transform
Learning Outcome	Understand Fundamental Quantum Mechanics Principles. Develop Skills in Quantum Circuit Computing and Analysis. Explore Advanced Quantum Computing Concepts. Gain proficiency in Master Quantum Algorithms.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Textbooks:

Nielsen, M.A. and Chuang, I.L., 2010. Quantum computation and quantum information.
Pittenger, A.O., 2012. An introduction to quantum computing algorithms (Vol. 19).
Relevant research articles.

Reference books:

Bernhardt, C., 2019. Quantum computing for everyone.

CS4207

Drone Data Processing

Course Number	CS4207
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Drone Data Processing
Learning Mode	Offline
Learning Objectives	This course aims to help the students (a) gain foundational knowledge of unmanned aerial systems (UAS), including their history, components, and classifications; (b) comprehend the various elements that make up a drone system, such as the air vehicle, communication data links, command and control elements, payloads, and launch/recovery systems; (c) acquire the ability to design and plan drone missions, including studying area maps, designing flight routes, and calibrating sensors; (d) learn the principles and practices of photogrammetry and geographic information systems (GIS) for processing and analyzing drone-collected data; (e) understand the importance of data quality, accuracy standards, error estimation, and strategies for achieving high-precision geospatial data.
Course Description	This course offers an in-depth exploration of Unmanned Aerial Systems (UAS) and drone operations, providing a comprehensive understanding of their history, types, and technological advancements. Students will learn about the various categories and missions of drones, the design and communication systems essential for drone functionality, and the roles and responsibilities in UAS operations. The course covers the fundamentals of geospatial data, photogrammetry, and GIS, emphasizing map accuracy and mission planning.
Course Outline	Introduction to Unmanned Aerial Systems (UAS). Types, Categories, and Missions of Drones. Drone Design and Communication Systems. Concepts of Operations (CONOP) and Risk Assessment Geospatial Data and Photogrammetry. Drone Mission Planning and Control. Route Planning and Operational Fundamentals. Regulatory Requirements and Guidelines. Applications and Challenges in Drone Operations
Learning Outcome	· Identify and categorize various types of unmanned aerial systems and their specific missions. · Create comprehensive mission plans, including route design, sensor selection, and calibration, ensuring optimal data collection. · Utilize photogrammetric methods and GIS tools to process and analyze drone-collected data, producing accurate geospatial products. · Assess data accuracy and quality, understand and apply mapping standards, and manage errors in measurements effectively. · Apply drone technology in diverse fields such as agriculture, construction, environmental monitoring, and disaster response.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Reading

Barnhart, R., Michael, M., Marshall, D., and Shappee, E. ed. 2016. Introduction to Unmanned Aircraft Systems, 2nd edition. Boca Raton. CRC Press.
Fahlstrom, P. and Gleason, T. 2012. Introduction to UAV Systems. 4th edition. United Kingdom. John Wiley & Sons Ltd.
Wolf, P., DeWitt, B., and Wilkinson, B. 2014. Elements of Photogrammetry with Applications in GIS, 4th edition. McGraw-Hil
Introduction to UAV Systems, Paul G. Fahlstrom and Thomas J. Gleason
Drone Technology in Architecture, Engineering, and Construction, Daniel Tal and Jon Altschuld
UAV or Drones for Remote Sensing Applications, edited by Felipe Gonzalez Toro and Antonios Tsourdos

CS4208

Edge Computing

Course Number	CS4208
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Edge Computing
Learning Mode	Offline
Learning Objectives	This course aims to help the students (a) to define edge computing and its role in modern computing paradigms; (b) to understand the principles and benefits of moving computation closer to the data source; (c) to identify various edge computing architectures, including fog computing and mobile edge computing; (d) to analyze and compare edge computing frameworks and platforms; (e) design and implement edge computing solutions to address latency, bandwidth, and privacy concerns; (f) to evaluate the impact of edge computing on traditional cloud computing models and network infrastructures; (g) to discuss emerging trends and challenges in edge computing, such as security, interoperability, and resource management; and (h) to apply edge computing principles and techniques to real-world scenarios and use cases.
Course Description	This course provides a comprehensive overview of edge computing, starting with the limitations of cloud computing in supporting low latency and round trip time (RTT), and the subsequent innovation waves leading to edge computing. Students will delve into edge computing architectures and their applications, including 5G slicing and self-driving cars. Key concepts of distributed systems such as time ordering, clock synchronization, and distributed snapshots will be explored within the context of edge computing. The course also introduces edge data centers, lightweight edge clouds, and services provided by various service providers. Practical knowledge of Docker containers and Kubernetes in edge computing, along with the design of edge storage systems like key-value stores, will be covered. Additionally, students will learn about MQTT and Kafka for creating end-to-end edge pipelines and edge analytics topologies for M2M and WSN networks. The course concludes with use cases of machine learning for edge sensor data, including predictive maintenance, image classification, and deep learning on-device inference to support latency-sensitive applications.
Course Outline	Introduction to Cloud and its limitations to support low latency and Round Trip Time (RTT). From Cloud to Edge computing: Waves of innovation. Introduction to Edge Computing Architectures. Edge Computing to support User Applications (5G-Slicing, self-driving cars and more) Concepts of distributed systems in edge computing such as time ordering and clock synchronization, distributed snapshot, etc. Introduction to Edge Data Center, Lightweight Edge Clouds and its services provided by different service providers. Introduction to docker container and Kubernetes in edge computing. Design of edge storage systems like key-value stores. Introduction to MQTT and Kafka for end-to-end edge pipeline. Edge analytics topologies for M2M and WSN network (MQTT) Use cases of machine learning for edge sensor data in predictive maintenance, image classifier and self-driving cars. Deep Learning On-Device inference at the edge to support latency-based application
Learning Outcome	· Define and explain the concept of edge computing and its significance in distributed computing architectures. · Analyze the advantages and limitations of edge computing compared to traditional centralized and cloud-based approaches. · Identify and describe different edge computing architectures, such as hierarchical, decentralized, and hybrid models. · Evaluate edge computing platforms and tools for their suitability in various application domains. · Design and implement edge computing solutions that leverage distributed computing principles to improve performance, reliability, and efficiency. · Analyze the impact of edge computing on network traffic, data privacy, and regulatory compliance. · Critically assess the security implications of deploying edge computing systems and propose mitigation strategies. · Collaborate in teams to develop and present case studies or projects demonstrating the practical application of edge computing concepts and techniques.
Assessment ethod	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Reading

Fog and Edge Computing: Principles and Paradigms, Rajkumar Buyya (Editor), Satish Narayana Srirama (Editor), Wiley, 2019
Cloud Computing: Principles and Paradigms, Editors: Rajkumar Buyya, James Broberg, Andrzej M. Goscinski, Wiley, 2011
Cloud and Distributed Computing: Algorithms and Systems, Rajiv Misra, Yashwant Patel, Wiley 2020.

Journal papers as references.

CS4209

Wireless Networks

Course Number	CS4209
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Wireless Networks
Learning Mode	Offline
Learning Objectives	In this subject, the students will be trained with the knowledge of 802.11 wireless networks, including protocol knowledge and the associated security vulnerabilities.
Course Description	In the consumer, industrial, and military sectors, 802.11-based wireless access networks have been widely used due to their convenience. This application, however, is reliant on the unstated assumptions of availability and anonymity. The management and media access protocols of 802.11 may be particularly vulnerable to malicious denial-of-service (DoS) and various security attacks. This course analyzes these 802.11-specific attacks, including their applicability, effectiveness, and proposed low-cost implementation improvements to mitigate the underlying vulnerabilities.
Course Outline	Overview of 802.11 networks, 802.11 MAC Layer, Wireless LAN physical components. Wireless LAN topologies and technologies - 802.11 a/b/g/n/ac features. Configure and install wireless adapters, access points. 802.11 architecture (access points, SSID, channels, beacons, scanning, association), Hidden terminal problem, RTS/CTS, 802.11 CSMA-CA protocol. Wireless communication technology: FHSS, DSSS, CDMA etc. Physical Layer, MAC Layer, MAC Management, Power Management. Multiple access protocols: ALOHA, Carrier sense multiple access protocols, collision free protocols. 802.11 Frame Structure & WLAN services-association, disassociation, re-association, distribution, integration, authentication, de-authentication and data delivery services. Security Features of 802.11: WEP, WPA1, and WPA2, PSK Authentication, TKIP Encryption and AES-CCMP Encryption.
Learning Outcome	On successful completion of the course, students should be able to: · Understand the fundamentals of 802.11 wireless networks · Describe the WLAN services-association, disassociation, re-association, distribution, integration, authentication, de authentication and data delivery services · Comprehend the vulnerabilities associated with 802.11 protocol.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Text Books and References:

"Wireless Communications: Principles and Practice" by Theodore S. Rappaport (2nd Edition)
"802.11 Wireless Networks: The Definitive Guide" by Matthew S. Gast (2nd Edition)
"Wireless Communications & Networks" by William Stallings (2nd Edition)
"Wireless Communications: Principles and Practice" by Andreas F. Molisch (2nd Edition)
"Fundamentals of Wireless Communication" by David Tse and Pramod Viswanath (1st Edition)
"Next Generation Wireless LANs: 802.11n and 802.11ac" by Eldad Perahia and Robert Stacey (2nd Edition)
"Wireless Networking: Understanding Internetworking Challenges" by Anurag Kumar, D. Manjunath, and Joy Kuri 1st Edition)
"Wireless Communications: Principles and Practice" by Kaveh Pahlavan and Prashant Krishnamurthy (1st Edition)

CS4215

Distributed Computing

Course Number	CS4215
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Distributed Computing
Learning Mode	Offline
Learning Objectives	This course aims to help the students (a) understand the fundamental concepts and challenges of distributed computing; (b) learn various communication models and protocols used in distributed computing; (c) explore distributed system architectures and design principles; (d) gain knowledge of distributed coordination algorithms and techniques; (e) study fault tolerance mechanisms and reliability in distributed computing; (f) investigate scalability issues and techniques for scaling distribute computing.; (g) understand security concerns and mechanisms in distributed environments; (h) analyze case studies of real-world distributed computing; and (i) explore emerging trends and technologies in distributed computing.
Course Description	This course offers a thorough exploration of distributed computing, focusing on the fundamental concepts, architectures, and algorithms that enable multiple computers to work together seamlessly. Students will study the principles of distributed systems, including communication, synchronization, fault tolerance, and consistency. Key topics include distributed databases, cloud computing, distributed file systems, and parallel processing. The course also covers contemporary technologies and frameworks such as Hadoop, Spark, and Kubernetes. Through practical assignments and projects, students will gain hands-on experience in designing, implementing, and managing distributed systems, preparing them to tackle complex problems in a distributed computing environment.
Course Outline	Introduction to Distributed Computing: Overview of distributed systems, Characteristics and challenges, Examples of distributed systems. Distributed System Models and Architectures: Client-server architecture, Peer-to-peer (P2P) architecture, Middleware-based architectures, Distributed object-based systems Communication in Distributed Systems: Message passing, Remote procedure calls (RPC), Message-oriented middleware (MOM), Publish-subscribe systems. Distributed Algorithms: Mutual exclusion, Leader election, Distributed consensus, Distributed transactions Distributed Data Management: Replication and consistency, Distributed file systems (e.g., HDFS), Distributed databases (e.g., Cassandra, MongoDB). Fault Tolerance and Reliability: Fault models, Failure detection and recovery, Replication and redundancy, Byzantine fault tolerance Scalability and Performance: Scalability patterns, Load balancing, Caching strategies, Performance measurement and optimization. Case Studies and Advanced Topics: Google File System (GFS), MapReduce, Apache Kafka, Docker and container orchestration (e.g., Kubernetes)
Learning Outcome	· Students will demonstrate a deep understanding of the fundamental concepts, principles, and challenges of distributed computing. · Students will be able to analyze, design, and implement distributed computing solutions to address specific problems or requirements. · Students will critically evaluate different distributed system architectures, algorithms, and technologies, considering their strengths, weaknesses, and trade-offs. · Students will work collaboratively in teams to design and implement distributed system projects, demonstrating effective teamwork and project management skills. · Students will apply distributed systems principles and techniques to solve real-world problems, demonstrating the relevance and applicability of course concepts. · Students will be able to adapt to new developments and emerging trends in distributed computing, demonstrating a continuous learning mindset. · Students will reflect on their learning experiences throughout the course, identifying areas of growth, challenges encountered, and lessons learned for future application.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term.

Suggested Reading

Designing Distributed Systems: Patterns and Paradigms for Scalable, Reliable Services, Brendon Burns
Distributed Systems: Principles and Paradigms, Andrew S. Tanenbaum and Maarten Van Steen
Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systemsm, Martin Kleppmann
Distributed Algorithms, Nancy Lynch
Distributed Systems: Concepts and Design, George Coulouris, Jean Dollimore, and Tim Kindberg
Distributed Systems for Fun and Profit, Mikito Takada

CS4216

Distributed Computing

Course number	CS4216
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Parallel computing
Learning Mode	offline
Learning Objectives	Fundamental theoretical issues in designing parallel algorithms and architectures. Shared memory models of parallel computation.
Course Description	This course will provide an overview of different methods and tricks to solve problems in a multiprocessor environment.
Course Outline	Parallel Programming Models: Shared-memory model (PRAM, MIMD, SIMD), network model (line, ring, mesh, hypercube), performance measurement of parallel algorithms. Algorithm Design Techniques for PRAM Models: Balancing, divide and conquer, parallel prefix computation, pointer jumping, symmetry breaking, pipelining, accelerated cascading. Algorithms for PRAM Models: List ranking, sorting and searching, tree algorithms, graph algorithms. Parallel Complexity: Lower bounds for PRAM models, the complexity class NC, P-completeness.
Learning Outcome	· An understanding of computer architectures at a high level, in order to understand what can and cannot be done in parallel, and the relative costs of operations like arithmetic, moving data, etc., · To recognize programming "patterns" to use the best available algorithms and software to implement them, · To understand sources of parallelism and locality in simulation in designing fast algorithms.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term.

Suggested Reading:

Michael J Quinn, Parallel Computing: Theory and Practice, second edition, McGraw Hill, 1994/2002.
Michael J Quinn, Parallel Programming in C with MPI and OpenMP, first edition, McGraw Hill, 2004/2003.
Ananth Grama, Anshul Gupta, George Karypis and Vipin Kumar, Introduction to Parallel Computing, second edition, Addison-Wesley/Pearson, 1994/2003.
Russ Miller and Laurence Boxer, Algorithms: Sequential and Parallel — A Unified Approach, third edition, Cengage, 2013.
Fayez Gebali, Algorithms and Parallel Computing, Wiley, 2011.
Seyed H Roosta, Parallel Processing and Parallel Algorithms: Theory and Computation, Springer, 2000.

Department Elective - VI

Department Elective - VI

Sl. No.

Course Code

Course Name

CS4210

Computer Security

Course Number	CS4210
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Computer Security
Learning Mode	Offline
Learning Objectives	To have a clear understanding of security and privacy issues in various aspects of computing, including: Programs, Operating systems & Networks
Course Description	The course covers. security and privacy issues in various aspects of computing, including: Programs, Operating systems, Networks, Web Applications
Course Outline	Introduction to Computer Security and Privacy: security and privacy; types of threats and attacks; methods of defense Program Security: nonmalicious program errors; vulnerabilities in code, Secure programs; malicious code; Malware detection Operating System Security: Methods of protection; access control; user authentication Network Security: Network threats; firewalls, intrusion detection systems Application Security and Privacy: Basics of cryptography; security and privacy for Internet applications, IPSEC, TLS
Learning Outcome	After completion of this course a student will have · Understanding of security issues in computing at program, , · Understand the operations of different malware · The ability to analysis Malwares · Ability to analyse the security of Operating system and Networks
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Readings:

Security in Computing, Charles P. Pfleeger and Shari Lawrence Pfleeger, 4^th edition or later Prentice-Hall, 2007
Computer Security: Principles and Practice, Dr. William Stallings and Lawrie Brown, Pearson

CS4211

Cryptography

Course Number	CS4211
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Cryptography
Learning Mode	physical
Learning Objectives	To have a clear understanding of design and analysis of different cryptographic primitives
Course Description	The course covers design and analysis of different cryptographic primitives including Symmetric and asymmetric key cryptography
Course Outline	Mathematical Background: Modular Arithmetic, Finite Fields, The Group Law, Elliptic Curves over Finite Fields , Projective Coordinates. Symmetric Encryption: Shift Cipher, Substitution Cipher, Permutation Cipher, Stream Cipher Basics, Linear Feedback Shift Registers, RC4; Block Ciphers: DES, AES, and Different modes of Block ciphers. Key Management, Secret Key Distribution. Hash Functions and Message Authentication Codes: SHA, MD5, HMAC. Public Key Encryption: RSA, ElGamal Encryption, Rabin Encryption, Elliptic curve based encryption. Digital Signatures: RSA based, DSA, ECDSA. Public key based infra structure. Key Exchange: Diffie–Hellman Key Exchange, Authenticated Key Agreement
Learning Outcome	After completion of this course a student will have · Understanding of modular arithmetic and Finite fields, · Understanding and analysis of symmetric key cryptography DES, AES · Understanding and analysis of Hash function, MAC function, · Understanding and analysis of asymmetric key cryptography · Understanding and analysis of key agreement protocols
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Readings:

Mao, Modern Cryptography: Theory and Practice. Pearson Education
Hand book of applied cryptography by A. Menezes, CRC press

Doug Stinson, Cryptography: Theory and Practice, Chapman and Hall/CRC,

CS4212

Big Data Analytics

Course Number	CS4212
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Big Data Analytics
Learning Mode	Offline
Learning Objectives	The objective of this course is to provide students (a) with a comprehensive understanding of Big Data analytics, covering the challenges, applications, and technologies involved in managing and analyzing large-scale data; (b) about the Big Data stack, various Big Data platforms (such as Apache Spark, HDFS, and YARN), and the MapReduce programming model; (c) knowledge of Big Data storage platforms, streaming platforms, and machine learning algorithms in Spark, including an introduction to deep learning for Big Data; (d) information about Big Data applications in graph processing.
Course Description	This comprehensive course provides an in-depth overview of big data and its significant impact across various industries. Students will explore the foundational characteristics of big data, including Volume, Velocity, Variety, Veracity, and Value, and understand the distinctions between big data and traditional data.
Course Outline	Introduction to Big Data: Overview of big data and its characteristics (Volume, Velocity, Variety, Veracity, Value), Big data vs. traditional data, Introduction to big data technologies and tools, Applications of big data in various industries. Big Data Architecture, Components of big data architecture, Distributed computing and storage, Introduction to Hadoop ecosystem (HDFS, YARN, MapReduce), Overview of other big data platforms (Spark, Flink, Storm) Data Ingestion and Storage, Data ingestion techniques and tools (Flume, Kafka, Sqoop), NoSQL databases (HBase, Cassandra, MongoDB), Data warehousing solutions (Hive, HBase), Real-time data processing. Data Processing with Hadoop, Hadoop Distributed File System (HDFS), MapReduce programming model, Writing and executing MapReduce jobs, Data processing workflows with Apache Pig. Data Processing with Apache Spark, Introduction to Apache Spark, Spark Core and RDDs (Resilient Distributed Datasets), Spark SQL and DataFrames, Spark Streaming for real-time data processing Data Analysis and Visualization, Exploratory Data Analysis (EDA) techniques, Data visualization tools (Tableau, Power BI, D3.js), Creating dashboards and reports, Visualizing big data with Python (Matplotlib, Seaborn). Applying machine learning algorithms to big data (classification, regression, clustering), MLlib: Spark’s machine learning library, Time-series analysis and forecasting, Text mining and sentiment analysis, Graph analytics with big data, Recommender systems Overview of cloud platforms for big data (AWS, Azure, Google Cloud), Cloud-based big data services and tools, Deploying big data applications in the cloud, Scalability and performance optimization. Security and Privacy in Big Data, Data privacy and security challenges in big data, Data anonymization and encryption techniques, Regulatory and compliance considerations (GDPR, CCPA), Best practices for securing big data, Real-world big data applications in healthcare, finance, marketing, and IoT.
Learning Outcome	· Comprehend the introduction, challenges, and applications of Big Data. · Understand the components and distribution packages of the Big Data stack. · Work with Apache Spark, HDFS, YARN, and implement the MapReduce programming model. · Manage Big Data Storage · Apply Machine Learning in Big Data · Explore Big Data Applications in Graph Processing · Understand and utilize Pregel, Giraph, and Spark GraphX for graph processing.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Reading

Bart Baesens, Analytics in a Big Data World: The Essential Guide to Data Science and its Applications, Wiley, 2014
Dirk Deroos et al., Hadoop for Dummies, Dreamtech Press, 2014.
Chuck Lam, Hadoop in Action, December, 2010
Mining of Massive Datasets. Leskovec, Rajaraman, Ullman, Cambridge University Press
Data Mining: Practical Machine learning tools and techniques, by I.H. Witten and E. Frank

CS4213

Computer Forensics

Course Number	CS4213
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Computer Forensics
Learning Mode	Offline/online
Learning Objectives	This course aims to: · impart principles and techniques for digital forensics investigation · make aware of various digital forensics tools · guide one how to perform forensics procedures to ensure court admissibility of evidence, as well as the legal and ethical implications
Course Description	Digital forensics involves the investigation of computer-related crimes with the goal of obtaining evidence to be presented in a court of law. In this course, students will learn the principles and techniques for digital forensics investigation and the spectrum of available computer forensics tools. One will learn about core forensics procedures to ensure court admissibility of evidence, as well as the legal and ethical implications. One will learn how to perform a forensic investigation on both Unix/Linux and Windows systems with different file systems. One will also be guided through forensic procedures and review and analyze forensics reports. Although the course does not have any lab components but students may have to work out some assignments/case project works related to data analysis and data recovery, data acquisition, recovering graphics file, validation of a forensic image file, etc.
Course Outline	Digital Forensics Fundamentals: Overview, Preparation for Digital Forensics, Conducting Investigation, Understanding Forensics Lab requirements, Cyber Laws Data Acquisition: Understanding the storage formats, Determining acquisition method, Use of acquisition tools, Validating data acquisition Processing crime and incident scenes: Identifying digital evidence, preparing for a search, Seizing and storing Digital Evidence Working with Windows and Linux File Systems: Understanding File Systems, Exploring Microsoft File Structure, Examining NTFS Disks, Windows Registry, Virtual Machine, File structure in Ext4, Some Forensics Tools: Software Tools, Hardware Tool, Validating and Testing Forensics Software, Password protection, Password Recovery Tools Recovering Graphics Files: Recognizing Graphics File, Understanding Data Compression, Identifying Unknown File Formats, Understanding Copyright Issues with Graphics Digital Forensics Analysis and Validation: Determining what data to collect and analyze, Validating Forensics Data, Addressing Data Hiding Techniques, Forensics handwriting and signature analysis Overview Email and Social Media Investigations, Mobile Device Forensics, Cloud Forensics, Memory Forensics
Learning Outcome	Upon successful completion of this course, the students will: · be able to perform forensics analysis using digital evidence · gain exposure on analyzing the performance of various forensics tools · obtain more in depth knowledge on various file system related artifacts
Assessment Methods	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Readings:

Amelia Phillips, Bill Nelson, Christopher Steuart - “Guide to Computer Forensics and Investigations”, 6^th Editon, Cengage
Darren Hayes: Practical Guide to Digital Forensics Investigations, Pearson
Michael K. Robinson: Digital Forensics: Hands-on Activities in Digital Forensics, Createspace Independent Pub; Workbook edition
Gerard Johnsen, Digital Forensics and Incident Response: Incident response tools and techniques for effective cyber threat response, 3rd Edition, 2022
William Oettinger, Learn Computer Forensics: Your one-stop guide to searching, analyzing, acquiring, and securing digital evidence, 2nd Edition, 2022
Thomas J. Holt, Adam M. Bossler, Kathryn C. Seigfried-Spellar, Cybercrime and Digital Forensics: An Introduction, 3rd Edition, 2022

IDE from CSE (Available to student other than Dept. of CSE)

IDE from CSE (Available to student other than Dept. of CSE)

Course Code

Course Name

IDE- I

CS2207

Introduction to Data Science

Course Number	CS2207 (IDE-1)
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Introduction to Data Science
Learning Mode	Offline
Learning Objectives	This course aims to help the students (a) to understand the fundamental concepts and principles of data science. (b) to provide an understanding of the data science process, including data collection, cleaning, analysis, and interpretation (c) to develop understanding in statistical and machine learning techniques for data analysis (d) to conduct exploratory data analysis (EDA) and to create predictive models (e) in developing of problem-solving skills using data science methodologies (f) to develop skills in visualizing data and creating compelling data stories (g) to highlight the importance of ethical decision-making in data science projects endeavors.
Course Description	This academic course on Introduction to Data Science aims to introduce methods for data collection and cleaning and finally inferring insightful information from the data and presenting that to audience in meaningful way. Major thrust is given on data processing and model preparation for some insightful information. Upon completion, students will excel in data handling, raising meaningful question for insights and come with model/ statistical test for acquiring the insight. Finally, a number of data representation methods are used to present the result in meaningful way.
Course Outline	Unit I Introduction to the data science and Python. Unit II Exploratory Data Analysis and the Data Science Process - Basic tools (Pandas, ScikitLearn, NumPy, Matplotlib, etc.). Unit III Python Programming for Statistics: Probability, Random Variable, Probability Distribution, central limit theorem Unit IV Inferential Statistics: population and sample, Point estimation, Interval estimation, hypothesis testing Unit V Supervised Learning- Linear Regression, k-Nearest Neighbors (kNN), Naïve Bayes, Decision Trees Unit VI Unsupervised Learning- k-means, DBSCAN, GMM, Principal Component Analysis
Learning Outcome	· A clear understanding of the core concepts and methodologies in data science. · Knowledge regarding programming languages (e.g., Python) and data manipulation libraries (e.g., pandas, NumPy) to clean, process, and analyze data. · Knowledge regarding exploratory data analysis (EDA) and capability to create predictive models using appropriate data science tools and techniques. · Drawing data-driven insights and recommendations from data. · Create visualizations and reports that convey findings in a compelling and understandable manner.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Reading:

Probability and Statistics for Engineers and Scientist by Ronald E. Walpole, Raymond H. Myers, Sharon L. Myers, Keying E. Ye, Pearson, 9th Edition
An Introduction to Statistical Learning with Applications in R by Gareth James Daniela Witten, Travor Hastie, Robert Tibshirani, Springer
Machine Learning by Tom Mitchel, McGraw Hill Education
Cathy O'Neil and Rachel Schutt. Doing Data Science, Straight Talk From The Frontline O'Reilly. 2014
Anil K. Jain, Richard C. Dubes, Algorithms for clustering data, Prentice Hall Advanced Reference Series: Computer Science, (2008)
Rajeev Motwani and Prabhakar Raghavan, Python for Rusers a data science approach, Wiley, Year: 2018
John D. Kelleher, Brendan Tierney, Data Science, The MIT Press, 2018

IDE –II

CS3106

Computer Graphics

Course Number	CS3106 (IDE-2)
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Computer Graphics
Learning Mode	Offline
Learning Objective	The objective of the course is to provide a conceptual and theoretical understanding of the organization and functioning of a computer graphics rendering pipeline.
Course Description	Computer Graphics comprises of a pipeline of technologies that play an important role in developing computer vision and image processing technologies with wide applications in the field of Artificial Intelligence (AI).
Course Outline	Graphics imaging pipeline, Rasterization, Display devices, CRT displays, Random scan display, Raster scan display, Raster Scan Basics. 2D transformations, 3D transformations, Vanishing points, Viewing Transformation. Coding sessions in class using C++, Python. Digital Differential Algorithms, Bresenham’s algorithms, polygon filling, Windowing and Clipping, problems of aliasing. Coding sessions in class using C++, Python. Graph based models, B-REP model, Constructive Solid Geometry (CSG), Octree based representation, Quadtree based representation. Parametric representation of curves, parametric cubic curves, Bezier curves, continuity of curves, modeling of surfaces. Hidden Surface Removal, Back face removal, Z-Buffer Algorithm, Scan-line algorithm for VSD, algorithm, BSP trees. Coding sessions in class using C++, Python.
Learning Outcome	· This course will teach the fundamentals of imaging graphics through which you will be able to develop various imaging applications. · This course also accompanies coding, using Python or C++ or Java and OpenGL, of every algorithm/technology that will be taught giving a first hand experience of imaging app development and how it works.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Readings:

Shirley, M. Ashikhmin and S. Marschner, Fundamentals of Computer Graphics, 3rd Edition, CRC Press, 2009.
Angel and D. Shreiner, Interactive Computer Graphics, A top-down approach with OpenGL, 6th Edition, Addison Wesley, 2012.
D. Foley, A. van Dam, S. Feiner, and J. F. Hughes, Computer Graphics: Principles and Practice, 2nd Ed, Addison-Wesley, 1996.

D. F. Rogers and J. A. Adams, Mathematical Elements for Computer Graphics, 2nd Edition, McGraw-Hill International Edition, 1990.

IDE -III

CS4113

Data Analysis and Visualization

Course Number	CS4113 (IDE-3)
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Data Analysis and Visualization
Learning Mode	Offline
Learning Objectives	· To understand the fundamental concepts and principles of data analysis. · To acquire skills in data collection, cleaning, and preparation for analysis. · To learn statistical techniques and methods for analyzing data. · To gain proficiency in using software tools for data analysis, such as Python, R, and Excel. · To develop the ability to create meaningful and effective data visualizations. · To interpret and communicate data findings clearly and accurately. · To apply data analysis and visualization techniques to real-world problems.
Course Description	This course provides a comprehensive introduction to data analysis and visualization techniques. Students will learn how to gather, clean, and analyze data using various tools and methodologies. The course covers statistical analysis, data manipulation, and visualization best practices. Through hands-on projects and real-world examples, students will develop the skills necessary to transform data into actionable insights and effectively communicate their findings using visualizations.
Course Outline	Introduction to Data Analysis and Visualization: Overview of Data Analysis and Visualization, Importance of Data in Decision Making, Data Preprocessing Tasks, Some Mathematical Preliminaries Introduction to various tools: Python, R, Tableau, etc. Exploratory Data Analysis programming: Descriptive Statistics, Data Cleaning and Handling Missing Values, Data Visualization with ggplot2, Correlation and Covariance, Data Distribution and Outliers, Introduction to Statistical Modeling programming: Linear Regression: Concepts and Implementation, Multiple Linear Regression Analysis, Supervised Data Analysis programming: Introduction of Supervised Analysis Techniques, Various Classifier Models- Logistic Regression, Naïve Bayes Classifier, LDA, KNN, SVM, Decision Trees. etc. Evaluation Parameters, Practice and Analysis using R Unsupervised Data Analysis programming: Introduction of Unsupervised Analysis, Various Clustering Strategies- K-Means, DBSCAN, Hierarchical. Evaluation Strategies, Practice and Analysis using R Real-world applications and case studies, industry-specific use cases, mini project
Learning Outcome	By the end of this course, students will be able to: · Apply various data analysis and visualization techniques using various tools. · Perform data preprocessing, including cleaning, handling missing values, and transforming data. · Conduct exploratory data analysis and create informative visualizations. · Implement and interpret statistical models and supervised learning techniques. · Execute unsupervised learning techniques and evaluate their effectiveness. · Apply learned techniques to real-world scenarios through case studies and projects, demonstrating their practical utility.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Readings:

Data Analytics & Visualization, Jack A. Hyman et al, April 2024
An Introduction to Statistical Learning with Application in R by Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, 2^nd Edition, Springer
Applied Predictive Modeling by Max Kuhn and Kjell Johnson, 2^nd Edition, Springer, ISBN: 978-1461468486
Visual Analytics with Tableau by Alexander Loth, ISBN: 978-1119560203
Introduction to Data Mining, by Pang-Ning Tan, Michael Steinbach, Anuj Karpatne, Vipin Kumar, 2nd Edition, Pearson.
Machine Learning with R by Brett Lantz, Packt Publishing
Practical Data Science with R by Nina Zumel, John Mount, Manning Publication, ISBN- 978-1617291562
The Art of R Programming by Norman Matloff, No Starch Press, ISBN: 9781593273842
R in a Nuttshell- A Desktop Quick Reference by Joseph Adler, Shroff/O'Reilly, ISBN: 978-9350239209
Hands-On Machine Learning with R by Brad Boehmke and Brandon Greenwell, CRC Press, 978-1138495685
Mastering Tableau 2023 by Marleen Meier, Packt Publishing; 4th ed. Edition, ISB: 978-1803233765

Minor in Computer Science & Engineering

Minor in Computer Science & Engineering

Minors

Course Code

Course Name

Minor-I

CS2101

Algorithm

Course Number	CS2101
Course Credit (L-T-P-C)	3-0-3-4.5
Course Title	Algorithm
Learning Mode	Offline
Learning Objectives	This course aims to help the students (a) to understand and explain fundamental concepts of computational complexity, including time and space complexity, and analyses the efficiency of algorithms; (b) to apply various algorithm design paradigms such as divide-and-conquer, dynamic programming, greedy algorithms, and backtracking to solve computational problems; (c) to develop and implement common algorithms for tasks such as sorting, searching, and graph traversal, and utilize well-known algorithms like Dijkstra's and Kruskal's; (d) to utilize fundamental data structures, including arrays, linked lists, stacks, queues, trees, and graphs, selecting and implementing the most appropriate one for specific problems; and (e) to evaluate the performance and scalability of algorithms and data structures, conducting empirical analysis to understand their practical performance, and enhancing problem-solving skills through theoretical knowledge application in practical scenarios.
Course Description	The course introduces the basics of computational complexity analysis and various algorithm design paradigms. The goal is to provide students with solid foundations to deal with a wide variety of computational problems, and to provide a thorough knowledge of the most common algorithms and data structures.
Course Outline	Unit I Role of algorithms in computing and elementary data structures. Unit II Analysis framework: Asymptotic notations, Analysis & Master Theorem Unfolding of recursion: review of sorting and searching algorithms, Huffman Encoding, String matching, hashing, Trees, Subset sum Unit III Algorithm design paradigm: · Brute force algorithms- Exhaustive search · Greedy algorithms · Divide and conquer algorithms, Branch-and-bound · Backtracking · Dynamic programming: Matrix Chain Multiplication, 0/1 Knapsack problem Unit IV Graph based algorithm: MST, Shortest distance, colouring, Vertex cover, TSP Unit V Reducibility: P, NP, NP complete, and NP hard Unit VI Elements of Randomized and approximation Algorithms Practical component: Lab to be conducted on a 3-hour slot weekly. It will be conducted with the theory course so the topics for problems given in the lab are already initiated in the theory class.
Learning Outcome	· Describe how efficiency affects the practical usage of algorithms and data structures. · Identify different algorithmic techniques for running programs at scale. · Construct programs that apply computational concepts as a tool in other domains. · Discuss how computer science interacts with and affects the world.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Reading

H. Carmen, C. E. Leiserson, R. L. Rivest and C. Stein, Introduction to Algorithms, MIT Press, 2001.
Aho, J. E. HopcroŌ and J. D. Ullman, The Design and Analysis of Computer Algorithms, Addison-Wesley, 1974.

M. T. Goodrich and R. Tamassia, Algorithm Design: Foundations, Analysis and Internet Examples, John Wiley & Sons, 2001

4.5

Minor-II

CS2202

Electronics and Communication Engineering

Course Number	CS2202
Course Credit (L-T-P-C)	3-0-2-4
Course Title	Database and Warehousing
Learning Mode	Offline
Learning Objectives	· Understand the fundamental principles of database systems and data warehousing. · Learn to design, implement, and manage databases using relational database management systems (RDBMS). · Explore the concepts and techniques of data warehousing and data mining. · Develop skills in SQL for querying and managing databases. · Analyze and optimize database performance and ensure data integrity and security.
Course Description	This course provides an in-depth exploration of database systems and data warehousing, covering essential concepts, technologies, and techniques. Students will learn about the design and implementation of relational databases, including data modeling, normalization, and SQL. The course will also introduce data warehousing concepts, focusing on data extraction, transformation, and loading (ETL), as well as data mining techniques. Through practical exercises and projects, students will gain hands-on experience in working with databases and data warehouses, preparing them for real-world applications.
Course Outline	1. Introduction to Databases, Overview of database systems, Types of databases and database models, Database architecture and components 2. Data Modeling, Entity-Relationship (ER) modeling, Relational model and schema design, Normalization and denormalization 3. Structured Query Language (SQL), Basic SQL queries (SELECT, INSERT, UPDATE, DELETE), Advanced SQL (joins, subqueries, indexing) , SQL functions and stored procedures 4. Database Design and Implementation, Database design principles, Creating and managing databases using RDBMS, Data integrity and constraints 5. Database Management and Administration, Database backup and recovery, User management and security, Performance tuning and optimization 6. Introduction to Data Warehousing, Concepts and architecture of data warehousing, Data warehousing vs. databases, Data modeling for data warehousing 7. ETL Processes, Data extraction, transformation, and loading (ETL), ETL tools and techniques, Data cleaning and integration 8. Data Mining and Analytics, Introduction to data mining, Data mining techniques and algorithms, Applications of data mining 9. Advanced Topics in Data Warehousing, Big data and data warehousing, Cloud-based data warehousing solutions, Data governance and data quality management Practical component: Lab to be conducted on a 2-hour slot weekly. It will be conducted with the theory course so the topics for problems given in the lab are already initiated in the theory class.
Learning Outcome	The student will be able to: · Demonstrate a thorough understanding of database and data warehousing principles. · Design, implement, and manage relational databases using RDBMS. · Write efficient SQL queries for data manipulation and retrieval. · Implement data warehousing solutions, including ETL processes and data mining techniques. · Analyze and optimize the performance of databases and data warehouses, ensuring data integrity and security.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Textbooks

"Database System Concepts" (7th Edition) by Abraham Silberschatz, Henry F. Korth, and S. Sudarshan
"Fundamentals of Database Systems" (7th Edition) by Ramez Elmasri and Shamkant B. Navathe
"Data Warehousing: The Ultimate Guide to Building a Data Warehouse for Business Intelligence" (1st Edition) by Erik Thomsen
"The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling" (3rd Edition) by Ralph Kimball and Margy Ross

5. "SQL: The Complete Reference" (3rd Edition) by James R. Groff and Paul N. Weinberg

Minor-III

CS3101

Operating System

Course Number	CS3101
Course Credit (L-T-P-C)	3-0-3-4.5
Course Title	Operating System
Learning Mode	Offline
Learning Objectives	This course provides an in-depth understanding of the fundamental concepts, principles, and mechanisms of operating systems. Topics include process management, memory management, file systems, concurrency, and scheduling.
Course Description	This course comprehensively introduces the fundamental concepts and principles underlying operating systems. Key topics include definitions of operating systems, the concept of a process, inter-process communication mechanisms, and multi-threading concepts. The course also addresses critical issues such as deadlock, discussing the necessary conditions for its occurrence and strategies for avoidance and prevention. In the realm of memory management, students will learn about both contiguous and non-contiguous allocation, paging concepts, and page table architecture. Further, the virtual memory concept will be explored, focusing on demand paging, replacement algorithms, and the phenomenon of thrashing. The course also includes a detailed study of file systems and disk management. By the end of this course, students will have a robust understanding of the essential components and functions of operating systems, preparing them for advanced studies and practical applications in the field of computer science.
Course Outline	Basics of Operating System: Definition and objectives of operating systems Types of operating systems: Batch, Time-sharing, Real-time, Distributed Systems Concept of process: Process control block, State transition, Scheduling algorithms, context switching, Process synchronization and inter-process communication Threads: Popular thread libraries, thread synchronization, multi-therading concepts Deadlock: necessary conditions, avoidance and prevention Memory management: Contiguous and non-contiguous allocation, Physical and logical addresses, Paging, different Page Table architectures, Virtual Memory: demand paging, replacement algorithms, thrashing. File systems: file operations, organization, mounting, sharing, File system implementation Disk management: disk structure, disk scheduling, disk management Practical component: Lab to be conducted on a 3-hour slot weekly. It will be conducted with the theory course so the topics for problems given in the lab are already initiated in the theory class.
Learning Outcome	· Understand the basic principles and functionalities of operating systems. · Analyse and evaluate different operating system components and their interactions. · Apply operating system concepts to solve real-world problems. · Develop an appreciation for the role of operating systems in modern computing environments.
Learning Outcome	· Understand the basic principles and functionalities of operating systems. · Analyse and evaluate different operating system components and their interactions. · Apply operating system concepts to solve real-world problems. · Develop an appreciation for the role of operating systems in modern computing environments.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested readings:

A. Silberschatz, P. B. Galvin and G. Gagne, Operating System Concepts, 7^th Ed, John Wiley and Sons, 2004.
M. Singhal and N. Shivratri, Advanced Concepts in Operating Systems, McGraw Hill, 1994.

3. David A Patterson and John L Hennessy, Computer Organisation and Design: The Hardware/Software Interface, Morgan Kaufmann, 1994. ISBN 1-55860-281-X.

4.5

Minor-IV

CS3201

Cyber Security

Course Number	CS3201
Course Credit (L-T-P-C)	3-0-2-4
Course Title	Cyber Security
Learning Mode	offline
Learning Objectives	To understand the basic concepts of cyber-attacks, legal issues and countermeasures.
Course Description	The course covers cyber-attacks, legal issues and countermeasures various aspects of cybersecurity, including basic principles, legal considerations, risk assessment, and security management. The course covers essential topics such as cybercrime, phishing attacks, cryptography basics, authentication mechanisms, and authorization protocols. Additionally, it delves into specific areas of vulnerability assessment and mitigation, focusing on secure programming practices and identifying threats to networks.
Course Outline	Introduction to cybersecurity: Basic concepts, cybercrime, legal issues, risk analysis and security management, phishing attack. Crypto basics, Authentication and authorization, Kerberos, PKI Vulnerabilities and Countermeasure: Vulnerabilities in code, Secure programming. Threats to network, network defense, social network security issues and countermeasures, email security Cyber system security: Hardware security, mobile security. Practical component: Lab to be conducted on a 2-hour slot weekly. It will be conducted with the theory course so the topics for problems given in the lab are already initiated in the theory class.
Learning Outcome	After completion of this course a student will have: · Understanding the legal aspects, risk and vulnerabilities in cyberspace. · Understanding the concepts of different attacks and their countermeasures in cyberspace.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Readings:

Nina Godbole and Sunit Belapure, Cyber Security, Wiley India

Total Credits

Program Learning Objectives: 1. Develop a solid foundation in electronics and communication engineering principles, including circuit analysis, electronic devices, signal processing, microprocessor/microcontroller systems, analog communication systems, digital communication, and RF circuits etc. 2. Develop electronics and communication project management skills, including the ability to plan, execute, and complete within specified timelines and budgets. 3. Work collaboratively in multidisciplinary teams, demonstrating effective teamwork and communication to solve complex engineering problems. 4. Recognize the importance of ongoing professional development, engaging in activities such as certifications, workshops, and conferences to stay updated of industry trends.	Program Learning Outcomes: The graduates of this program will have a successful career in an Academia/Industry/Entrepreneur. strong fundamentals in electronics and communications engineering. ability to design prototypes for real world problems related to electronics, communications and interdisciplinary fields. ability to develop soft skills such as effective communications in both verbal and written forms, body language, time management, problem-solving, leadership, work in both team as well as individual in a professional manner.
Program Goal 1: Academic excellence by providing a curriculum that aligns with industry standards and encourages critical thinking in the field of electronics and communication engineering.	Program Learning Outcome 1a: Highly skilled market ready man power to serve the emerging electronic sectors Program Learning Outcome 1b: Skilled Human resource to cater the needs of next generation communication sectors
Program Goal 2: A culture of research and innovation by promoting faculty and student involvement in cutting-edge projects in electronic and communication technologies.	Program Learning Outcome 2a: Trained researchers for implementing research projects in line with national priorities such as CPS, Semiconductors, Clean Energy, Green Technologies Program Learning Outcome 2b: Design and develop innovative smart electronics products as per the societal need
Program Goal 3:. To design dynamic and flexible course structures for UG and PG programs as per the changing requirement of the industries	Program Learning Outcome 3a: Industry relevant UG, PG, and research programs Program Learning Outcome 3b: Trained manpower as per the industry requirement
Program Goal 4: To promote entrepreneurship among the students in the field of electronics and communication engineering	Program Learning Outcome 4a: Realization of working prototype towards product development Program Learning Outcome 4b: Promotion of in house technology based ventures catering societal needs
Program Goal 5: Equip students with strong communication skills, enabling them to articulate technical concepts clearly and effectively in both written and oral forms.	Program Learning Outcome 5a: Man power with enhanced soft skills to support the vision of developed India Program Learning Outcome 5b: Responsible citizen for the holistic growth of the country

Sl. No.

Subject Code

SEMESTER I

MA1101

Course Number	MA1101
Course Credit (L-T-P-C)	3-1-0-4
Course Title	Calculus and Linear Algebra
Learning Mode	Lectures and Tutorials
Learning Objectives	To provide the essential knowledge of basic tools of Differential Calculus, Integral Calculus, Vector spaces and Matrix Algebra.
Course Description	This course provides a foundation for Calculus and Linear Algebra. Topics related to properties of single and two variable functions along with their applications will be discussed. In addition fundamentals of linear algebra and matrix theory with applications will also be discussed.
Course Content	Differential Calculus (12 Lectures): Limit and continuity of one variable function (including ε-δ definition). Limit, continuity and differentiability of functions of two variables, Tangent plane and normal, Change of variables, chain rule, Jacobians, Taylor’s Theorem for two variables, Extrema of functions of two or more variables, Lagrange’s method of undetermined multipliers. Integral Calculus (10 Lectures): Riemann integral for one variable functions, Double and Triple integrals, Change of order of integration. Change of variables, Applications of Multiple integrals such as surface area and volume. Vector Spaces (12 Lectures): Vector spaces (over the field of real numbers), subspaces, spanning set, linear independence, basis and dimension. Linear transformations, range and null space, rank-nullity theorem, matrix of a linear transformation. Matrix Algebra (8 Lectures): Elementary operations and their use in getting the rank, inverse of a matrix and solution of linear simultaneous equations, Orthogonal, symmetric, skew-symmetric, Hermitian, skew-Hermitian, normal and unitary matrices and their elementary properties, Eigenvalues and Eigenvectors of a matrix, Cayley-Hamilton theorem, Diagonalization of a matrix.
Learning Outcome	Students completing this course will be able to: 1. Understand various properties of functions such as limit, continuity and differentiability. 2. Learn about integrations in various dimension and their applications. 3. learn about the concept of basis and dimension of a vector space. 4. define Linear Transformations and compute the domain, range, kernel, rank, and nullity of a linear transformation. 5. compute the inverse of an invertible matrix. 6. solve the system of linear equations. 7. Apply linear algebra concepts to model, solve, and analyze real-world problems.
Assessment Method	Quiz /Assignment/ MSE / ESE

Textbooks:

Thomas, G. B., Hass, J., Heil, C. and Weir M. D., “Thomas’ Calculus”, 14th Ed., Pearson Education, 2018
Kreyszig, E., “Advanced Engineering Mathematics”, 10th Ed., Wiley India Pvt. Ltd, 2015

Reference Books:

Jain, R. K. and Iyenger, S. R. K., “Advanced Engineering Mathematics”, 5^th, Narosa Publishing House, 2017
Axler, S., “Linear Algebra Done Right”, 3rd Ed., Springer Nature, 2015
Strang, G., “Linear Algebra and Its Applications” 4th Ed., Cengage India Private Limited, 2005

4.0

CS1101

Course Number	CS1101
Course Credit	3-0-3-4.5
Course Title	Foundations of Programming
Learning Mode	Offline
Learning Objectives	· To understand the fundamental concepts of programming · To develop the basic problem-solving skills by designing algorithms and implementing them. · To learn about various data types, control statements, functions, arrays, pointers, and file handling. · To achieve proficiency in debugging and testing a C program
Course Description	This introductory course provides a solid foundation in programming principles and techniques. Designed for students with little to no prior programming experience, it covers fundamental concepts such as variables, data types, control structures, functions, and basic data structures. Students will learn to write, debug, and execute programs using a high-level programming language. Emphasis is placed on developing problem-solving skills, logical thinking, and the ability to write clear and efficient code. By the end of the course, students will be equipped with the essential skills needed to pursue more advanced studies in computer science and software development.
Course Outline	Introduction and Programming basics, Expressions Control and Iterative statements, Functions, Arrays, Recursion vs. Iteration Pointers, 2D-Array with pointers, Structures, String, Dynamic memory allocation, File handling, Contemporary programming languages, and applications Practical component: Lab to be conducted on a 3-hour slot weekly. It will be conducted with the theory course so the topics for problems given in the lab are already initiated in the theory class.
Learning Outcome	· Understanding of Basic Syntax and Structure in C language · Proficiency in Data Types, Operators, and Control Structures · Function Implementation and learn to use them appropriately · Efficient Use of Arrays and Strings · Pointer Utilization · Ability to perform dynamic memory allocation and deallocation using malloc (), calloc (), realloc (), and free () functions. · Structured data management with structures and unions · Exposure of file Handling · Learning debugging and error Handling
Assessment Method	Internal (Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Reading

Knuth, Donald E. The art of computer programming, volume 4A: combinatorial algorithms, part 1. Pearson Education India, 2011.
P.J. Deitel and H.M. Deitel, C How To Program, Pearson Education (7th Edition)
Brian W. Kernighan and Dennis M. Ritchie, The C Programming Language, Prentice−Hall
A. Kelley and I. Pohl, A Book on C, Pearson Education (4th Edition)

K. N. King, C PROGRAMMING A Modern Approach, W. W. Norton & Company

4.5

PH1101/PH1201

Course Number	PH1101/PH1201
Course Credit	3-1-3-5.5
Course Title	Physics
Learning Mode	Lectures and Tutorials
Learning Objectives	Complies with Program Goals 1 and 2
Course Description	This course deals with fundamentals in Classical mechanics, Waves and Oscillations and Quantum Mechanics. As a prerequisite, the mathematical preliminaries such as coordinate systems, vector calculus etc will be discussed in the beginning.
Course Outline	Orthogonal coordinate systems (Plane polar, Spherical, Cylindrical), concept of generalised coordinates, generalised velocity and phase space for a mechanical system, Introduction to vector operators, Gradient, divergence, curl and Laplacian in different co-ordinate systems. Central force problem and its applications. Rigid body rotation, vector nature of angular velocity, Finding the principal axes, Euler's equations; Gyroscopic motion and its application; Accelerated frame of reference, Fictitious forces. Potential energy and concept of equilibrium, Lennard-Jones and double-well potentials, Small oscillations, Harmonic oscillator, damped and forced oscillations, resonance and its different examples, oscillator states in phase space, coupled oscillations, normal modes, longitudinal and transverse waves, wave equation, plane waves, examples two- and three-dimensional waves. Michelson-Morley experiment, Lorentz transformation, Postulates of special theory of relativity, Time dilation and length contraction, Applications of special theory of relativity.
Learning Outcome	Complies with PLO 1a, 2a, 3a
Assessment Method	Quiz, Assignments and Exams

Suggested Readings:

Textbooks:

Engineering Mechanics, M. K. Harbola, 2^nd ed., Cengage, 2012
D. Kleppner and R. J. Kolenkow, An introduction to Mechanics, Tata McGraw-Hill, New Delhi, 2000.
I. G. Main, Oscillations and Waves
H. G. Pain, The Physics of Vibrations and Waves, 1968
Frank S. Crawford, Berkeley Physics Course Vol 3: Waves and Oscillations, McGraw Hill, 1966.

References:

R. P. Feynman, R. B. Leighton and M. Sands, The Feynman Lecture in Physics, Vol I, Narosa Publishing House, New Delhi, 2009.
David Morin, Introduction to Classical Mechanics, Cambridge University Press, NY, 2007.

3. P. C. Deshmukh, Foundations of Classical Mechanics, Cambridge University Press, 2019

5.5

CE1101/CE1201

Course code	CE1101/CE1201
Course Credit (L-T-P-C)	1-0-3-2.5
Course Title	Engineering Graphics
Learning Mode	Lectures and Practical
Learning Objectives	Complies with PLO-1a 1. The course on engineering drawing is designed to introduce the fundamentals of technical drawing as an important form of conveying information. 2. Apply principles of engineering visualization and projection theory to prepare engineering drawings, using conventional and modern drawing tools. 3. Practice drawing orthographic projections, isometric views, and sectional views, of simple and combined solids in different orientations.
Course Description	This course will introduce drawing as a tool to represent a complex three-dimensional object on two-dimensional paper through methods of projections. The course explains the use of different drafting tools and the importance of conventions for uniformity and standardization of the interpretation of the drawings.
Course Outline	Fundamental of engineering drawing, line types, dimensioning, and scales. Conic sections: ellipse, parabola, hyperbola; cycloidal curves. Principle of projection, method of projection, orthographic projection, plane of projection, first angle of projection, Projection of points, lines, planes and solids. Section of solids: Sectional views of simple solids- prism, pyramid, cylinder, cone, sphere; the true shape of the section. Methods of development, development of surfaces. Isometric projections: construction of isometric view of solids and combination of solids from orthographic projections. Introduction to AutoCad and solving isometric problems.
Learning Outcome	After attending this course, the following outcomes are expected: 1. The student will understand the basic concepts of engineering drawing. 2. The student will be able to use basic drafting tools, drawing instruments, and sheets. 3. The student will be able to represent three-dimensional simple and combined solid objects on two-dimensional paper. 4. The student will be able to visualize and interpret the orientation of simple and combine solid objects.
Assessment Method	Laboratory Assignments (30%), Mid-semester examination (25%) and End-semester examination (45%).

Suggested Readings:

Textbooks:

D. Bhatt, Engineering Drawing, Charotar Publishing House.
Agrawal & Agrawal, Engineering Drawing, McGraw Hill.
Jolhe, Engineering Drawing.

References:

Engineering Drawing and Design by David Madsen

2.5

EE1101/EE1201

Course Number	EE1101/EE1201
Course Credit	3-0-3-4.5
Course Title	Electrical Sciences
Learning Mode	Lectures and Experiments
Learning Objectives	Complies with Program goals 1, 2 and 3
Course Description	The course is designed to meet the requirements of all B. Tech programmes. The course aims at giving an overview of the entire electrical engineering domain from the concepts of circuits, devices, digital systems and magnetic circuits.
Course Outline	Circuit Analysis Techniques, Circuit elements, Simple RL and RC Circuits, Kirchoff’s law, Nodal Analysis, Mesh Analysis, Linearity and Superposition, Source Transformations, Thevenin’s and Norton’s Theorems, Time Domain Response of RC, RL and RLC circuits, Sinusoidal Forcing Function, Phasor Relationship for R, L and C, Impedance and Admittance, Instantaneous power, Real, reactive power and power factor. Semiconductor Diode, Zener Diode, Rectifier Circuits, Clipper, Clamper, UJT, Bipolar Junction Transistors, MOSFET, Transistor Biasing, Transistor Small Signal Analysis, Transistor Amplifier and their types, Operational Amplifiers, Op-amp Equivalent Circuit, Practical Op-amp Circuits, Power Opamp, DC Offset, Constant Gain Multiplier, Voltage Summing, Voltage Buffer, Controlled Sources, Instrumentation Amplifier, Active Filters and Oscillators. Number Systems, Logic Gates, Boolean Theorem, Algebraic Simplification, K-map, Combinatorial Circuits, Encoder, Decoder, Combinatorial Circuit Design, Introduction to Sequential Circuits. Magnetic Circuits, Mutually Coupled Circuits, Transformers, Equivalent Circuit and Performance, Analysis of Three-Phase Circuits, Power measurement in three phase system, Electromechanical Energy Conversion, Introduction to Rotating Machines (DC and AC Machines). Laboratory: Experiments to verify Circuit Theorems; Experiments using diodes and bipolar junction transistor (BJT): design and analysis of half -wave and full-wave rectifiers, clipping and clamping circuits and Zener diode characteristics and its regulators, BJT characteristics (CE, CB and CC) and BJT amplifiers; Experiment on MOSFET characteristics (CS, CG, and CD), parameter extraction and amplifier; Experiments using operational amplifiers (op-amps): summing amplifier, comparator, precision rectifier, Astable and Monostable Multivibrators and oscillators; Experiments using logic gates: combinational circuits such as staircase switch, majority detector, equality detector, multiplexer and demultiplexer; Experiments using flip-flops: sequential circuits such as non-overlapping pulse generator, ripple counter, synchronous counter, pulse counter and numerical display; Power Measurement by two Wattmeter method; Open and Short Circuit Tests of Transformer.
Learning Outcomes	Complies with PLO 1a, 2a and 3a
Assessment Method	Quiz, Assignments and Exams

Texts/References

K. Alexander, M. N. O. Sadiku, Fundamentals of Electric Circuits, 3rd Edition, McGraw-Hill, 2008.
H. Hayt and J. E. Kemmerly, Engineering Circuit Analysis, McGraw-Hill, 1993.
L. Boylestad and L. Nashelsky, Electronic Devices and Circuit Theory, 6th Edition, PHI, 2001.
M. Mano, M. D. Ciletti, Digital Design, 4th Edition, Pearson Education, 2008.
Floyd, Jain, Digital Fundamentals, 8th Edition, Pearson.
David V. Kerns, Jr. J. David Irwin, Essentials of Electrical and Computer Engineering, Pearson, 2004.
Donald A Neamen, Electronic Circuits; analysis and Design, 3rd Edition, Tata McGraw-Hill Publishing Company Limited.
Adel S. Sedra, Kenneth C. Smith, Microelectronic Circuits, 5th Edition, Oxford University Press, 2004.
E. Fitzgerald, C. Kingsley Jr., S. D. Umans, Electric Machinery, 6th Edition, Tata McGraw-Hill, 2003.
P. Kothari, I. J. Nagrath, Electric Machines, 3rd Edition, McGraw-Hill, 2004.

Del Toro, Vincent. "Principles of electrical engineering." (No Title) (1972).

4.5

HS1101

Course Number	HS1101
Course Credit	2-0-1-2.5
Course Title	English for Professionals
Learning Mode	Offline
Learning Objectives	This course aims to help the students (a) attain proficiency in written English through the construction of grammatically correct sentences, utilization of subject-verb agreement principles, mastery of various tenses, and effective deployment of active and passive voice to ensure coherent and impactful written expression; (b) enhance oral communication skills by honing public speaking abilities, acquiring strategies to deliver persuasive presentations, and cultivating a polished telephone etiquette, enabling confident and articulate verbal communication; (c) foster active listening capabilities by recognizing different types of listening, and applying proven methods and strategies to improve active listening skills; (d) strengthen reading skills, including comprehension, interpretation, and critical analysis, to grasp diverse written materials and derive meaning from various types of texts encountered in academic and professional contexts; (e) develop adeptness in written communication for business purposes, encompassing the understanding of essential writing elements, mastery of appropriate writing styles thereby enhancing prospects for successful job interviews and subsequent professional endeavors.
Course Description	This academic course on communication skills aims to equip students with fluency in spoken and written English for effective expression in both academic and professional settings. By focusing on essential communication principles and providing practical experiences, students develop clarity, precision, and confidence in their communication. Through interactive discussions and exercises, students enhance critical thinking and adaptability in diverse contexts. Upon completion, students will excel in formal presentations, group discussions, and persuasive writing, enhancing their overall communication proficiency.
Course Outline	Unit I: Introduction to professional communication – LSRW - Phonetics and phonology Sounds in English Language – production and articulation – rhythm and intonation – connected speech - Basic Grammar and Advanced Vocabulary Sounds in English Language – production and articulation – rhythm and intonation – connected speech – persuading and negotiating – brevity and clarity in language. Unit II: Characteristics of Technical Communication: Types of communication and forms of communication - Formal and informal communication Verbal and non-Verbal Communication – Communication barriers and remedies Intercultural communication – neutral language Unit III: Comprehension and Composition – summarization, precis writing Business Letter Writing CV/ Resume – E-Communication Unit IV: Statement of Purpose, Writing Project Reports, Writing research proposal, writing abstracts, developing presentations, interviews – combating nervousness Tutorial: Listening Exercises, Speaking Practice (GDs, and Presentations), and Writing Practice Learning Outcome · Attain proficiency in written English, enabling the construction of grammatically correct sentences and coherent written expression through the use of appropriate grammar, tenses, and voice. · Enhance oral communication skills, including public speaking, persuasive presentation, and polished telephone etiquette, fostering confident and articulate verbal expression. · Cultivate active listening abilities, recognizing different listening types, overcoming obstacles, and employing strategies for attentive and effective communication. · Develop proficient written communication skills for business purposes, demonstrating understanding of essential writing elements, appropriate styles, and the creation of reports, notices, agendas, and minutes that effectively convey information.
Assessment Method	Class test + Quiz = 20%; Mid-semester = 25%; Assignment = 15%; End semester = 40%

Suggested Reading

Balzotti, Jon. Technical Communication: A Design-Centric Approach. Routledge, 2022.
Kaul, Asha, Business Communication. PHI Learning Pvt. Ltd. 2009
Laplante, Phillip A. Technical Writing: A Practical Guide for Engineers, Scientists, and Nontechnical Professionals. CRC Press, 2018.
Lawson, Celeste, et al. Communication Skills for Business Professionals, Second Edition. CUP, 2019.
Sharon Gerson and Steven Gerson. Technical Writing: Process and Product (8th Edition), London: Longman, 2013
Rentz, Kathryn, Marie E. Flatley & Paula Lentz. Lesikar’s Business Communication Connecting in a Digital world, McGraw-Hill, Irwin.2012
Allan & Barbara Pease. The Definitive Book of Body Language, New York, Bantam,2004
Jones, Daniel. The Pronunciation of English, New Delhi, Universal Book Stall.2010
Savage, Alice. Effective Academic Writing. OUP. 2014
Swan and Alter. Oxford English grammar course. OUP. 201

2.5

TOTAL

23.5

Probability Theory and Ordinary Differential Equations

Sl. No.

Subject Code

SEMESTER II

MA1201

Course Number	MA1201
Course Credit (L-T-P-C)	3-1-0-4
Course Title	Probability Theory and Ordinary Differential Equations
Learning Mode	Lectures and Tutorials
Learning Objectives	To introduce the basic concepts of probability, statistics, and Differential equations.
Course Description	This course aims to cover basic concepts of probability, statistics and ordinary differential equations. In particular, popular distributions, random sampling, various estimators and hypothesis testing will be discussed. Students will also get exposure to the linear ordinary differential equations and their solution techniques.
Course Content	Probability (12 Lectures): Random variables and their probability distributions, Cumulative distribution functions, Expectation and Variance, probability inequalities, Binomial, Poisson, Geometric, negative binomial distributions, Uniform, Exponential, beta, Gamma, Normal and lognormal distributions. Statistics (10 Lectures): Random sampling, sampling distributions, Parameter estimation, Point estimation, unbiased estimators, maximum likelihood estimation, Confidence intervals for normal mean, Simple and composite hypothesis, Type I and Type II errors, Hypothesis testing for normal mean. Ordinary Differential Equations (20 Lectures): First order ordinary differential equations, exactness and integrating factors, Picard's iteration, Ordinary linear differential equations of n-th order, solutions of homogeneous and non-homogeneous equations (Method of variation of parameters). Systems of ordinary differential equations, Power series methods for solutions of ordinary differential equations. Legendre equation and Legendre polynomials, Bessel equation and Bessel functions.
Learning Outcome	Students will get exposure and understanding of: 1. Random variables and their probability distributions 2. Understand popular distributions and their properties 3. Sampling, estimation and hypothesis testing 4. Solution of ordinary differential equations 5. Solution of system of ordinary differential equations 6. Special functions arising as power series solutions of ordinary differential equations
Assessment Method	Quiz /Assignment/ MSE / ESE

Text Books:

Hogg, R. V., Mckean, J. and Craig, A. T., “Introduction to Mathematical Statistics”, 8th Ed., Pearson Education India, 2021
M. Ross “An introduction to Probability Models, Academic Press INC, 11th edition.
Miller, I. and Miller, M., “John E. Freund's Mathematical Statistics with Applications”, 8th Ed., Pearson Education India, 2013
L. Ross, Differential equations, 3rd Edition, Wiley, 1984

W. E. Boyce and R. C. Di Prima, Elementary Differential equations and Boundary Value Problems, 7th Edition, Wiley, 2001.

CS1201

Course Number	CS1201
Course Credit	3-0-3-4.5
Course Title	Data Structure
Learning Mode	Offline
Learning Objectives	· Understand the principles and concepts of data structures and their importance in computer science. · Learn to implement various data structures and understand how different algorithms works. · Develop problem-solving skills by applying appropriate data structures to different computational problems. · Achieving proficiency in designing efficient algorithms.
Course Description	This course provides a comprehensive study of data structures and their applications in computer science. It focuses on the implementation, analysis, and use of various data structures such as arrays, linked lists, stacks, queues, trees, and graphs. Through theoretical concepts and practical programming exercises, this course aims to develop problem-solving and algorithmic thinking skills essential for advanced topics in computer science and software development.
Course Outline	· Introduction to Data Structure, · Time and space requirements, Asymptotic notations · Abstraction and Abstract data types · Linear Data Structure: stack, queue, list, and linked structure · Unfolding the recursion · Tree, Binary Tree, traversal · Search and Sorting, · Graph, traversal, MST, Shortest distance · Balanced Tree Practical component: Lab to be conducted on a 3-hour slot weekly. It will be conducted with the theory course so the topics for problems given in the lab are already initiated in the theory class.
Learning Outcome	· Understand Data Structure Fundamentals · Implement Basic Data Structures using a programming language · Analyse and Apply Algorithms · Design and Analyse Tree Structures · Understand the usage of graph and its related algorithms · Design and Implement Sorting and Searching Algorithms · Debug and Optimize Code
Assessment Method	Internal (Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Reading

Alfred V. Aho, John E. Hopcroft, Jeffrey D. Ullman, Data Structures and Algorithms, Published by Addison-Wesley
Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest and Clifford Stein., Introduction to Algorithms,
Mark Allen Weiss, Data Structures and Algorithm Analysis in Java
Robert Sedgewick and Kevin Wayne, Algorithms

Narasimha Karumanchi, Data Structures and Algorithms Made Easy

4.5

CH1201/CH1101

Course Number	CH1201/CH1101
Course Credit	3-1-3-5.5
Course Title	Chemistry
Learning Mode	Offline
Learning Objectives	The course aims to lay a foundation for all three branches of chemistry, viz. Organic, Inorganic, and Physical Chemistry. The course aims to nurture knowledge to appreciate the interface of chemistry with other science and Engineering branches by combining theoretical concepts and experimental studies.
Course Description	This course introduces basic organic chemistry, inorganic chemistry and Physical chemistry to understand fundamental laws that governs various reactions, reaction rates, equilibrium, and their applications in daily life through relevant experimentation.
Course Outline	Module 1: Thermodynamics: The fundamental definition and concept, the zeroth and first law. Work, heat, energy and enthalpies. Second law: entropy, free energy and chemical potential. Change of Phase. Third law. Chemical equilibrium. Conductance of solutions, Kohlrausch’s law-ionic mobilities, Basic Electrochemistry. Module 2: Coordination chemistry: Crystal field theory and consequences color, magnetism, J.T distortion. Bioinorganic chemistry: Trace elements in biology, heme and non-heme oxygen carriers, haemoglobin and myoglobin; Organometallic chemistry. Module 3: Stereo and regio-chemistry of organic compounds, conformational analysis and conformers, Molecules devoid of point chirality (allenes and biphenyls); Significance of chirality in living systems, organic photochemistry, Modern techniques in structural elucidation of compounds (UV–Vis, IR, NMR). Module 4 (Lab Component): Experiments based on redox and complexometric titrations; synthesis and characterization of inorganic complexes and nanomaterials; synthesis and characterization of organic compounds; experiments based on chromatography; experiments based on pH and conductivity measurement; experiment related to chemical kinetics and spectroscopy.
Learning Outcome	Students will be able to 1. identify organic and inorganic molecules and relate them to daily life applications through experiments. 2. understand important hypothesis, laws and their derivations to intercept physical phenomenon of chemical reactions and apply them in hands-on experiments. 3. understand the importance of organic and inorganic molecules in our body and environment. 4. know important analytical techniques to intercept chemical entity. 5. approach organic and inorganic synthesis as a skillset for drug manufacturing, calculate limiting reagents and yields, use various analytical tools to characterize organic compounds, interpret and ascertain data related to Physical chemistry aspects and know laboratory safety measures, risk factors and scientific report writing skills.
Assessment Method	Theory: 20% Quiz and assignment, 30% Mid sem and 50% End semester exams for theory part (4 credits). Lab: 60% lab report, lab performance and assignment, 20% End semester exam for practical part, 20% viva/quiz (1.5 credits). Overall Weightage: Theory (70%), Lab (30%).

Suggested Reading:

Text books:

Vogel's Qualitative Inorganic Analysis, G. Svehla, 7^th Edition, Revised, Prentice Hall, 1996.
J. Elias, S. S. Manoharan and H. Raj, "Experiments in General Chemistry", Universities Press (India) Pvt. Ltd., 1997.

A. J. Elias, A Collection of Interesting General Chemistry Experiments, revised edition, Universities Press (India) Pvt. Ltd., 2007.

Albert Cotton, G. Wilkinson, C. A. Murillo, M. Bochmann, Advanced Inorganic Chemistry - 6^th Edition New Delhi: Wiley India, 2008.
Mukkanti, Practical Engineering Chemistry, B.S. Publications, Hyderabad, 2009.
Shriver and Atkins inorganic chemistry / Peter Atkins, Tina Overton, Jonathan Rourke, Mark Weller, Fraser Armstrong-5^th Edition – Oxford: UOP. 2012.
Atkins’ Physical Chemistry, Peter Atkins, Julio de Paula, James Keeler, Oxford University Press, 11^th Edition 2017.
L. Kapoor, A Textbook of Physical Chemistry, Vol: 1, 2 (6^th Edition, 2019), Vol: 3 (5^th Edition, 2020) MaGraw Hill.

G. R. Chatwal, S. K. Anand, Instrumental Methods of Chemical Analysis, 5^th Edition, Himalaya Publications, 2023.

5.5

ME1201/ME1101

Course Number	ME1101/ME1201
Course Credit	0-0-3-1.5
Course Title	Mechanical Fabrication
Learning Mode	Fabrication work – hands on fabrication work in Workshop
Learning Objectives	Complies with PLOs 3-4. · This course aims to develop the concepts and skills of various mechanical fabrication methods. · Fabrication of metallic and non-metallic components, fabrication using bulk and sheet metals, subtractive and additive manufacturing methods, and assemble the parts
Course Description	This course is designed to fulfil the need of hand on experience about various approaches (conventional and CNC, subtractive and additive) of mechanical fabrication approaches. Prerequisite: NIL
Course Outline	The jobs for various shops should be planned such that they are the parts of an assembled item. The student groups will fabricate different parts in various shops which will involve some amount of their creativeness/input particularly in design and/or planning. Various components as required for the assembled part can be made using the following shops: Sheet Metal Working: Development, sheet cutting and fabrication of designated job using sheet metal (ferrous/nonferrous); Joining of required portions by soldering, in case part is desired to be made leak proof. Pattern Making and Foundry: Making of suitable pattern (wood); making of sand mould, melting of non-ferrous metal/alloy (Al or Al alloys), pouring, solidification. Observation/identification of various defects appeared on the component. Joining: Butt/lap/corner joint job fabrication as required of low carbon steel plates; weld quality inspection by dye-penetration test (non-destructive testing approach)of the component made. Demonstration of semi-automatic Gas Metal Arc welding (GMAW). Conventional machining: Operations on lathe and vertical milling to fabricate the required component. The fabrication of the component should cover various lathe operations like straight turning, facing, thread cutting, parting off etc., and operations using indexing mechanism on vertical milling. CNC centre: Fundamentals of CNC programming using G and M code; setting and operations of job using CNC lathe or milling, tool reference, work reference, tool offset, tool radius compensation to fabricate the component with a designed profile on Al/Al-alloy plate. 3D printing (Fused Filament Fabrication): (2 weeks) Create the model, select appropriate slicing and path for fabrication of a 3D job by layer deposition (additive manufacturing approach) using polymeric material. Demonstration on pattern fabrication using 3D printing.
Learning Outcome	· This course would enable the students to develop the concept of design, fabrication (subtractive and additive) for various engineering applications. Fabrication of components and assemble them. · The practical skill and hands on experience for various fabrication methods from bulk, sheet metal using conventional as well as CNC machines.
Assessment Method	Fabrication of components in each of the shops required for assembly of the given part; submission of reports for each shop, and quiz assessment.

Text and Reference books:

Hajra Choudhury, HazraChoudhary and Nirjhar Roy, 2007, Elements of Workshop Technology, vol. I,Mediapromoters and Publishers Pvt. Ltd.
W A J Chapman, Workshop Technology, 1998, Part -1, 1st South Asian Edition, Viva Book Pvt Ltd.
N. Rao, 2009, Manufacturing Technology, Vol.1, 3rd Ed., Tata McGraw Hill Publishing Company.
Adithan, B.S. Pabla, 2012, CNC machines, New Age International Publishers

1.5

ME1202/ME1102

Course Number	ME1102/ME1202
Course Number	Engineering Mechanics
L-T-P-C	3-1-0-4
Pre-requisites	Nil
Semester	Spring
Learning Mode	Lectures
Learning Objectives	Complies with PLOs 1, 4 · The objective of this first course in mechanics is to enable engineering students to analyze basic mechanics problems and apply vector-based approach to solve them.
Course Outline	1. Rigid body statics: Equivalent force system. Equations of equilibrium, Free body diagram, Reaction, Static indeterminacy. 2. Structures: 2D truss, Method of joints, Method of section. Beam, Frame, types of loading and supports, axial force, Bending moment, Shear force and Torque Diagrams for a member. 3. Friction: Dry friction (static and kinetic), wedge friction, disk friction (thrust bearing), belt friction, square threaded screw, journal bearings, Wheel friction, Rolling resistance. 4. Centroid and Moment of Inertia 5. Introduction to stress and strain: Definition of Stress, Normal and shear Stress. Relation between stress and strain, Cauchy formula. Stress in an axially loaded member and stress due to torsion in axisymmetric section
Learning Outcomes:	Following learning outcomes are expected after going through this course. · Learn and apply general mathematical and computer skills to solve basic mechanics problems. · Apply the vector-based approach to solve mechanics problems.
Assessment Method	Mid semester examination, End semester examination, Class test/Quiz, Tutorials

Reference Books

Shames, Engineering Mechanics: Statics and dynamics, 4th Ed, PHI, 2002.
P. Beer and E. R. Johnston, Vector Mechanics for Engineers, Vol I - Statics, 3rd Ed, Tata McGraw Hill, 2000.
L. Meriam and L. G. Kraige, Engineering Mechanics, Vol I - Statics, 5th Ed, John Wiley, 2002.
P. Popov, Engineering Mechanics of Solids, 2nd Ed, PHI, 1998.
P. Beer and E. R. Johnston, J.T. Dewolf, and D.F. Mazurek, Mechanics of Materials, 6th Ed, McGraw Hill Education (India) Pvt. Ltd., 2012.

IK1201

Indian Knowledge System (IKS)

TOTAL

22.5

Measurements and Instrumentation

Sl. No.

Subject Code

SEMESTER III

EC2101

Analog Circuits

Course Number	EC2101
Course Credit	3-0-2-4
Course Title	Analog Circuits
Learning Mode	Lectures and Labs
Learning Objectives	Complies with Program Goal 1 and 2
Course Description	The course deals with various analog sub circuits including analog circuits such as amplifiers, differential amplifiers, filters and oscillators. It also focuses on design and implementation of various analog circuits like amplifiers - single transistor amplifiers, cascode amplifiers, differential amplifiers, filters and oscillators.
Course Outline	CMOS realizations: current source, sink and mirrors, differential amplifiers, multistage amplifiers; Differential amplifiers: DC and small signal analysis, CMRR, current mirrors, active load and cascode configurations; Frequency response of amplifiers: high frequency device models, frequency responses of various amplifiers, GBW, methods of short circuit and open circuit time constants, dominant pole approximation; Analog subsystems: analog switches, voltage comparator, voltage regulator, switching regulator, bandgap reference voltage source, analog multiplier, Filter approximations: Butterworth, Chebyshev, first order and second order passive/active filter realizations of LPF, HPF, BPF. Signal generation and waveform shaping: Schmitt trigger, relaxation oscillators, sinusoidal oscillators – RC, LC, and crystal oscillator; Feedback amplifiers: basic feedback topologies and their properties, analysis of practical feedback amplifiers, stability; Power amplifiers: efficiency of class A, B, AB, C, D stages, output stages, short circuit protection, power transistors and thermal design considerations; Case study: 741 op-amp - DC and small signal analysis, frequency response, frequency compensation, GBW, phase margin, slew rate, offsets; Laboratory Experiments on advanced applications of BJTs- and FETs-based circuits, Op-amps and other integrated circuits, Multistage amplifiers, Automatic gain controlled amplifiers, programmable gain amplifiers, Frequency response of amplifiers; waveform generators, Active filters, Feedback circuits and analysis, Current mirroring, 555 timer-based circuit design.
Learning Outcomes	Complies with PLO 1a, 2a, 2b
Assessment Method	Quiz, Experiments and Exams
Suggested Reading	Textbooks: A. S. Sedra and K. C. Smith, Microelectronics Circuits, 5th Edition, 2005, Oxford University Press. P. Gray, P. Hurst, S. Lewis and R. Meyer, Analysis & Design of Analog Integrated Circuits, 4th Edition, 2001, Wiley. B. Razavi, Fundamental of Microelectronics, 1st Edition, 2009, Wiley. A. Malvino and D. Bates, Electronic Principles, 7th Edition, 2017, McGraw-Hill. R. A. Gayakwad, Op-Amps and Linear Integrated Circuit, 4th Edition, 2002, Prentice Hall. Reference Books: B. Carter and R. Mancini, Op Amaps for Everyone, 3rd Edition, 2009, Texas Instruments. D. Johns, T. C. Carusone and K. Martin, Analog Integrated Circuit Design, 2nd Edition, 2011, Wiley. R. A. Gayakwad, Op-Amps and Linear Integrated Circuit, 4th Edition, 2002, Prentice Hall. P. E. Allen and D. R. Holberg, CMOS Analog Circuit Design, 2nd Edition, 1997, Oxford University Press.

EC2102

Signals and Systems

Course Number	EC2102
Course Credit	3-1-0-4
Course Title	Signals and Systems
Learning Mode	Lectures and Tutorials
Learning Objectives	Complies with Program Goal 1 and 2
Course Description	The course deals with fundamental concepts of signals and systems including its application, analysis of impulse response of systems and frequency response using transforms such as CTFT, Laplace, DTFT, ZT, DFT.
Course Outline	Signals: classification of signals; signal operations: scaling, shifting and inversion; signal properties: symmetry, periodicity and absolute integrability; elementary signals. Systems: classification of systems; system properties: linearity, time/shift-invariance, causality, stability; continuous-time linear time invariant (LTI) and discrete-time linear shift invariant (LSI) systems: impulse response and step response; Response to an arbitrary input: convolution; system representation using differential and difference equations; Eigenfunctions of LTI/ LSI systems, frequency response and its relation to the impulse response. Signal representation: signal space and orthogonal bases; Fourier series representation of continuous-time and discrete-time signals; continuous-time Fourier transform and its properties; Parseval's relation, time-bandwidth product; discrete-time Fourier transform and its properties; relations among various Fourier representations. Sampling: sampling theorem; aliasing; signal reconstruction: ideal interpolator, zero-order hold, first-order hold; discrete Fourier transform and its properties. Laplace transform and Z-transform: definition, region of convergence, properties; transform-domain analysis of LTI/LSI systems, system function: poles and zeros; stability.
Learning Outcomes	Complies with PLO 1b, 2a and 2b
Assessment Method	Quiz, Assignments and Exams
Suggested Reading	Textbooks: 1. A.V. Oppenheim, A.S. Willsky and H.S. Nawab, Signals and Systems, 2nd Edition, 2006, Prentice Hall.. 2. S. Haykin and B. V. Veen, Signals and Systems, 2nd Edition, 1998, John Wiley and Sons. Reference Books: B. P. Lathi, Signal Processing and Linear Systems, 1998, Oxford University Press.

EC2103

Semiconductor Devices

Course Number	EC2103
Course Credit	3-0-2-4
Course Title	Semiconductor Devices
Learning Mode	Lectures and Labs
Learning Objectives	Complies with Program Goal 1, 2 and 4
Course Description	The course deals with major properties of semiconductor materials, explains energy band diagrams and connections with the device structures and properties. It also focuses on basic equations to analyze semiconductor devices and design semiconductor devices and estimate device characteristics.
Course Outline	Energy bands; semiconductors; charge carriers: electrons and holes, effective mass, doping. Carrier concentration: Fermi level, temperature dependence of carrier concentration. Drift and diffusion of carriers: excess carriers; recombination and lifetime P-N Junction: depletion region, forward and reverse- bias, depletion and diffusion capacitances, switching characteristics; breakdown mechanisms; SPICE model. Metal-semiconductor junctions: rectifying and Ohmic contacts. BJT: carrier distribution; current gain, transit time, secondary effects MOSFET: MOS capacitor; C-V and I-V characteristics; threshold voltage; Short-channel effects. Body effect in MOSFET, Other Semiconductor Devices: MESFET: Working mechanism, I-V characteristics, HEMT: Working mechanism, I-V characteristics, Tunnel Diode: Working mechanism, I-V characteristics, Introduction to Power Semiconductor Devices (diode, IGBT and MOSFETs) Laboratory: Characterization and parameter extraction of various diodes Measurement and h parameter extraction of BJTs CV characteristics of MOS Capacitor Measurement and parameter extraction of MOSFETs TCAD Simulation of semiconductor devices
Learning Outcomes	Complies with PLO 1b, 2a, 2b and 4a
Assessment Method	Quiz, Assignments and Exams
Suggested Reading	Textbooks: S. M. Sze and M. K. Lee, Semiconductor Devices: Physics and Technology, 3rd Edition, 2013, Wiley. A. K. Dutta, Semiconductor Devices and Circuits, Illustrated Edition, 2008, Oxford University Press.   Reference Books: J. Millman, C. C. Halkias and S. Jit, Electronics Devices and Circuits, 4th Edition, 2015, McGraw-Hill. A. S. Sedra and K. C. Smith, Microelectronics Circuits, 5th Edition, 2005, Oxford University Press. B. Streetman and S. Banerjee, Solid State Electronic Devices, 7th Edition, 2015, Pearson Education Limited. D. A. Neamen, Semiconductor Physics and Devices, 4th Edition, 2011, McGraw-Hill.

EE2101

Course Number	EE2101
Course Credit	3-0-0-3
Course Title	Measurements and Instrumentation
Learning Mode	Lectures and Experiments
Learning Objectives	Complies with Program goals 1, 2 and 3
Course Description	The course is designed to meet the requirements of B. Tech. The course aims at giving detail of construction, operation and modelling of transformer and DC machines. Transformer and DC machines will be discussed.
Course Outline	Definition of instrumentation. Static characteristics of measuring devices. Error analysis, standards and calibration. Dynamic characteristics of instrumentation systems. Electromechanical indicating instruments: ac/dc current and voltage meters, ohmmeter; loading effect. Measurement of power and energy; Instrument transformers. Measurement of resistance, inductance, capacitance. ac/dc bridges. Measurement of non-electrical quantities: transducers classification; measurement of displacement, strain, pressure, flow, temperature, force, level and humidity. Signal conditioning; Instrumentation amplifier, Isolation amplifier, and other special purpose amplifiers. EMI and EMC, shielding, earthing and grounding. Signal recovery, data transmission and telemetry. Data acquisition and conversion. Modern electronic test equipment: oscilloscope, DMM, frequency counter, wave/ network/ harmonic distortion/ spectrum analyzers, logic probe and logic analyzer. Data acquisition system; PC based instrumentation. Programmable logic controller: ladder diagram. Computer controlled test systems, serial and parallel interfaces, Field buses. Smart sensors (Voltage, Current and Temperature sensors). Laboratory: Experiments on displacement, temperature, strain, flow, acceleration measurements, AC bridges, PLC, instrumentation amplifier, encoder, Measurement of capacitance, inductance and resistance.
Learning Outcomes	Complies with PLO 1a, 2a and 3a
Assessment Method	Quiz, Assignments and Exams
Suggested Reading	Texts/References 1. A. D. Helfrick and W. D. Cooper, Modern Electronic Instrumentation and Measuring Techniques, Pearson Education, 1996. 2. M. M. S. Anand, Electronic Instruments and Instrumentation Technology, PHI, 2006. 3. E. O. Deobelin, Measurement Systems - Application and Design, Tata McGraw-Hill, 1990. 4. B. E. Jones, Instrumentation, measurement, and Feedback, Tata McGraw-Hill, 2000. 5. R. P. Areny and T. G. Webster, Sensors and Signal Conditioning, John Wiley, 1991. 6. B. M. Oliver and J. M. Cage, Electronic Measurements and Instrumentation, McGraw-Hill, 1975. 7. C. F. Coombs, Electronic Instruments Handbook, McGraw-Hill, 1995. 8. R. A. Witte, Electronic Test Instruments, Pearson Education, 1995. 9. B. G. Liptak, Instrument Engineers' Handbook: Process Measurement and Analysis, Chilton Book, 1995.

EE2102

Network Analysis and Synthesis

Course Number	EE2102
Course Credit	3-0-0-3
Course Title	Network Analysis and Synthesis
Learning Mode	Lectures
Learning Objectives	Complies with Program goals 1, 2 and 3
Course Description	The course is designed to meet the requirements of B. Tech. The course aims at giving detail of network theorems, graph theory and analysing and designing electrical circuits.
Course Outline	Overview of network analysis techniques, network theorems, transient and steady state sinusoidal response. Graph theory: basic definitions of loop (or tie set), cut-set, mesh matrices and their relationships, applications of graph theory in solving network equations. Two-port and N-Port networks, Z, Y, h, g and transmission parameters, combination of two ports, Analysis of common two port networks, pie and t-networks. Network functions, parts of network functions, obtaining a network function from a given part. Network transmission criteria; delay and rise time. Elements of network synthesis techniques, Cauer and Foster forms, Butterworth and Chebyshev Approximation.
Learning Outcomes	Complies with PLO 1a, 2a and 3a
Assessment Method	Quiz, Assignments, and Exams
Suggested Reading	Texts/ References: 1. F. F. Kuo, Network Analysis and Synthesis, John. Wiley, 2006. 2. M. E. V. Valkenburg, Network Analysis 3rd Edition 3. R. J. Trudeau,  Introduction to graph theory. Courier Corporation, 2013.

HS21XX

HSS Elective - I

TOTAL

Computer Organization and Architecture

Sl. No.

Subject Code

SEMESTER IV

EC2201

Digital Electronics

Course Number	EC2201
Course Credit	3-0-2-4
Course Title	Digital Electronics
Learning Mode	Lectures and Labs
Learning Objectives	Complies with Program Goal 1, 2 and 4
Course Description	The course deals with the fundamental concepts used in digital electronics, analyzing and designing of various combinational and sequential circuits, identifying the basic requirements for a design application with focus on a cost effective solution, understanding the digital signals, and developing skills for designing combinational and sequential logic circuits and their practical implementation on breadboard.
Course Outline	Introduction to digital circuits: Logic families (RTL, TTL, ECL and MOS), Integer and floating point representation. Logic gates representation and combinational circuit realization, Boolean functions and simplification. Karnaugh Maps and logic optimization. Macro level combinational circuits and their realization: Multiplexers, Code converters, Decoders, parity Generators, 7-segment display decoder; Digital Arithmetic Circuits: Adders, Subtractor, BCD adders. Introduction to sequential elements (Latches and Flip-flops) and sequential circuit design, State machines. Finite state machines and examples: shift registers and counters. Introduction to memory circuits: RAM, ROM, EEPROM Introduction to programmable and reconfigurable devices. Digital logic realization using programmable Logic devices. Laboratory: To set up circuits for Bipolar (RTL, DTL, TTL) and Unipolar (MOS, CMOS) Logic families, Logic Gate verification Introduction to Combinational circuits, Realization of Decoder, Design and realization of a Multiplexer and Magnitude Comparator Verification of basic Flip Flops using 74XXICS, Implementation of basic Latches, Asynchronous Counters, Synchronous Counters, Pattern Generation and Detection
Learning Outcomes	Complies with PLO 1b, 2a, 2b and 4a
Assessment Method	Quiz, Assignments and Exams
Suggested Reading	Textbooks: D. P. Leach, A. P. Malvino and G. Saha, Digital Principal and Applications, 2nd Edition, 2006, McGraw-Hill. J. F. Wakerly, Digital Design Principles and Practices, 4th Edition, 2006, Pearson Education. M. Mano and M. D. Cilietti, Digital Design, 4th Edition, 2008, Pearson Education. C. H. Roth, Fundamentals of Logic Design, 5th Edition, 2004, Cengage Learning. N. Wirth, Digital Circuit Design: An Introductory Textbook, 1st Edition, 1995, Springer. D. P. Leach, A. P. Malvino and G. Saha, Digital Principal and Applications, 2nd Edition, 2006, McGraw-Hill.   Reference Books: D. J. Corner, Digital Logic and State Machine Design, 3rd Edition, 2012, Oxford University Press. H. Taub and D. Schilling, Digital Integrated Electronics, Illustrated Edition, 1977, McGraw-Hill.

EC2202

Microprocessor

Course Number	EC2202
Course Credit	2-0-2-3
Course Title	Microprocessor
Learning Mode	Lectures and Labs
Learning Objectives	Complies with Program Goal 1, 2 and 4
Course Description	The course deals with architecture & organization of 8085 & 8086 Microprocessor, classification of the instruction set of 8086 microprocessor and distinguishing the use of different instructions and applying it in assembly language programming. It also focuses on realization of the Interfacing of memory & various I/O devices with 8086 Microprocessor, familiarization of the architecture and operation of Programmable Interface Devices and realization of the programming & interfacing of it with 8086 Microprocessor. The course covers hands-on experiments on emulators and hardware kits and gives exposure to advanced microprocessor architectures.
Course Outline	Introduction to Microprocessor and Microcomputer, Introduction to 8-bit microprocessor: Internal architecture of Intel 8085 microprocessor Introduction to 8086: Block diagram, Registers, Internal Bus Organization, Functional details of pins, Control signals, External Address / Data bus multiplexing, Demultiplexing. 8086 Architecture: Addressing Modes, Instruction Set Architecture, Instruction Coding Format, Instruction Description and Assembler directives, Standard program Structure, Assembly Language Programming, Strings, Procedures, Macros,. Pinouts: minimum mode and maximum mode configurations, Bus structure, bus buffering, latching, system bus timing with diagram, Interrupt Controller. Timing, I/ O mapped I/ O, and memory mapped I/ O techniques. I/O and memory interfacing using 8086: Memory interfacing and I/O interfacing with 8086, Parallel communication interface (8255), Timer (8253 / 8254) , Keyboard / Display controller (8279), Priority Interrupt controller (8259) , DMA controller (8257). Coprocessor (8087) architecture and interfacing with 8086 Microprocessor Introduction to advanced Microprocessors (X86). Laboratory: Hands-on laboratory experiment based on assembly language to program microprocessor using emulator/hardware kit to implement various algorithms and applications along with peripherals.
Learning Outcomes	Complies with PLO 1b, 2a, 2b and 4a
Assessment Method	Quiz, Assignments and Exams
Suggested Reading	Textbooks: R. S. Gaonkar, Microprocessor – Architecture, Programming and Applications with the 8085, 6th Edition, 2013, Penram International Publisher. D. V. Hall, Microprocessors and Interfacing, 2nd Edition, 2012, McGraw-Hill. Reference Books: B. B. Brey, The INTEL Microprocessors – 8086 / 8088, 80186 / 80188, 80286, 80386, 80486 Pentium and Pentium pro processor, Pentium II, Pentium III and Pentium IV - Architecture, Programming and Interfacing, 8th Edition, 2012, Pearson Education.

EC2203

Course Number	EC2203
Course Credit	3-0-0-3
Course Title	Computer Organization and Architecture
Learning Mode	Lectures
Learning Objectives	Complies with Program Goal 1, 2 and 4
Course Description	This course introduces the basic organization and architecture of computing systems, various CPU architectures and peripherals, and designing of both programmable and reconfigurable architecture. It also covers the state of art embedded processor architecture and their applications.
Course Outline	Introduction: Evolution of computing systems and applications, Introduction to computing system, top level view of computer function and interconnection, computing performances and measures, Register transfer and micro-operations, Basic Computer Arithmetic architectures. Basic CPU architecture: Data Path and Control Path, hardwired and microprogrammed control architecture, Timing of control units, Basic CPU Design using HDL. General purpose CPU organization and architecture: CISC and RISC features, Processor structure and function, Instruction Set Architecture, Addressing Mode, RTL representation of Instructions, Assembly Language and Programming, Introduction to Assembler. Memory Organization and Architecture: Types of memory and interfacing, Virtual memory, paging, Cache Memory. I/O and peripheral organization and architecture: programmable I/O architecture, Programmable Timers, Interrupts and exception handling, Priority Interrupt Controller, DMA Controller Introduction to high performance computing architecture: pipeline architecture, Pipeline hazard, Hazard control unit. Embedded and reconfigurable computing architecture: Embedded CPU organization and architecture, RISC ISA, Embedded CPU programming, Assembly Language, Embedded Bus protocol and architecture, FPGA Architecture, FPGA programming, Implementation and prototype methods: Case studies, IP and its reuse, Introduction to Operating System: Embedded Operating System and RTOS.
Learning Outcomes	Complies with PLO 1b, 2a, 2b and 4a
Assessment Method	Quiz, Assignments and Exams
Suggested Reading	Textbooks: M Morris Mano, Computer System Architecture, 3rd Edition, 2017, Prentice Hall. J. Hayes, Computer Architecture and Organization, 3rd Edition, 2017, McGraw Hill. W. Stallings, Computer Architecture and Organization, 3rd Edition, 2013, Pearson Education. Reference Books: D. V. Hall and S.S.S.P. Rao, Microprocessors and Interfacing, 3rd Edition, 2017, McGraw Hill. R. Kamal, Embedded Systems: Architecture, Programming and Design, 3rd Edition, 2017, McGraw Hill.. M. A. Mazidi, R. D. Mckinlay and D. Causey, PIC Microcontroller and Embedded Systems, 1st Edition, 2008, Pearson Education. S. Palnitkar, VerilogHDL, 2nd Edition, 2003, Pearson Education. F. Bruno, FPGA Programming for Beginners, 1st Edition, 2021, Packt Publishing. P. P. Chu, FPGA Prototyping by VerilogHDL examples, 1st Edition, 2008, Wiley.

EC2204

Internet of Things (IoT)

Course Number	EC2204
Course Credit	3-0-0-3
Course Title	Internet of Things (IoT)
Learning Mode	Lectures
Learning Objectives	Complies with Program Goal 1, 2 and 4
Course Description	The course deals with fundamental building blocks of the Internet of Things components and its underlying concepts. It also covers the design aspect of various IoT applications.
Course Outline	Motivation, Applications and Objectives of Internet of Things (IoT), Cyber-Physical Systems and Wireless Sensor Networks. Sensors and Actuators, Sensor Types, Sensor Characteristics, Actuator Types, Controlling IoT Devices. Radio Frequency Identification (RFID) Technology, Connectivity Protocols in IoT: Bluetooth Low Energy, ZigBee, and LoRa. Data messaging Protocols in IoT: Message Queue Telemetry Transport (MQTT), Hyper-Text Transport Protocol (HTTP), Constrained Application Protocol (CoAP). Localization in IoT: Localization using Received Signal Strength (RSS), Time and Time difference of arrival (ToA/TdoA) and Angle based Localization. Sensor Fusion, Fog Computing and Edge Computing, Task Offloading. Security in IoT Networks. Use Cases of IoT for Smart Environments: Healthcare, Agriculture, and Smart City
Learning Outcomes	Complies with PLO 1b, 2a and 2b
Assessment Method	Quiz, Assignments and Exams
Suggested Reading	Textbooks:  Raj, P., and Raman A.C., The Internet of Things: Enabling Technologies, Platforms, and Use Cases, 1st Edition, 2017, Auerbach Publications. Rayes, A., and Salam, S., Internet of Things from Hype to Reality: The Road to Digitization, 2nd Edition, 2018, Springer. Kumar S., Fundamentals of Internet of Things, 1st Edition, 2021, CRC Press. Reference Books: Song H. et al., Cyber-Physical Systems: Foundations, Principles and Applications, 1st Edition, 2016, Academic Press Inc. Yan, L., et al., The Internet of Things: From RFID to the Next-Generation Pervasive Networked Systems, 1st Edition, 2008, CRC Press. Waher, P. , Learning Internet of Things, 2015, Packt Publishing Ltd.

EE2201

Control Systems

Course Number	EE2201
Course Credit	3-0-2-4
Course Title	Control Systems
Learning Mode	Lectures and Experiments
Learning Objectives	Complies with Program goals 1, 2 and 3
Course Description	This course gives the idea of classical methods of Control Systems to be useful in Engineering applications. The prerequisite for this course is signal and systems.
Course Outline	Basic concepts: Notion of feedback, open- and closed-loop systems; Modeling and representations of control systems: Ordinary differential equations, Transfer functions, Block diagrams, Signal flow graphs, State-space representations; Performance and stability: Time-domain analysis, Second-order systems, Characteristic-equation and roots, Routh-Hurwitz criteria; Frequency-domain techniques: Root-locus methods, Frequency responses, Bode-plots, Gain-margin and phase-margin, Nyquist plots; Compensator design: Proportional, PI and PID controllers, Lead-lag compensators; State-space concepts: Controllability, Observability, pole placement result, Minimal representations; Introduction to nonlinear control. Laboratory: To Study the DC Modular Servo System and to obtain the characteristics of the constituent components. Also, to set up a closed loop position control system and study the system performance; Controller design for position and velocity control of servo motors; Modeling and analysis of Magnetic Levitation System; Design a PD/PID controller for the Magnetic Levitation System; Determine the transfer function of black box from the Bode plot Level control of three/ four coupled tanks; Study and design of controller for Inverted Pendulum System; Introduction to Matlab and analysis of basic control theory in Matlab; Linearisation and Simulation of Nonlinear Ship Roll Dynamics Twin rotor control using PI/PID controller
Learning Outcomes	Complies with PLO 1a, 2a and 3a
Assessment Method	Quiz, Assignments and Exams
Suggested Reading	Text/References 1. N. S. Nise, Control Systems Engineering, 4th edition, New York, John Wiley, 2003. (Indian edition) 2. G. Franklin, J.D. Powell and A. Emami-Naeini, Feedback Control of Dynamic Systems, Addison Wesley, 1986. 3. I. J. Nagrath and M. Gopal, Control System Engineering, 2nd Edn.Wiley Eastern, New Delhi, 1982. 4. C. L. Phillips and R.D. Harbour, Feedback Control Systems, Prentice Hall, 1985 5. B.C. Kuo, Automatic Control Systems, 4th Edn. Prentice Hall of India, New Delhi, 1985. 6. K. Ogata,  Modern control systems. Prentice Hall, 1997.

XX22PQ

IDE I

TOTAL

Microcontroller and Embedded Systems

Sl. No.

Subject Code

SEMESTER V

EC3101

Course Number	EC3101
Course Credit	3-0-2-4
Course Title	Microcontroller and Embedded Systems
Learning Mode	Lectures and Labs
Learning Objectives	Complies with Program Goal 1, 2 and 4
Course Description	The course deals with the fundamentals as well as advanced concepts in microcontroller and embedded systems. This also focuses on writing assembly and high level programs on real-time microcontrollers, developing the optimized embedded systems, and applying the ideas in different applications. Further it covers hands on experiments on commercially available embedded kits and components.
Course Outline	Introduction to microcontroller and embedded system, Introduction to CISC and RISC microcontroller, Registers, Pin diagram, I/O ports functions, 16-bits microcontroller architecture, Addressing modes, Internal memory organization, External memory (ROM & RAM) interfacing. Instruction set Architecture Data Transfer instructions, Arithmetic instructions, Logical instructions, Branch instructions, Bit manipulation instructions. Peripherals: Timers and Counters, PWM, Interrupts, communication protocols: UART, SPI. Embedded System Interfacing: ADC, DAC, Sensors, Display, keyboard. Embedded system models and development cycle, Embedded system components, Embedded processor and memory architecture. Hierarchical state machine, Embedded OS and RTOS, Scheduling, Multi-tasking. Experiments on microcontrollers: Programming and interfacing. Lab: PIC Microcontroller-Based Experiments: Write and implement a program to read input through a momentary switch and toggle the ON/OFF of led blinking. Write and implement a program to realize a simple calculator. Write and implement a program to generate precise delay and pulse by using TIMER Write and implement a program to interface a seven segment display and scroll the roll number on single/multiple seven segment display. Write and implement a program to interface both keyboard and LCD display. Write and implement a program to interface a ADC peripheral and control LED brightness depending on ADC value. Write and implement a program to interface 16×2 LCD display and display the ADC value Write and implement a program to use microcontroller as function generator and interface DAC to display generated signals in DSO. Write and implement a program to generate PWM and controlling a lightweight DC Motor Write and implement a program to control speed and direction of the stepper Motor and use it as Clock. Arduino/Raspberry-Pi/Galileo-based Experiments: Write and implement a program to interface I2C IMU sensor and display over LCD display. Write and implement a program to interface blue tooth and Wi-Fi Devices
Learning Outcomes	Complies with PLO 1b, 2a and 2b
Assessment Method	Quiz, Assignments and Exams
Suggested Reading	Textbooks: M. A. Mazidi, R. D. McKinlay, D. Causey, PIC Microcontroller and Embedded Systems, 1st Edition, 2008, Pearson Education. P. Marvedel, Embedded System Design, 4th Edition, 2021, Springer.   Reference Books: R. Kamal, Embedded Systems: Architecture, Programming and Design, 3rd Edition, 2017, McGraw Hill.

EC3102

VLSI Design

Course Number	EC3102
Course Credit	3-0-2-4
Course Title	VLSI Design
Learning Mode	Lectures and Labs
Learning Objectives	Complies with Program Goal 1, 2 and 4
Course Description	The course deals with the design and implementation methods of VLSI Chip starting from full custom circuit to semi-custom architecture, FPGA and ASIC Architectures, and basic VLSI testing and validation methodologies. The course also covers various EDA tools and soft skills for designing VLSI Chip, full custom circuit simulation and verification, design and simulation of digital VLSI Systems using HD, synthesis and physical design along with analysis and FPGA implementation and prototype of digital VLSI systems.
Course Outline	Introduction to CMOS Technology, VLSI Design Flow, ASIC and FPGA Design Approaches. CMOS Process Flow, Design Rules, Layout and Stick Diagram. Design matrices: Area, Power and Performance Optimization, CMOS Inverter, Static and Dynamic of Inverter, Inverter Sizing. Speed and Power Dissipation: Static and Dynamic Power Consumption, Static CMOS Design, and different CMOS Logic Design approaches. Wire Delay model, Elmore Delay Model, CMOS Logic Sizing, Worst-case and Best-case Delays, Pass Transistors and Transmission Gates. Dynamic Logic, Domino Logic, Sequential Circuits, Latches and Flip-flops Arithmetic and Logic Circuits : Pipelining and Adders, Carry Save Adder & Multipliers. Memories: Working and Design aspects of DRAM, SRAM, and Flash memories, Design and Implementation of Digital Subsystems: Case Studies such as Neuromorphic Computing, In-memory Computing, and AI/Cryptographic Accelerators Laboratory: Introduction to the Cadence VLSI EDA software, develop schematics for NMOS/PMOS as pass gates, INV, NAND, and NOR as logic gates. Design and analyse the inverter and the universal gates (NAND and NOR). Design and analyse the sequential circuits, such as D-latch/flip-flop using transmission gates and other building blocks (library cells developed in this lab) and their behavior characterization Design of simplified state machines that generates a sequence of patterns
Learning Outcomes	Complies with PLO 1b, 2a, 2b and 4a
Assessment Method	Quiz, Assignments and Exams
Suggested Reading	Textbooks: W. Wolf, Modern VLSI Design - System on Chip design, 3rd Edition, 2004, Pearson Education. J. M. Rabaey, A. Chandrakasan and B. Nikolic, Digital Integrated Circuits- A Design Perspective, 2nd Edition, 2003, Prentice Hall of India. N. Weste and D. Harris, CMOS VLSI Design: A Circuits and Systems Perspective, 3rd Edition, 2007, Pearson Education India. Kang, Sung Mo, and Yusuf Leblebici. CMOS digital integrated circuits. New York, NY, USA: MacGraw-Hill, 2003. M. H. Rashid, Introduction to PSpice Using OrCAD for Circuits and Electronics, 3rd Edition, 2005, Prentice Hall. C. H. Roth Jr., Digital systems Design Using VHDL, 8th Edition, 2006, Thomson Learning Inc.   Reference Books: M. J. S. Smith, Application Specific Integrated Circuit, 1st Edition, Pearson India. R. J. Baker, CMOS Circuit Design, Layout and Simulation, 1st Edition, 2009, Wiley. S. M. Kang and Y. Leblevici, CMOS Digital Integrated Circuits Analysis and Design, 3rd Edition, 2003, McGraw Hill. J. P. Uyemura, Introduction to VLSI Circuits and Systems, 2002, John Wiley & Sons. C. H. Roth Jr., Fundamentals of Logic Design, 5th Edition, 2007, Thomson Learning Inc. J. M. Rabaey, A. Chandrakasan and B. Nikolic, Digital Integrated Circuits- A Design Perspective, 2nd Edition, 2003, Pearson Education. P. E. Allen and D. R. Holberg, CMOS Analog Circuit Design, 2nd Edition, 1997, Oxford University Press.

EC3103

Analog Communications

Course Number	EC3103
Course Credit	3-0-2-4
Course Title	Analog Communications
Learning Mode	Lectures and Labs
Learning Objectives	Complies with Program Goal 1, 2 and 4
Course Description	The course focuses on building blocks of communication systems, and different modulation formats; their usage along with their advantages and limitations. In particular, it covers design and performance analysis of analog communication systems, design of transmitter and receivers for different analog modulation formats from scratch using both discrete. component and software configurable system. The focus would be on understanding of baseband, passband modulation and demodulation techniques using experiments, advantages and disadvantages of various modulation and demodulation techniques and encoding and decoding using self-made hardware system and estimate their performance.
Course Outline	Review of Fourier Series and Transforms. Hilbert Transforms, Band pass signal and System Representation. Random Processes, Stationarity, Power Spectral Density, Gaussian Process, Noise. Amplitude Modulation, DSBSC, SSB, VSB: Signal Representation, Generation and Demodulation. Frequency Modulation: Signal Representation, Generation, and Demodulation. Mixing, Super-heterodyne Receiver, Phase Recovery with PLLs. Noise in AM Receivers using Coherent Detection, in AM Receivers using Envelope Detection, in FM Receivers. Fidelity of AM and FM Receivers. Sampling, Pulse-Amplitude Modulation. Quantization, Pulse-Code Modulation. Noise considerations in PCM, Time Division Multiplexing, Delta Modulation, DPCM and ADPCM. Inter symbol Interference Laboratory: Amplitude modulation and demodulation (AM with carrier & DSB-SC AM); Frequency modulation and demodulation (using VCO & PLL); automatic gain control (AGC); Pulse amplitude modulation (PAM): Natural Sampling and Flat Top Sampling; Pulse Code Modulation (PCM); Pulse Width Modulation and Demodulation; Pulse Position Modulation and Demodulation. Delta Modulation and DPCM Transmitter & Receiver.
Learning Outcomes	Complies with PLO 1b, 2a and 4a
Assessment Method	Quiz, Assignments and Exams
Suggested Reading	Textbooks: H. Taub and D. L. Schilling, Principles of Communication Systems, 2nd Edition, 1986, McGraw Hill. S. Haykin, Digital Communications, Student Edition, 2004, Wiley. B. P. Lathi, Modern Analog and Digital Communication Systems, 3rd Edition, 1998, Oxford University Press. H. Taub and D. L. Schilling, Principles of Communication Systems, 4th Edition, 2017, McGraw-Hill. W. Tomasi, Electronic Communications Systems - Fundamentals Through Advanced, 4th Edition, 2003, Pearson. S. Haykin and M. Moher, An Introduction to Analog and Digital Communication Systems, 2nd Edition, 2012, Wiley.   Reference Books: K. S. Sanmugan, Digital and Analog Communication Systems, Student Edition, 2006, John Wiley & Sons L. W. Couch, Digital and Analog Communication Systems, 8th Edition, 2012, Pearson

EC3104

Engineering Electromagnetics

Course Number	EC3104
Course Credit	3-0-0-3
Course Title	Engineering Electromagnetics
Learning Mode	Lectures
Learning Objectives	Complies with Program Goal 1, 2 and 4
Course Description	The course deals with frequency dependent circuit designs, and various aspects of wave propagation and mechanism. The focus would be on visualizing various field interactions and phenomena and hands-on with several electromagnetic simulators and components.
Course Outline	An overview of electrostatics, electromagnetic fields, and vector calculus. Time-varying EM fields: Maxwell’s equations, wave equation, and plane waves: Helmholtz wave equation, Solution to wave equations and plane waves, wave polarization, Poynting vector and power flow in EM fields. Wave Propagation: Wave propagations in unbounded & moving medium. boundary conditions, reflection, and refraction of plane waves. Transmission Lines: distributed parameter circuits, traveling and standing waves, impedance matching, Smith chart, stub matching. Introduction to antenna, Dipole antenna. Radio-wave propagation: ground-wave, sky-wave, and space-wave. Diversity techniques. Assignments on numerical methods using computational tools: FDTD, FEM.
Learning Outcomes	Complies with PLO 1b, 2a and 4a
Assessment Method	Quiz, Assignments and Exams
Suggested Reading	Textbooks: M. N. O. Sadiku, Elements of Electromagnetics, 3rd Edition, 2000, Oxford University Press. R. F. Harrington, Time-Harmonic Electromagnetic Fields, 2nd Edition, 2001, Wiley-IEEE Press. J. Griffiths, Introduction to Electrodynamics, 3rd Edition, 1999, Pearson Education. E. C. Jordan and K. G. Balmain, Electromagnetic Waves and Radiating Systems, 2nd Edition, 2016, Pearson   Reference Books: K. E. Lonngren and S. V. Savov, Fundamentals Electromagnetics with MATLAB, 1st Edition, 2005, Pearson Education. D. K. Cheng, Field and Wave Electromagnetics, 2nd Edition, 2001, Pearson Education. N. Ida, Engineering Electromagnetics, 1st Edition, 2000, Springer. W. H. Hayt Jr, J. A. Buck and M. J. Akhtar, Engineering Electromagnetics, 9th Edition, 2020, McGraw Hill.

EC3105

Random Signals & Stochastic Processes

Course Number	EC3105
Course Credit	3-0-0-3
Course Title	Random Signals & Stochastic Processes
Learning Mode	Lectures
Learning Objectives	Complies with Program Goal 1, 2 and 4
Course Description	The course deals with frequently encountered random variables, mathematical tools to analyze random process and development of analytical skills to model systems exhibiting random behavior
Course Outline	Random process: Concept of random process, ensemble, mathematical tools for studying random process, correlation function, stationarity, ergodicity, a few known stochastic processes: random walk, Poisson process, Gaussian random process, Markov chains, Brownian motion etc., pseudorandom process, nonlinear transformation of random process. Random process in frequency domain: Peridogram and power spectral density, Weiner-Khintchine-Einstein Theorem, concept of bandwidth, spectral estimation. Linear system: Discrete time and continuous time LTI system, concept of convolution, system described in frequency domain, state space description of the system. Linear systems with random inputs: Linear system fundamentals, response of a linear system, convolution, mean, autocorrelation and cross correlation function in LTI system, power spectral density in LTI, cross power spectral density in LTI. Processing of random signals: Noise in systems, noise bandwidth, SNR, bandlimited random process, noise reduction, matched filter, Wiener filter, Kalman filter, extended Kalman filter. Engineering examples.
Learning Outcomes	Complies with PLO 1b, 2a and 2b
Assessment Method	Quiz, Assignments and Exams
Suggested Reading	Text/Reference Books: 1. Miller, Scott, and Donald Childers, “Probability and random processes: with applications to signal processing and communications”, Academic Press, 2012. 2. Wim C. van Etten, “Introduction to random signals and Noise”, Chichester, England: Wiley, 2005. 3. Peyton Z. Peebles, “Probability, random variables, and random signal principles”. McGraw Hill Book Company, 1987. 4. Geoffrey R. Grimmett, and David Stirzaker, “Probability and random processes”, Oxford university press, 2001. 5. Alberto Leon-Garcia, “Probability, statistics, and random processes for Electrical engineering”, Upper Saddle River, NJ: Pearson/Prentice Hall, 2008. 6. Grewal, Mohinder, and Angus P. Andrews, “Kalman filtering: theory and practice with MATLAB”, John Wiley & Sons, 2014.

XX31PQ

IDE - II

TOTAL

Digital Signal Processing

Sl. No.

Subject Code

SEMESTER VI

EC3201

Digital Communications

Course Number	EC3201
Course Credit	3-0-2-4
Course Title	Digital Communications
Learning Mode	Lectures and Labs
Learning Objectives	Complies with Program Goal 1, 2 and 4
Course Description	The course deals with the fundamentals as well as advanced concepts in digital communications such as modulation, demodulation, detection, channel estimation and equalization. It also covers the implementation of various digital communications techniques. comparison of different techniques and apply for different applications, and design of transmitter and receivers for different digital modulation formats from scratch using both discrete component and software configurable system
Course Outline	Overview of Random Variables, Random Processes and Linear Algebra: Signal Space Concepts, Orthogonal Representation of Signals, Gram-Schmidt Procedure and Karhunen-Loeve (KL) Expansion. Communication Channel Models, Bandpass & Lowpass Signals Digital Modulation Schemes and their Performance Analysis: Memoryless and with Memory Modulation Methods, Pulse Amplitude Modulation (PAM), Phase Modulation, Quadrature Amplitude Modulation (QAM), Continuous-Phase Frequency-Shift Keying (CPFSK), and Continuous-Phase Modulation (CPM) Optimum Receiver in Presence of Additive White Gaussian Noise: Maximum a Posteriori Probability (MAP) and Maximum Likelihood (ML) Receivers, Coherent versus Non-coherent Detection, Binary Signal Detection in AWGN, M-ary Signal Detection in AWGN. Probability of Error Analysis Introduction to Receiver Synchronization Laboratory: Pseudo-random (PN) sequence generation; Amplitude shift keying (ASK) Generation and Detection; Frequency shift keying (FSK) Generation and Detection; Binary phase shift keying (BPSK) Generation and Detection; binary frequency shift keying (BFSK) Generation and Detection; Quadrature phase shift keying (QPSK) Generation and Detection; Orthogonal frequency division multiple access; Code division multiple access (CDMA) and direct sequence spread spectrum (DSSS) system.
Learning Outcomes	Complies with PLO 1b, 2a and 4a
Assessment Method	Quiz, Assignments and Exams
Suggested Reading	Textbooks: J. G. Proakis, M. Salehi, Digital Communications, 5th Edition, 2008, McGraw Hill. R. G. Gallager, Principles of Digital Communication, 2009, Cambridge University Press. S. S. Haykin, Digital Communications, 2001, Wiley-India. B.P. Lathi, Zhi Ding, and Hari Mohan Gupta, Modern Digital And Analog Communication Systems, 4th Edition, 2017, Oxford University Press. W. Tomasi, Electronic Communications Systems - Fundamentals through advanced, 4/e, Pearson, 2003. S.S. Haykin, An Introduction to Analog and Digital Communication Systems, Wiley Eastern 1989. Reference Books: P. B Crilly, A. B. Carlson, Communication Systems, 5th Edition, 2011, Tata McGraw-Hill Education. U. Madhow, Fundamentals of Digital Communication, 2008, Cambridge University Press. J.M Wozencraft, I.M. Jacobs, Principles of Communication Engineering, 1965, JohnWiley. A. Glover, P. M. Grant, Digital Communications, 5th Impression, 2012, Pearson. P. Z. Peeples, Digital Communication Systems, 1987, Prentice Hall International.

EC3202

Course Number	EC3202
Course Credit	3-0-2-4
Course Title	Digital Signal Processing
Learning Mode	Lectures and Labs
Learning Objectives	Complies with Program Goal 1, 2 and 4
Course Description	The course deals with the illustration of digital signals, systems and their significance. understanding of the analytical tools such as Fourier transforms, Discrete Fourier transforms, Fast Fourier Transforms and Z-Transforms required for digital signal processing. It also covers the design and development of the basic digital system, familiarization with various structures of IIR and FIR systems, design and realization of various digital filters for digital signal processing, interpretation of the finite word length effects on functioning of digital filters. Experimental concepts of DSP and its applications using MATLAB Software is also included.
Course Outline	Review of discrete time signals, systems and transforms and sampling theorems (bandlimited and bandpass signals) Discrete Fourier Transform (DFT): Computational problem, DFT relations, DFT properties, fast Fourier transform (FFT) algorithms (radix-2, decimation-in-time, decimation-in-frequency), Goertzel algorithm, linear convolution using DFT. Frequency selective filters: Ideal filter characteristics, lowpass, highpass, bandpass and bandstop filters, Paley-Wiener criterion, digital resonators, notch filters, comb filters, all-pass filters, inverse systems, minimum phase, maximum phase and mixed phase systems. Structures for discrete-time systems: Signal flow graph representation, basic structures for FIR and IIR systems (direct, parallel, cascade and polyphase forms), transposition theorem, ladder and lattice structures. Design of FIR and IIR filters: Design of FIR filters using windows, frequency sampling, Remez algorithm and least mean square error methods; Design of IIR filters using impulse invariance, bilinear transformation and frequency transformations. Laboratory DSK6713 Signal Processing Kit and MATLAB are used for the experiments: Familiarization with Kits and MATLAB Linear and Circular Convolution Z Transform Discrete Fourier Transform & Fast Fourier Transform IIR Filter Design – Analog Filter Filter Design using Windowing Techniques
Learning Outcomes	Complies with PLO 1b, 2a and 4a
Assessment Method	Quiz, Assignments and Exams
Suggested Reading	Textbooks: S. K. Mitra, Digital Signal Processing: A computer-Based Approach, TMH, 2/e, 2001. A. V. Oppenheim and R. W. Shafer, Discrete-Time Signal Processing, PHI, 2/e, 2004. J. G. Proakis and D. G. Manolakis, Digital Signal Processing: Principles, Algorithms and Applications, PHI, 1997 TMS320C6XXX CPU and Instruction Set Reference Guide, Texas Instruments, 2000 (www.ti.com) V. K. Ingle and J. G. Proakis, Digital signal processing using MATLAB, Thompson Brooks/Cole, Singapore, 2007. MATLAB and Signal Processing Toolbox User's Guide (www.mathworks.com)   Reference Books: L. R. Rabiner and B. Gold, Theory and Application of Digital Signal Processing, Prentice Hall India, 2005. A. Antoniou, Digital Filters: Analysis, Design and Applications, Tata McGraw-Hill, New Delhi, 2003.

EC3203

Introduction to AI/ ML

Course Number	EC3203
Course Credit	3-0-0-3
Course Title	Introduction to AI/ ML
Learning Mode	Lectures
Learning Objectives	Complies with Program Goal 1,2 and 4
Course Description	The course deals with the comprehension of AI to analyze and map real world problem. and identification of electrical engineering problems (communication, power, control, signal processing) that is solved by AI techniques. It also focuses on different learning techniques and program/code in AI languages
Course Outline	Introduction: Foundations of Artificial Intelligence, Definitions; Problem solving: Problem-Solving Agents, Searching for Solutions, Uninformed Search, Breadth-first search, Depth-first search, Heuristic Search, Domain Relaxations, Local Search, Adversarial Search, Greedy best-first search; Logic and reasoning: Knowledge-Based systems, Propositional Logic, Reasoning Patterns in Propositional Logic, Resolution, Forward and Backward chaining, Syntax and Semantics of First-Order Logic, Using First-Order Logic, Propositional vs. First-Order Inference, Unification and Lifting, Forward Chaining, Backward Chaining, Resolution; Machine Learning: kNN, SVM, PCA, ICA, Clustering and ANN algorithms. Applications of AI in healthcare, communication, speech processing, electrical power and control engineering
Learning Outcomes	Complies with PLO 1b, 2a and 4a
Assessment Method	Quiz, Assignments and Exams
Suggested Reading	Textbooks: Patrick Henry Winston, Artificial Intelligence, Third Edition, Addison-Wesley Publishing Company, 2004. Nils J Nilsson, Principles of Artificial Intelligence, Illustrated Reprint Edition, Springer Heidelberg, 2014 Duda, Richard O., and Peter E. Hart. Pattern classification. John Wiley & Sons, 2006   Reference Books: Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach, 3rd Edition, PHI 2009.

EC3204

Low Power MOSFETs Design and Modeling

Course Number	EC3204
Course Credit	3-0-0-3
Course Title	Low Power MOSFETs Design and Modeling
Learning Mode	Lectures
Learning Objectives	Complies with Program Goal 1, 2 and 4
Course Description	The course deals with Low-Power Design Principles for MOSFETs, design of Low-Power MOSFET Architectures, modelling and simulation of Low-Power MOSFET Devices and implementation and validation of compact models for Low-Power MOSFETs:
Course Outline	Overview of Low-Power Design: Importance of low-power MOSFETs in modern electronics, Concepts of Power Consumption, Static power vs. dynamic power, Sources of power dissipation: Leakage currents, switching power, short-circuit power Fundamentals of MOSFET Operation: Structure and operation principles, Key parameters: Threshold voltage, mobility, subthreshold slope, Short-channel effects and their impact on power consumption, Quantum mechanical effects at nanoscale dimensions Design Techniques for Low-Power MOSFETs: Techniques for adjusting threshold voltage, Impact of threshold voltage on power and performance, Strained silicon and other materials for mobility enhancement, Use of high-k/metal gate stacks to reduce leakage, Leakage Reduction Techniques, Power gating and sleep modes, Use of multiple threshold voltages (Multi-Vth), Dynamic Power Reduction, Voltage scaling and Dynamic Voltage and Frequency Scaling (DVFS), Clock gating and its impact on dynamic power Advanced Low-Power MOSFET Architectures: FinFETs and Multi-Gate MOSFETs and their Structure, operation, and benefits for low-power applications, Design, considerations and modeling techniques, Tunnel FETs (TFETs):Principles of operation and advantages for low-power design, Design challenges and modeling approaches, Negative Capacitance FETs (NC-FETs): Concept of negative capacitance and its impact on power consumption, Integration with existing MOSFET technology Compact Modeling of Low-Power MOSFETs: Introduction to Compact Models, Importance of compact models for circuit simulation, Key parameters and their significance, BSIM Models, Overview of BSIM models for traditional and advanced MOSFETs, Parameter extraction and fitting techniques Compact Models for Advanced MOSFETs: BSIM-CMG for FinFETs, Models for TFETs and NC-FETs, Custom models for emerging low-power MOSFETs Numerical Simulation Techniques: TCAD Simulation Tools, Overview of TCAD tools (e.g., Synopsys Sentaurus, Silvaco ATLAS), Setting up simulations for low-power MOSFETs, Process Simulation, Simulation of fabrication processes and their impact on device performance, Analyzing process variations and their effect on power consumption, Device Simulation, Electrical characterization and parameter extraction, Analyzing simulation results for low-power performance
Learning Outcomes	Complies with PLO 1b, 2a, 2b and 4a
Assessment Method	Quiz, Assignments and Exams
Suggested Reading	Textbooks: Roy, Kaushik, and Sharat C. Prasad. Low-power CMOS VLSI circuit design. John Wiley & Sons, 2009. Taur, Yuan, and Tak H. Ning. Fundamentals of modern VLSI devices. Cambridge university press, 2021.   Reference Books: Saha, Samar K. Compact models for integrated circuit design: conventional transistors and beyond. Taylor & Francis, 2015.

EC3205

Introduction of Wireless Communications

Course Number	EC3205
Course Credit	3-0-0-3
Course Title	Introduction of Wireless Communications
Learning Mode	Lectures
Learning Objectives	Complies with Program Goal 1, 2 and 4
Course Description	The course deals with the basic concepts of radio wave propagation, channel models and fading, effect of fading on data transmission and understanding of the modern communication technologies
Course Outline	Wireless Transmission, Radio wave propagation issues in wireless systems, Path Loss and Shadowing: Radio Wave Propagation, Transmit and Receive Signal Models, Free-Space Path Loss, Ray Tracing Models, Different Propagation models, Shadow Fading, Combined Path Loss and Shadowing. Multipath Fading and Time-Varying Channel Impulse Response, Narrowband and Wideband Fading Models. Capacity in AWGN and Fading Channels. Performance of different modulation over Wireless Channels, Analysis of BPSK, QPSK, M-PSK, M-QAM over fading channels; Estimation of Spectral efficiency, Outage probability and Average probability of error. Diversity techniques: Space, Different types of diversity techniques: Time, Frequency, Polarization, Angular, Transmitter and Receiver. Introduction to Multi-input Multi-output (MIMO) Systems. Introduction to Multicarrier Modulation (MCM): OFDM, OFDMA, MCCDMA and their Performance Analysis
Learning Outcomes	Complies with PLO 1b, 2a and 4a
Assessment Method	Quiz, Assignments and Exams
Suggested Reading	Textbooks: K. Feher, Wireless Digital Communication, Prentice Hall of India, New Delhi, 1995. T.S. Rappaport, Wireless Communication; Principles and Practice, Prentice Hall, NJ, 1996.   Reference Books: Andrea Goldsmith, Wireless Communications, Cambridge University Press, 2005.

EC3206

RF Systems

Course Number	EC3206
Course Credit	3-0-0-3
Course Title	RF Systems
Learning Mode	Lectures
Learning Objectives	Complies with Program Goal 1, 2 and 4
Course Description	The course deals with the design of microwave coupler and dividers, filters and their implementation, microwave amplifiers, active microwave devices, oscillators and mixers.
Course Outline	Microstrip Transmission line, propagation module, Scattering parameters; Wave Guides, Rectangular Wave Guide, Circular Wave Guide, propagation modules, guided propagation. Microwave components, Filters, Planar transmission lines, Filters lumped as well as distributed element realization, Implementation using simulators. Analysis and design of passive components, Phase shifters, Directional couplers, Junctions, Power dividers, Isolators and circulators, Resonant circuits, Transmission line resonators. Radiation: Antenna fundamentals, potentials, Hertzian dipole, short loop, different antenna types, antenna parameters, gain, arrays-active/passive, antenna measurement techniques. Antenna Synthesis, Antenna Analysis. RF systems, RF Front end, design and analysis. Basics of RADAR principle.
Learning Outcomes	Complies with PLO 1b, 2a and 4a
Assessment Method	Quiz, Assignments and Exams
Suggested Reading	Textbooks: David M. Pozar, Microwave Engineering, Wiley India Private Limited; Fourth edition (14 May 2013). C. A. Balanis: Antenna Theory: Analysis and Design, John Wiley, 2005, 3/e. R. E. Collin, Foundations for Microwave Engineering, Wiley-Blackwell; 2nd Edition   Reference Books: D. M. Sullivan: Electromagnetic Simulation using the FDTD Method, Wiley-IEEE, 2000, 1/e. B. S. Guru & H. R. Hiziroglu: Electromagnetic Field Theory Fundamentals, Thomson, 1997, 1/e Skolnik, Merrill I. "Introduction to radar." Radar handbook, 1962

TOTAL

Sl. No.	Subject Code	SEMESTER VII	L	T	P	C
1.	EC41XX	Department Elective - I	3	0	0	3
2.	EC41XX	Department Elective - II	3	0	0	3
3.	XX41PQ	IDE - III	3	0	0	3
4.	HS41XX	HSS Elective - II	3	0	0	3
5.	EC4198	Summer Internship*	0	0	12	3
6.	EC4199	Project – I	0	0	12	6
TOTAL			12	0	24	21

* For specific cases of internship after 6^th Semester, the performance evaluation would be made on joining the VII^th Semester and graded accordingly in the VII^th Semester:

Note :

a) (i) Summer internship (*) period of at least 60 days’ (8 weeks) duration begins in the intervening vacation between semester VI and VII that may be done in industry / R&D / Academic Institutions including IIT Patna. The evaluation would comprise combined grading based on host supervisor evaluation, project internship report after plagiarism check and seminar presentation at the Department (DAPC to coordinate) with equal weightage of each of the three components stated herein.

a) (ii) Further, on return from internship, students will be evaluated for internship work through combined grading based on host supervisor evaluation, project internship report after plagiarism check, and presentation evaluation by the parent department with equal weightage of each component.

b) (i) In the VII^th semester, students can opt for a semester long internship on recommendation of the DAPC and approval of the Competent Authority.

b) (ii) On approval of semester long internship, at the maximum two courses (properly mapped/aligned syllabus) at par with institute electives may be opted from NPTEL and / or SWAYAM and the other two more should be done at the institute through course overloading in any other semester (either before or after the internship) and/or during following summer semester.
b) (iii) The candidates opting two courses from NPTEL and / or SWAYAM would be required to appear in the examination at the Institute as scheduled in the Academic Calendar.

Introduction to Quantum Computing

Department Elective I

Department Elective I

Sl. No.

Subject Code

Course

EC4101

Course Number	EC4101
Course Credit	3-0-0-3
Course Title	Introduction to Quantum Computing
Learning Mode	Lectures
Learning Objectives	Complies with Program Goal 1, 2 and 3
Course Description	The course deals with the key components and architecture of quantum computing systems, including qubits, quantum gates, and quantum circuits. It also focuses on comprehending the principles of quantum information theory, including quantum entanglement, quantum entropy, and quantum teleportation. Implementation and analysis of quantum algorithms, such as Shor's algorithm for factoring and Grover's algorithm for search problems is also included.
Course Outline	Introduction: History, Motivation, Need of quantum bits, quantum states, quantum computations, quantum information, and quantum algorithms Overview of complex numbers and Linear Algebra, Introduction to quantum mechanics and its postulates, Bloch sphere Quantum gates: X, Z, Y, H, R, S, T, Square root of NOT Quantum Circuits: Single qubits and multiple qubits operations and quantum teleportation Quantum Algorithms: Deutsch’s algorithm, Deutsch-Jozsa algorithm, Simon’s algorithm Quantum Tools and Applications: Goal Challenges, Lights and Photon, Decoherence, Ion Trap, Quantum Simulation
Learning Outcomes	Complies with PLO 1b, 2a and 3a
Assessment Method	Quiz, Assignments and Exams
Suggested Reading	Textbooks: Nielsen, M. A., and Chuang, I. L., Quantum computation and quantum information, 10th Anniversary Edition, 2010, Cambridge university press. Yanofsky, N. S., and Mannucci, M. A., Quantum computing for computer scientists, 1st Edition, 2008, Cambridge University Press. Reference Books: Johnston, E. R., Harrigan, N., and Gimeno-Segovia, M., Programming quantum computers: essential algorihms and code samples, 1st Edition, 2019, O'Reilly Media.

EC4102

Deep Learning for Video Surveillance Systems

Course Number	EC4102
Course Credit	3-0-0-3
Course Title	Deep Learning for Video Surveillance Systems
Learning Mode	Lectures
Learning Objectives	Complies with Program Goal 1, 2 and 3
Course Description	The course deals with video surveillance tasks such as monitoring and processing of video footage, and understanding and analyzing of machine and deep learning models. The course also develop competence to take logical, scientific and correct decisions while predicting model outcomes. Aptitude and ability of performance measurement and management of video surveillance cameras is also covered.
Course Outline	Introduction to Video Surveillance Systems: Introduction to image processing methods, Edge detection and linking, Image transforms, Introduction to video processing techniques, Video compression standards. Motion detection using optical flow method, motion modeling, Background modeling, Basic building blocks of video surveillance systems. Introduction to Deep Learning: Introduction to neural networks with linear algebra, Matrix mathematics and probability, Introduction to multilayer perceptron networks, forward and back propagation, Hyper-parameter tuning, Regularization and optimization in neural networks, Introduction to tensor-flow. Convolutional Neural Nets: Introduction to convolutional neural networks, Key concepts like convolution and pooling. Stacking convolutional layers for object detection. Recurrent Neural Nets: Introduction to recurrent neural networks (RNN, LSTM, GRU) for sequence level tasks (time series, video). Bidirectional and deep recurrent nets. Use them for activity recognition. Object Detection and Classification using Deep Learning: Haar like feature based object detection, Viola Jones object detection framework, Deep learning based object classification. Object Tracking using Deep Learning: Video monitoring for detection and tracking of single as well as multiple interacting objects, Region-based tracking, Contourbased tracking, Feature-based tracking, Model-based tracking, KLT tracker, Meanshift based tracking. Deep Learning based Human Activity Recognition: Template based activity recognition, CNN based activity recognition, RNN based activity recognition, abnormal behavior detection in crowded environments using deep learning Camera Networks for Surveillance: Types of CCTV (closed circuit television) camera- PTZ (pan-tilt zoom) camera, IR (Infrared) camera, IP (Internet protocol) camera, wireless security camera, multiple view geometry, camera network calibration, PTZ camera calibration, camera placement, smart imagers and smart cameras, Introducing graph signal processing, consensus networks. Emerging Techniques of Deep Learning in Visual Surveillance System: Augmented surveillance system, Operator attention based visual surveillance system, EEG and eye tracking based visual surveillance system, ONVIF standard for the interface of IP-based physical security products.
Learning Outcomes	Complies with PLO 1b, 2a and 3b
Assessment Method	Quiz, Assignments and Exams
Suggested Reading	Textbooks: 1. M H Kolekar, “Intelligent Video Surveillance Systems: An Algorithmic Approach”, CRC press Taylor and Francis Group, 2018 2. Q. Huihuan, X. Wu, Y. Xu, “Intelligent Surveillance Systems”, Springer Publication, 2011. 3. Ian Goodfellow, Yoshua Bengio and Aaron Courville, “Deep Learning”, The MIT Press, 2017.   Reference Books: 1. Murat A. Tekalp, “Digital Video Processing”, Prentice Hall, 1995. 2. Pradeep K Atrey, Mohan Kankanhalli, A Cavallaro, “Intelligent Multimedia Surveillance: Current Trends and Research” Springer Publication, 2013. 3. Y. Ma and G. Qian (Ed.), “Intelligent Video Surveillance: Systems and Technology”, CRC Press, 2009. 4. H. Aghajan and A. Cavallaro (Ed.), Multi-Camera Network: Principles and Applications”, Elsevier, 2009.

EC4103

FPGA based System Design

Course Number	EC4103
Course Credit	3-0-0-3
Course Title	FPGA based System Design
Learning Mode	Lectures
Learning Objectives	Complies with Program Goal 1, 2 and 3
Course Description	The course deals with design of complex digital systems & use the design flow for using FPGA. This also gives exposure to Softcore Processor IP, Memory and other IO IPs and digital IPs, understanding of IP integration for large scale FPGA based digital System. Also, it covers performance analysis and issues of large scale digital system on FPGA and completion of a significant project on the FPGA platform.
Course Outline	Introduction to reconfigurable and FPGA based system Design; Basic and Advanced FPGA Fabrics; Combinational, Sequential logic and FSM realization on FPGA; FPGA Architecting: Speed, Area and Power; Issues on FPGA based system Design: Area, Timing and Power; Design Methodologies: Behavioral /high level Design and Implementation methodologies: RTL, IP Core, System Generator; Processor and memory cores; Timing analysis; Clock distribution and management systems; IP Cores for FPGA: Block and Distributed memory, FIFO, CORDIC, Clock distribution and management systems; Large scale System Design: Platform FPGA, Multi-FPGA System; Busses and I/O communication system; System Design and Implementation using FPGA: DSP and Communication Blocks and Cryptography blocks Introduction to FPGA based Embedded system platform: Soft processor, AHB Bus and I/O interfacing – Case studies.
Learning Outcomes	Complies with PLO 1b, 2b and 3b
Assessment Method	Quiz, Assignments and Exams
Suggested Reading	Text/Reference Books: Wayne Wolf, “FPGA Based System Design”, Prentice Hall Modern Semiconductor Design Series, 2004. Steve Kilts, “Advanced FPGA design – Architecture, Implementation and Optimization”, Wiley publications,2007. Ron Sass and Andrew G. Schmidt, Morgan Kaufmann (MK), “Embedded System design with Platform FPGAs”, Elsevier,2010.

Department Elective II

Department Elective II

Sl. No.

Subject Code

Course

EC4104

Introduction to Information Theory

Course Number	EC4104
Course Credit	3-0-0-3
Course Title	Introduction to Information Theory
Learning Mode	Lectures
Learning Objectives	Complies with Program Goal 1, 2 and 3
Course Description	The course deals with the fundamental concepts of information theory, understanding of coding, quantification, storage, and communication of information and analysis of source coding and channel coding.
Course Outline	Overview of random variable, Function of a random variable and its distribution, Discrete distributions, Continuous distributions, Random vector and its joint distribution. The concept of Amount of Information, Average Information, Information rate. Entropy, Joint Entropy and Conditional Entropy, Relative Entropy and Mutual Information, Chain rule for entropy, relative entropy, and mutual information. Source Coding: Fixed and Variable Length Codes, Kraft Inequality, Shannon-Fano Algorithm, Huffman Algorithm. Maximum entropy distribution for continuous and discrete random variables. Channel Capacity, Capacity of different channels: Noiseless binary channel, Noisy channel with nonoverlapping outputs, Binary symmetric channels, Binary erasure channel, symmetric channels, AWGN channels; Shannon Theorem, Bandwidth-SNR Trade-off, Channel Capacity Theorem, Shannon Limit. Maximum entropy distributions for continuous and discrete cases.
Learning Outcomes	Complies with PLO 1b, 2a and 3b
Assessment Method	Quiz, Assignments and Exams
Suggested Reading	Textbooks: T.M. Cover and J. A. Thomas, Elements of Information Theory, 2nd edition, 2006, Wiley. R. Bose, Information Theory and applications, 2nd Edition, 2008, TMH.   Reference Books: J. G. Proakis, Digital Communications, 1995, McGraw-Hill. Ross, Sheldon. A First Course in Probability. 8th Edition, 2009, Pearson Prentice Hall.

EC4105

Digital Image Processing

Course Number	EC4105
Course Credit	3-0-0-3
Course Title	Digital Image Processing
Learning Mode	Lectures
Learning Objectives	Complies with Program Goal 1, 2 and 3
Course Description	The course deals with the fundamental concepts of digital image processing, including filtering, transforms, morphology, colour and image analysis. It also covers the basic image processing algorithms in C or Matlab or Python and make ready the students for advanced version of the course.
Course Outline	Introduction to Digital Image Processing & Applications, Sampling, Quantization, Basic Relationship between Pixels, ImagingGeometry, Image Transforms, Image Enhancement, Image Restoration, Image Segmentation, Morphological Image Processing, Shape Representation and Description, Object Recognition and Image Understanding, Texture Image Analysis, Motion Picture Analysis, Image Data Compression.
Learning Outcomes	Complies with PLO 1b, 2a and 3b
Assessment Method	Quiz, Assignments and Exams
Suggested Reading	Textbooks: Rafael C. Gonzalez and Richard E. Woods, Digital Image Processing, Pearson Anil K. Jain, Fundamentals of Digital Image Processing, Prentice Hall   Reference Books: Milan Sonka, Vaclav Hlavac and Roger Boyle, Image Processing, Analysis and Machine Vision, Springer

EC4106

Graph Signal Processing

Course Number	EE4106
Course Credit	3-0-0-3
Course Title	Graph Signal Processing
Learning Mode	Lectures
Learning Objectives	Complies with Program Goal 1, 2 and 3
Course Description	The course deals with explanation of basic graph theory concepts and their extension to graph signal processing, and implementation of filtering, sampling, and reconstruction techniques for graph signals. It also covers spectral analysis of graph signals using the graph Fourier transform, and design and evaluation of graph filters for practical applications in various domains.
Course Outline	Introduction: Why Graph Signal Processing: concepts, applications and challenges Introduction to graph concepts: Linear algebra review, graph shift, graph shift invariance, graph signals, graph filtering, graph Fourier transform, graph convolution and modulation, graph frequency and graph spectral analysis of graph signals, Spectral graph theory: Orthogonal transforms review, Frequency interpretation – Nodal Theorems, Graph filtering: Vertex and Spectral interpretations, Advanced Topic 1: Shift invariance, localization and uncertainty principles, Advanced Topic 2: Down sampling, Advanced Topic 3: Wavelets, Advanced Topic 4: Multiresolution and graph approximation, Advanced Topic 5: Directed Graphs, Geometric Learning to extend deep learning models to learning with data supported by graphs.
Learning Outcomes	Complies with PLO 1b, 2a and 3b
Assessment Method	Quiz, Assignments and Exams
Suggested Reading	Textbooks: F. R. Chung, Spectral graph theory, volume92, AMS Bookstore, 1997. D. M. Cvetkovic, P. Rowlinson, and S. Simic, An introduction to the theory of graph spectra. Cambridge University Press Cambridge, 2010. D. K. Hammond, P. Vandergheynst, and R. Gribonval. Wavelets on graphs via spectral graph theory. Applied and Computational Harmonic Analysis, 30(2):129--150, 2011.   Reference Books: P. Milanfar. A tour of modern image filtering: new insights and methods, both practical and theoretical. Signal Processing Magazine, IEEE , 30(1):106--128, 2013. S. K. Narang and A. Ortega. Perfect reconstruction two-channel wavelet filter banks for graph structured data. Signal Processing, IEEE Transactions on , 60(6):2786--2799, 2012. A. Sandryhaila and J. M. Moura. Discrete signal processing on graphs. IEEE transactions on signal processing, 61(5-8):1644--1656, 2013. D. I. Shuman, S. K. Narang, P. Frossard, A. Ortega, and P. Vandergheynst. The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. Signal Processing Magazine, IEEE, 30(3):83--98, 2013. D. Spielman, Spectral graph theory, Lecture Notes, Yale University, 2009.

Department Elective III

Department Elective III

Sl. No.

Subject Code

Course

EC4201

Mobile Communications

Course Number	EC4201
Course Credit	3-0-0-3
Course Title	Mobile Communications
Learning Mode	Lectures
Learning Objectives	Complies with Program Goal 1, 2 and 3
Course Description	The course deals with the basic concepts of mobile communication system, cellular architecture of mobile communication, design and analysis of the wireless communication systems, and design and development of the prototypes for next generation communication systems
Course Outline	History and evolution of mobile radio systems. Standards of mobile cellular networks (e.g. 2G, 3G, 4G, 5G and beyond). Global System for Mobile Communication (GSM) and the System Architecture of GSM. The Mobile Station and the Subscriber Identity Module, The Base Station Subsystem: Base Transceiver Station, Architecture and Functionality, Base Transceiver Station Configurations, Base Station Controller, Architecture and Tasks of the Base Station Controller. The Network Switching Subsystem, Home Location Register and Authentication Center, Visitor Location Register, The Mobile-Services Switching Center, Gateway MSC, The Relationship Between MSC and VLR. Overview of OSI Reference Model. Quality of Service, Tools for Protocol Measurements. Code Division Multiple Access (CDMA)-based mobile systems, Pseudo-random codes, modulation and demodulation techniques, synchronization. Wideband CDMA System. Cellular concept and frequency reuse, Multiple Access Schemes, Channel assignment and handoff, Interface and system capacity, Trunking and Erlang capacity calculations.
Learning Outcomes	Complies with PLO 1b, 2a and 3b
Assessment Method	Quiz, Assignments and Exams
Suggested Reading	Textbooks: Gunnar Heine, GSM Networks: Protocols, Terminology and Implementation, Artech House Publishers (31 December 1998). Vijay K. Garg and Joseph E. Wilkes, Principles and Applications of GSM, Prentice Hall, 1998. T.S. Rappaport, Wireless Communication; Principles and Practice, Prentice Hall, NJ, 1996.   Reference Books: J. S. Lee and L. E. Miller, CDMA Systems Engineering Handbook, Artech House, 1998. R. L. Peterson, R. E. Ziemer, and D. E. Borth, Introduction to Spread Spectrum Communications, Prentice Hall, 1995.

EC4202

Opto Electronic Devices

Course Number	EC4202
Course Credit	3-0-0-3
Course Title	Opto Electronic Devices
Learning Mode	Lectures
Learning Objectives	Complies with Program Goal 1, 2 and 3
Course Description	The course deals with the basic working mechanism of the devices, and governing equations to be able to perform calculations to characterize the performance of the devices. The practical knowledge and an understanding of the trade-offs when using these devices in their respective applications is also included.
Course Outline	Elements Of Light And Solid-State Physics: Wave and particle nature of light, Polarization, Interference, Diffraction, Light Source, review of Quantum Mechanical concept, Review of Solid-State Physics, Lithography Process, Characterization tools. Display Devices And Lasers: Introduction, Photo Luminescence, Cathode Luminescence, Electro Luminescence, Injection Luminescence, Injection Luminescence, LED, Plasma Display, Population Inversion, Optical Feedback, Threshold condition, Laser Modes, Classes of Lasers, Mode Locking, laser applications. Optical Detection Devices: Photo detector, Thermal detector, Photovoltaics, Photo Conductors, Sensors, Detector Performance. Optoelectronic Integrated Circuits: Introduction, hybrid and Monolithic Integration, Application of Opto Electronic Integrated Circuits, Integrated transmitters and Receivers, Guided wave devices.
Learning Outcomes	Complies with PLO 1b, 2b and 3b
Assessment Method	Quiz, Assignments and Exams
Suggested Reading	Textbooks: Pallab Bhattacharya “Semiconductor Opto Electronic Devices”, Prentice Hall of India Pvt., Ltd., New Delhi, 2006. Jasprit Singh, “Opto Electronics – As Introduction to materials and devices”, McGraw-Hill International Edition, 1998   Reference Books: S C Gupta, Opto Electronic Devices and Systems, Prentice Hal of India,2005. J. Wilson and J.Haukes, “Opto Electronics – An Introduction”, Prentice Hall, 1995.

EE4203

Introduction to Energy Storage Techniques

Course Number	EE4203
Course Credit	3-0-0-3
Course Title	Introduction to Energy Storage Techniques
Learning Mode	Lectures
Learning Objectives	Complies with Program goals 1, 2, 3 and 4
Course Description	The course is designed to meet the requirements of B. Tech. The course aims at giving a brief of energy storage techniques. Various storage techniques such as Battery, Fuel Cell etc will be discussed.
Course Outline	Energy storage systems overview - Scope of energy storage, needs and opportunities in energy storage, Technology overview and key disciplines, comparison of time scale of storages and applications, Energy storage in the power and transportation sectors. Thermal storage system-heat pumps, hot water storage tank, solar thermal collector, application of phase change materials for heat storage-organic and inorganic materials, efficiencies, and economic evaluation of thermal energy storage systems. Chemical storage system- hydrogen, methane etc., concept of chemical storage of solar energy, application of chemical energy storage system, advantages and limitations of chemical energy storage, challenges, and future prospects of chemical storage systems. Electromagnetic storage systems - double layer capacitors with electrostatically charge storage, superconducting magnetic energy storage (SMES), concepts, advantages and limitations of electromagnetic energy storage systems, and future prospects of electromagnetic storage systems. Electrochemical storage system (a) Batteries-Working principle of battery, primary and secondary (flow) batteries, battery performance evaluation methods, major battery chemistries and their voltages- Li-ion battery& Metal hydride battery vs lead-acid battery. (b) Supercapacitors- Working principle of supercapacitor, types of supercapacitors, cycling and performance characteristics, difference between battery and supercapacitors, Introduction to Hybrid electrochemical supercapacitors. (c) Fuel cell: Operational principle of a fuel cell, types of fuel cells, hybrid fuel cell-battery systems, hybrid fuel cell-supercapacitor systems.
Learning Outcomes	Complies with PLO 1b, 2a, 2b, 4a, 4b
Assessment Method	Quiz, Assignments and Exams
Suggested Reading	Text books: F. S. Barnes and J. G. Levine: Large Energy Storage Systems Handbook (Mechanical and Aerospace Engineering Series), 2011, CRC press. R. Zito: Energy storage: A new approach, 2010, Wiley. References: G. Pistoia, and L. Boryann, Behaviour of Lithium-Ion Batteries in Electric Vehicles: Battery Health, Performance, Safety, and Cost, 2018, Springer International Publishing AG. 2. R. A. Huggins: Energy storage, 2010, Springer Science & Business Media. P. Denholm, E. Ela, Brendan Kirby and Michael Milligan: The Role of Energy Storage with Renewable Electricity Generation, National Renewable Energy Laboratory (NREL) -a National Laboratory of the U.S. Department of Energy.

Department Elective IV

Department Elective IV

Sl. No.

Subject Code

Course

EC4203

Introduction to Optical Communication

Course Number	EC4203
Course Credit	3-0-0-3
Course Title	Introduction to Optical Communication
Learning Mode	Lectures
Learning Objectives	Complies with Program Goal 1, 2 and 3
Course Description	The course deals with the needs and types of optical communication systems, basic elements of any optical transmission link, characteristics of optical fiber, which is a majorly used in optical channel and estimation/measurement of the performance of an optical communication link.
Course Outline	Overview of optical communication: Fiber Optic, Free Space, Underwater, Chip-to-chip. Optical Fiber: Structure, ray theory of light propagation, numerical aperture, modes. Types of optical fiber: Step index, graded index, single mode, multi-mode. Signal degradation in optical fiber: Loss, chromatic dispersion, polarization mode dispersion, nonlinearity. Bit rate distance product: Intermodal, chromatic dispersion. Optical Sources and Detectors: Light emitting diode, Laser Diode, PIN photodetector, avalanche photodiode. Wavelength division multiplexing. Optical system performance metrics: Eye-opening penalty, Q, BER, OSNR. Channel model: Free space optical communication, Underwater optical wireless communication. Link Analysis: Single channel point-to-point, WDM point-to-point.
Learning Outcomes	Complies with PLO 1b, 2a and 3b
Assessment Method	Quiz, Assignments and Exams
Suggested Reading	Textbooks: G Keiser, Optical fiber communications, 5th edition, McGraw Hill J M Senior, Optical fiber communications principles and practice, 3rd edition, Pearson J C Palais, Fiber optic communications, 5th edition, Pearson Pallab Bhattacharya, Semiconductor optoelectronic devices, 2nd Edition, Phi Le   Reference Books: R Ramaswami, K.N. Sivarajan, G. H. Sasaki, Optical Networks: A Practical Perspective, 2009 Elsevier. G. P. Agrawal, Fiber-optic communication systems, 3rd Edition, 2007, Wiley India. M Cvijetic, Optical transmission systems engineering, 2004, Artech House Publishers.

EC4204

Low Power Circuits

Course Number	EC4204
Course Credit	3-0-0-3
Course Title	Low Power Circuits
Learning Mode	Lectures
Learning Objectives	Complies with Program Goal 1, 2 and 3
Course Description	The course deals with the different sources of power dissipation in circuits and systems, design of low power device architectures, and design of low power subsystems such as adder and multipliers
Course Outline	Introduction: Need for Low Power Circuits, Low Power Techniques at different Hierarchical levels MOS Transistors, Working principle, ON Current, subthreshold current, Short Channel Effects, Level 1, Level 2, Level 3 and BISIM Models Low Power Devices: DG MOSFETs., FinFETs, GAA MOSFETs, Tunnel FETs Low Power circuits: Inverters, CMOS inverters, Delay Estimation, driving large load MOS Logic Styles: Static CMOS, Dynamic CMOS and Pass transistor circuits, BiCMOS circuits Subsystem Design: 1 Bit Full adders, Full adders architectures, Multipliers architectures Low Power Memory Circuits: SRAM, DRAM, Static Power dissipation and minimization Techniques, MT CMOS, VT CMOS, DT CMOS and other techniques Dynamic Power dissipation and Minimization Techniques: Device, Circuits, and system level techniques, Minimizing switching capacitance at circuits and system level , short circuit power dissipation and minimization techniques Memory Design: SRAM, DRAM, 1T DRAM Adiabatic Circuits for Low Power Electronics
Learning Outcomes	Complies with PLO 1b, 2b and 3b
Assessment Method	Quiz, Assignments and Exams
Suggested Reading	Textbooks:  Ajit Pal, “Low-Power VLSI Circuits and Systems”, Springer, 2015 J. B. Kuo and J-H. Lou, “Low-Voltage CMOS VLSI Circuits”, Wiley, 1999. Reference Books: K. Roy and S. C. Prasad, “Low-Power CMOS VLSI Circuit Design”, Wiley, 2000.

EE4206

Fundamentals of Electric Vehicle Technology

Course Number	EE4206
Course Credit	3-0-0-3
Course Title	Fundamentals of Electric Vehicle Technology
Learning Mode	Lectures
Learning Objectives	Complies with Program goals 1, 2 and 3
Course Description	The course is designed to meet the requirements of B. Tech. The course aims at giving a brief overview of electric vehicle technology. Drive power train concept, inverter design, charger design and motor control will be discussed.
Course Outline	History of electric vehicle journey, Electric vehicle architecture and its type and challenges, Dynamics of electric vehicle, Benefits of using electric vehicle, Concept of drive cycle, Electric vehicle drivetrain components, Electric vehicle auxiliaries. 3-phase inverter design & analysis and its control, Multilevel inverter design & analysis and its control. Power factor correction AC-DC converter and its control, Phase -shifted full bridge converter and its control. Basics of Batteries, Lithium-ion vs Lead Acid Battery, Modelling of Battery, Supercapacitor, Fuel Cell. Introduction motor drive and its control, Permanent magnet motor drive and its control, Switched reluctance drive and its control.
Learning Outcomes	Complies with PLO 1a, 1b, 2a and 2b
Assessment Method	Quiz, Assignments and Exams
Suggested Reading	Textbooks: 1. N. Mohan, T. M, Undelnad, W. P, Robbins: Power Electronics: Converters, Applications and Design, 3rd Edition, 2002, Wiley. M. Eshani, Y. Gao, Sebastien E Gay, Ali Emadi: Modern electric, hybrid electric and fuel cell vehicles, Fundamentals, Theory, and Design. 2005, Boca Raton, FL, CRC. References: 1. R. Ericson Fundamentals of Power Electronics, 2004, Chapman & Hall. F. A. Silva; M. P. Kazmierkowski: Energy Storage Systems for Electric Vehicles, 2021, MDPI.

Department Elective V

Department Elective V

Sl. No.

Subject Code

Course

EC4205

Biomedical Signal Processing

Course Number	EC4205
Course Credit	3-0-0-3
Course Title	Biomedical Signal Processing
Learning Mode	Lectures
Learning Objectives	Complies with Program Goal 1, 2 and 3
Course Description	The course deals with.various Biomedical Signal Processing and Monitoring Tasks, analyzing machine and deep learning biomedical models. The course also develop competence to take logical, scientific and correct decisions while predicting model outcomes
Course Outline	Introduction of Biomedical signals: Nervous system, Neuron anatomy, Basic Electrophysiology, Biomedical signal’s origin and dynamic characteristics, biomedical signal acquisition and processing, Different transforms techniques. The Electrical Activity of Heart: Heart Rhythms, Components of ECG signal, Heart beat Morphologies, Noise and Artifacts, Muscle Noise Filtering, QRS Detection Algorithm, ECG compression techniques (Direct Time Domain (TP, AZTECH, CORTES, SAPA, Entropy Coding), Frequency Domain (DFT, DCT, DWT, KLT, Walsh Transform), Parameter Extraction: Heart rate variability, acquisition and RR Interval conditioning, Spectral analysis of heart rate variability. The Electrical Activity of Brain: Electroencephalogram, Types of artifacts and characteristics, Filtration techniques using FIR and IIR filters, Independent component analysis, Nonparametric and Model-based spectral analysis, Joint Time-Frequency Analysis, Event Related Potential, Noise reduction by Ensemble Averaging and Linear Filtering, Single-Trail Analysis and adaptive analysis using basis functions. The Electrical Activity of Neuromuscular System: Human muscular system, Electrical signals of motor unit and gross muscle, Electromyogram signal recording, analysis, EMG applications. Frequency-Time Analysis of Bioelectric Signal and Wavelet Transform: Frequency domain representations for biomedical Signals, Higher-order spectral analysis, correlation analysis, wavelet analysis: continuous wavelet transform, discrete wavelet transform, reconstruction, recursive multi resolution decomposition, causality analysis, nonlinear dynamics and chaos: fractal dimension, correlation dimension, Lyapunov exponent.
Learning Outcomes	Complies with PLO 1b, 2a and 3b
Assessment Method	Quiz, Assignments and Exams
Suggested Reading	Textbooks: Willis J. Tompkins, Biomedical Digital Signal Processing: C Language Examples and Laboratory Experiments for the IBM PC, Prentice Hall India Eugene N. Bruce, Biomedical Signal Processing and Signal Modeling, John Wiley & Sons, 2006. Rangaraj M. Rangayyan, Biomedical Signal Analysis: A Case-Study Approach, John Wiley & Sons, 2002 Steven J. Luck, An Introduction to the Event-Related Potential Technique, Second Edition, THE MIT PRESS Leif Sornmo and Pablo Laguna, Bioelectrical Signal Processing in Cardiac and Neurological Applications, Academic Press, 2005 Reference Books: Hojjat Adeli & Samanway Ghosh-Dastidar, Automated EEG based Diagnosis of Neurological Disorders, CRC Press. Thomas P. Trappenberg, Fundamentals of Computational Neuroscience, Oxford University Press. 2002. Mike X Cohen, Analyzing Neural Time Series Data Theory and Practice, THE MIT PRESS Nait-Ali, Amine, Advanced Biosignal Processing, Spingers(Ed.). 2009 C. Koch, Biophysics of Computation. Information Processing in Single Neurons, Oxford University Press: New York, Oxford Peter Dayan and LF Abbott, Theoretical Neuroscience Computational and Mathematical Modeling of Neural Systems, MIT 2001. F. Rieke and D. Warland and R. de Ruyter van Steveninck and W. Bialek, Spikes: Exploring the Neuronal Code, A Bradford Book. MIT Press.

EC4206

High-Power Semiconductor Devices

Course Number	EC4206
Course Credit	3-0-0-3
Course Title	High-Power Semiconductor Devices
Learning Mode	Lectures
Learning Objectives	Complies with Program Goal 1, 2, 3 and 4
Course Description	The course deals with the fundamental principles and physics of high-power semiconductor devices, analysing the performance characteristics and limitations of various high-power semiconductor devices, designing and simulating high-power semiconductor devices using advanced computational tools, assessing the impact of material properties and device architecture on the performance and reliability of high-power semiconductor devices, applying knowledge of high-power devices in the development of power electronic systems and evaluating the latest research and technological advancements in high-power semiconductor devices.
Course Outline	Introduction to High-Power Semiconductor Devices: Overview of high-power devices, Applications in power electronics Semiconductor Physics for High-Power Devices: Charge carrier dynamics, Breakdown mechanisms Power Diodes: Structure, operation, and types (e.g., Schottky, PiN), Performance characteristics and applications Power Bipolar Junction Transistors (BJTs): Structure and operation principles, High-power performance characteristics Insulated Gate Bipolar Transistors (IGBTs): Design and operation principles, Power MOSFETs: Structure, operation, and characteristics, Comparison with other high-power devices Thyristors and Related Devices: Structure and types (e.g., SCR, GTO), Switching characteristics and applications Thermal Management in High-Power Devices: Heat generation and dissipation, Thermal modeling and packaging techniques Reliability and Failure Mechanisms: Degradation and failure modes, Reliability testing and improvement strategies Advanced Materials for High-Power Devices: Wide bandgap materials (e.g., SiC, GaN), Advantages and challenges Integration and Application of High-Power Devices: Power modules and converters, Applications in renewable energy and electric vehicles Recent Advances and Research Trends: Innovations in high-power device technology,
Learning Outcomes	Complies with PLO 1a, 2a, 2b, 3a, and 4a
Assessment Method	Quiz, Assignments and Exams
Suggested Reading	Textbooks:  B. Jayant Baliga, Power Semiconductor Devices, 1st Edition,Publisher: PWS Publishing Company, Year: 1995 B. Jayant Baliga, Fundamentals of Power Semiconductor Devices, 2nd Edition, Publisher: Springer, Year: 2010 Reference Books: Josef Lutz, Heinrich Schlangenotto, Uwe Scheuermann, Rik De Doncker, Semiconductor Power Devices: Physics, Characteristics, Reliability, 2nd Edition, Publisher: Springer Ned Mohan, Tore M. Undeland, William P. Robbins, Power Electronics: Converters, Applications, and Design, 3rd Edition, Publisher: Wiley, Year: 2002 B. Jayant Baliga, Wide Bandgap Semiconductor Power Devices: Materials, Physics, Design, and Applications, Publisher: Woodhead Publishing, Year: 2018

EC4207

Biomedical Instrumentation

Course Number	EC4207
Course Credit	3-0-0-3
Course Title	Biomedical Instrumentation
Learning Mode	Lectures
Learning Objectives	Complies with Program Goal 1, 2, 3 and 4
Course Description	The course deals with the basic principles and functions of biomedical instruments, design and developing biomedical instruments for diagnostic and therapeutic purposes, analysing and interpreting data from biomedical instruments, applying knowledge of electronics, signal processing, and instrumentation in biomedical applications and addressing challenges in the design and application of biomedical instruments considering ethical and regulatory standards.
Course Outline	Introduction to Biomedical Instrumentation: Overview of biomedical engineering and instrumentation, History and evolution of biomedical devices, Types of biomedical instruments, Ethical and regulatory aspects in biomedical instrumentation Biosignal Acquisition and Processing: Types of biosignals (ECG, EEG, EMG), Basic transducer principles, Signal conditioning and processing techniques, Filtering and noise reduction Biomedical Sensors and Measurement: Types of biomedical sensors (e.g., temperature, pressure, flow sensors), Sensor characteristics and selection criteria, Measurement techniques and signal conditioning, Design principles Materials used in biomedical devices, Prototyping and testing Diagnostic Instruments, Therapeutic and Prosthetic Devices: Electrocardiographs (ECG), Electroencephalographs (EEG), Electromyographs (EMG), Imaging: X-ray, MRI, CT, Ultrasound; Pacemakers and defibrillators, Infusion pumps, Dialysis machines, Prosthetics and orthotics, Laser applications in medicine Clinical Laboratory Instruments: Blood gas analyzers, Hematology analyzers, Spectrophotometers Chromatography and electrophoresis, Immunoassay systems Recent Advances in Biomedical Instrumentation: Wearable health technology, Telemedicine and remote monitoring, Nanotechnology in medical devices Biomedical microelectromechanical systems (BioMEMS) Artificial intelligence and machine learning in biomedical instrumentation Project and Case Studies: Design and implementation of a biomedical device Case studies of biomedical instrumentation applications
Learning Outcomes	Complies with PLO 1a, 2a, 2b, 3a, 3b, 4a and 4b
Assessment Method	Quiz, Assignments and Exams
Suggested Reading	Textbooks: Webster, John G., ed. Medical instrumentation: Application and Design. John Wiley & Sons, 2009. Carr, Joseph J., and John Michael Brown. Introduction to Biomedical Equipment technology. John Wiley & Sons, 1981. Reddy, Narender P. "Book review: biomedical signal analysis: a case-study approach, by Rangaraja M. Rangayyan." Annals of Biomedical Engineering 30 (2002): 983-983. Bronzino, Joseph D. Biomedical Engineering Handbook. Springer Science & Business Media, 2000. Chatterjee, Shakti, and Aubert Miller. Biomedical Instrumentation Systems. Cengage Learning, 2012. Khandpur, Raghbir Singh. Compendium of Biomedical Instrumentation, John Wiley & Sons, 2020. Reference Books: Geddes, L.A., and Baker, L.E. "Principles of Applied Biomedical Instrumentation", Wiley-Interscience. Carr, J.J., and Brown, J.M. "Introduction to Biomedical Equipment Technology", Pearson. Pallás-Areny, R., and Webster, J.G. "Sensors and Signal Conditioning", John Wiley & Sons.

Interdisciplinary Electives (Available to students of B. Tech. other than Dept. of EE)

Sl. No.

Subject Code

Subject

IDE-I

EE2203

Introduction to Electric Vehicle Technology

Course Number	EE2203 (B. Tech IDE I)
Course Credit	3-0-0-3
Course Title	Introduction to Electric Vehicle Technology
Learning Mode	Lectures
Learning Objectives	Complies with EE Program goals 1, 2 and 3
Course Description	The course is designed to meet the requirements of all the B. Tech branches. The course aims at giving a brief overview of electric vehicle technology. Drive power train concept, basic inverter design concept, basic of charger converter and basic of motor control will be discussed.
Course Outline	History of electric vehicle journey, Electric vehicle architecture and its type and challenges, Dynamics of electric vehicle, Benefits of using electric vehicle, Concept of drive cycle, Electric vehicle drivetrain components, Electric vehicle auxiliaries. Concept of Inverter, Single Phase Inverter, Basic of Three Phase Inverter, Modulation Strategy, AC-DC converter, Boost converter, State space modelling of Boost Converter, Buck Converter, State space modelling of Buck converter, Concept of Power Factor Correction Basics of Batteries, Lithium-ion vs Lead Acid Battery, Modelling of Battery Introduction to Induction motor drive and its control,
Learning Outcomes	Complies with EE PLO 1a, 2a and 3a
Assessment Method	Quiz, Assignments and Exams
Suggested Reading	Textbooks: 1. N. Mohan, T. M, Undelnad, W. P, Robbins: Power Electronics: Converters, Applications and Design, 3rd Edition, 2002, Wiley. 2. M. Eshani, Y. Gao, Sebastien E Gay, Ali Emadi: Modern electric, hybrid electric and fuel cell vehicles, Fundamentals, Theory, and Design. 2005, Boca Raton, FL, CRC. References: 1. R. Ericson Fundamentals of Power Electronics, 2004, Chapman & Hall. 2. F. A. Silva; M. P. Kazmierkowski: Energy Storage Systems for Electric Vehicles, 2021, MDPI.

IDE-II

EC3106

Introduction to Communication System

Course Number	EC3106 (B.Tech IDE-II)
Course Credit	3-0-0-3
Course Title	Introduction to Communication System
Learning Mode	Lectures
Learning Objectives	Complies with Program Goal 1, 2 and 4
Course Description	This course deals with the basics of communications systems and data transmission over wireless networks along with the next generation communication technologies. The prerequisite is Mathematics I & II.
Course Outline	Fourier analysis and its applications in communication systems. Signal spectra and filtering. Study of different analog and digital modulation and demodulation techniques: AM, FM, PAM, BPSK, QPSK, and QAM. Applications of modulation techniques in radio, television and telemetry. Noise modelling and its impact on the performance of communication systems. Performance metrics in communication systems: Q factor, SNR, noise figure, bit error rate, symbol error rate. Different blocks in data transmission: source coding and channel coding. Introduction to wireless communication, radio wave propagation issues in wireless systems, path loss, shadowing, and fading. Capacity in AWGN and fading channels. Cellular architecture, frequency reuse, handover, and multiple access schemes. Base station, mobile station, MSC, and other subsystems of cellular architecture. History and evolution of mobile radio systems, standards of mobile cellular networks (e.g. 2G, 3G, 4G, 5G and beyond). Introduction for fiber communication, aerial communication, near-field communications, quantum communications, and molecular communications.
Learning Outcome	Complies with PLO 1b, 2a and 4a
Assessment Method	Quiz, Assignments, and Exams
Suggested Readings	Text Books: 1. Michael Moher and Simon S. Haykin, Communication Systems, Wiley, 2006. 2. T.S. Rappaport, Wireless Communication; Principles and Practice, Prentice Hall, NJ, 1996. Reference Books: 1. Gunnar Heine, GSM Networks: Protocols, Terminology and Implementation, Artech House Publishers, 1998. 2. K. Feher, Wireless Digital Communication, Prentice Hall of India, New Delhi, 1995. 3. B. P. Lathi and Zhi Ding, Modern Digital and Analog Communication, Oxford Univ Press, 2018. 4. Govind P. Agrawal, Fiber-Optic Communication Systems, John Wiley & Sons, 2012.

IDE-III

EC4107

Quantum Computing Fundamentals

Course Number	EC4107 (B. Tech IDE-III)
Course Credit	3-0-0-3
Course Title	Quantum Computing Fundamentals
Learning Mode	Lectures
Learning Objectives	Complies with EE Program goals 1, 2 and 3
Course Description	This course offers a comprehensive introduction to the principles and applications of quantum information systems (QIS) and the associated hardware. It provides a foundational understanding of quantum computing, focusing on both the theoretical concepts and practical implementations. Students will explore key quantum phenomena and operations, learn about quantum circuits, and examine various quantum algorithms. The course also covers advanced topics such as quantum error correction and quantum cryptography, equipping students with the knowledge needed to understand and contribute to the evolving field of quantum information science.
Course Outlines	Quantum information system (QIS) applications and hardware, intuitive introduction of quantum operations and underlying quantum computing. Symbolic and mathematical representation of qubit. Measurement, Superposition, Multi-Qubit Operations, Quantum Circuits, Entanglement, Toffoli Gate, Phase-Flip, EPR Pairs. Deutsch’s algorithm, the Deutsch-Jozsa Algorithm and the Bernstein-Vazirani Algorithm, Simon’s algorithm, and Shor’s algorithm for factoring/discrete log And Grover’s algorithm for searching. Quantum error correction and quantum cryptography.
Learning Outcomes	Complies with EE PLO 1a, 2a and 3a
Assessment Methods	Quizzes, Assignments, Exams
Suggested Readings	Books Text/Reference: 1. Paul Kaye, Raymond Laflamme, and Michele Mosca, An Introduction to Quantum Computing, Oxford University Press (2007). 2. Scott Aaronson's Introduction to Quantum Information Science (UT Austin 2017). 3. M. Nielsen and I. Chuang. Quantum Computation and Quantum Information, Cambridge University Press; 10 Anv edition, 2011. 4. A. Yu. Kitaev, A. H. Shen and M. N. Vyalyi. Classical and Quantum Computation (Graduate Studies in Mathematics), AMS, 2002. 5. John Watrous. The Theory of Quantum Information, Cambridge University Press, 2018.

Minor in Communication Engineering

Sl. No.

Subject Code

Course Name

EC2102

Signals and Systems

Course Number	EC2102
Course Credit	3-1-0-4
Course Title	Signals and Systems
Learning Mode	Lectures and Tutorials
Learning Objectives	Complies with Program Goal 1 and 2
Course Description	The course deals with fundamental concepts of signals and systems including its application, analysis of impulse response of systems and frequency response using transforms such as CTFT, Laplace, DTFT, ZT, DFT.
Course Outline	Signals: classification of signals; signal operations: scaling, shifting and inversion; signal properties: symmetry, periodicity and absolute integrability; elementary signals. Systems: classification of systems; system properties: linearity, time/shift-invariance, causality, stability; continuous-time linear time invariant (LTI) and discrete-time linear shift invariant (LSI) systems: impulse response and step response; Response to an arbitrary input: convolution; system representation using differential and difference equations; Eigenfunctions of LTI/ LSI systems, frequency response and its relation to the impulse response. Signal representation: signal space and orthogonal bases; Fourier series representation of continuous-time and discrete-time signals; continuous-time Fourier transform and its properties; Parseval's relation, time-bandwidth product; discrete-time Fourier transform and its properties; relations among various Fourier representations. Sampling: sampling theorem; aliasing; signal reconstruction: ideal interpolator, zero-order hold, first-order hold; discrete Fourier transform and its properties. Laplace transform and Z-transform: definition, region of convergence, properties; transform-domain analysis of LTI/LSI systems, system function: poles and zeros; stability.
Learning Outcomes	Complies with PLO 1b, 2a and 2b
Assessment Method	Quiz, Assignments and Exams
Suggested Reading	Textbooks: 1. A.V. Oppenheim, A.S. Willsky and H.S. Nawab, Signals and Systems, 2nd Edition, 2006, Prentice Hall.. 2. S. Haykin and B. V. Veen, Signals and Systems, 2nd Edition, 1998, John Wiley and Sons. Reference Books: B. P. Lathi, Signal Processing and Linear Systems, 1998, Oxford University Press.

EC2201

Digital Electronics

Course Number	EC2201
Course Credit	3-0-2-4
Course Title	Digital Electronics
Learning Mode	Lectures and Labs
Learning Objectives	Complies with Program Goal 1, 2 and 4
Course Description	The course deals with the fundamental concepts used in digital electronics, analyzing and designing of various combinational and sequential circuits, identifying the basic requirements for a design application with focus on a cost effective solution, understanding the digital signals, and developing skills for designing combinational and sequential logic circuits and their practical implementation on breadboard.
Course Outline	Introduction to digital circuits: Logic families (RTL, TTL, ECL and MOS), Integer and floating point representation. Logic gates representation and combinational circuit realization, Boolean functions and simplification. Karnaugh Maps and logic optimization. Macro level combinational circuits and their realization: Multiplexers, Code converters, Decoders, parity Generators, 7-segment display decoder; Digital Arithmetic Circuits: Adders, Subtractor, BCD adders. Introduction to sequential elements (Latches and Flip-flops) and sequential circuit design, State machines. Finite state machines and examples: shift registers and counters. Introduction to memory circuits: RAM, ROM, EEPROM Introduction to programmable and reconfigurable devices. Digital logic realization using programmable Logic devices. Laboratory: To set up circuits for Bipolar (RTL, DTL, TTL) and Unipolar (MOS, CMOS) Logic families, Logic Gate verification Introduction to Combinational circuits, Realization of Decoder, Design and realization of a Multiplexer and Magnitude Comparator Verification of basic Flip Flops using 74XXICS, Implementation of basic Latches, Asynchronous Counters, Synchronous Counters, Pattern Generation and Detection
Learning Outcomes	Complies with PLO 1b, 2a, 2b and 4a
Assessment Method	Quiz, Assignments and Exams
Suggested Reading	Textbooks: D. P. Leach, A. P. Malvino and G. Saha, Digital Principal and Applications, 2nd Edition, 2006, McGraw-Hill. J. F. Wakerly, Digital Design Principles and Practices, 4th Edition, 2006, Pearson Education. M. Mano and M. D. Cilietti, Digital Design, 4th Edition, 2008, Pearson Education. C. H. Roth, Fundamentals of Logic Design, 5th Edition, 2004, Cengage Learning. N. Wirth, Digital Circuit Design: An Introductory Textbook, 1st Edition, 1995, Springer. D. P. Leach, A. P. Malvino and G. Saha, Digital Principal and Applications, 2nd Edition, 2006, McGraw-Hill.   Reference Books: D. J. Corner, Digital Logic and State Machine Design, 3rd Edition, 2012, Oxford University Press. H. Taub and D. Schilling, Digital Integrated Electronics, Illustrated Edition, 1977, McGraw-Hill.

EC3103

Analog Communications

Course Number	EC3103
Course Credit	3-0-2-4
Course Title	Analog Communications
Learning Mode	Lectures and Labs
Learning Objectives	Complies with Program Goal 1, 2 and 4
Course Description	The course focuses on building blocks of communication systems, and different modulation formats; their usage along with their advantages and limitations. In particular, it covers design and performance analysis of analog communication systems, design of transmitter and receivers for different analog modulation formats from scratch using both discrete. component and software configurable system. The focus would be on understanding of baseband, passband modulation and demodulation techniques using experiments, advantages and disadvantages of various modulation and demodulation techniques and encoding and decoding using self-made hardware system and estimate their performance.
Course Outline	Review of Fourier Series and Transforms. Hilbert Transforms, Band pass signal and System Representation. Random Processes, Stationarity, Power Spectral Density, Gaussian Process, Noise. Amplitude Modulation, DSBSC, SSB, VSB: Signal Representation, Generation and Demodulation. Frequency Modulation: Signal Representation, Generation, and Demodulation. Mixing, Super-heterodyne Receiver, Phase Recovery with PLLs. Noise in AM Receivers using Coherent Detection, in AM Receivers using Envelope Detection, in FM Receivers. Fidelity of AM and FM Receivers. Sampling, Pulse-Amplitude Modulation. Quantization, Pulse-Code Modulation. Noise considerations in PCM, Time Division Multiplexing, Delta Modulation, DPCM and ADPCM. Inter symbol Interference Laboratory: Amplitude modulation and demodulation (AM with carrier & DSB-SC AM); Frequency modulation and demodulation (using VCO & PLL); automatic gain control (AGC); Pulse amplitude modulation (PAM): Natural Sampling and Flat Top Sampling; Pulse Code Modulation (PCM); Pulse Width Modulation and Demodulation; Pulse Position Modulation and Demodulation. Delta Modulation and DPCM Transmitter & Receiver.
Learning Outcomes	Complies with PLO 1b, 2a and 4a
Assessment Method	Quiz, Assignments and Exams
Suggested Reading	Textbooks: H. Taub and D. L. Schilling, Principles of Communication Systems, 2nd Edition, 1986, McGraw Hill. S. Haykin, Digital Communications, Student Edition, 2004, Wiley. B. P. Lathi, Modern Analog and Digital Communication Systems, 3rd Edition, 1998, Oxford University Press. H. Taub and D. L. Schilling, Principles of Communication Systems, 4th Edition, 2017, McGraw-Hill. W. Tomasi, Electronic Communications Systems - Fundamentals Through Advanced, 4th Edition, 2003, Pearson. S. Haykin and M. Moher, An Introduction to Analog and Digital Communication Systems, 2nd Edition, 2012, Wiley.   Reference Books: K. S. Sanmugan, Digital and Analog Communication Systems, Student Edition, 2006, John Wiley & Sons L. W. Couch, Digital and Analog Communication Systems, 8th Edition, 2012, Pearson

EC3201

Digital Communications

Course Number	EC3201
Course Credit	3-0-2-4
Course Title	Digital Communications
Learning Mode	Lectures and Labs
Learning Objectives	Complies with Program Goal 1, 2 and 4
Course Description	The course deals with the fundamentals as well as advanced concepts in digital communications such as modulation, demodulation, detection, channel estimation and equalization. It also covers the implementation of various digital communications techniques. comparison of different techniques and apply for different applications, and design of transmitter and receivers for different digital modulation formats from scratch using both discrete component and software configurable system
Course Outline	Overview of Random Variables, Random Processes and Linear Algebra: Signal Space Concepts, Orthogonal Representation of Signals, Gram-Schmidt Procedure and Karhunen-Loeve (KL) Expansion. Communication Channel Models, Bandpass & Lowpass Signals Digital Modulation Schemes and their Performance Analysis: Memoryless and with Memory Modulation Methods, Pulse Amplitude Modulation (PAM), Phase Modulation, Quadrature Amplitude Modulation (QAM), Continuous-Phase Frequency-Shift Keying (CPFSK), and Continuous-Phase Modulation (CPM) Optimum Receiver in Presence of Additive White Gaussian Noise: Maximum a Posteriori Probability (MAP) and Maximum Likelihood (ML) Receivers, Coherent versus Non-coherent Detection, Binary Signal Detection in AWGN, M-ary Signal Detection in AWGN. Probability of Error Analysis Introduction to Receiver Synchronization Laboratory: Pseudo-random (PN) sequence generation; Amplitude shift keying (ASK) Generation and Detection; Frequency shift keying (FSK) Generation and Detection; Binary phase shift keying (BPSK) Generation and Detection; binary frequency shift keying (BFSK) Generation and Detection; Quadrature phase shift keying (QPSK) Generation and Detection; Orthogonal frequency division multiple access; Code division multiple access (CDMA) and direct sequence spread spectrum (DSSS) system.
Learning Outcomes	Complies with PLO 1b, 2a and 4a
Assessment Method	Quiz, Assignments and Exams
Suggested Reading	Textbooks: J. G. Proakis, M. Salehi, Digital Communications, 5th Edition, 2008, McGraw Hill. R. G. Gallager, Principles of Digital Communication, 2009, Cambridge University Press. S. S. Haykin, Digital Communications, 2001, Wiley-India. B.P. Lathi, Zhi Ding, and Hari Mohan Gupta, Modern Digital And Analog Communication Systems, 4th Edition, 2017, Oxford University Press. W. Tomasi, Electronic Communications Systems - Fundamentals through advanced, 4/e, Pearson, 2003. S.S. Haykin, An Introduction to Analog and Digital Communication Systems, Wiley Eastern 1989. Reference Books: P. B Crilly, A. B. Carlson, Communication Systems, 5th Edition, 2011, Tata McGraw-Hill Education. U. Madhow, Fundamentals of Digital Communication, 2008, Cambridge University Press. J.M Wozencraft, I.M. Jacobs, Principles of Communication Engineering, 1965, JohnWiley. A. Glover, P. M. Grant, Digital Communications, 5th Impression, 2012, Pearson. P. Z. Peeples, Digital Communication Systems, 1987, Prentice Hall International.

Electrical and Electronics Engineering

Program Learning Objectives: 1. Develop a solid foundation in electrical and electronics engineering principles, including circuit analysis, electromagnetic field theory, electrical machines, power systems, control systems, power electronics, signal processing, and microprocessor/microcontroller systems. 2. Develop electrical and electronics project management skills, including the ability to plan, execute, and complete within specified timelines and budgets. 3. Work collaboratively in multidisciplinary teams, demonstrating effective teamwork and communication to solve complex engineering problems. 4. Recognize the importance of ongoing professional development, engaging in activities such as certifications, workshops, and conferences to stay updated of industry trends.	Program Learning Outcomes: The graduates of this program will have 1. a successful career in an Academia/Industry/Entrepreneur 2. strong fundamentals in electrical and electronics engineering. 3. ability to design prototypes for real world problems related to electrical, electronics, and interdisciplinary fields. 4. ability to develop soft skills such as effective communications in both verbal and written forms, body language, time managements, problem-solving, leadership, work in both team as well as individual in a professional manner
Program Goal 1: Academic excellence by providing a curriculum that aligns with industry standards and encourages critical thinking in electrical and electronics engineering.	Program Learning Outcome 1a: Highly skilled market ready manpower to serve the emerging electrical and electronic sectors Program Learning Outcome 1b: Skilled Human resource to cater the needs of next generation power systems and EV technologies.
Program Goal 2: A culture of research and innovation by promoting faculty and student involvement in innovative projects in electrical and electronic technologies.	Program Learning Outcome 2a: Trained researchers for implementing research projects in line with national priorities such as Energy, EVs, Smart Grids, Green Technologies Program Learning Outcome 2b: Design and develop innovative smart technologies/products in energy and EVs as per the societal need
Program Goal 3: To design dynamic and flexible course structures for UG and PG programs as per the changing requirement of the industries	Program Learning Outcome 3a: Industry relevant UG, PG, and research programs Program Learning Outcome 3b: Trained manpower as per the industry requirement
Program Goal 4: To promote entrepreneurship among the students in the field of electrical and electronics engineering	Program Learning Outcome 4a: Realization of working prototype towards product development Program Learning Outcome 4b: Promotion of in-house technology-based ventures catering societal needs
Program Goal 5: Equip students with effective communication skills, enabling them to articulate technical concepts clearly and effectively in both written and oral forms.	Program Learning Outcome 5a: Manpower with enhanced soft skills to support the vision of developed India Program Learning Outcome 5b: Responsible citizen for the holistic growth of the country

Sl. No.

Subject Code

SEMESTER I

MA1101

Course Number	MA1101
Course Credit (L-T-P-C)	3-1-0-4
Course Title	Calculus and Linear Algebra
Learning Mode	Lectures and Tutorials
Learning Objectives	To provide the essential knowledge of basic tools of Differential Calculus, Integral Calculus, Vector spaces and Matrix Algebra.
Course Description	This course provides a foundation for Calculus and Linear Algebra. Topics related to properties of single and two variable functions along with their applications will be discussed. In addition fundamentals of linear algebra and matrix theory with applications will also be discussed.
Course Content	Differential Calculus (12 Lectures): Limit and continuity of one variable function (including ε-δ definition). Limit, continuity and differentiability of functions of two variables, Tangent plane and normal, Change of variables, chain rule, Jacobians, Taylor’s Theorem for two variables, Extrema of functions of two or more variables, Lagrange’s method of undetermined multipliers. Integral Calculus (10 Lectures): Riemann integral for one variable functions, Double and Triple integrals, Change of order of integration. Change of variables, Applications of Multiple integrals such as surface area and volume. Vector Spaces (12 Lectures): Vector spaces (over the field of real numbers), subspaces, spanning set, linear independence, basis and dimension. Linear transformations, range and null space, rank-nullity theorem, matrix of a linear transformation. Matrix Algebra (8 Lectures): Elementary operations and their use in getting the rank, inverse of a matrix and solution of linear simultaneous equations, Orthogonal, symmetric, skew-symmetric, Hermitian, skew-Hermitian, normal and unitary matrices and their elementary properties, Eigenvalues and Eigenvectors of a matrix, Cayley-Hamilton theorem, Diagonalization of a matrix.
Learning Outcome	Students completing this course will be able to: 1. Understand various properties of functions such as limit, continuity and differentiability. 2. Learn about integrations in various dimension and their applications. 3. learn about the concept of basis and dimension of a vector space. 4. define Linear Transformations and compute the domain, range, kernel, rank, and nullity of a linear transformation. 5. compute the inverse of an invertible matrix. 6. solve the system of linear equations. 7. Apply linear algebra concepts to model, solve, and analyze real-world problems.
Assessment Method	Quiz /Assignment/ MSE / ESE

Textbooks:

Thomas, G. B., Hass, J., Heil, C. and Weir M. D., “Thomas’ Calculus”, 14th Ed., Pearson Education, 2018
Kreyszig, E., “Advanced Engineering Mathematics”, 10th Ed., Wiley India Pvt. Ltd, 2015

Reference Books:

Jain, R. K. and Iyenger, S. R. K., “Advanced Engineering Mathematics”, 5^th, Narosa Publishing House, 2017
Axler, S., “Linear Algebra Done Right”, 3rd Ed., Springer Nature, 2015
Strang, G., “Linear Algebra and Its Applications” 4th Ed., Cengage India Private Limited, 2005

4.0

CS1101

Course Number	CS1101
Course Credit	3-0-3-4.5
Course Title	Foundations of Programming
Learning Mode	Offline
Learning Objectives	· To understand the fundamental concepts of programming · To develop the basic problem-solving skills by designing algorithms and implementing them. · To learn about various data types, control statements, functions, arrays, pointers, and file handling. · To achieve proficiency in debugging and testing a C program
Course Description	This introductory course provides a solid foundation in programming principles and techniques. Designed for students with little to no prior programming experience, it covers fundamental concepts such as variables, data types, control structures, functions, and basic data structures. Students will learn to write, debug, and execute programs using a high-level programming language. Emphasis is placed on developing problem-solving skills, logical thinking, and the ability to write clear and efficient code. By the end of the course, students will be equipped with the essential skills needed to pursue more advanced studies in computer science and software development.
Course Outline	Introduction and Programming basics, Expressions Control and Iterative statements, Functions, Arrays, Recursion vs. Iteration Pointers, 2D-Array with pointers, Structures, String, Dynamic memory allocation, File handling, Contemporary programming languages, and applications Practical component: Lab to be conducted on a 3-hour slot weekly. It will be conducted with the theory course so the topics for problems given in the lab are already initiated in the theory class.
Learning Outcome	· Understanding of Basic Syntax and Structure in C language · Proficiency in Data Types, Operators, and Control Structures · Function Implementation and learn to use them appropriately · Efficient Use of Arrays and Strings · Pointer Utilization · Ability to perform dynamic memory allocation and deallocation using malloc (), calloc (), realloc (), and free () functions. · Structured data management with structures and unions · Exposure of file Handling · Learning debugging and error Handling
Assessment Method	Internal (Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Reading

Knuth, Donald E. The art of computer programming, volume 4A: combinatorial algorithms, part 1. Pearson Education India, 2011.
P.J. Deitel and H.M. Deitel, C How To Program, Pearson Education (7th Edition)
Brian W. Kernighan and Dennis M. Ritchie, The C Programming Language, Prentice−Hall
A. Kelley and I. Pohl, A Book on C, Pearson Education (4th Edition)
K. N. King, C PROGRAMMING A Modern Approach, W. W. Norton & Company

4.5

PH1101/PH1201

Course Number	PH1101/PH1201
Course Credit	3-1-3-5.5
Course Title	Physics
Learning Mode	Lectures and Tutorials
Learning Objectives	Complies with Program Goals 1 and 2
Course Description	This course deals with fundamentals in Classical mechanics, Waves and Oscillations and Quantum Mechanics. As a prerequisite, the mathematical preliminaries such as coordinate systems, vector calculus etc will be discussed in the beginning.
Course Outline	Orthogonal coordinate systems (Plane polar, Spherical, Cylindrical), concept of generalised coordinates, generalised velocity and phase space for a mechanical system, Introduction to vector operators, Gradient, divergence, curl and Laplacian in different co-ordinate systems. Central force problem and its applications. Rigid body rotation, vector nature of angular velocity, Finding the principal axes, Euler's equations; Gyroscopic motion and its application; Accelerated frame of reference, Fictitious forces. Potential energy and concept of equilibrium, Lennard-Jones and double-well potentials, Small oscillations, Harmonic oscillator, damped and forced oscillations, resonance and its different examples, oscillator states in phase space, coupled oscillations, normal modes, longitudinal and transverse waves, wave equation, plane waves, examples two- and three-dimensional waves. Michelson-Morley experiment, Lorentz transformation, Postulates of special theory of relativity, Time dilation and length contraction, Applications of special theory of relativity.
Learning Outcome	Complies with PLO 1a, 2a, 3a
Assessment Method	Quiz, Assignments and Exams

Suggested Readings:

Textbooks:

Engineering Mechanics, M. K. Harbola, 2^nd ed., Cengage, 2012
D. Kleppner and R. J. Kolenkow, An introduction to Mechanics, Tata McGraw-Hill, New Delhi, 2000.
I. G. Main, Oscillations and Waves
H. G. Pain, The Physics of Vibrations and Waves, 1968
Frank S. Crawford, Berkeley Physics Course Vol 3: Waves and Oscillations, McGraw Hill, 1966.

References:

R. P. Feynman, R. B. Leighton and M. Sands, The Feynman Lecture in Physics, Vol I, Narosa Publishing House, New Delhi, 2009.
David Morin, Introduction to Classical Mechanics, Cambridge University Press, NY, 2007.

3. P. C. Deshmukh, Foundations of Classical Mechanics, Cambridge University Press, 2019

5.5

CE1101/CE1201

Course code	CE1101/CE1201
Course Credit (L-T-P-C)	1-0-3-2.5
Course Title	Engineering Graphics
Learning Mode	Lectures and Practical
Learning Objectives	Complies with PLO-1a 1. The course on engineering drawing is designed to introduce the fundamentals of technical drawing as an important form of conveying information. 2. Apply principles of engineering visualization and projection theory to prepare engineering drawings, using conventional and modern drawing tools. 3. Practice drawing orthographic projections, isometric views, and sectional views, of simple and combined solids in different orientations.
Course Description	This course will introduce drawing as a tool to represent a complex three-dimensional object on two-dimensional paper through methods of projections. The course explains the use of different drafting tools and the importance of conventions for uniformity and standardization of the interpretation of the drawings.
Course Outline	Fundamental of engineering drawing, line types, dimensioning, and scales. Conic sections: ellipse, parabola, hyperbola; cycloidal curves. Principle of projection, method of projection, orthographic projection, plane of projection, first angle of projection, Projection of points, lines, planes and solids. Section of solids: Sectional views of simple solids- prism, pyramid, cylinder, cone, sphere; the true shape of the section. Methods of development, development of surfaces. Isometric projections: construction of isometric view of solids and combination of solids from orthographic projections. Introduction to AutoCad and solving isometric problems.
Learning Outcome	After attending this course, the following outcomes are expected: 1. The student will understand the basic concepts of engineering drawing. 2. The student will be able to use basic drafting tools, drawing instruments, and sheets. 3. The student will be able to represent three-dimensional simple and combined solid objects on two-dimensional paper. 4. The student will be able to visualize and interpret the orientation of simple and combine solid objects.
Assessment Method	Laboratory Assignments (30%), Mid-semester examination (25%) and End-semester examination (45%).

Suggested Readings:

Textbooks:

D. Bhatt, Engineering Drawing, Charotar Publishing House.
Agrawal & Agrawal, Engineering Drawing, McGraw Hill.
Jolhe, Engineering Drawing.

References:

Engineering Drawing and Design by David Madsen

2.5

EE1101/EE1201

Course Number	EE1101/EE1201
Course Credit	3-0-3-4.5
Course Title	Electrical Sciences
Learning Mode	Lectures and Experiments
Learning Objectives	Complies with Program goals 1, 2 and 3
Course Description	The course is designed to meet the requirements of all B. Tech programmes. The course aims at giving an overview of the entire electrical engineering domain from the concepts of circuits, devices, digital systems and magnetic circuits.
Course Outline	Circuit Analysis Techniques, Circuit elements, Simple RL and RC Circuits, Kirchoff’s law, Nodal Analysis, Mesh Analysis, Linearity and Superposition, Source Transformations, Thevenin’s and Norton’s Theorems, Time Domain Response of RC, RL and RLC circuits, Sinusoidal Forcing Function, Phasor Relationship for R, L and C, Impedance and Admittance, Instantaneous power, Real, reactive power and power factor. Semiconductor Diode, Zener Diode, Rectifier Circuits, Clipper, Clamper, UJT, Bipolar Junction Transistors, MOSFET, Transistor Biasing, Transistor Small Signal Analysis, Transistor Amplifier and their types, Operational Amplifiers, Op-amp Equivalent Circuit, Practical Op-amp Circuits, Power Opamp, DC Offset, Constant Gain Multiplier, Voltage Summing, Voltage Buffer, Controlled Sources, Instrumentation Amplifier, Active Filters and Oscillators. Number Systems, Logic Gates, Boolean Theorem, Algebraic Simplification, K-map, Combinatorial Circuits, Encoder, Decoder, Combinatorial Circuit Design, Introduction to Sequential Circuits. Magnetic Circuits, Mutually Coupled Circuits, Transformers, Equivalent Circuit and Performance, Analysis of Three-Phase Circuits, Power measurement in three phase system, Electromechanical Energy Conversion, Introduction to Rotating Machines (DC and AC Machines). Laboratory: Experiments to verify Circuit Theorems; Experiments using diodes and bipolar junction transistor (BJT): design and analysis of half -wave and full-wave rectifiers, clipping and clamping circuits and Zener diode characteristics and its regulators, BJT characteristics (CE, CB and CC) and BJT amplifiers; Experiment on MOSFET characteristics (CS, CG, and CD), parameter extraction and amplifier; Experiments using operational amplifiers (op-amps): summing amplifier, comparator, precision rectifier, Astable and Monostable Multivibrators and oscillators; Experiments using logic gates: combinational circuits such as staircase switch, majority detector, equality detector, multiplexer and demultiplexer; Experiments using flip-flops: sequential circuits such as non-overlapping pulse generator, ripple counter, synchronous counter, pulse counter and numerical display; Power Measurement by two Wattmeter method; Open and Short Circuit Tests of Transformer.
Learning Outcomes	Complies with PLO 1a, 2a and 3a
Assessment Method	Quiz, Assignments and Exams

Texts/References

K. Alexander, M. N. O. Sadiku, Fundamentals of Electric Circuits, 3rd Edition, McGraw-Hill, 2008.
H. Hayt and J. E. Kemmerly, Engineering Circuit Analysis, McGraw-Hill, 1993.
L. Boylestad and L. Nashelsky, Electronic Devices and Circuit Theory, 6th Edition, PHI, 2001.
M. Mano, M. D. Ciletti, Digital Design, 4th Edition, Pearson Education, 2008.
Floyd, Jain, Digital Fundamentals, 8th Edition, Pearson.
David V. Kerns, Jr. J. David Irwin, Essentials of Electrical and Computer Engineering, Pearson, 2004.
Donald A Neamen, Electronic Circuits; analysis and Design, 3rd Edition, Tata McGraw-Hill Publishing Company Limited.
Adel S. Sedra, Kenneth C. Smith, Microelectronic Circuits, 5th Edition, Oxford University Press, 2004.
E. Fitzgerald, C. Kingsley Jr., S. D. Umans, Electric Machinery, 6th Edition, Tata McGraw-Hill, 2003.
P. Kothari, I. J. Nagrath, Electric Machines, 3rd Edition, McGraw-Hill, 2004.
Del Toro, Vincent. "Principles of electrical engineering." (No Title) (1972).

4.5

HS1101

Course Number	HS1101
Course Credit	2-0-1-2.5
Course Title	English for Professionals
Learning Mode	Offline
Learning Objectives	This course aims to help the students (a) attain proficiency in written English through the construction of grammatically correct sentences, utilization of subject-verb agreement principles, mastery of various tenses, and effective deployment of active and passive voice to ensure coherent and impactful written expression; (b) enhance oral communication skills by honing public speaking abilities, acquiring strategies to deliver persuasive presentations, and cultivating a polished telephone etiquette, enabling confident and articulate verbal communication; (c) foster active listening capabilities by recognizing different types of listening, and applying proven methods and strategies to improve active listening skills; (d) strengthen reading skills, including comprehension, interpretation, and critical analysis, to grasp diverse written materials and derive meaning from various types of texts encountered in academic and professional contexts; (e) develop adeptness in written communication for business purposes, encompassing the understanding of essential writing elements, mastery of appropriate writing styles thereby enhancing prospects for successful job interviews and subsequent professional endeavors.
Course Description	This academic course on communication skills aims to equip students with fluency in spoken and written English for effective expression in both academic and professional settings. By focusing on essential communication principles and providing practical experiences, students develop clarity, precision, and confidence in their communication. Through interactive discussions and exercises, students enhance critical thinking and adaptability in diverse contexts. Upon completion, students will excel in formal presentations, group discussions, and persuasive writing, enhancing their overall communication proficiency.
Course Outline	Unit I: Introduction to professional communication – LSRW - Phonetics and phonology Sounds in English Language – production and articulation – rhythm and intonation – connected speech - Basic Grammar and Advanced Vocabulary Sounds in English Language – production and articulation – rhythm and intonation – connected speech – persuading and negotiating – brevity and clarity in language. Unit II: Characteristics of Technical Communication: Types of communication and forms of communication - Formal and informal communication Verbal and non-Verbal Communication – Communication barriers and remedies Intercultural communication – neutral language Unit III: Comprehension and Composition – summarization, precis writing Business Letter Writing CV/ Resume – E-Communication Unit IV: Statement of Purpose, Writing Project Reports, Writing research proposal, writing abstracts, developing presentations, interviews – combating nervousness Tutorial: Listening Exercises, Speaking Practice (GDs, and Presentations), and Writing Practice Learning Outcome · Attain proficiency in written English, enabling the construction of grammatically correct sentences and coherent written expression through the use of appropriate grammar, tenses, and voice. · Enhance oral communication skills, including public speaking, persuasive presentation, and polished telephone etiquette, fostering confident and articulate verbal expression. · Cultivate active listening abilities, recognizing different listening types, overcoming obstacles, and employing strategies for attentive and effective communication. · Develop proficient written communication skills for business purposes, demonstrating understanding of essential writing elements, appropriate styles, and the creation of reports, notices, agendas, and minutes that effectively convey information.
Assessment Method	Class test + Quiz = 20%; Mid-semester = 25%; Assignment = 15%; End semester = 40%

Suggested Reading

Balzotti, Jon. Technical Communication: A Design-Centric Approach. Routledge, 2022.
Kaul, Asha, Business Communication. PHI Learning Pvt. Ltd. 2009
Laplante, Phillip A. Technical Writing: A Practical Guide for Engineers, Scientists, and Nontechnical Professionals. CRC Press, 2018.
Lawson, Celeste, et al. Communication Skills for Business Professionals, Second Edition. CUP, 2019.
Sharon Gerson and Steven Gerson. Technical Writing: Process and Product (8th Edition), London: Longman, 2013
Rentz, Kathryn, Marie E. Flatley & Paula Lentz. Lesikar’s Business Communication Connecting in a Digital world, McGraw-Hill, Irwin.2012
Allan & Barbara Pease. The Definitive Book of Body Language, New York, Bantam,2004
Jones, Daniel. The Pronunciation of English, New Delhi, Universal Book Stall.2010
Savage, Alice. Effective Academic Writing. OUP. 2014
Swan and Alter. Oxford English grammar course. OUP. 201

2.5

TOTAL

23.5

Probability Theory and Ordinary Differential Equations

Sl. No.

Subject Code

SEMESTER II

MA1201

Course Number	MA1201
Course Credit (L-T-P-C)	3-1-0-4
Course Title	Probability Theory and Ordinary Differential Equations
Learning Mode	Lectures and Tutorials
Learning Objectives	To introduce the basic concepts of probability, statistics, and Differential equations.
Course Description	This course aims to cover basic concepts of probability, statistics and ordinary differential equations. In particular, popular distributions, random sampling, various estimators and hypothesis testing will be discussed. Students will also get exposure to the linear ordinary differential equations and their solution techniques.
Course Content	Probability (12 Lectures): Random variables and their probability distributions, Cumulative distribution functions, Expectation and Variance, probability inequalities, Binomial, Poisson, Geometric, negative binomial distributions, Uniform, Exponential, beta, Gamma, Normal and lognormal distributions. Statistics (10 Lectures): Random sampling, sampling distributions, Parameter estimation, Point estimation, unbiased estimators, maximum likelihood estimation, Confidence intervals for normal mean, Simple and composite hypothesis, Type I and Type II errors, Hypothesis testing for normal mean. Ordinary Differential Equations (20 Lectures): First order ordinary differential equations, exactness and integrating factors, Picard's iteration, Ordinary linear differential equations of n-th order, solutions of homogeneous and non-homogeneous equations (Method of variation of parameters). Systems of ordinary differential equations, Power series methods for solutions of ordinary differential equations. Legendre equation and Legendre polynomials, Bessel equation and Bessel functions.
Learning Outcome	Students will get exposure and understanding of: 1. Random variables and their probability distributions 2. Understand popular distributions and their properties 3. Sampling, estimation and hypothesis testing 4. Solution of ordinary differential equations 5. Solution of system of ordinary differential equations 6. Special functions arising as power series solutions of ordinary differential equations
Assessment Method	Quiz /Assignment/ MSE / ESE

Text Books:

Hogg, R. V., Mckean, J. and Craig, A. T., “Introduction to Mathematical Statistics”, 8th Ed., Pearson Education India, 2021
M. Ross “An introduction to Probability Models, Academic Press INC, 11th edition.
Miller, I. and Miller, M., “John E. Freund's Mathematical Statistics with Applications”, 8th Ed., Pearson Education India, 2013
L. Ross, Differential equations, 3rd Edition, Wiley, 1984
E. Boyce and R. C. Di Prima, Elementary Differential equations and Boundary Value Problems, 7th Edition, Wiley, 2001.

CS1201

Course Number	CS1201
Course Credit	3-0-3-4.5
Course Title	Data Structure
Learning Mode	Offline
Learning Objectives	· Understand the principles and concepts of data structures and their importance in computer science. · Learn to implement various data structures and understand how different algorithms works. · Develop problem-solving skills by applying appropriate data structures to different computational problems. · Achieving proficiency in designing efficient algorithms.
Course Description	This course provides a comprehensive study of data structures and their applications in computer science. It focuses on the implementation, analysis, and use of various data structures such as arrays, linked lists, stacks, queues, trees, and graphs. Through theoretical concepts and practical programming exercises, this course aims to develop problem-solving and algorithmic thinking skills essential for advanced topics in computer science and software development.
Course Outline	· Introduction to Data Structure, · Time and space requirements, Asymptotic notations · Abstraction and Abstract data types · Linear Data Structure: stack, queue, list, and linked structure · Unfolding the recursion · Tree, Binary Tree, traversal · Search and Sorting, · Graph, traversal, MST, Shortest distance · Balanced Tree Practical component: Lab to be conducted on a 3-hour slot weekly. It will be conducted with the theory course so the topics for problems given in the lab are already initiated in the theory class.
Learning Outcome	· Understand Data Structure Fundamentals · Implement Basic Data Structures using a programming language · Analyse and Apply Algorithms · Design and Analyse Tree Structures · Understand the usage of graph and its related algorithms · Design and Implement Sorting and Searching Algorithms · Debug and Optimize Code
Assessment Method	Internal (Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Reading

Alfred V. Aho, John E. Hopcroft, Jeffrey D. Ullman, Data Structures and Algorithms, Published by Addison-Wesley
Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest and Clifford Stein., Introduction to Algorithms,
Mark Allen Weiss, Data Structures and Algorithm Analysis in Java
Robert Sedgewick and Kevin Wayne, Algorithms
Narasimha Karumanchi, Data Structures and Algorithms Made Easy

4.5

CH1201/CH1101

Course Number	CH1201/CH1101
Course Credit	3-1-3-5.5
Course Title	Chemistry
Learning Mode	Offline
Learning Objectives	The course aims to lay a foundation for all three branches of chemistry, viz. Organic, Inorganic, and Physical Chemistry. The course aims to nurture knowledge to appreciate the interface of chemistry with other science and Engineering branches by combining theoretical concepts and experimental studies.
Course Description	This course introduces basic organic chemistry, inorganic chemistry and Physical chemistry to understand fundamental laws that governs various reactions, reaction rates, equilibrium, and their applications in daily life through relevant experimentation.
Course Outline	Module 1: Thermodynamics: The fundamental definition and concept, the zeroth and first law. Work, heat, energy and enthalpies. Second law: entropy, free energy and chemical potential. Change of Phase. Third law. Chemical equilibrium. Conductance of solutions, Kohlrausch’s law-ionic mobilities, Basic Electrochemistry. Module 2: Coordination chemistry: Crystal field theory and consequences color, magnetism, J.T distortion. Bioinorganic chemistry: Trace elements in biology, heme and non-heme oxygen carriers, haemoglobin and myoglobin; Organometallic chemistry. Module 3: Stereo and regio-chemistry of organic compounds, conformational analysis and conformers, Molecules devoid of point chirality (allenes and biphenyls); Significance of chirality in living systems, organic photochemistry, Modern techniques in structural elucidation of compounds (UV–Vis, IR, NMR). Module 4 (Lab Component): Experiments based on redox and complexometric titrations; synthesis and characterization of inorganic complexes and nanomaterials; synthesis and characterization of organic compounds; experiments based on chromatography; experiments based on pH and conductivity measurement; experiment related to chemical kinetics and spectroscopy.
Learning Outcome	Students will be able to 1. identify organic and inorganic molecules and relate them to daily life applications through experiments. 2. understand important hypothesis, laws and their derivations to intercept physical phenomenon of chemical reactions and apply them in hands-on experiments. 3. understand the importance of organic and inorganic molecules in our body and environment. 4. know important analytical techniques to intercept chemical entity. 5. approach organic and inorganic synthesis as a skillset for drug manufacturing, calculate limiting reagents and yields, use various analytical tools to characterize organic compounds, interpret and ascertain data related to Physical chemistry aspects and know laboratory safety measures, risk factors and scientific report writing skills.
Assessment Method	Theory: 20% Quiz and assignment, 30% Mid sem and 50% End semester exams for theory part (4 credits). Lab: 60% lab report, lab performance and assignment, 20% End semester exam for practical part, 20% viva/quiz (1.5 credits). Overall Weightage: Theory (70%), Lab (30%).

Suggested Reading:

Text books:

Vogel's Qualitative Inorganic Analysis, G. Svehla, 7^th Edition, Revised, Prentice Hall, 1996.
J. Elias, S. S. Manoharan and H. Raj, "Experiments in General Chemistry", Universities Press (India) Pvt. Ltd., 1997.

A. J. Elias, A Collection of Interesting General Chemistry Experiments, revised edition, Universities Press (India) Pvt. Ltd., 2007.

Albert Cotton, G. Wilkinson, C. A. Murillo, M. Bochmann, Advanced Inorganic Chemistry - 6^th Edition New Delhi: Wiley India, 2008.
Mukkanti, Practical Engineering Chemistry, B.S. Publications, Hyderabad, 2009.
Shriver and Atkins inorganic chemistry / Peter Atkins, Tina Overton, Jonathan Rourke, Mark Weller, Fraser Armstrong-5^th Edition – Oxford: UOP. 2012.
Atkins’ Physical Chemistry, Peter Atkins, Julio de Paula, James Keeler, Oxford University Press, 11^th Edition 2017.
L. Kapoor, A Textbook of Physical Chemistry, Vol: 1, 2 (6^th Edition, 2019), Vol: 3 (5^th Edition, 2020) MaGraw Hill.
R. Chatwal, S. K. Anand, Instrumental Methods of Chemical Analysis, 5^th Edition, Himalaya Publications, 2023.

5.5

ME1201/ME1101

Course Number	ME1101/ME1201
Course Credit	0-0-3-1.5
Course Title	Mechanical Fabrication
Learning Mode	Fabrication work – hands on fabrication work in Workshop
Learning Objectives	Complies with PLOs 3-4. · This course aims to develop the concepts and skills of various mechanical fabrication methods. · Fabrication of metallic and non-metallic components, fabrication using bulk and sheet metals, subtractive and additive manufacturing methods, and assemble the parts
Course Description	This course is designed to fulfil the need of hand on experience about various approaches (conventional and CNC, subtractive and additive) of mechanical fabrication approaches. Prerequisite: NIL
Course Outline	The jobs for various shops should be planned such that they are the parts of an assembled item. The student groups will fabricate different parts in various shops which will involve some amount of their creativeness/input particularly in design and/or planning. Various components as required for the assembled part can be made using the following shops: Sheet Metal Working: Development, sheet cutting and fabrication of designated job using sheet metal (ferrous/nonferrous); Joining of required portions by soldering, in case part is desired to be made leak proof. Pattern Making and Foundry: Making of suitable pattern (wood); making of sand mould, melting of non-ferrous metal/alloy (Al or Al alloys), pouring, solidification. Observation/identification of various defects appeared on the component. Joining: Butt/lap/corner joint job fabrication as required of low carbon steel plates; weld quality inspection by dye-penetration test (non-destructive testing approach)of the component made. Demonstration of semi-automatic Gas Metal Arc welding (GMAW). Conventional machining: Operations on lathe and vertical milling to fabricate the required component. The fabrication of the component should cover various lathe operations like straight turning, facing, thread cutting, parting off etc., and operations using indexing mechanism on vertical milling. CNC centre: Fundamentals of CNC programming using G and M code; setting and operations of job using CNC lathe or milling, tool reference, work reference, tool offset, tool radius compensation to fabricate the component with a designed profile on Al/Al-alloy plate. 3D printing (Fused Filament Fabrication): (2 weeks) Create the model, select appropriate slicing and path for fabrication of a 3D job by layer deposition (additive manufacturing approach) using polymeric material. Demonstration on pattern fabrication using 3D printing.
Learning Outcome	· This course would enable the students to develop the concept of design, fabrication (subtractive and additive) for various engineering applications. Fabrication of components and assemble them. · The practical skill and hands on experience for various fabrication methods from bulk, sheet metal using conventional as well as CNC machines.
Assessment Method	Fabrication of components in each of the shops required for assembly of the given part; submission of reports for each shop, and quiz assessment.

Text and Reference books:

Hajra Choudhury, HazraChoudhary and Nirjhar Roy, 2007, Elements of Workshop Technology, vol. I,Mediapromoters and Publishers Pvt. Ltd.
W A J Chapman, Workshop Technology, 1998, Part -1, 1st South Asian Edition, Viva Book Pvt Ltd.
N. Rao, 2009, Manufacturing Technology, Vol.1, 3rd Ed., Tata McGraw Hill Publishing Company.
Adithan, B.S. Pabla, 2012, CNC machines, New Age International Publishers

1.5

ME1202/ME1102

Course Number	ME1102/ME1202
Course Number	Engineering Mechanics
L-T-P-C	3-1-0-4
Pre-requisites	Nil
Semester	Spring
Learning Mode	Lectures
Learning Objectives	Complies with PLOs 1, 4 · The objective of this first course in mechanics is to enable engineering students to analyze basic mechanics problems and apply vector-based approach to solve them.
Course Outline	1. Rigid body statics: Equivalent force system. Equations of equilibrium, Free body diagram, Reaction, Static indeterminacy. 2. Structures: 2D truss, Method of joints, Method of section. Beam, Frame, types of loading and supports, axial force, Bending moment, Shear force and Torque Diagrams for a member. 3. Friction: Dry friction (static and kinetic), wedge friction, disk friction (thrust bearing), belt friction, square threaded screw, journal bearings, Wheel friction, Rolling resistance. 4. Centroid and Moment of Inertia 5. Introduction to stress and strain: Definition of Stress, Normal and shear Stress. Relation between stress and strain, Cauchy formula. Stress in an axially loaded member and stress due to torsion in axisymmetric section
Learning Outcomes:	Following learning outcomes are expected after going through this course. · Learn and apply general mathematical and computer skills to solve basic mechanics problems. · Apply the vector-based approach to solve mechanics problems.
Assessment Method	Mid semester examination, End semester examination, Class test/Quiz, Tutorials

Reference Books

Shames, Engineering Mechanics: Statics and dynamics, 4th Ed, PHI, 2002.
P. Beer and E. R. Johnston, Vector Mechanics for Engineers, Vol I - Statics, 3rd Ed, Tata McGraw Hill, 2000.
L. Meriam and L. G. Kraige, Engineering Mechanics, Vol I - Statics, 5th Ed, John Wiley, 2002.
P. Popov, Engineering Mechanics of Solids, 2nd Ed, PHI, 1998.
P. Beer and E. R. Johnston, J.T. Dewolf, and D.F. Mazurek, Mechanics of Materials, 6th Ed, McGraw Hill Education (India) Pvt. Ltd., 2012.

IK1201

Indian Knowledge System (IKS)

TOTAL

22.5

Measurements and Instrumentation

Sl. No.

Subject Code

SEMESTER III

EE2101

Course Number	EE2101
Course Credit	3-0-0-3
Course Title	Measurements and Instrumentation
Learning Mode	Lectures and Experiments
Learning Objectives	Complies with Program goals 1, 2 and 3
Course Description	The course deals with the basic of instrumentation and measurements of several commonly known physical variables. It also introduces signal conditioning and modern electronics equipment.
Course Outline	Definition of instrumentation. Static characteristics of measuring devices. Error analysis, standards and calibration. Dynamic characteristics of instrumentation systems. Electromechanical indicating instruments: ac/dc current and voltage meters, ohmmeter; loading effect. Measurement of power and energy; Instrument transformers. Measurement of resistance, inductance, capacitance. ac/dc bridges. Measurement of non-electrical quantities: transducers classification; measurement of displacement, strain, pressure, flow, temperature, force, level and humidity. Signal conditioning; Instrumentation amplifier, Isolation amplifier, and other special purpose amplifiers. EMI and EMC, shielding, earthing and grounding. signal recovery, data transmission and telemetry. data acquisition and conversion. Modern electronic equipment: oscilloscope, DMM, frequency counter, wave/ network/ harmonic distortion/ spectrum analyzers, logic probe and logic analyzer. Data acquisition system; PC based instrumentation. Programmable logic controller: ladder diagram. Computer controlled test systems, serial and parallel interfaces, Field buses. Smart sensors (Voltage, Current and Temperature sensors). Laboratory: Experiments on displacement, temperature, strain, flow, acceleration measurements, AC bridges, PLC, instrumentation amplifier, encoder, Measurement of capacitance, inductance and resistance.
Learning Outcomes	Complies with PLO 1a, 2a and 3a
Assessment Method	Quiz, Assignments and Exams
Suggested Reading	Text/References A. D. Helfrick and W. D. Cooper, Modern Electronic Instrumentation and Measuring Techniques, Pearson Education, 1996. M. M. S. Anand, Electronic Instruments and Instrumentation Technology, PHI, 2006. E. O. Deobelin, Measurement Systems - Application and Design, Tata McGraw-Hill, 1990. B. E. Jones, Instrumentation, measurement, and Feedback, Tata McGraw-Hill, 2000. R. P. Areny and T. G. Webster, Sensors and Signal Conditioning, John Wiley, 1991. B. M. Oliver and J. M. Cage, Electronic Measurements and Instrumentation, McGraw-Hill, 1975. C. F. Coombs, Electronic Instruments Handbook, McGraw-Hill, 1995. R. A. Witte, Electronic Test Instruments, Pearson Education, 1995. B. G. Liptak, Instrument Engineers' Handbook: Process Measurement and Analysis, Chilton Book, 1995.

EE2102

Network Analysis and Synthesis

Course Number	EE2102
Course Credit	3-0-0-3
Course Title	Network Analysis and Synthesis
Learning Mode	Lectures
Learning Objectives	Complies with Program goals 1, 2 and 3
Course Description	The course is designed to meet the requirements of B. Tech. The course aims at giving detail of network theorems, graph theory and analysing and designing electrical circuits.
Course Outline	Overview of network analysis techniques, network theorems, transient and steady state sinusoidal response. Graph theory: basic definitions of loop (or tie set), cut-set, mesh matrices and their relationships, applications of graph theory in solving network equations. Two-port and N-Port networks, Z, Y, h, g and transmission parameters, combination of two ports, Analysis of common two port networks, pie and t-networks. Network functions, parts of network functions, obtaining a network function from a given part. Network transmission criteria; delay and rise time. Elements of network synthesis techniques, Cauer and Foster forms, Butterworth and Chebyshev Approximation.
Learning Outcomes	Complies with PLO 1a, 2a and 3a
Assessment Method	Quiz, Assignments, and Exams
Suggested Reading	Text/ References: 1. F. F. Kuo, Network Analysis and Synthesis, John. Wiley, 2006. 2. M. E. V. Valkenburg, Network Analysis 3rd Edition 3. R. J. Trudeau, Introduction to graph theory. Courier Corporation, 2013.

EE2103

Electrical Machines-I

Course Number	EE2103
Course Credit	2-0-2-3
Course Title	Electrical Machines-I
Learning Mode	Lectures and Experiments
Learning Objectives	Complies with Program goals 1, 2 and 3a
Course Description	The course is designed to meet the requirements of B. Tech. The course aims at giving detail of construction, operation and modelling of transformer and DC machines. Transformer and DC machines will be discussed.
Course Outline	Principles of Electromechanical Energy Conversions: Introduction, Flow of Energy in Electromechanical devices, Energy in Magnetic Systems, Singly Excited System, Determination of Mechanical Force, Mechanical Energy, Torque Equation, Doubly Excited System, energy stored in magnetic field, Electromagnetic Torque, Generated EMF in Machines, Torque in Machines with Cylindrical air-gap, General classifications of Electrical Machines. DC Machines: DC Generator: Parts of generator, Armature Winding, coil pitch, back pitch, front pitch, Resultant pitch, commutator pitch, single layer winding, two layer winding, Multiplex winding, lap & wave winding, dummy coils, Types of generators, Equalizer connections, EMF & Torque Equation, total losses and efficiency, Armature reaction, Demagnetizing and Cross Magnetizing Effects, Compensating winding Commutation, Methods for Improving Commutation, Interpoles, Performance Characteristics of DC generators, Critical speed, Critical resistance, Parallel operation, DC Motor: Principle of Motor, comparison of generator and motor action, Back Emf, Power & torque, Shaft torque, Performance characteristics of DC Motors, Losses & efficiency, power stages, speed control of DC motors, Electric Braking, Necessity of a starter, three point & four-point starters, Starting of DC motors. Transformers: Construction and principle, Types & Classification, operation at no load and on load, vector diagrams, equivalent circuit, losses, efficiency and regulation, determination of regulation and efficiency by direct load test and indirect test methods, Sumpner’s test, parallel operation, auto transformer, condition for maximum efficiency, all day efficiency. Star/star, Star/delta, Delta/delta, Delta/Star, delta/zigzag, terminal marking, Nomenclature, Vector diagram, Phase groups, Parallel operation of 3-phase Transformer, Scott connection, V-V connections, tertiary winding, Testing of transformers, Transients in transformers - voltage regulation - off load and on load tap changers, Introduction of harmonics in Transformer Laboratory: Open circuit and short circuit tests of a single-phase transformer, Load Test on a single phase transformer, Sumpner’s Test, Speed Control of DC Shunt motor, Open circuit test and load characteristics of DC generator, Speed control and output characteristics of DC motor
Learning Outcomes	Complies with PLO 1a, 2a and 3a
Assessment Method	Quiz, Assignments and Exams
Suggested Reading	Textbooks: 1. S. Chapman “Electric Machinery Fundamentals” 4th edition, 2003, McGraw-Hill. 2. B. S. Guru and H. R. Hiziroglu “Electrical Machinery and Transformers” 3rd edition, 2003, Oxford University Press. Reference Books: 1. I. L. Kosow “Electrical Machinery and Transformers” 2 edition, 2003, Prentice- Hall of India Pvt. Ltd.. 2. R. K. Rajput “Electrical Machines” 3 edition, 2003, Laxmi Publications (P) Ltd. 3. M.G. Say and E. M. Pink "The performance and design of alternating current machines: transformers, three-phase induction motors and synchronous machines" 2002, CBS. 4. A. E. Fitzgerald, K. Charles, and S. D. Umans "Electric machinery." 6th edition, 2017, McGraw Hill. 5. A.S. Langsdorf “Theory of Alternating Current Machinery”, 2nd edition, 1984, McGraw Hill.

EC2101

Analog Circuits

Course Number	EC2101
Course Credit	3-0-2-4
Course Title	Analog Circuits
Learning Mode	Lectures and Experiments
Learning Objectives	Complies with Program Goal 1 and 2
Course Description	The course deals with various analog sub circuits including analog circuits such as amplifiers, differential amplifiers, filters and oscillators. It also focuses on design and implementation of various analog circuits like amplifiers - single transistor amplifiers, cascade amplifiers, differential amplifiers, filters and oscillators.
Course Outline	CMOS realizations: current source, sink and mirrors, differential amplifiers, multistage amplifiers; Differential amplifiers: DC and small signal analysis, CMRR, current mirrors, active load and cascade configurations; Frequency response of amplifiers: high frequency device models, frequency responses of various amplifiers, GBW, methods of short circuit and open circuit time constants, dominant pole approximation; Analog subsystems: analog switches, voltage comparator, voltage regulator, switching regulator, bandgap reference voltage source, analog multiplier, Filter approximations: Butterworth, Chebyshev, first order and second order passive/active filter realizations of LPF, HPF, BPF. Signal generation and waveform shaping: Schmitt trigger, relaxation oscillators, sinusoidal oscillators – RC, LC, and crystal oscillator; Feedback amplifiers: basic feedback topologies and their properties, analysis of practical feedback amplifiers, stability; Power amplifiers: efficiency of class A, B, AB, C, D stages, output stages, short circuit protection, power transistors and thermal design considerations; Case study: 741 op-amp - DC and small signal analysis, frequency response, frequency compensation, GBW, phase margin, slew rate, offsets; Laboratory: Experiments on advanced applications of BJTs- and FETs-based circuits, Op-amps and other integrated circuits, Multistage amplifiers, Automatic gain controlled amplifiers, programmable gain amplifiers, Frequency response of amplifiers; waveform generators, Active filters, Feedback circuits and analysis, Current mirroring, 555 timer-based circuit design.
Learning Outcomes	Complies with PLO 1a, 2a, 2b
Assessment Method	Quiz, Assignments, and Exams
Suggested Reading	Textbooks: 1. A. S. Sedra and K. C. Smith, Microelectronics Circuits, 5th Edition, 2005, Oxford University Press. 2. P. Gray, P. Hurst, S. Lewis and R. Meyer, Analysis & Design of Analog Integrated Circuits, 4th Edition, 2001, Wiley. 3. B. Razavi, Fundamental of Microelectronics, 1st Edition, 2009, Wiley. 4. A. Malvino and D. Bates, Electronic Principles, 7th Edition, 2017, McGraw-Hill. 5. R. A. Gayakwad, Op-Amps and Linear Integrated Circuit, 4th Edition, 2002, Prentice Hall. Reference Books: 1. B. Carter and R. Mancini, Op Amaps for Everyone, 3rd Edition, 2009, Texas Instruments. 2. D. Johns, T. C. Carusone and K. Martin, Analog Integrated Circuit Design, 2nd Edition, 2011, Wiley. 3. R. A. Gayakwad, Op-Amps and Linear Integrated Circuit, 4th Edition, 2002, Prentice Hall. 4. P. E. Allen and D. R. Holberg, CMOS Analog Circuit Design, 2nd Edition, 1997, Oxford University Press.

EC2102

Signals and Systems

Course Number	EC2102
Course Credit	3-1-0-4
Course Title	Signals and Systems
Learning Mode	Lectures and Tutorials
Learning Objectives	Complies with Program Goal 1 and 2
Course Description	The course deals with fundamental concepts of signals and systems including its application, analysis of impulse response of systems and frequency response using transforms such as CTFT, Laplace, DTFT, ZT, DFT.
Course Outline	Signals: classification of signals; signal operations: scaling, shifting and inversion; signal properties: symmetry, periodicity and absolute integrability; elementary signals. Systems: classification of systems; system properties: linearity, time/shift-invariance, causality, stability; continuous-time linear time invariant (LTI) and discrete-time linear shift invariant (LSI) systems: impulse response and step response; Response to an arbitrary input: convolution; system representation using differential and difference equations; Eigenfunctions of LTI/ LSI systems, frequency response and its relation to the impulse response. Signal representation: signal space and orthogonal bases; Fourier series representation of continuous-time and discrete-time signals; continuous-time Fourier transform and its properties; Parseval's relation, time-bandwidth product; discrete-time Fourier transform and its properties; relations among various Fourier representations. Sampling: sampling theorem; aliasing; signal reconstruction: ideal interpolator, zero-order hold, first-order hold; discrete Fourier transform and its properties. Laplace transform and Z-transform: definition, region of convergence, properties; transform-domain analysis of LTI/LSI systems, system function: poles and zeros; stability.
Learning Outcomes	Complies with PLO 1b, 2a and 2b
Assessment Method	Quiz, Assignments and Exams
Suggested Reading	Textbooks: 1. A.V. Oppenheim, A.S. Willsky and H.S. Nawab, Signals and Systems, 2nd Edition, 2006, Prentice Hall. 2. S. Haykin and B. V. Veen, Signals and Systems, 2nd Edition, 1998, John Wiley and Sons. Reference Books: 1. B. P. Lathi, Signal Processing and Linear Systems, 1998, Oxford University Press.

HS21XX

HSS Elective - I

TOTAL

Sl. No.

Subject Code

SEMESTER IV

EC2201

Digital Electronics

Course Number	EC2201
Course Credit	3-0-2-4
Course Title	Digital Electronics
Learning Mode	Lectures and Labs
Learning Objectives	Complies with Program Goal 1, 2 and 4
Course Description	The course deals with the fundamental concepts used in digital electronics, analyzing and designing of various combinational and sequential circuits, identifying the basic requirements for a design application with focus on a cost effective solution, understanding the digital signals, and developing skills for designing combinational and sequential logic circuits and their practical implementation on breadboard.
Course Outline	Introduction to digital circuits: Logic families (RTL, TTL, ECL and MOS), Integer and floating point representation. Logic gates representation and combinational circuit realization, Boolean functions and simplification. Karnaugh Maps and logic optimization. Macro level combinational circuits and their realization: Multiplexers, Code converters, Decoders, parity Generators, 7-segment display decoder; Digital Arithmetic Circuits: Adders, Subtractor, BCD adders. Introduction to sequential elements (Latches and Flip-flops) and sequential circuit design, State machines. Finite state machines and examples: shift registers and counters. Introduction to memory circuits: RAM, ROM, EEPROM Introduction to programmable and reconfigurable devices. Digital logic realization using programmable Logic devices. Laboratory: To set up circuits for Bipolar (RTL, DTL, TTL) and Unipolar (MOS, CMOS) Logic families, Logic Gate verification; Introduction to Combinational circuits, Realization of Decoder, Design and realization of a Multiplexer and Magnitude Comparator; Verification of basic Flip Flops using 74XXICS, Implementation of basic Latches, Asynchronous Counters, Synchronous Counters, Pattern Generation and Detection
Learning Outcomes	Complies with PLO 1b, 2a, 2b and 4a
Assessment Method	Quiz, Assignments and Exams
Suggested Reading	Textbooks: 1. D. P. Leach, A. P. Malvino and G. Saha, Digital Principal and Applications, 2nd Edition, 2006, McGraw-Hill. 2. J. F. Wakerly, Digital Design Principles and Practices, 4th Edition, 2006, Pearson Education. 3. M. Mano and M. D. Cilietti, Digital Design, 4th Edition, 2008, Pearson Education. 4. C. H. Roth, Fundamentals of Logic Design, 5th Edition, 2004, Cengage Learning. 5. N. Wirth, Digital Circuit Design: An Introductory Textbook, 1st Edition, 1995, Springer. 6. D. P. Leach, A. P. Malvino and G. Saha, Digital Principal and Applications, 2nd Edition, 2006, McGraw-Hill. Reference Books: 1. D. J. Corner, Digital Logic and State Machine Design, 3rd Edition, 2012, Oxford University Press. 2. H. Taub and D. Schilling, Digital Integrated Electronics, Illustrated Edition, 1977, McGraw-Hill.

EC2202

Microprocessor

Course Number	EC2202
Course Credit	2-0-2-3
Course Title	Microprocessor
Learning Mode	Lectures and Labs
Learning Objectives	Complies with Program Goal 1, 2 and 4
Course Description	The course deals with architecture & organization of 8085 & 8086 Microprocessor, classification of the instruction set of 8086 microprocessor and distinguishing the use of different instructions and applying it in assembly language programming. It also focuses on realization of the Interfacing of memory & various I/O devices with 8086 Microprocessor, familiarization of the architecture and operation of Programmable Interface Devices and realization of the programming & interfacing of it with 8086 Microprocessor. The course covers hands on experiments on emulator and hardware kits and give exposure to advanced microprocessor architectures.
Course Outline	Introduction to Microprocessor and Microcomputer, Introduction to 8-bit microprocessor: Internal architecture of Intel 8085 microprocessor Introduction to 8086: Block diagram, Registers, Internal Bus Organization, Functional details of pins, Control signals, External Address / Data bus multiplexing, Demultiplexing. 8086 Architecture: Addressing Modes, Instruction Set Architecture, Instruction Coding Format, Instruction Description and Assembler directives, Standard program Structure, Assembly Language Programming, Strings, Procedures, Macros,. Pinouts: minimum mode and maximum mode configurations, Bus structure, bus buffering, latching, system bus timing with diagram, Interrupt Controller. Timing, I/ O mapped I/ O, and memory mapped I/ O techniques. I/O and memory interfacing using 8086: Memory interfacing and I/O interfacing with 8086, Parallel communication interface (8255), Timer (8253 / 8254) , Keyboard / Display controller (8279), Priority Interrupt controller (8259) , DMA controller (8257). Coprocessor (8087) architecture and interfacing with 8086 Microprocessor Introduction to advanced Microprocessors (X86). Laboratory: Hands on laboratory experiment based on assembly language to program microprocessor using emulator/hardware kit to implement various algorithms and applications along with peripherals.
Learning Outcomes	Complies with PLO 1b, 2a, 2b and 4a
Assessment Method	Quiz, Assignments and Exams
Suggested Reading	Textbooks: 1. R. S. Gaonkar, Microprocessor – Architecture, Programming and Applications with the 8085, 6th Edition, 2013, Penram International Publisher. 2. D. V. Hall, Microprocessors and Interfacing, 2nd Edition, 2012, McGraw-Hill. Reference Books: 1. B. B. Brey, The INTEL Microprocessors – 8086 / 8088, 80186 / 80188, 80286, 80386, 80486 Pentium and Pentium pro processor, Pentium II, Pentium III and Pentium IV - Architecture, Programming and Interfacing, 8th Edition, 2012, Pearson Education.

EC2204

Internet of Things (IoT)

Course Number	EC2204
Course Credit	3-0-0-3
Course Title	Internet of Things (IoT)
Learning Mode	Lectures
Learning Objectives	Complies with Program Goal 1, 2 and 4
Course Description	The course deals with fundamental building blocks of the Internet of Things components and its underlying concepts. It also covers the design aspect of various IoT applications.
Course Outline	Motivation, Applications and Objectives of Internet of Things (IoT), Cyber-Physical Systems and Wireless Sensor Networks. Sensors and Actuators, Sensor Types, Sensor Characteristics, Actuator Types, Controlling IoT Devices. Radio Frequency Identification (RFID) Technology, Connectivity Protocols in IoT: Bluetooth Low Energy, ZigBee, and LoRa. Data messaging Protocols in IoT: Message Queue Telemetry Transport (MQTT), Hyper-Text Transport Protocol (HTTP), Constrained Application Protocol (CoAP). Localization in IoT: Localization using Received Signal Strength (RSS), Time and Time difference of arrival (ToA/TdoA) and Angle based Localization. Sensor Fusion, Fog Computing and Edge Computing, Task Offloading. Security in IoT Networks. Use Cases of IoT for Smart Environments: Healthcare, Agriculture, and Smart City
Learning Outcomes	Complies with PLO 1b, 2a and 2b
Assessment Method	Quiz, Assignments and Exams
Suggested Reading	Textbooks: 1. Raj, P., and Raman A.C., The Internet of Things: Enabling Technologies, Platforms, and Use Cases, 1st Edition, 2017, Auerbach Publications. 2. Rayes, A., and Salam, S., Internet of Things from Hype to Reality: The Road to Digitization, 2nd Edition, 2018, Springer. 3. Kumar S., Fundamentals of Internet of Things, 1st Edition, 2021, CRC Press. Reference Books: 1. Song H. et al., Cyber-Physical Systems: Foundations, Principles and Applications, 1st Edition, 2016, Academic Press Inc. 2. Yan, L., et al., The Internet of Things: From RFID to the Next-Generation Pervasive Networked Systems, 1st Edition, 2008, CRC Press. 3. Waher, P. , Learning Internet of Things, 2015, Packt Publishing Ltd.

EE2201

Control Systems

Course Number	EE2201
Course Credit	3-0-2-4
Course Title	Control Systems
Learning Mode	Lectures and Experiments
Learning Objectives	Complies with Program goals 1, 2 and 3
Course Description	This course gives the idea of classical methods of Control Systems to be useful in Engineering applications. The prerequisite for this course is signal and systems.
Course Outline	Basic concepts: Notion of feedback, open- and closed-loop systems; Modeling and representations of control systems: Ordinary differential equations, Transfer functions, Block diagrams, Signal flow graphs, State-space representations; Performance and stability: Time-domain analysis, Second-order systems, Characteristic-equation and roots, Routh-Hurwitz criteria; Frequency-domain techniques: Root-locus methods, Frequency responses, Bode-plots, Gain-margin and phase-margin, Nyquist plots; Compensator design: Proportional, PI and PID controllers, Lead-lag compensators; State-space concepts: Controllability, Observability, pole placement result, Minimal representations; Introduction to nonlinear control. Laboratory: To Study the DC Modular Servo System and to obtain the characteristics of the constituent components. Also, to set up a closed loop position control system and study the system performance; Controller design for position and velocity control of servo motors; Modeling and analysis of Magnetic Levitation System; Design a PD/PID controller for the Magnetic Levitation System; Determine the transfer function of black box from the Bode plot Level control of three/ four coupled tanks; Study and design of controller for Inverted Pendulum System; Introduction to Matlab and analysis of basic control theory in Matlab; Linearisation and Simulation of Nonlinear Ship Roll Dynamics Twin rotor control using PI/PID controller
Learning Outcomes	Complies with PLO 1a, 2a and 3a
Assessment Method	Quiz, Assignments and Exams
Suggested Reading	Text/References N. S. Nise, Control Systems Engineering, 4th edition, New York, John Wiley, 2003. (Indian edition) G. Franklin, J.D. Powell and A. Emami-Naeini, Feedback Control of Dynamic Systems, Addison Wesley, 1986. I. J. Nagrath and M. Gopal, Control System Engineering, 2nd Edn.Wiley Eastern, New Delhi, 1982. C. L. Phillips and R.D. Harbour, Feedback Control Systems, Prentice Hall, 1985 B.C. Kuo, Automatic Control Systems, 4th Edn. Prentice Hall of India, New Delhi, 1985. K. Ogata, Modern control systems. Prentice Hall, 1997.

EE2202

Electrical Machines-II

Course Number	EE2202
Course Credit	2-0-2-3
Course Title	Electrical Machines-II
Learning Mode	Lectures and Experiments
Learning Objectives	Complies with Program goals 1, 2 and 3
Course Description	The course is designed to meet the requirements of B. Tech. The course aims at giving detail of construction, operation and modelling of AC machines. Induction and synchronous machine will be discussed.
Course Outline	AC Armature Winding: Number of Phases & Phase spread, Concentric winding, Mush winding, Double layer winding, Integral slot, lap & wave winding, Fractional Slot winding, Concentrated & Distributed winding in machines Three Phase Induction Motor: Classification of AC motors, working principle, Synchronous Speed, speed of rotor field, slip, starting & running torque, torque-slip characteristics, Starting & maximum torque, Rotor emf, effect of change in voltage & frequency on torque, speed & slip, Measurement of Slip, No-load & blocked rotor test, equivalent circuit, Phasor diagram, Circle diagram, Effect of rotor resistance on performance of induction motor, Double cage squirrel cage I.M. and its equivalent circuit, Basic of D-Q Control, Synchronous Machines: Alternator: Introduction, Stationary armature, rotor, Armature winding, Damper winding, Distribution factor, Emf equation, Alternator on load, Synchronous reactance, Voltage regulation, Methods of Voltage regulation i.e. EMF method, MMF method, Potier Triangle method, Torque, Operations, Machine efficiency, Armature reaction and it’s compensation, Short circuit ratio, Effect of increase in excitation, Brushless excitation, Effect of change in torque and speed, Determination of Synchronous reactance, AIEE methods, Synchronizing & load sharing between two machines Operating characteristics, Load angle and Power flow equations, Capability curves, Two reaction model of Salient pole machines, Effect of unequal voltages & percentage impedance, Short circuit transients, single phase generators, Slip test for measurement of Xd and Xq, Sudden short circuit of Synchronous machine. Synchronous Motor: Methods of starting of synchronous motors, Different torques in Synchronous motor, Synchronous motor with different excitation, V-curve and inverted V-curve, Stability, Power developed by synchronous motor, Synchronous condenser, Synchronous phase modifiers. Laboratory: No load and blocked rotor tests on a three phase squirrel cage induction motor; Load Test on a three phase squirrel cage induction motor; Load Test on three phase slip induction motor with different rotor resistances; open circuit and short circuit tests of an alternator; Load test on a three phase alternator; Synchronization of three phase alternator with grid supply
Learning Outcomes	Complies with PLO 1a, 2a and 3a
Assessment Method	Quiz, Assignments and Exams
Suggested Reading	Textbooks: 1. S. Chapman “Electric Machinery Fundamentals” 4th edition, 2003, McGraw-Hill. 2. B. S. Guru and H. R. Hiziroglu “Electrical Machinery and Transformers” 3rd edition, 2003, Oxford University Press. Reference Books: 1. I. L. Kosow “Electrical Machinery and Transformers” 2 edition, 2003, Prentice- Hall of India Pvt. Ltd.. 2. R. K. Rajput “Electrical Machines” 3 edition, 2003, Laxmi Publications (P) Ltd. 3. M.G. Say and E. M. Pink "The performance and design of alternating current machines: transformers, three-phase induction motors and synchronous machines" 2002, CBS. 4. A. E. Fitzgerald, K. Charles, and S. D. Umans "Electric machinery." 6th edition, 2017, McGraw Hill. 5. A.S. Langsdorf “Theory of Alternating Current Machinery”, 2nd edition, 1984, McGraw Hill.

XX22PQ

IDE - I

TOTAL

Microcontroller and Embedded Systems

Sl. No.

Subject Code

SEMESTER V

EE3101

Power Systems - I

Course Number	EE3101
Course Credit	2-0-2-3
Course Title	Power Systems - I
Learning Mode	Lectures and Experiments
Learning Objectives	Complies with Program goals 1, 2 and 3
Course Description	The course is designed to meet the requirements of B. Tech. The course aims at giving detail of power systems, generation methods, transmission line modelling and distribution systems.
Course Outline	Introduction: Basic structure of power system; demand of electrical system – base load, peak load; controlling power balance between generator and load, advantages of interconnected system. Generation of Electrical Energy: Thermal power plant – general layout, turbines, alternators, excitation system, governing system, efficiency; Hydel power plant – typical layout, turbines, alternators; Nuclear power plant – principle of energy conversion, types of nuclear reactors; brief overview of renewable energy sources: Solar Energy, Wind Energy etc. Transmission of Electrical Energy: Evaluation of Transmission line parameters- types of conductors, representation of transmission line, inductance calculation of single/three phase lines, concept of GMD and GMR, transposition of lines, bundled conductors, skin effect, proximity effect, capacitance calculation of single/three phase lines, effect of earth on calculation of capacitance, line resistance, line conductance; Analysis of transmission lines – representation, short/medium/long transmission lines, nominal T/π network, ABCD parameters, surge impedance, Ferranti effect, power flow through a transmission line, reactive power compensation of transmission line; corona loss; Insulators for overhead transmission lines – types of insulators, string efficiency, methods to improve string efficiency; Insulated cables – insulating material, grading of cables, capacitance of single/three core cable, dielectric loss; methods of grounding; Transient analysis – travelling waves, reflection and refraction, lattice diagram; mechanical design of transmission line. Distribution of Electrical Energy: D.C. and A.C. distribution, radial and ring main distribution, medium voltage distribution network, low voltage distribution network, single line diagram, substation layout, substation equipment Laboratory: Transmission Line Parameters; Transmission Line Modeling; Transmission Line Performance
Learning Outcomes	Complies with PLO 1a, 2a and 3a
Assessment Method	Quiz, Assignments, Lab, and Exams
Suggested Reading	Text/References 1. J. D. Glover, M. S. Sarma and T. J. Overbye, Power System Analysis and Design, 4/e, Thomson Learning Inc., 2007. 2. J. J. Grainger and W. D. Stevenson, Jr., Power System Analysis, Tata McGraw-Hill, 2003. 3. H. Saadat, Power System Analysis, Tata Mc-Graw Hill, 2002. 4. P. Kundur, Power System Stability and Control, Tata McGraw-Hill Edition, 2009. 5. J. Green and R. Wolson, Control and Automation of Electric Power Distribution System, Taylor and Francis, 2006. 6. T. Gonen, Electric Power Distribution System, McGraw-Hill, 1986. 7. S. N. Singh: Electric Power Generation, Transmission and Distribution, Prentice-Hall, 2007 8. D. P. Kothari, I. J. Nagrath, R. K. Saket “Modern Power System Analysis” McGraw Hill, 2022

EE3102

Modern Control Theory

Course Number	EE3102
Course Credit	3-0-2-4
Course Title	Modern Control Theory
Learning Mode	Lectures and Experiments
Learning Objectives	Complies with Program goals 1, 2 and 3
Course Description	This course focuses on providing the knowledge of modern control theory. With the laboratory components, the course further attempts to provide the skill for implementing the control theories taught in the class for addressing real-life problems. The scope of this covers both linear and nonlinear systems.
Course Outline	State Variable Approach: Derivation of state model of linear time invariant (LTI) continuous systems, transfer function from ordinary differential equations, canonical variable diagonalization, system analysis by transfer function and state space methods for continuous systems convolution integral; State transition matrices and solution of state equations for continuous and discrete time systems. Discrete Systems: Introduction to discrete time systems, sample and hold circuits, pulse transfer function, representation by difference equations and its solution using z-transform and inverse z transforms, analysis of LTI systems, unit circle concepts; Stability criterion Controllability and Observability: Concept of controllability and observability, definitions, state and output controllability and observability tests for continuous and discrete systems, controllability and observability of time varying systems Introduction, effect of state feedback on controllability and observability, design via state feedback full order observer, reduced order observers design of state observers and controllers, pole placement, Ackerman’s formula. Non-Linear Systems: Characteristics - different types of nonlinearities and their occurrence Phase plane analysis – Isocline, method - limit cycles in phase plane - stability of limit cycles – existence of limit cycle – Nonlinear feedback systems - Filter hypothesis - Describing functions - describing function for single valued and double valued nonlinear elements - amplitude and frequency of limit cycles. Stability of nonlinear Systems Linearization and equilibrium points - stability of equilibrium points - Lyapunov’s First method - Stability of non-linear systems - Lyapunov method for nonlinear systems – Variable Gradient Method for generation of Lyapunov function. Laboratory: Discretization and its effect on linear systems; Observer design; SoC estimation; Study and analysis of limit cycle and phase portrait; Bifurcation analysis, Design of controller for various nonlinear systems: DOF helicopter/twin-rotor/inverted pendulum/ball beam balance/etc.
Learning Outcomes	Complies with PLO 1a, 2a and 3a
Assessment Method	Quiz, Assignments and Exams
Suggested Reading	Text/References 1. K. Ogata , “Modern Control Engineering”, 4th Ed., Pearson Education 2. I. J. Nagrath and M. Gopal, “Control System Engineering”, 5th Ed., New Age International Private Ltd. Publishers 3. B. C. Kuo, “Automatic Control Systems”, 8th Ed., Wiley India. 4. R. C. Dorf and R. H. Bishop, “Modern Control Systems” Pearson Education. 5. S. N. Norman , “Control Systems Engineering”, 4th Ed., Wiley India 6. K. P. Mohandas, Modern Control Engineering, Revised Edition, Sanguine Pearson, 2010. 7. H. K. Khalil, Nonlinear Systems, Prentice Hall International (UK), 1996.

EC3101

Course Number	EC3101
Course Credit	3-0-2-4
Course Title	Microcontroller and Embedded Systems
Learning Mode	Lectures and Labs
Learning Objectives	Complies with Program Goal 1, 2 and 4
Course Description	The course deals with the fundamentals as well as advanced concepts in microcontroller and embedded systems. This also focuses on writing assembly and high level programs on real-time microcontrollers, developing the optimized embedded systems, and applying the ideas in different applications. Further it covers hands on experiments on commercially available embedded kits and components.
Course Outline	Introduction to microcontroller and embedded system, Introduction to CISC and RISC microcontroller, Registers, Pin diagram, I/O ports functions, 16-bits microcontroller architecture, Addressing modes, Internal memory organization, External memory (ROM & RAM) interfacing. Instruction set Architecture Data Transfer instructions, Arithmetic instructions, Logical instructions, Branch instructions, Bit manipulation instructions. Peripherals: Timers and Counters, PWM, Interrupts, communication protocols: UART, SPI. Embedded System Interfacing: ADC, DAC, Sensors, Display, keyboard. Embedded system models and development cycle, Embedded system components, Embedded processor and memory architecture. Hierarchical state machine, Embedded OS and RTOS, Scheduling, Multi-tasking. Experiments on microcontrollers: Programming and interfacing. Lab: PIC Microcontroller-Based Experiments: Write and implement a program to read input through a momentary switch and toggle the ON/OFF of led blinking; Write and implement a program to realize a simple calculator; Write and implement a program to generate precise delay and pulse by using TIMER; Write and implement a program to interface a seven segment display and scroll the roll number on single/multiple seven segment display; Write and implement a program to interface both keyboard and LCD display; Write and implement a program to interface a ADC peripheral and control LED brightness depending on ADC value; Write and implement a program to interface 16×2 LCD display and display the ADC value; Write and implement a program to use microcontroller as function generator and interface DAC to display generated signals in DSO; Write and implement a program to generate PWM and controlling a lightweight DC Motor; Write and implement a program to control speed and direction of the stepper Motor and use it as Clock. Arduino/Raspberry-Pi/Galileo-based Experiments: Write and implement a program to interface I2C IMU sensor and display over LCD display; Write and implement a program to interface blue tooth and Wi-Fi Devices
Learning Outcomes	Complies with PLO 1b, 2a and 2b
Assessment Method	Quiz, Assignments and Exams
Suggested Reading	Textbooks: 1. M. A. Mazidi, R. D. McKinlay, D. Causey, PIC Microcontroller and Embedded Systems, 1st Edition, 2008, Pearson Education. 2. P. Marvedel, Embedded System Design, 4th Edition, 2021, Springer. Reference Books: 1. R. Kamal, Embedded Systems: Architecture, Programming and Design, 3rd Edition, 2017, McGraw Hill.

EC3104

Engineering Electromagnetics

Course Number	EC3104
Course Credit	3-0-0-3
Course Title	Engineering Electromagnetics
Learning Mode	Lectures
Learning Objectives	Complies with Program Goal 1, 2 and 4
Course Description	The course deals with frequency dependent circuit designs, and various aspects of wave propagation and mechanism. The focus would be on visualizing various field interactions and phenomena and hands-on with several electromagnetic simulators and components.
Course Outline	An overview of electrostatics, electromagnetic fields, and vector calculus. Time-varying EM fields: Maxwell’s equations, wave equation, and plane waves: Helmholtz wave equation, Solution to wave equations and plane waves, wave polarization, Poynting vector and power flow in EM fields. Wave Propagation: Wave propagations in unbounded & moving medium. boundary conditions, reflection, and refraction of plane waves. Transmission Lines: distributed parameter circuits, traveling and standing waves, impedance matching, Smith chart, stub matching. Introduction to antenna, Dipole antenna. Radio-wave propagation: ground-wave, sky-wave, and space-wave. Diversity techniques. Assignments on numerical methods using computational tools: FDTD, FEM.
Learning Outcomes	Complies with PLO 1b, 2a and 4a
Assessment Method	Quiz, Assignments and Exams
Suggested Reading	Textbooks: 1. M. N. O. Sadiku, Elements of Electromagnetics, 3rd Edition, 2000, Oxford University Press. 2. R. F. Harrington, Time-Harmonic Electromagnetic Fields, 2nd Edition, 2001, Wiley-IEEE Press. 3. J. Griffiths, Introduction to Electrodynamics, 3rd Edition, 1999, Pearson Education. 4. E. C. Jordan and K. G. Balmain, Electromagnetic Waves and Radiating Systems, 2nd Edition, 2016, Pearson Reference Books: 1. K. E. Lonngren and S. V. Savov, Fundamentals Electromagnetics with MATLAB, 1st Edition, 2005, Pearson Education. 2. D. K. Cheng, Field and Wave Electromagnetics, 2nd Edition, 2001, Pearson Education. 3. N. Ida, Engineering Electromagnetics, 1st Edition, 2000, Springer. 4. W. H. Hayt Jr, J. A. Buck and M. J. Akhtar, Engineering Electromagnetics, 9th Edition, 2020, McGraw Hill.

EC3105

Random Signals & Stochastic Processes

Course Number	EC3105
Course Credit	3-0-0-3
Course Title	Random Signals & Stochastic Processes
Learning Mode	Lectures
Learning Objectives	Complies with Program Goal 1, 2 and 4
Course Description	The course deals with frequently encountered random variables, mathematical tools to analyze random process and development of analytical skills to model systems exhibiting random behavior
Course Outline	Random process: Concept of random process, ensemble, mathematical tools for studying random process, correlation function, stationarity, ergodicity, a few known stochastic processes: random walk, Poisson process, Gaussian random process, Markov chains, Brownian motion etc., pseudorandom process, nonlinear transformation of random process. Random process in frequency domain: Peridogram and power spectral density, Weiner-Khintchine-Einstein Theorem, concept of bandwidth, spectral estimation. Linear system: Discrete time and continuous time LTI system, concept of convolution, system described in frequency domain, state space description of the system. Linear systems with random inputs: Linear system fundamentals, response of a linear system, convolution, mean, autocorrelation and cross correlation function in LTI system, power spectral density in LTI, cross power spectral density in LTI. Processing of random signals: Noise in systems, noise bandwidth, SNR, bandlimited random process, noise reduction, matched filter, Wiener filter, Kalman filter, extended Kalman filter. Engineering examples.
Learning Outcomes	Complies with PLO 1b, 2a and 2b
Assessment Method	Quiz, Assignments and Exams
Suggested Reading	Text/Reference Books: 1. Miller, Scott, and Donald Childers, “Probability and random processes: with applications to signal processing and communications”, Academic Press, 2012. 2. Wim C. van Etten, “Introduction to random signals and Noise”, Chichester, England: Wiley, 2005. 3. Peyton Z. Peebles, “Probability, random variables, and random signal principles”. McGraw Hill Book Company, 1987. 4. Geoffrey R. Grimmett, and David Stirzaker, “Probability and random processes”, Oxford university press, 2001. 5. Alberto Leon-Garcia, “Probability, statistics, and random processes for Electrical engineering”, Upper Saddle River, NJ: Pearson/Prentice Hall, 2008. 6. Grewal, Mohinder, and Angus P. Andrews, “Kalman filtering: theory and practice with MATLAB”, John Wiley & Sons, 2014.

XX31PQ

IDE - II

TOTAL

Fundamental of electric drives

Sl. No.

Subject Code

SEMESTER VI

EE3201

Course Number	EE3201
Course Credit	3-0-2-4
Course Title	Fundamental of electric drives
Learning Mode	Lectures and Experiments
Learning Objectives	Complies with Program goals 1, 2 and 3
Course Description	The course is designed to meet the requirements of B. Tech. The course aims at giving detail of various electrical drives and analysis of them under various conditions.
Course Outline	Introduction to Electrical Drives, Dynamics of Electrical Drives, Review of Load Torque-Speed Characteristics of DC Motor Drives and Load, Solid-state Control of DC Motor Drives Controlled Rectifier-fed DC Drive, Chopper Controlled DC Drives, Synchronous link converter. Induction Motor Drives Operation of Induction Motor with Unbalanced Source Voltages Analysis of Induction Motor from Non-sinusoidal Voltage Supply Starting and Braking of Induction Motor Variable Voltage/ Current, Variable Frequency Control of Induction Motor Fed from VSI and CSI Control of Slip-ring Induction Motor, Kramer’s and Scherbius Drives, Synchronous, Brushless DC Motor Drives, Stepper Motor and Switched Reluctance Motor Drives Laboratory: Chopper Based Control of DC Motor, Rectifier Based Control of DC Motor, Variable voltage and variable frequency Control of Induction Motor, Voltage control of Induction Motor, Inverter based Control of BLDC Motor, Control of Synchronous Motor, Control of Switched Reluctance Motor
Learning Outcomes	Complies with PLO 1a, 2a and 3a
Assessment Method	Quiz, Assignments and Exams
Suggested Reading	Text Books: 1. G. K. Dubey “Fundamentals of electrical drives” 2nd edition, 2001, Alpha Science Int'l Ltd. 2. A. D. Veltman, W. J. Pulle, and R.W. D. Doncker. “Fundamentals of electrical drives” 2nd edition, 2016, Springer. Reference Books: R. Ericson, D. Maksimovic, “Fundamentals of Power Electronics”, 3rd edition, 2020, Springer. I. Boldea, and S. A. Nasar. “Electric drives” 3rd edition, 2017, CRC press. V. K. Yadav, R. K. Behera, Dheeraj Joshi, and Ramesh Bansal, “Power Electronics, Drives and Advanced Applications,” 1st edition, 2020, CRC Press. M Chilikin, “Electric Drives”, 2nd edition, 1970, Mir Publishers.

EE3202

Power Systems - II

Course Number	EE3202
Course Credit	3-0-2-4
Course Title	Power Systems - II
Learning Mode	Lectures and Experiments
Learning Objectives	Complies with Program goals 1, 2 and 3
Course Description	The course is designed to meet the requirements of B. Tech. The course aims at giving detail of analysis of power systems in steady state and faulted conditions. Economics of power systems is also discussed.
Course Outline	Power System Analysis: Integrated operation of power systems, modeling of power system components, load flow studies, economic load dispatch, load frequency control, automatic generation control (AGC), power system stability. Power System Protection: Symmetrical components, fault analysis, switchgear, fuses, circuit breakers and relays. Economics of Power Supply Systems: Economic choice of conductor size and voltage level, maximum demand and diversity factor, tariffs, power factor correction. Special Topics: Introduction to high voltage DC transmission (HVDC), flexible AC transmission system (FACTS), supervisory control and data acquisition (SCADA). Laboratory: Formation of network matrices; Load Flow Analysis; Economic Dispatch; Automatic Generation Control; Power System Stability
Learning Outcomes	Complies with PLO 1a, 2a and 3a
Assessment Method	Quiz, Assignments and Exams
Suggested Reading	Texts & References 1. D. P. Kothari and I. J. Nagrath, R. K. Saket, Modern Power System Analysis, McGraw-Hill, 2022. 2. P. Kundur, Power System Stability and Control, McGraw-Hill, 1995. 3. N. G. Hingorani and L. Gyugyi, Understanding FACTS, Wiley-IEEE Press, 1999. 4. J. Arrillaga, High voltage direct current transmission, IEE Power Engineering Series, 2/e, 1998. 5. A. J. Wood and B. F. Wollenberg, Power Generation Operation and Control, John Wiley and Sons, 2/e, 1996. 6. A. Wright and C. Christopoulos, Electrical Power system protection, Chapman & Hall, 1993.

EE3203

Power Electronics

Course Number	EE3203
Course Credit	3-0-2-4
Course Title	Power Electronics
Learning Mode	Lectures and Experiments
Learning Objectives	Complies with Program goals 1, 2 and 3
Course Description	The course is designed to meet the requirements of B. Tech. The course aims at giving detail of power semiconductor devices, rectifiers, dc-dc converters, and inverters.
Course Outline	Power semiconductor devices: structure and characteristics; snubber circuits, switching loss. Controlled rectifiers: full/half controlled converters for R, RL ,RLE load with source inductance and without source inductance, dual converters, sequence control. AC regulator circuits, reactive power compensators. dc-dc converters, switching dc power supplies. Inverters: square wave and PWM types, filters, inverters for induction heating and UPS. Wide Band Gap Devices, EMI and EMC. Laboratory: Rectifiers and applications; DC-DC Converters and applications; DC-AC Converters and applications; AC regulator circuits; Design of PWM generators and projects.
Learning Outcomes	Complies with PLO 1a, 2a and 3a
Assessment Method	Quiz, Assignments and Exams
Suggested Reading	Texts: 1. N. Mohan: Power Electronics- Converters, Applications and Design, 3/e, 2003, John Wiley & Sons.. 2. G. K. Dubey: Fundamentals of Electrical Drives, 2003, Narosa Publishing House. References: 1. M. Rashid: Power Electronics- Circuits, Devices and Applications, 3/e, 2004, Prentice Hall. 2. B. K. Bose: Modern Power Electronics and AC Drives, 2003, Pearson Education. 3. A. M. Trzynadlowski: Introduction to Modern Power Electronics, 1998, John Wiley & Sons. 4. M. Rashid: Power Electronics Handbook, 2001, Academic Press-Elsevier.

EE3204

Electrical Machine Design

Course Number	EE3204
Course Credit	1-0-2-2
Course Title	Electrical Machine Design
Learning Mode	Lectures and Experiments
Learning Objectives	Complies with Program goals 1, 2 and 3
Course Description	The course is designed to meet the requirements of B. Tech. The course aims at giving design detail of electrical machines. A detailed design guidelines for transformer and rotating machine will be discussed.
Course Outline	Introduction: Design of Machines, Factors, limitations, Carter’s coefficient, UMP, Axial and Radial duct, Modern trends. Materials: Conducting, magnetic and insulating materials. Magnetic Circuits: Calculations of mmf for air gap and teeth, real and apparent flux densities, iron losses, field form, leakage flux, specific permanence. Heating and Cooling: Modes of heat dissipation, Temperature gradients, types of enclosures, types of ventilation, conventional and direct cooling, amount of coolants used, Ratings. Armature Windings: Windings for DC and AC machines and their layout. Design of Transformers: Output equation, Types of transformer windings, design of core and windings and cooling tank, performance calculations. Concepts and Constraints in Design of Rotating Machines: Specific loading, output equation and output coefficient, effects of variation of linear dimension. Skeleton Design of Rotating Machines: Calculation of D and L for DC, induction and synchronous machines, length of air gap, design of field coils for DC and synchronous machines, selection of rotor slots of squirrel cage induction motors, design of bars and ends, design of rotor for wound rotor for induction motors, design of commutator and inter poles for DC machines. Computer Aided Design of Electrical Machines: Analysis and synthesis approaches, design algorithms, Introduction to optimization techniques, Implementing computer program for design of three phase induction motor, Introduction to Ansys Maxwell software for Electrical machine design
Learning Outcomes	Complies with PLO 1a, 2a and 3a
Assessment Method	Quiz, Assignments and Exams
Suggested Reading	Textbooks: 1. M. Ramamoorthy, “Computer Aided Design of Electrical Equipment” 2nd edition, 2008, East West Press Private Limited. 2. A.K. Sawhney, “ A Course in Electrical Machine Design” 6th edition, 2017, Dhanpat Rai & CO. References Books: 1. M.G. Say and E. M. Pink. “The performance and design of alternating current machines: transformers, three-phase induction motors and synchronous machines” 2nd edition, 2002, CBS. 2. E. S. Hamdi, “Design of Small Electrical Machine” 1st edition, 1994, John Wiley and Sons. 3. S. P. Smith, and M. G. Say, “Electrical Engineering Design Manual” 2nd edition, 1984, Chapman and Hall.

EC3202

Digital Signal Processing

Course Number	EC3202
Course Credit	3-0-2-4
Course Title	Digital Signal Processing
Learning Mode	Lectures and Labs
Learning Objectives	Complies with Program Goal 1, 2 and 4
Course Description	The course deals with the illustration of digital signals, systems and their significance. understanding of the analytical tools such as Fourier transforms, Discrete Fourier transforms, Fast Fourier Transforms and Z-Transforms required for digital signal processing. It also covers the design and development of the basic digital system, familiarization with various structures of IIR and FIR systems, design and realization of various digital filters for digital signal processing, interpretation of the finite word length effects on functioning of digital filters. Experimental concepts of DSP and its applications using MATLAB Software is also included.
Course Outline	Review of discrete time signals, systems and transforms and sampling theorems (bandlimited and bandpass signals) Discrete Fourier Transform (DFT): Computational problem, DFT relations, DFT properties, fast Fourier transform (FFT) algorithms (radix-2, decimation-in-time, decimation-in-frequency), Goertzel algorithm, linear convolution using DFT. Frequency selective filters: Ideal filter characteristics, lowpass, highpass, bandpass and bandstop filters, Paley-Wiener criterion, digital resonators, notch filters, comb filters, all-pass filters, inverse systems, minimum phase, maximum phase and mixed phase systems. Structures for discrete-time systems: Signal flow graph representation, basic structures for FIR and IIR systems (direct, parallel, cascade and polyphase forms), transposition theorem, ladder and lattice structures. Design of FIR and IIR filters: Design of FIR filters using windows, frequency sampling, Remez algorithm and least mean square error methods; Design of IIR filters using impulse invariance, bilinear transformation and frequency transformations. Laboratory ; DSK6713 Signal Processing Kit and MATLAB are used for the experiments: Familiarization with Kits and MATLAB, Linear and Circular Convolution, Z Transform, Discrete Fourier Transform & Fast Fourier Transform, IIR Filter Design – Analog Filter, Filter Design using Windowing Techniques
Learning Outcomes	Complies with PLO 1b, 2a and 4a
Assessment Method	Quiz, Assignments and Exams
Suggested Reading	Textbooks: 1. S. K. Mitra, Digital Signal Processing: A computer-Based Approach, TMH, 2/e, 2001. 2. A. V. Oppenheim and R. W. Shafer, Discrete-Time Signal Processing, PHI, 2/e, 2004. 3. J. G. Proakis and D. G. Manolakis, Digital Signal Processing: Principles, Algorithms and Applications, PHI, 1997 4. TMS320C6XXX CPU and Instruction Set Reference Guide, Texas Instruments, 2000 (www.ti.com) 5. V. K. Ingle and J. G. Proakis, Digital signal processing using MATLAB, Thompson Brooks/Cole, Singapore, 2007. 6. MATLAB and Signal Processing Toolbox User's Guide (www.mathworks.com) Reference Books: 1. L. R. Rabiner and B. Gold, Theory and Application of Digital Signal Processing, Prentice Hall India, 2005. 2. A. Antoniou, Digital Filters: Analysis, Design and Applications, Tata McGraw-Hill, New Delhi, 2003.

EC3203

Introduction to AI/ ML

Course Number	EC3203
Course Credit	3-0-0-3
Course Title	Introduction to AI/ ML
Learning Mode	Lectures
Learning Objectives	Complies with Program Goal 1,2 and 4
Course Description	The course deals with the comprehension of AI to analyze and map real world problem. and identification of electrical engineering problems (communication, power, control, signal processing) that is solved by AI techniques. It also focuses on different learning techniques and program/code in AI languages
Course Outline	Introduction: Foundations of Artificial Intelligence, Definitions; Problem solving: Problem-Solving Agents, Searching for Solutions, Uninformed Search, Breadth-first search, Depth-first search, Heuristic Search, Domain Relaxations, Local Search, Adversarial Search, Greedy best-first search; Logic and reasoning: Knowledge-Based systems, Propositional Logic, Reasoning Patterns in Propositional Logic, Resolution, Forward and Backward chaining, Syntax and Semantics of First-Order Logic, Using First-Order Logic, Propositional vs. First-Order Inference, Unification and Lifting, Forward Chaining, Backward Chaining, Resolution; Machine Learning: KNN, SVM, PCA, ICA, Clustering and ANN algorithms. Applications of AI in healthcare, communication, speech processing, electrical power and control engineering
Learning Outcomes	Complies with PLO 1b, 2a and 4a
Assessment Method	Quiz, Assignments and Exams
Suggested Reading	Textbooks: 1. Patrick Henry Winston, Artificial Intelligence, Third Edition, Addison-Wesley Publishing Company, 2004. 2. Nils J Nilsson, Principles of Artificial Intelligence, Illustrated Reprint Edition, Springer Heidelberg, 2014 3. Duda, Richard O., and Peter E. Hart. Pattern classification. John Wiley & Sons, 2006 Reference Books: 1. Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach, 3rd Edition, PHI 2009.

TOTAL

Sl. No.	Subject Code	SEMESTER VII	L	T	P	C
1.	EE41XX	Departmental Elective – I	3	0	0	3
2.	EE41XX	Departmental Elective – II	3	0	0	3
3.	HS41XX	HSS Elective - II	3	0	0	3
4.	XX41PQ	IDE - III	3	0	0	3
5.	EE4198	Summer Internship*	0	0	12	3
6.	EE4199	Project – I	0	0	12	6
TOTAL			12	0	24	21

* For specific cases of internship after 6^th Semester, the performance evaluation would be made on joining the VII^th Semester and graded accordingly in the VII^th Semester:

Note :

a) (i) Summer internship (*) period of at least 60 days’ (8 weeks) duration begins in the intervening vacation between semester VI and VII that may be done in industry / R&D / Academic Institutions including IIT Patna. The evaluation would comprise combined grading based on host supervisor evaluation, project internship report after plagiarism check and seminar presentation at the Department (DAPC to coordinate) with equal weightage of each of the three components stated herein.

a) (ii) Further, on return from internship, students will be evaluated for internship work through combined grading based on host supervisor evaluation, project internship report after plagiarism check, and presentation evaluation by the parent department with equal weightage of each component.

b) (i) In the VII^th semester, students can opt for a semester long internship on recommendation of the DAPC and approval of the Competent Authority.

b) (ii) On approval of semester long internship, at the maximum two courses (properly mapped/aligned syllabus) at par with institute electives may be opted from NPTEL and / or SWAYAM and the other two more should be done at the institute through course overloading in any other semester (either before or after the internship) and/or during following summer semester.

b) (iii) The candidates opting two courses from NPTEL and / or SWAYAM would be required to appear in the examination at the Institute as scheduled in the Academic Calendar.

Electric Traction and Propulsion

Department Elective I

Department Elective I

Sl. No.

Subject Code

Course

EE4101

Course Number	EE4101
Course Credit	3-0-0-3
Course Title	Electric Traction and Propulsion
Learning Mode	Lectures
Learning Objectives	Complies with Program goals 1, 2, 3 and 4
Course Description	The course is designed to meet the requirements of B. Tech. The course aims at giving an introduction to electric traction, traction systems and drives, and propulsion mechanism
Course Outline	Electric Traction Introduction, Traction Systems and Latest Trends, Mechanics of Train Movement, Traction Motors and Their Control, Electric Locomotives and Auxiliary Equipment, Feeding and Distribution System. Direct Drive Linear Motors and applications. Fundamentals of electric propulsion system including land, water and space, including space flight dynamics, rocket propulsion systems overview, nozzle theory, combustion processes, and flight performance.
Learning Outcomes	Complies with PLO 1b, 2b and 3b
Assessment Method	Quiz, Assignments and Exams
Suggested Reading	Texts/References: 1. Modern Electric Traction H. Partab Dhanpat Rai and Sons, New Delhi 2. Electric Traction J. Upadhyay S. N. Mahendra Allied Publishers Ltd., Dhanpat Rai and Sons, New Delhi 3. Electric Traction A.T. Dover Mac millan, Dhanpat Rai and Sons, New Delhi 4. Electric Traction Hand Book R. B. Brooks. Sir Isaac Pitman and sons ltd. London.

EC4102

Deep Learning for Video Surveillance Systems

Course Number	EC4102
Course Credit	3-0-0-3
Course Title	Deep Learning for Video Surveillance Systems
Learning Mode	Lectures
Learning Objectives	Complies with Program Goal 1, 2 and 3
Course Description	The course deals with video surveillance tasks such as monitoring and processing of video footage, and understanding and analyzing of machine and deep learning models. The course also develop competence to take logical, scientific and correct decisions while predicting model outcomes. Aptitude and ability of performance measurement and management of video surveillance cameras is also covered.
Course Outline	Introduction to Video Surveillance Systems: Introduction to image processing methods, Edge detection and linking, Image transforms, Introduction to video processing techniques, Video compression standards. Motion detection using optical flow method, motion modeling, Background modeling, Basic building blocks of video surveillance systems. Introduction to Deep Learning: Introduction to neural networks with linear algebra, Matrix mathematics and probability, Introduction to multilayer perceptron networks, forward and back propagation, Hyper-parameter tuning, Regularization and optimization in neural networks, Introduction to tensor-flow. Convolutional Neural Nets: Introduction to convolutional neural networks, Key concepts like convolution and pooling. Stacking convolutional layers for object detection. Recurrent Neural Nets: Introduction to recurrent neural networks (RNN, LSTM, GRU) for sequence level tasks (time series, video). Bidirectional and deep recurrent nets. Use them for activity recognition. Object Detection and Classification using Deep Learning: Haar like feature based object detection, Viola Jones object detection framework, Deep learning based object classification. Object Tracking using Deep Learning: Video monitoring for detection and tracking of single as well as multiple interacting objects, Region-based tracking, Contourbased tracking, Feature-based tracking, Model-based tracking, KLT tracker, Meanshift based tracking. Deep Learning based Human Activity Recognition: Template based activity recognition, CNN based activity recognition, RNN based activity recognition, abnormal behavior detection in crowded environments using deep learning Camera Networks for Surveillance: Types of CCTV (closed circuit television) camera- PTZ (pan-tilt zoom) camera, IR (Infrared) camera, IP (Internet protocol) camera, wireless security camera, multiple view geometry, camera network calibration, PTZ camera calibration, camera placement, smart imagers and smart cameras, Introducing graph signal processing, consensus networks. Emerging Techniques of Deep Learning in Visual Surveillance System: Augmented surveillance system, Operator attention based visual surveillance system, EEG and eye tracking based visual surveillance system, ONVIF standard for the interface of IP-based physical security products.
Learning Outcomes	Complies with PLO 1b, 2a and 3b
Assessment Method	Quiz, Assignments and Exams
Suggested Reading	Textbooks: 1. M H Kolekar, “Intelligent Video Surveillance Systems: An Algorithmic Approach”, CRC press Taylor and Francis Group, 2018 2. Q. Huihuan, X. Wu, Y. Xu, “Intelligent Surveillance Systems”, Springer Publication, 2011. 3. Ian Goodfellow, Yoshua Bengio and Aaron Courville, “Deep Learning”, The MIT Press, 2017. Reference Books: 1. Murat A. Tekalp, “Digital Video Processing”, Prentice Hall, 1995. 2. Pradeep K Atrey, Mohan Kankanhalli, A Cavallaro, “Intelligent Multimedia Surveillance: Current Trends and Research” Springer Publication, 2013. 3. Y. Ma and G. Qian (Ed.), “Intelligent Video Surveillance: Systems and Technology”, CRC Press, 2009. 4. H. Aghajan and A. Cavallaro (Ed.), Multi-Camera Network: Principles and Applications”, Elsevier, 2009.

EC4103

FPGA based System Design

Course Number	EC4103
Course Credit	3-0-0-3
Course Title	FPGA based System Design
Learning Mode	Lectures
Learning Objectives	Complies with Program Goal 1, 2 and 3
Course Description	The course deals with design of complex digital systems & use the design flow for using FPGA. This also gives exposure to Softcore Processor IP, Memory and other IO IPs and digital IPs, understanding of IP integration for large scale FPGA based digital System. Also, it covers performance analysis and issues of large scale digital system on FPGA and completion of a significant project on the FPGA platform.
Course Outline	Introduction to reconfigurable and FPGA based system Design; Basic and Advanced FPGA Fabrics; Combinational, Sequential logic and FSM realization on FPGA; FPGA Architecting: Speed, Area and Power; Issues on FPGA based system Design: Area, Timing and Power; Design Methodologies: Behavioral /high level Design and Implementation methodologies: RTL, IP Core, System Generator; Processor and memory cores; Timing analysis; Clock distribution and management systems; IP Cores for FPGA: Block and Distributed memory, FIFO, CORDIC, Clock distribution and management systems; Large scale System Design: Platform FPGA, Multi-FPGA System; Busses and I/O communication system; System Design and Implementation using FPGA: DSP and Communication Blocks and Cryptography blocks Introduction to FPGA based Embedded system platform: Soft processor, AHB Bus and I/O interfacing – Case studies.
Learning Outcomes	Complies with PLO 1b, 2b and 3b
Assessment Method	Quiz, Assignments and Exams
Suggested Reading	Text/Reference Books: 1. Wayne Wolf, “FPGA Based System Design”, Prentice Hall Modern Semiconductor Design Series, 2004. 2. Steve Kilts, “Advanced FPGA design – Architecture, Implementation and Optimization”, Wiley publications,2007. 3. Ron Sass and Andrew G. Schmidt, Morgan Kaufmann (MK), “Embedded System design with Platform FPGAs”, Elsevier,2010.

Department Elective II

Department Elective II

Sl. No.

Subject Code

Course

EE4102

Power System Reliability

Course Number	EE4102
Course Credit	3-0-0-3
Course Title	Power System Reliability
Learning Mode	Lectures
Learning Objectives	Complies with Program goals 1, 2 and 3
Course Description	The course is designed to meet the requirements of B. Tech. The course aims at giving reliability, application of probability distributions to evaluate the reliability of power systems.
Course Outline	Introduction to Reliability, Basic Probability Theory, Application of the binomial distribution, Network modelling and evaluation of simple systems, Network modelling and evaluation of complex systems, Probability distributions in reliability evaluation, System reliability evaluation using probability distributions, Distribution systems reliability-basic techniques and radial networks, Plant and Station availability.
Learning Outcomes	Complies with PLO 1a, 2a and 3a
Assessment Method	Quiz, Assignments and Exams
Suggested Reading	Texts/References: 1. R. Billinton, R.N.Allan, BS Publications, Reliability Evaluation of Power systems, 2007. 2. J. Endrenyi, John Wiley and Sons,Reliability Modeling in Electric Power Systems, 1978

EC4101

Introduction to Quantum Computing

Course Number	EC4101
Course Credit	3-0-0-3
Course Title	Introduction to Quantum Computing
Learning Mode	Lectures
Learning Objectives	Complies with Program Goal 1, 2 and 3
Course Description	The course deals with the key components and architecture of quantum computing systems, including qubits, quantum gates, and quantum circuits. It also focuses on comprehending the principles of quantum information theory, including quantum entanglement, quantum entropy, and quantum teleportation. Implementation and analysis of quantum algorithms, such as Shor's algorithm for factoring and Grover's algorithm for search problems is also included.
Course Outline	Introduction: History, Motivation, Need of quantum bits, quantum states, quantum computations, quantum information, and quantum algorithms Overview of complex numbers and Linear Algebra, Introduction to quantum mechanics and its postulates, Bloch sphere Quantum gates: X, Z, Y, H, R, S, T, Square root of NOT Quantum Circuits: Single qubits and multiple qubits operations and quantum teleportation Quantum Algorithms: Deutsch’s algorithm, Deutsch-Jozsa algorithm, Simon’s algorithm Quantum Tools and Applications: Goal Challenges, Lights and Photon, Decoherence, Ion Trap, Quantum Simulation
Learning Outcomes	Complies with PLO 1b, 2a and 3a
Assessment Method	Quiz, Assignments and Exams
Suggested Reading	Textbooks: 1. Nielsen, M. A., and Chuang, I. L., Quantum computation and quantum information, 10th Anniversary Edition, 2010, Cambridge university press. 2. Yanofsky, N. S., and Mannucci, M. A., Quantum computing for computer scientists, 1st Edition, 2008, Cambridge University Press. Reference Books: 1. Johnston, E. R., Harrigan, N., and Gimeno-Segovia, M., Programming quantum computers: essential algorihms and code samples, 1st Edition, 2019, O'Reilly Media.

EC4105

Digital Image Processing

Course Number	EC4105
Course Credit	3-0-0-3
Course Title	Digital Image Processing
Learning Mode	Lectures
Learning Objectives	Complies with Program Goal 1, 2 and 3
Course Description	The course deals with the fundamental concepts of digital image processing, including filtering, transforms, morphology, colour and image analysis. It also covers the basic image processing algorithms in C or Matlab or Python and make ready the students for advanced version of the course.
Course Outline	Introduction to Digital Image Processing & Applications, Sampling, Quantization, Basic Relationship between Pixels, ImagingGeometry, Image Transforms, Image Enhancement, Image Restoration, Image Segmentation, Morphological Image Processing, Shape Representation and Description, Object Recognition and Image Understanding, Texture Image Analysis, Motion Picture Analysis, Image Data Compression.
Learning Outcomes	Complies with PLO 1b, 2a and 3b
Assessment Method	Quiz, Assignments and Exams
Suggested Reading	Textbooks: 1. Rafael C. Gonzalez and Richard E. Woods, Digital Image Processing, Pearson 2. Anil K. Jain, Fundamentals of Digital Image Processing, Prentice Hall Reference Books: 1. Milan Sonka, Vaclav Hlavac and Roger Boyle, Image Processing, Analysis and Machine Vision, Springer

Department Elective III

Department Elective III

Sl. No.

Subject Code

Course

EE4201

Power System Protection

Course Number	EE4201
Course Credit	3-0-0-3
Course Title	Power System Protection
Learning Mode	Lectures
Learning Objectives	Complies with Program goals 1, 2 and 3
Course Description	The course is designed to meet the requirements of B. Tech. The course aims at giving the necessity of protecting power system components. The course discusses protection of generators, transformers and transmission lines protection.
Course Outline	Introduction to Power System Protection: Need for protective schemes, Nature and Cause of Faults, Types of Faults, Effects of Faults, Fault Statistics, Zones of Protection, Primary and Backup Protection, Essential Qualities of Protection, Performance of Protective Relaying, Classification of Protective Relays, Automatic Reclosing, Current Transformers for protection, Voltage Transformers for Protection. Relay Construction and Operating Principles: Introduction, Electromechanical Relays, Static Relays – Merits and Demerits of Static Relays, Numerical Relays, Comparison between Electromechanical Relays and Numerical Relays. Overcurrent Protection: Introduction, Time – current Characteristics, Current Setting, Time Setting. Overcurrent Protective Schemes, Reverse Power or Directional Relay, Protection of Parallel Feeders, Protection of Ring Mains, Earth Fault and Phase Fault Protection, Combined Earth Fault and Phase Fault Protective Scheme, Phase Fault Protective Scheme, Directional Earth Fault Relay, Static Overcurrent Relays, Numerical Overcurrent Relays. Distance Protection: Introduction, Impedance Relay, Reactance Relay, Mho Relay, Angle Impedance Relay, Effect of Arc Resistance on the Performance of Distance Relays, Reach of Distance Relays. Effect of Power Surges (Power Swings) on Performance of Distance Relays, Effect of Line Length and Source Impedance on Performance of Distance Relays. Differential Protection: Introduction, Differential Relays, Simple Differential Protection, Percentage or Biased Differential Relay, Differential Protection of 3 Phase Circuits, Balanced (Opposed) Voltage Differential Protection. Rotating Machines Protection: Introduction, Protection of Generators. Transformer and Bus zone Protection: Introduction, Transformer Protection, Buszone Protection, Frame Leakage Protection. Protection against Overvoltage: Causes of Overvoltage, Lightning phenomena, Wave Shape of Voltage due to Lightning, Over Voltage due to Lightning, Klydonograph and Magnetic Link, Protection of Transmission Lines against Direct Lightning Strokes, Protection of Stations and Sub – Stations from Direct Strokes, Protection against Travelling Waves, Insulation Coordination, Basic Impulse Insulation Level (BIL).
Learning Outcomes	Complies with PLO 1a, 2a and 3a
Assessment Method	Quiz, Assignments, and Exams
Suggested Reading	Text Books: 1. B. Ram, D.N. Vishwakarma “Power System Protection and Switchgear” 2 nd Edition, 2017, McGraw Hill. 2. H. J. A. Ferrer, and E. O. Schweitzer, eds. “Modern solutions for protection, control, and monitoring of electric power systems” 1st edition, 2010, Schweitzer Engineering Laboratories. 3. B. Oza et al “Power System Protection and Switchgear” 1 st Edition, 2010, McGraw Hill. Reference Books: 1. Bhavesh et al “Protection and Switchgear” 1 st Edition, 2011, Oxford. 2. N. Veerappan S.R. Krishnamurthy “Power System Switchgear and Protection” 1 st Edition, 2009, S. Chand. 3. S. H. Horowitz, A. G. Phadke, and C. F. Henville “Power system relaying” 3rd edition, 2014, John Wiley & Sons.

EE4202

Digital Control System

Course Number	EE4202
Course Credit	3-0-0-3
Course Title	Digital Control System
Learning Mode	Lectures
Learning Objectives	Complies with Program goals 1, 2 and 3
Course Description	The scope of digital systems is very wide in the modern engineering era. Therefore, it is necessary that the students are taught with the control tools for handling the digital systems. This course focuses on the same.
Course Outline	Introduction: Structure and examples of digital control systems; input signals. Sampling and Reconstruction of Signals: Zero-order hold (ZOH); D-A conversion; sampling theory; aliasing; choice of the sampling period. z-transform theory: z-transforms of standard discrete-time signals; properties of z-transform; inversion of z-transform; final value theorem. Modeling of Digital Control Systems: ADC model; DAC model; transfer function of ZOH; DAC; analog subsystem; ADC combination transfer function; systems with transport lag; closed-loop transfer function; analog disturbances in a digital system; steady-state error and error constants for different input signals. Stability of Digital Control Systems: stable z-domain pole locations; asymptotic stability; BIBO stability; internal stability Routh-Hurwitz stability criterion; Nyquist stability criterion; phase margin; gain margin. Digital Control System Design: z-Domain root locus; z-Domain digital control system design (z-Domain contours, proportional control design in z-domain); Digital implementation of analog controller design (differencing methods, pole-zero matching, bilinear transformation, empirical digital PID controller tuning); direct z-domain digital controller design; frequency response design; direct control design; finite settling time design. State Space Analysis of Discrete-time Systems: discrete-time state space equations; z-transform solution of discrete-time state equations; z-transfer function from state space equations; controllability and stabilizability; observability and detectability.
Learning Outcomes	Complies with PLO 1a, 2a and 3a
Assessment Method	Quiz, Assignments and Exams
Suggested Reading	Text Books: 1. M. S. Fadali and Antonio Visioli, Digital Control Engineering Analysis and Design. Academic Press (Elsevier), Third Edition, 2020. 2. C. L. Phillips, H. Troy Nagle, Aranya Chakrabortty, Digital Control System Analysis & Design, Pearson Prentice Hall, 2015. 3. B. C. Kuo, Digital Control Systems, Oxford University Press, 1992. References: 1. S. Monaco and D. Normand-Cyrot, Issues on nonlinear digital control. European Journal of Control, vol. 7, no. 2-3, pp. 160-177, 2001. 2. J. R. Leigh, Applied digital control: theory, design and implementation. Courier Dover Publications, 2006. 3. B. Wittenmark, K. E. Årzén, and K. J. Astrom, Computer control: An overview. International Federation of Automatic Control, 2002. 4. K. Warwick and D. Rees, Industrial digital control systems. IET, 1988.

EE4203

Introduction to Energy Storage Techniques

Course Number	EE4203
Course Credit	3-0-0-3
Course Title	Introduction to Energy Storage Techniques
Learning Mode	Lectures
Learning Objectives	Complies with Program goals 1, 2, 3 and 4
Course Description	The course is designed to meet the requirements of B. Tech. The course aims at giving a brief of energy storage technique. Various storage technique such as Battery, Fuel Cell etc will be discussed.
Course Outline	Energy storage systems overview - Scope of energy storage, needs and opportunities in energy storage, Technology overview and key disciplines, comparison of time scale of storages and applications, Energy storage in the power and transportation sectors. Thermal storage system-heat pumps, hot water storage tank, solar thermal collector, application of phase change materials for heat storage-organic and inorganic materials, efficiencies, and economic evaluation of thermal energy storage systems. Chemical storage system- hydrogen, methane etc., concept of chemical storage of solar energy, application of chemical energy storage system, advantages and limitations of chemical energy storage, challenges, and future prospects of chemical storage systems. Electromagnetic storage systems - double layer capacitors with electrostatically charge storage, superconducting magnetic energy storage (SMES), concepts, advantages and limitations of electromagnetic energy storage systems, and future prospects of electromagnetic storage systems. Electrochemical storage system (a) Batteries-Working principle of battery, primary and secondary (flow) batteries, battery performance evaluation methods, major battery chemistries and their voltages- Li-ion battery& Metal hydride battery vs lead-acid battery. (b) Supercapacitors- Working principle of supercapacitor, types of supercapacitors, cycling and performance characteristics, difference between battery and supercapacitors, Introduction to Hybrid electrochemical supercapacitors. (c) Fuel cell: Operational principle of a fuel cell, types of fuel cells, hybrid fuel cell-battery systems, hybrid fuel cell-supercapacitor systems.
Learning Outcomes	Complies with PLO 1b, 2a, 2b, 4a, 4b
Assessment Method	Quiz, Assignments and Exams
Suggested Reading	Text books: 1. F. S. Barnes and J. G. Levine: Large Energy Storage Systems Handbook (Mechanical and Aerospace Engineering Series), 2011, CRC press. 2. R. Zito: Energy storage: A new approach, 2010, Wiley. References: 1. G. Pistoia, and L. Boryann, Behaviour of Lithium-Ion Batteries in Electric Vehicles: Battery Health, Performance, Safety, and Cost, 2018, Springer International Publishing AG. 2. R. A. Huggins: Energy storage, 2010, Springer Science & Business Media. 3. P. Denholm, E. Ela, Brendan Kirby and Michael Milligan: The Role of Energy Storage with Renewable Electricity Generation, National Renewable Energy Laboratory (NREL) -a National Laboratory of the U.S. Department of Energy.

Department Elective IV

Department Elective IV

Sl. No.

Subject Code

Course

EE4204

Special Electrical Machines

Course Number	EE4204
Course Credit	3-0-0-3
Course Title	Special Electrical Machines
Learning Mode	Lectures
Learning Objectives	Complies with Program goals 1, 2 and 3
Course Description	The course is designed to meet the requirements of B. Tech. The course aims at giving a detail of special electrical machines. Synchronous reluctance motor, switched reluctance motor, stepping motor, PMSM, PMBLDC will be discussed.
Course Outline	STEPPING MOTORS: Constructional features, principle of operation, types, modes of excitation, Torque production in Variable Reluctance (VR) stepping motor, Static and Dynamic characteristics, Introduction to Drive circuits for stepper motor, suppressor circuits, Closed loop control of stepper motor- Applications. SWITCHED RELUCTANCE MOTORS: Principle of Operation, Constructional features, Torque equation, Power Semi-Conductor Switching Circuits, frequency of variation of inductance of each phase winding - Control circuits of SRM-Torque - Speed Characteristics, Microprocessor based control of SRM Drive, Applications. SYNCHRONOUS RELUCTANCE MOTORS: Constructional features: axial and radial air gap Motors. Operating principle, reluctance torque - Phasor diagram, Speed torque characteristics, Applications. PERMANENT MAGNET BRUSHLESS DC MOTORS: Commutation in DC motors, Electronic Commutation - Difference between mechanical and electronic commutators- Hall sensors, Optical sensors, Construction and principle of PM BLDC Motor, Torque and E.M.F equation, Torque-speed characteristics, Power Controllers-Drive Circuits, Applications. PERMANENT MAGNET SYNCHRONOUS MOTORS: Construction and types, Principle of operation, EMF and Torque equation, Phasor diagram Torque Speed Characteristics.
Learning Outcomes	Complies with PLO 1a, 1b, 2a, 2b and 4b
Assessment Method	Quiz, Assignments and Exams
Suggested Reading	Text/Reference books: 1. M., T. JE Brushless permanent-magnet and reluctance motor drives., 1989, Clarendon Press. 2. R. Krishnan, Permanent magnet synchronous and brushless DC motor drives, 2017, CRC press. 3. V. V. Athani, Stepper motors: fundamentals, applications and design, 1997, New Age International. 4. P. Acarnley, Stepping motors: a guide to theory and practice. No. 63., 2002, IET. 5. B. Bilgin, J. W. Jiang, and A. Emadi Switched reluctance motor drives: fundamentals to applications., 2018, Boca Raton, FL. 6. N. Bianchi, B. Cristian, and G. Bacco Synchronous Reluctance Machines: Analysis, Optimization and Applications. vol. 186., 2021, IET.

EE4205

High Voltage Engineering

Course Number	EE4205
Course Credit	3-0-0-3
Course Title	High Voltage Engineering
Learning Mode	Lectures
Learning Objectives	Complies with Program goals 1, 2 and 3
Course Description	This course provides students with a comprehensive understanding of high voltage engineering, including the principles of electric field stress control, insulation technology, and high voltage testing techniques. Emphasis is placed on real-world applications, safety protocols, and the design and maintenance of high voltage equipment and systems.
Course Outline	Electric Field Strength (Electric Stress) : Introduction, Importance of Electric Field Intensity in the dielectrics, Types of electric fields and degree of uniformity fields, Utilization of dielectric properties and stress control. Gaseous Dielectrics : Properties of atmospheric air, SF6 and vacuum, Development of electron avalanche, Breakdown mechanisms, Breakdown in uniform fields, Breakdown of gaseous dielectrics in weakly non-uniform fields. Properties of liquid and solid dielectrics : Classification and properties, permittivity and polarization, Insulation resistance, conductivity, losses in dielectrics, Partial breakdown phenomenon in dielectrics. Generation of High Test Voltages : Methods of generation of power frequency high test voltage, transformers in cascade, Resonance transformers, Generation of high DC voltage, Impulse voltage generator. Measurement of High voltage: Peak high voltage measurement techniques, Sphere gap; Construction; Effects of earthed objects and atmospheric conditions, Electrostatic Voltmeters, Principle and Construction, Potential Dividers, their types and applications.
Learning Outcomes	Complies with PLO 1a, 3a and 3b
Assessment Method	Quiz, Assignments and Exams
Suggested Reading	Text/References: E. Kuffel, W. S. Zaengl, and J. Kuffel, 'High Voltage Engineering Fundamentals', Butterworth-Heineman press, Oxford, 2000. M. S. Naidu & V. Kamaraju, High Voltage Engineering, Tata McGraw Hill, 2004\

EE4206

Fundamentals of Electric Vehicle Technology

Course Number	EE4206
Course Credit	3-0-0-3
Course Title	Fundamentals of Electric Vehicle Technology
Learning Mode	Lectures
Learning Objectives	Complies with Program goals 1, 2 and 3
Course Description	The course is designed to meet the requirements of B. Tech. The course aims at giving a brief overview of electric vehicle technology. Drive power train concept, inverter design, charger design and motor control will be discussed.
Course Outline	History of electric vehicle journey, Electric vehicle architecture and its type and challenges, Dynamics of electric vehicle, Benefits of using electric vehicle, Concept of drive cycle, Electric vehicle drivetrain components, Electric vehicle auxiliaries. 3-phase inverter design & analysis and its control, Multilevel inverter design & analysis and its control. Power factor correction AC-DC converter and its control, Phase -shifted full bridge converter and its control. Basics of Batteries, Lithium-ion vs Lead Acid Battery, Modelling of Battery, Supercapacitor, Fuel Cell. Introduction motor drive and its control, Permanent magnet motor drive and its control, Switched reluctance drive and its control.
Learning Outcomes	Complies with PLO 1a, 1b, 2a and 2b
Assessment Method	Quiz, Assignments and Exams
Suggested Reading	Textbooks: 1. N. Mohan, T. M, Undelnad, W. P, Robbins: Power Electronics: Converters, Applications and Design, 3rd Edition, 2002, Wiley. 2. M. Eshani, Y. Gao, Sebastien E Gay, Ali Emadi: Modern electric, hybrid electric and fuel cell vehicles, Fundamentals, Theory, and Design. 2005, Boca Raton, FL, CRC. References: 1. R. Ericson Fundamentals of Power Electronics, 2004, Chapman & Hall. 2. F. A. Silva; M. P. Kazmierkowski: Energy Storage Systems for Electric Vehicles, 2021, MDPI.

Department Elective V

Department Elective V

Sl. No.

Subject Code

Course

EC4205

Biomedical Signal Processing

Course Number	EC4205
Course Credit	3-0-0-3
Course Title	Biomedical Signal Processing
Learning Mode	Lectures
Learning Objectives	Complies with Program Goal 1, 2 and 3
Course Description	The course deals with.various Biomedical Signal Processing and Monitoring Tasks, analyzing machine and deep learning biomedical models. The course also develop competence to take logical, scientific and correct decisions while predicting model outcomes
Course Outline	Introduction of biomedical signals: Nervous system, Neuron anatomy, Basic Electrophysiology, Biomedical signal’s origin and dynamic characteristics, biomedical signal acquisition and processing, Different transforms techniques. The Electrical Activity of Heart: Heart Rhythms, Components of ECG signal, Heart beat Morphologies, Noise and Artifacts, Muscle Noise Filtering, QRS Detection Algorithm, ECG compression techniques (Direct Time Domain (TP, AZTECH, CORTES, SAPA, Entropy Coding), Frequency Domain (DFT, DCT, DWT, KLT, Walsh Transform), Parameter Extraction: Heart rate variability, acquisition and RR Interval conditioning, Spectral analysis of heart rate variability. The Electrical Activity of Brain: Electroencephalogram, Types of artifacts and characteristics, Filtration techniques using FIR and IIR filters, Independent component analysis, Nonparametric and Model-based spectral analysis, Joint Time-Frequency Analysis, Event Related Potential, Noise reduction by Ensemble Averaging and Linear Filtering, Single-Trail Analysis and adaptive analysis using basis functions. The Electrical Activity of Neuromuscular System: Human muscular system, Electrical signals of motor unit and gross muscle, Electromyogram signal recording, analysis, EMG applications. Frequency-Time Analysis of Bioelectric Signal and Wavelet Transform: Frequency domain representations for biomedical Signals, Higher-order spectral analysis, correlation analysis, wavelet analysis: continuous wavelet transform, discrete wavelet transform, reconstruction, recursive multi resolution decomposition, causality analysis, nonlinear dynamics and chaos: fractal dimension, correlation dimension, Lyapunov exponent.
Learning Outcomes	Complies with PLO 1b, 2a and 3b
Assessment Method	Quiz, Assignments and Exams
Suggested Reading	Textbooks: 1. Willis J. Tompkins, Biomedical Digital Signal Processing: C Language Examples and Laboratory Experiments for the IBM PC, Prentice Hall India 2. Eugene N. Bruce, Biomedical Signal Processing and Signal Modeling, John Wiley & Sons, 2006. 3. Rangaraj M. Rangayyan, Biomedical Signal Analysis: A Case-Study Approach, John Wiley & Sons, 2002 4. Steven J. Luck, An Introduction to the Event-Related Potential Technique, Second Edition, THE MIT PRESS 5. Leif Sornmo and Pablo Laguna, Bioelectrical Signal Processing in Cardiac and Neurological Applications, Academic Press, 2005 Reference Books: 1. Hojjat Adeli & Samanway Ghosh-Dastidar, Automated EEG based Diagnosis of Neurological Disorders, CRC Press. 2. Thomas P. Trappenberg, Fundamentals of Computational Neuroscience, Oxford University Press. 2002. 3. Mike X Cohen, Analyzing Neural Time Series Data Theory and Practice, THE MIT PRESS 4. Nait-Ali, Amine, Advanced Biosignal Processing, Spingers(Ed.). 2009 5. C. Koch, Biophysics of Computation. Information Processing in Single Neurons, Oxford University Press: New York, Oxford 6. Peter Dayan and LF Abbott, Theoretical Neuroscience Computational and Mathematical Modeling of Neural Systems, MIT 2001. 7. F. Rieke and D. Warland and R. de Ruyter van Steveninck and W. Bialek, Spikes: Exploring the Neuronal Code, A Bradford Book. MIT Press.

EC4206

High-Power Semiconductor Devices

Course Number	EC4206
Course Credit	3-0-0-3
Course Title	High-Power Semiconductor Devices
Learning Mode	Lectures
Learning Objectives	Complies with Program Goal 1, 2, 3 and 4
Course Description	The course deals with the fundamental principles and physics of high-power semiconductor devices, analysing the performance characteristics and limitations of various high-power semiconductor devices, designing and simulating high-power semiconductor devices using advanced computational tools, assessing the impact of material properties and device architecture on the performance and reliability of high-power semiconductor devices, applying knowledge of high-power devices in the development of power electronic systems and evaluating the latest research and technological advancements in high-power semiconductor devices.
Course Outline	Introduction to High-Power Semiconductor Devices: Overview of high-power devices, Applications in power electronics Semiconductor Physics for High-Power Devices: Charge carrier dynamics, Breakdown mechanisms Power Diodes: Structure, operation, and types (e.g., Schottky, PiN), Performance characteristics and applications Power Bipolar Junction Transistors (BJTs): Structure and operation principles, High-power performance characteristics Insulated Gate Bipolar Transistors (IGBTs): Design and operation principles, Power MOSFETs: Structure, operation, and characteristics, Comparison with other high-power devices Thyristors and Related Devices: Structure and types (e.g., SCR, GTO), Switching characteristics and applications Thermal Management in High-Power Devices: Heat generation and dissipation, Thermal modeling and packaging techniques Reliability and Failure Mechanisms: Degradation and failure modes, Reliability testing and improvement strategies Advanced Materials for High-Power Devices: Wide bandgap materials (e.g., SiC, GaN), Advantages and challenges Integration and Application of High-Power Devices: Power modules and converters, Applications in renewable energy and electric vehicles Recent Advances and Research Trends: Innovations in high-power device technology,
Learning Outcomes	Complies with PLO 1a, 2a, 2b, 3a, and 4a
Assessment Method	Quiz, Assignments and Exams
Suggested Reading	Textbooks: 1. B. Jayant Baliga, Power Semiconductor Devices, 1st Edition,Publisher: PWS Publishing Company, Year: 1995 2. B. Jayant Baliga, Fundamentals of Power Semiconductor Devices, 2nd Edition, Publisher: Springer, Year: 2010 Reference Books: 1. Josef Lutz, Heinrich Schlangenotto, Uwe Scheuermann, Rik De Doncker, Semiconductor Power Devices: Physics, Characteristics, Reliability, 2nd Edition, Publisher: Springer 2. Ned Mohan, Tore M. Undeland, William P. Robbins, Power Electronics: Converters, Applications, and Design, 3rd Edition, Publisher: Wiley, Year: 2002 3. B. Jayant Baliga, Wide Bandgap Semiconductor Power Devices: Materials, Physics, Design, and Applications, Publisher: Woodhead Publishing, Year: 2018

EC4207

Biomedical Instrumentation

Course Number	EC4207
Course Credit	3-0-0-3
Course Title	Biomedical Instrumentation
Learning Mode	Lectures
Learning Objectives	Complies with Program Goal 1, 2, 3 and 4
Course Description	The course deals with the basic principles and functions of biomedical instruments, design and developing biomedical instruments for diagnostic and therapeutic purposes, analysing and interpreting data from biomedical instruments, applying knowledge of electronics, signal processing, and instrumentation in biomedical applications and addressing challenges in the design and application of biomedical instruments considering ethical and regulatory standards.
Course Outline	Introduction to Biomedical Instrumentation: Overview of biomedical engineering and instrumentation, History and evolution of biomedical devices, Types of biomedical instruments, Ethical and regulatory aspects in biomedical instrumentation Biosignal Acquisition and Processing: Types of biosignals (ECG, EEG, EMG), Basic transducer principles, Signal conditioning and processing techniques, Filtering and noise reduction Biomedical Sensors and Measurement: Types of biomedical sensors (e.g., temperature, pressure, flow sensors), Sensor characteristics and selection criteria, Measurement techniques and signal conditioning, Design principles Materials used in biomedical devices, Prototyping and testing Diagnostic Instruments, Therapeutic and Prosthetic Devices: Electrocardiographs (ECG), Electroencephalographs (EEG), Electromyographs (EMG), Imaging: X-ray, MRI, CT, Ultrasound; Pacemakers and defibrillators, Infusion pumps, Dialysis machines, Prosthetics and orthotics, Laser applications in medicine Clinical Laboratory Instruments: Blood gas analyzers, Hematology analyzers, Spectrophotometers Chromatography and electrophoresis, Immunoassay systems Recent Advances in Biomedical Instrumentation: Wearable health technology, Telemedicine and remote monitoring, Nanotechnology in medical devices Biomedical microelectromechanical systems (BioMEMS) Artificial intelligence and machine learning in biomedical instrumentation Project and Case Studies: Design and implementation of a biomedical device Case studies of biomedical instrumentation applications
Learning Outcomes	Complies with PLO 1a, 2a, 2b, 3a, 3b, 4a and 4b
Assessment Method	Quiz, Assignments and Exams
Suggested Reading	Textbooks: 1. Webster, John G., ed. Medical instrumentation: Application and Design. John Wiley & Sons, 2009. 2. Carr, Joseph J., and John Michael Brown. Introduction to Biomedical Equipment technology. John Wiley & Sons, 1981. 3. Reddy, Narender P. "Book review: biomedical signal analysis: a case-study approach, by Rangaraja M. Rangayyan." Annals of Biomedical Engineering 30 (2002): 983-983. 4. Bronzino, Joseph D. Biomedical Engineering Handbook. Springer Science & Business Media, 2000. 5. Chatterjee, Shakti, and Aubert Miller. Biomedical Instrumentation Systems. Cengage Learning, 2012. 6. Khandpur, Raghbir Singh. Compendium of Biomedical Instrumentation, John Wiley & Sons, 2020. Reference Books: 1. Geddes, L.A., and Baker, L.E. "Principles of Applied Biomedical Instrumentation", Wiley-Interscience. 2. Carr, J.J., and Brown, J.M. "Introduction to Biomedical Equipment Technology", Pearson. 3. Pallás-Areny, R., and Webster, J.G. "Sensors and Signal Conditioning", John Wiley & Sons.

Interdisciplinary Electives (Available to students of B. Tech. other than Dept. of EE)

Sl. No.

Subject Code

Subject

IDE-I

EE2203

Introduction to Electric Vehicle Technology

Course Number	EE2203 (B. Tech IDE I)
Course Credit	3-0-0-3
Course Title	Introduction to Electric Vehicle Technology
Learning Mode	Lectures
Learning Objectives	Complies with EE Program goals 1, 2 and 3
Course Description	The course is designed to meet the requirements of all the B. Tech branches. The course aims at giving a brief overview of electric vehicle technology. Drive power train concept, basic inverter design concept, basic of charger converter and basic of motor control will be discussed.
Course Outline	History of electric vehicle journey, Electric vehicle architecture and its type and challenges, Dynamics of electric vehicle, Benefits of using electric vehicle, Concept of drive cycle, Electric vehicle drivetrain components, Electric vehicle auxiliaries. Concept of Inverter, Single Phase Inverter, Basic of Three Phase Inverter, Modulation Strategy, AC-DC converter, Boost converter, State space modelling of Boost Converter, Buck Converter, State space modelling of Buck converter, Concept of Power Factor Correction Basics of Batteries, Lithium-ion vs Lead Acid Battery, Modelling of Battery Introduction to Induction motor drive and its control,
Learning Outcomes	Complies with EE PLO 1a, 2a and 3a
Assessment Method	Quiz, Assignments and Exams
Suggested Reading	Textbooks: 1. N. Mohan, T. M, Undelnad, W. P, Robbins: Power Electronics: Converters, Applications and Design, 3rd Edition, 2002, Wiley. 2. M. Eshani, Y. Gao, Sebastien E Gay, Ali Emadi: Modern electric, hybrid electric and fuel cell vehicles, Fundamentals, Theory, and Design. 2005, Boca Raton, FL, CRC. References: 1. R. Ericson Fundamentals of Power Electronics, 2004, Chapman & Hall. 2. F. A. Silva; M. P. Kazmierkowski: Energy Storage Systems for Electric Vehicles, 2021, MDPI.

IDE-II

EC3106

Introduction to Communication System

Course Number	EC3106 (B.Tech IDE-II)
Course Credit	3-0-0-3
Course Title	Introduction to Communication System
Learning Mode	Lectures
Learning Objectives	Complies with Program Goal 1, 2 and 4
Course Description	This course deals with the basics of communications systems and data transmission over wireless networks along with the next generation communication technologies. The prerequisite is Mathematics I & II.
Course Outline	Fourier analysis and its applications in communication systems. Signal spectra and filtering. Study of different analog and digital modulation and demodulation techniques: AM, FM, PAM, BPSK, QPSK, and QAM. Applications of modulation techniques in radio, television and telemetry. Noise modelling and its impact on the performance of communication systems. Performance metrics in communication systems: Q factor, SNR, noise figure, bit error rate, symbol error rate. Different blocks in data transmission: source coding and channel coding. Introduction to wireless communication, radio wave propagation issues in wireless systems, path loss, shadowing, and fading. Capacity in AWGN and fading channels. Cellular architecture, frequency reuse, handover, and multiple access schemes. Base station, mobile station, MSC, and other subsystems of cellular architecture. History and evolution of mobile radio systems, standards of mobile cellular networks (e.g. 2G, 3G, 4G, 5G and beyond). Introduction for fiber communication, aerial communication, near-field communications, quantum communications, and molecular communications.
Learning Outcome	Complies with PLO 1b, 2a and 4a
Assessment Method	Quiz, Assignments, and Exams
Suggested Readings	Text Books: 1. Michael Moher and Simon S. Haykin, Communication Systems, Wiley, 2006. 2. T.S. Rappaport, Wireless Communication; Principles and Practice, Prentice Hall, NJ, 1996. Reference Books: 1. Gunnar Heine, GSM Networks: Protocols, Terminology and Implementation, Artech House Publishers, 1998. 2. K. Feher, Wireless Digital Communication, Prentice Hall of India, New Delhi, 1995. 3. B. P. Lathi and Zhi Ding, Modern Digital and Analog Communication, Oxford Univ Press, 2018. 4. Govind P. Agrawal, Fiber-Optic Communication Systems, John Wiley & Sons, 2012.

IDE-III

EC4107

Quantum Computing Fundamentals

Course Number	EC4107 (B. Tech IDE-III)
Course Credit	3-0-0-3
Course Title	Quantum Computing Fundamentals
Learning Mode	Lectures
Learning Objectives	Complies with EE Program goals 1, 2 and 3
Course Description	This course offers a comprehensive introduction to the principles and applications of quantum information systems (QIS) and the associated hardware. It provides a foundational understanding of quantum computing, focusing on both the theoretical concepts and practical implementations. Students will explore key quantum phenomena and operations, learn about quantum circuits, and examine various quantum algorithms. The course also covers advanced topics such as quantum error correction and quantum cryptography, equipping students with the knowledge needed to understand and contribute to the evolving field of quantum information science.
Course Outlines	Quantum information system (QIS) applications and hardware, intuitive introduction of quantum operations and underlying quantum computing. Symbolic and mathematical representation of qubit. Measurement, Superposition, Multi-Qubit Operations, Quantum Circuits, Entanglement, Toffoli Gate, Phase-Flip, EPR Pairs. Deutsch’s algorithm, the Deutsch-Jozsa Algorithm and the Bernstein-Vazirani Algorithm, Simon’s algorithm, and Shor’s algorithm for factoring/discrete log And Grover’s algorithm for searching. Quantum error correction and quantum cryptography.
Learning Outcomes	Complies with EE PLO 1a, 2a and 3a
Assessment Methods	Quizzes, Assignments, Exams
Suggested Readings	Books Text/Reference: 1. Paul Kaye, Raymond Laflamme, and Michele Mosca, An Introduction to Quantum Computing, Oxford University Press (2007). 2. Scott Aaronson's Introduction to Quantum Information Science (UT Austin 2017). 3. M. Nielsen and I. Chuang. Quantum Computation and Quantum Information, Cambridge University Press; 10 Anv edition, 2011. 4. A. Yu. Kitaev, A. H. Shen and M. N. Vyalyi. Classical and Quantum Computation (Graduate Studies in Mathematics), AMS, 2002. 5. John Watrous. The Theory of Quantum Information, Cambridge University Press, 2018.

Mathematics & Computing

Program Learning Objectives:	Program Learning Outcomes:
Program Goal 1: To learn and excel in rigor of Mathematics	Program Learning Outcome 1a: The students are equipped with a mixture of basic and advanced mathematics courses during the program Program Learning Outcome 1b: A rigorous training in all basic courses in Mathematics is obtained
Program Goal 2: To be able to apply the concepts of Mathematics in problems	Program Learning Outcome 2a: Students pursue application-oriented courses in the form of electives Program Learning Outcome 2b: Application skills of using mathematics is acquired.
Program Goal 3: To learn and excel in contemporary courses in Computer Science domain	Program Learning Outcome 3 a: Students are exposed to both hardware and software courses. Program Learning Outcome 3 b: Acquainted with advanced courses in computer science.
Program Goal 4: To be leader in the area where both Mathematics and computer science skills are required	Program Learning Outcome 4: Leadership skills are developed through overall exposure to various components

Sl. No.

Subject Code

SEMESTER I

MA1101

Course Number	MA1101
Course Credit (L-T-P-C)	3-1-0-4
Course Title	Calculus and Linear Algebra
Learning Mode	Lectures and Tutorials
Learning Objectives	To provide the essential knowledge of basic tools of Differential Calculus, Integral Calculus, Vector spaces and Matrix Algebra.
Course Description	This course provides a foundation for Calculus and Linear Algebra. Topics related to properties of single and two variable functions along with their applications will be discussed. In addition fundamentals of linear algebra and matrix theory with applications will also be discussed.
Course Content	Differential Calculus (12 Lectures): Limit and continuity of one variable function (including ε-δ definition). Limit, continuity and differentiability of functions of two variables, Tangent plane and normal, Change of variables, chain rule, Jacobians, Taylor’s Theorem for two variables, Extrema of functions of two or more variables, Lagrange’s method of undetermined multipliers. Integral Calculus (10 Lectures): Riemann integral for one variable functions, Double and Triple integrals, Change of order of integration. Change of variables, Applications of Multiple integrals such as surface area and volume. Vector Spaces (12 Lectures): Vector spaces (over the field of real numbers), subspaces, spanning set, linear independence, basis and dimension. Linear transformations, range and null space, rank-nullity theorem, matrix of a linear transformation. Matrix Algebra (8 Lectures): Elementary operations and their use in getting the rank, inverse of a matrix and solution of linear simultaneous equations, Orthogonal, symmetric, skew-symmetric, Hermitian, skew-Hermitian, normal and unitary matrices and their elementary properties, Eigenvalues and Eigenvectors of a matrix, Cayley-Hamilton theorem, Diagonalization of a matrix.
Learning Outcome	Students completing this course will be able to: 1. Understand various properties of functions such as limit, continuity and differentiability. 2. Learn about integrations in various dimension and their applications. 3. learn about the concept of basis and dimension of a vector space. 4. define Linear Transformations and compute the domain, range, kernel, rank, and nullity of a linear transformation. 5. compute the inverse of an invertible matrix. 6. solve the system of linear equations. 7. Apply linear algebra concepts to model, solve, and analyze real-world problems.
Assessment Method	Quiz /Assignment/ MSE / ESE

Textbooks:

Thomas, G. B., Hass, J., Heil, C. and Weir M. D., “Thomas’ Calculus”, 14th Ed., Pearson Education, 2018
Kreyszig, E., “Advanced Engineering Mathematics”, 10th Ed., Wiley India Pvt. Ltd, 2015

Reference Books:

Jain, R. K. and Iyenger, S. R. K., “Advanced Engineering Mathematics”, 5^th, Narosa Publishing House, 2017
Axler, S., “Linear Algebra Done Right”, 3rd Ed., Springer Nature, 2015
Strang, G., “Linear Algebra and Its Applications” 4th Ed., Cengage India Private Limited, 2005

4.0

CS1101

Course Number	CS1101
Course Credit	3-0-3-4.5
Course Title	Foundations of Programming
Learning Mode	Offline
Learning Objectives	· To understand the fundamental concepts of programming · To develop the basic problem-solving skills by designing algorithms and implementing them. · To learn about various data types, control statements, functions, arrays, pointers, and file handling. · To achieve proficiency in debugging and testing a C program
Course Description	This introductory course provides a solid foundation in programming principles and techniques. Designed for students with little to no prior programming experience, it covers fundamental concepts such as variables, data types, control structures, functions, and basic data structures. Students will learn to write, debug, and execute programs using a high-level programming language. Emphasis is placed on developing problem-solving skills, logical thinking, and the ability to write clear and efficient code. By the end of the course, students will be equipped with the essential skills needed to pursue more advanced studies in computer science and software development.
Course Outline	Introduction and Programming basics, Expressions Control and Iterative statements, Functions, Arrays, Recursion vs. Iteration Pointers, 2D-Array with pointers, Structures, String, Dynamic memory allocation, File handling, Contemporary programming languages, and applications Practical component: Lab to be conducted on a 3-hour slot weekly. It will be conducted with the theory course so the topics for problems given in the lab are already initiated in the theory class.
Learning Outcome	· Understanding of Basic Syntax and Structure in C language · Proficiency in Data Types, Operators, and Control Structures · Function Implementation and learn to use them appropriately · Efficient Use of Arrays and Strings · Pointer Utilization · Ability to perform dynamic memory allocation and deallocation using malloc (), calloc (), realloc (), and free () functions. · Structured data management with structures and unions · Exposure of file Handling · Learning debugging and error Handling
Assessment Method	Internal (Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Reading

Knuth, Donald E. The art of computer programming, volume 4A: combinatorial algorithms, part 1. Pearson Education India, 2011.
P.J. Deitel and H.M. Deitel, C How To Program, Pearson Education (7th Edition)
Brian W. Kernighan and Dennis M. Ritchie, The C Programming Language, Prentice−Hall
A. Kelley and I. Pohl, A Book on C, Pearson Education (4th Edition)
K. N. King, C PROGRAMMING A Modern Approach, W. W. Norton & Company

4.5

PH1101/PH1201

Course Number	PH1101/PH1201
Course Credit	3-1-0-4
Course Title	Physics
Learning Mode	Lectures and Tutorials
Learning Objectives	Complies with Program Goals 1 and 2
Course Description	This course deals with fundamentals in Classical mechanics, Waves and Oscillations and Quantum Mechanics. As a prerequisite, the mathematical preliminaries such as coordinate systems, vector calculus etc will be discussed in the beginning.
Course Outline	Orthogonal coordinate systems (Plane polar, Spherical, Cylindrical), concept of generalised coordinates, generalised velocity and phase space for a mechanical system, Introduction to vector operators, Gradient, divergence, curl and Laplacian in different co-ordinate systems. Central force problem and its applications. Rigid body rotation, vector nature of angular velocity, Finding the principal axes, Euler's equations; Gyroscopic motion and its application; Accelerated frame of reference, Fictitious forces. Potential energy and concept of equilibrium, Lennard-Jones and double-well potentials, Small oscillations, Harmonic oscillator, damped and forced oscillations, resonance and its different examples, oscillator states in phase space, coupled oscillations, normal modes, longitudinal and transverse waves, wave equation, plane waves, examples two- and three-dimensional waves. Michelson-Morley experiment, Lorentz transformation, Postulates of special theory of relativity, Time dilation and length contraction, Applications of special theory of relativity.
Learning Outcome	Complies with PLO 1a, 2a, 3a
Assessment Method	Quiz, Assignments and Exams

Suggested Readings:

Textbooks:

Engineering Mechanics, M. K. Harbola, 2^nd ed., Cengage, 2012
D. Kleppner and R. J. Kolenkow, An introduction to Mechanics, Tata McGraw-Hill, New Delhi, 2000.
I. G. Main, Oscillations and Waves
H. G. Pain, The Physics of Vibrations and Waves, 1968
Frank S. Crawford, Berkeley Physics Course Vol 3: Waves and Oscillations, McGraw Hill, 1966.

References:

R. P. Feynman, R. B. Leighton and M. Sands, The Feynman Lecture in Physics, Vol I, Narosa Publishing House, New Delhi, 2009.
David Morin, Introduction to Classical Mechanics, Cambridge University Press, NY, 2007.

3. P. C. Deshmukh, Foundations of Classical Mechanics, Cambridge University Press, 2019

5.5

CE1101/CE1201

Course code	CE1101/CE1201
Course Credit (L-T-P-C)	1-0-3-2.5
Course Title	Engineering Graphics
Learning Mode	Lectures and Practical
Learning Objectives	Complies with PLO-1a 1. The course on engineering drawing is designed to introduce the fundamentals of technical drawing as an important form of conveying information. 2. Apply principles of engineering visualization and projection theory to prepare engineering drawings, using conventional and modern drawing tools. 3. Practice drawing orthographic projections, isometric views, and sectional views, of simple and combined solids in different orientations.
Course Description	This course will introduce drawing as a tool to represent a complex three-dimensional object on two-dimensional paper through methods of projections. The course explains the use of different drafting tools and the importance of conventions for uniformity and standardization of the interpretation of the drawings.
Course Outline	Fundamental of engineering drawing, line types, dimensioning, and scales. Conic sections: ellipse, parabola, hyperbola; cycloidal curves. Principle of projection, method of projection, orthographic projection, plane of projection, first angle of projection, Projection of points, lines, planes and solids. Section of solids: Sectional views of simple solids- prism, pyramid, cylinder, cone, sphere; the true shape of the section. Methods of development, development of surfaces. Isometric projections: construction of isometric view of solids and combination of solids from orthographic projections. Introduction to AutoCad and solving isometric problems.
Learning Outcome	After attending this course, the following outcomes are expected: 1. The student will understand the basic concepts of engineering drawing. 2. The student will be able to use basic drafting tools, drawing instruments, and sheets. 3. The student will be able to represent three-dimensional simple and combined solid objects on two-dimensional paper. 4. The student will be able to visualize and interpret the orientation of simple and combine solid objects.
Assessment Method	Laboratory Assignments (30%), Mid-semester examination (25%) and End-semester examination (45%).

Suggested Readings:

Textbooks:

D. Bhatt, Engineering Drawing, Charotar Publishing House.
Agrawal & Agrawal, Engineering Drawing, McGraw Hill.
Jolhe, Engineering Drawing.

References:

Engineering Drawing and Design by David Madsen

2.5

EE1101/EE1201

Course Number	EE1101/EE1201
Course Credit	3-0-3-4.5
Course Title	Electrical Sciences
Learning Mode	Lectures and Experiments
Learning Objectives	Complies with Program goals 1, 2 and 3
Course Description	The course is designed to meet the requirements of all B. Tech programmes. The course aims at giving an overview of the entire electrical engineering domain from the concepts of circuits, devices, digital systems and magnetic circuits.
Course Outline	Circuit Analysis Techniques, Circuit elements, Simple RL and RC Circuits, Kirchoff’s law, Nodal Analysis, Mesh Analysis, Linearity and Superposition, Source Transformations, Thevenin’s and Norton’s Theorems, Time Domain Response of RC, RL and RLC circuits, Sinusoidal Forcing Function, Phasor Relationship for R, L and C, Impedance and Admittance, Instantaneous power, Real, reactive power and power factor. Semiconductor Diode, Zener Diode, Rectifier Circuits, Clipper, Clamper, UJT, Bipolar Junction Transistors, MOSFET, Transistor Biasing, Transistor Small Signal Analysis, Transistor Amplifier and their types, Operational Amplifiers, Op-amp Equivalent Circuit, Practical Op-amp Circuits, Power Opamp, DC Offset, Constant Gain Multiplier, Voltage Summing, Voltage Buffer, Controlled Sources, Instrumentation Amplifier, Active Filters and Oscillators. Number Systems, Logic Gates, Boolean Theorem, Algebraic Simplification, K-map, Combinatorial Circuits, Encoder, Decoder, Combinatorial Circuit Design, Introduction to Sequential Circuits. Magnetic Circuits, Mutually Coupled Circuits, Transformers, Equivalent Circuit and Performance, Analysis of Three-Phase Circuits, Power measurement in three phase system, Electromechanical Energy Conversion, Introduction to Rotating Machines (DC and AC Machines). Laboratory: Experiments to verify Circuit Theorems; Experiments using diodes and bipolar junction transistor (BJT): design and analysis of half -wave and full-wave rectifiers, clipping and clamping circuits and Zener diode characteristics and its regulators, BJT characteristics (CE, CB and CC) and BJT amplifiers; Experiment on MOSFET characteristics (CS, CG, and CD), parameter extraction and amplifier; Experiments using operational amplifiers (op-amps): summing amplifier, comparator, precision rectifier, Astable and Monostable Multivibrators and oscillators; Experiments using logic gates: combinational circuits such as staircase switch, majority detector, equality detector, multiplexer and demultiplexer; Experiments using flip-flops: sequential circuits such as non-overlapping pulse generator, ripple counter, synchronous counter, pulse counter and numerical display; Power Measurement by two Wattmeter method; Open and Short Circuit Tests of Transformer.
Learning Outcomes	Complies with PLO 1a, 2a and 3a
Assessment Method	Quiz, Assignments and Exams

Texts/References

K. Alexander, M. N. O. Sadiku, Fundamentals of Electric Circuits, 3rd Edition, McGraw-Hill, 2008.
H. Hayt and J. E. Kemmerly, Engineering Circuit Analysis, McGraw-Hill, 1993.
L. Boylestad and L. Nashelsky, Electronic Devices and Circuit Theory, 6th Edition, PHI, 2001.
M. Mano, M. D. Ciletti, Digital Design, 4th Edition, Pearson Education, 2008.
Floyd, Jain, Digital Fundamentals, 8th Edition, Pearson.
David V. Kerns, Jr. J. David Irwin, Essentials of Electrical and Computer Engineering, Pearson, 2004.
Donald A Neamen, Electronic Circuits; analysis and Design, 3rd Edition, Tata McGraw-Hill Publishing Company Limited.
Adel S. Sedra, Kenneth C. Smith, Microelectronic Circuits, 5th Edition, Oxford University Press, 2004.
E. Fitzgerald, C. Kingsley Jr., S. D. Umans, Electric Machinery, 6th Edition, Tata McGraw-Hill, 2003.
P. Kothari, I. J. Nagrath, Electric Machines, 3rd Edition, McGraw-Hill, 2004.

Del Toro, Vincent. "Principles of electrical engineering." (No Title) (1972).

4.5

HS1101

Course Number	HS1101
Course Credit	L-T-P-W: 2-0-1-2.5
Course Title	English for Professionals
Learning Mode	Offline
Learning Objectives	This course aims to help the students (a) attain proficiency in written English through the construction of grammatically correct sentences, utilization of subject-verb agreement principles, mastery of various tenses, and effective deployment of active and passive voice to ensure coherent and impactful written expression; (b) enhance oral communication skills by honing public speaking abilities, acquiring strategies to deliver persuasive presentations, and cultivating a polished telephone etiquette, enabling confident and articulate verbal communication; (c) foster active listening capabilities by recognizing different types of listening, and applying proven methods and strategies to improve active listening skills; (d) strengthen reading skills, including comprehension, interpretation, and critical analysis, to grasp diverse written materials and derive meaning from various types of texts encountered in academic and professional contexts; (e) develop adeptness in written communication for business purposes, encompassing the understanding of essential writing elements, mastery of appropriate writing styles thereby enhancing prospects for successful job interviews and subsequent professional endeavors.
Course Description	This academic course on communication skills aims to equip students with fluency in spoken and written English for effective expression in both academic and professional settings. By focusing on essential communication principles and providing practical experiences, students develop clarity, precision, and confidence in their communication. Through interactive discussions and exercises, students enhance critical thinking and adaptability in diverse contexts. Upon completion, students will excel in formal presentations, group discussions, and persuasive writing, enhancing their overall communication proficiency.
Course Outline	Unit I: Introduction to professional communication – LSRW - Phonetics and phonology Sounds in English Language – production and articulation – rhythm and intonation – connected speech - Basic Grammar and Advanced Vocabulary Sounds in English Language – production and articulation – rhythm and intonation – connected speech – persuading and negotiating – brevity and clarity in language. Unit II: Characteristics of Technical Communication: Types of communication and forms of communication - Formal and informal communication Verbal and non-Verbal Communication – Communication barriers and remedies Intercultural communication – neutral language Unit III: Comprehension and Composition – summarization, precis writing Business Letter Writing CV/ Resume – E-Communication Unit IV: Statement of Purpose, Writing Project Reports, Writing research proposal, writing abstracts, developing presentations, interviews – combating nervousness Tutorial: Listening Exercises, Speaking Practice (GDs, and Presentations), and Writing Practice Learning Outcome · Attain proficiency in written English, enabling the construction of grammatically correct sentences and coherent written expression through the use of appropriate grammar, tenses, and voice. · Enhance oral communication skills, including public speaking, persuasive presentation, and polished telephone etiquette, fostering confident and articulate verbal expression. · Cultivate active listening abilities, recognizing different listening types, overcoming obstacles, and employing strategies for attentive and effective communication. · Develop proficient written communication skills for business purposes, demonstrating understanding of essential writing elements, appropriate styles, and the creation of reports, notices, agendas, and minutes that effectively convey information.
Assessment Method	Class test + Quiz = 20%; Mid-semester = 25%; Assignment = 15%; End semester = 40%

Suggested Reading

Balzotti, Jon. Technical Communication: A Design-Centric Approach. Routledge, 2022.
Kaul, Asha, Business Communication. PHI Learning Pvt. Ltd. 2009

Laplante, Phillip A. Technical Writing: A Practical Guide for Engineers, Scientists, and Nontechnical Professionals. CRC Press, 2018.

2.5

TOTAL

23.5

Probability Theory and Ordinary Differential Equations

Sl. No.

Subject Code

SEMESTER II

MA1201

Course Number	MA1201
Course Credit (L-T-P-C)	3-1-0-4
Course Title	Probability Theory and Ordinary Differential Equations
Learning Mode	Lectures and Tutorials
Learning Objectives	To introduce the basic concepts of probability, statistics, and Differential equations.
Course Description	This course aims to cover basic concepts of probability, statistics and ordinary differential equations. In particular, popular distributions, random sampling, various estimators and hypothesis testing will be discussed. Students will also get exposure to the linear ordinary differential equations and their solution techniques.
Course Content	Probability (12 Lectures): Random variables and their probability distributions, Cumulative distribution functions, Expectation and Variance, probability inequalities, Binomial, Poisson, Geometric, negative binomial distributions, Uniform, Exponential, beta, Gamma, Normal and lognormal distributions. Statistics (10 Lectures): Random sampling, sampling distributions, Parameter estimation, Point estimation, unbiased estimators, maximum likelihood estimation, Confidence intervals for normal mean, Simple and composite hypothesis, Type I and Type II errors, Hypothesis testing for normal mean. Ordinary Differential Equations (20 Lectures): First order ordinary differential equations, exactness and integrating factors, Picard's iteration, Ordinary linear differential equations of n-th order, solutions of homogeneous and non-homogeneous equations (Method of variation of parameters). Systems of ordinary differential equations, Power series methods for solutions of ordinary differential equations. Legendre equation and Legendre polynomials, Bessel equation and Bessel functions.
Learning Outcome	Students will get exposure and understanding of: 1. Random variables and their probability distributions 2. Understand popular distributions and their properties 3. Sampling, estimation and hypothesis testing 4. Solution of ordinary differential equations 5. Solution of system of ordinary differential equations 6. Special functions arising as power series solutions of ordinary differential equations
Assessment Method	Quiz /Assignment/ MSE / ESE

Text Books:

Hogg, R. V., Mckean, J. and Craig, A. T., “Introduction to Mathematical Statistics”, 8th Ed., Pearson Education India, 2021
M. Ross “An introduction to Probability Models, Academic Press INC, 11th edition.
Miller, I. and Miller, M., “John E. Freund's Mathematical Statistics with Applications”, 8th Ed., Pearson Education India, 2013
L. Ross, Differential equations, 3rd Edition, Wiley, 1984
E. Boyce and R. C. Di Prima, Elementary Differential equations and Boundary Value Problems, 7th Edition, Wiley, 2001.

CS1201

Course Number	CS1201
Course Credit	3-0-3-4.5
Course Title	Data Structure
Learning Mode	Offline
Learning Objectives	· Understand the principles and concepts of data structures and their importance in computer science. · Learn to implement various data structures and understand how different algorithms works. · Develop problem-solving skills by applying appropriate data structures to different computational problems. · Achieving proficiency in designing efficient algorithms.
Course Description	This course provides a comprehensive study of data structures and their applications in computer science. It focuses on the implementation, analysis, and use of various data structures such as arrays, linked lists, stacks, queues, trees, and graphs. Through theoretical concepts and practical programming exercises, this course aims to develop problem-solving and algorithmic thinking skills essential for advanced topics in computer science and software development.
Course Outline	· Introduction to Data Structure, · Time and space requirements, Asymptotic notations · Abstraction and Abstract data types · Linear Data Structure: stack, queue, list, and linked structure · Unfolding the recursion · Tree, Binary Tree, traversal · Search and Sorting, · Graph, traversal, MST, Shortest distance · Balanced Tree Practical component: Lab to be conducted on a 3-hour slot weekly. It will be conducted with the theory course so the topics for problems given in the lab are already initiated in the theory class.
Learning Outcome	· Understand Data Structure Fundamentals · Implement Basic Data Structures using a programming language · Analyse and Apply Algorithms · Design and Analyse Tree Structures · Understand the usage of graph and its related algorithms · Design and Implement Sorting and Searching Algorithms · Debug and Optimize Code
Assessment Method	Internal (Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Reading

Alfred V. Aho, John E. Hopcroft, Jeffrey D. Ullman, Data Structures and Algorithms, Published by Addison-Wesley
Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest and Clifford Stein., Introduction to Algorithms,
Mark Allen Weiss, Data Structures and Algorithm Analysis in Java
Robert Sedgewick and Kevin Wayne, Algorithms
Narasimha Karumanchi, Data Structures and Algorithms Made Easy

4.5

CH1201/CH1101

Course Number	CH1101/CH1201
Course Credit	L-T-P-C: 3-1-3-5.5
Course Title	Chemistry
Learning Mode	Offline
Learning Objectives	The course aims to lay a foundation for all three branches of chemistry, viz. Organic, Inorganic, and Physical Chemistry. The course aims to nurture knowledge to appreciate the interface of chemistry with other science and Engineering branches by combining theoretical concepts and experimental studies.
Course Description	This course introduces basic organic chemistry, inorganic chemistry and Physical chemistry to understand fundamental laws that governs various reactions, reaction rates, equilibrium, and their applications in daily life through relevant experimentation.
Course Outline	Module 1: Thermodynamics: The fundamental definition and concept, the zeroth and first law. Work, heat, energy and enthalpies. Second law: entropy, free energy and chemical potential. Change of Phase. Third law. Chemical equilibrium. Conductance of solutions, Kohlrausch’s law-ionic mobilities, Basic Electrochemistry. Module 2: Coordination chemistry: Crystal field theory and consequences color, magnetism, J.T distortion. Bioinorganic chemistry: Trace elements in biology, heme and non-heme oxygen carriers, haemoglobin and myoglobin; Organometallic chemistry. Module 3: Stereo and regio-chemistry of organic compounds, conformational analysis and conformers, Molecules devoid of point chirality (allenes and biphenyls); Significance of chirality in living systems, organic photochemistry, Modern techniques in structural elucidation of compounds (UV–Vis, IR, NMR). Module 4 (Lab Component): Experiments based on redox and complexometric titrations; synthesis and characterization of inorganic complexes and nanomaterials; synthesis and characterization of organic compounds; experiments based on chromatography; experiments based on pH and conductivity measurement; experiment related to chemical kinetics and spectroscopy.
Learning Outcome	Students will be able to 1. identify organic and inorganic molecules and relate them to daily life applications through experiments. 2. understand important hypothesis, laws and their derivations to intercept physical phenomenon of chemical reactions and apply them in hands-on experiments. 3. understand the importance of organic and inorganic molecules in our body and environment. 4. know important analytical techniques to intercept chemical entity. 5. approach organic and inorganic synthesis as a skillset for drug manufacturing, calculate limiting reagents and yields, use various analytical tools to characterize organic compounds, interpret and ascertain data related to Physical chemistry aspects and know laboratory safety measures, risk factors and scientific report writing skills.
Assessment Method	Theory: 20% Quiz and assignment, 30% Mid sem and 50% End semester exams for theory part (4 credits). Lab: 60% lab report, lab performance and assignment, 20% End semester exam for practical part, 20% viva/quiz (1.5 credits). Overall Weightage: Theory (70%), Lab (30%).

Suggested Reading:

Text books:

Vogel's Qualitative Inorganic Analysis, G. Svehla, 7^th Edition, Revised, Prentice Hall, 1996.
J. Elias, S. S. Manoharan and H. Raj, "Experiments in General Chemistry", Universities Press (India) Pvt. Ltd., 1997.

A. J. Elias, A Collection of Interesting General Chemistry Experiments, revised edition, Universities Press (India) Pvt. Ltd., 2007.

Albert Cotton, G. Wilkinson, C. A. Murillo, M. Bochmann, Advanced Inorganic Chemistry - 6^th Edition New Delhi: Wiley India, 2008.
Mukkanti, Practical Engineering Chemistry, B.S. Publications, Hyderabad, 2009.
Shriver and Atkins inorganic chemistry / Peter Atkins, Tina Overton, Jonathan Rourke, Mark Weller, Fraser Armstrong-5^th Edition – Oxford: UOP. 2012.
Atkins’ Physical Chemistry, Peter Atkins, Julio de Paula, James Keeler, Oxford University Press, 11^th Edition 2017.
L. Kapoor, A Textbook of Physical Chemistry, Vol: 1, 2 (6^th Edition, 2019), Vol: 3 (5^th Edition, 2020) MaGraw Hill.
R. Chatwal, S. K. Anand, Instrumental Methods of Chemical Analysis, 5^th Edition, Himalaya Publications, 2023.

5.5

ME1201/ME1101

Course Number	ME1101/ME1201
Course Credit	L-T-P-C: 0-0-3-1.5
Course Title	Mechanical Fabrication
Learning Mode	Fabrication work – hands on fabrication work in Workshop
Learning Objectives	Complies with PLOs 3-4. · This course aims to develop the concepts and skills of various mechanical fabrication methods. · Fabrication of metallic and non-metallic components, fabrication using bulk and sheet metals, subtractive and additive manufacturing methods, and assemble the parts
Course Description	This course is designed to fulfil the need of hand on experience about various approaches (conventional and CNC, subtractive and additive) of mechanical fabrication approaches. Prerequisite: NIL
Course Outline	The jobs for various shops should be planned such that they are the parts of an assembled item. The student groups will fabricate different parts in various shops which will involve some amount of their creativeness/input particularly in design and/or planning. Various components as required for the assembled part can be made using the following shops: Sheet Metal Working: Development, sheet cutting and fabrication of designated job using sheet metal (ferrous/nonferrous); Joining of required portions by soldering, in case part is desired to be made leak proof. Pattern Making and Foundry: Making of suitable pattern (wood); making of sand mould, melting of non-ferrous metal/alloy (Al or Al alloys), pouring, solidification. Observation/identification of various defects appeared on the component. Joining: Butt/lap/corner joint job fabrication as required of low carbon steel plates; weld quality inspection by dye-penetration test (non-destructive testing approach)of the component made. Demonstration of semi-automatic Gas Metal Arc welding (GMAW). Conventional machining: Operations on lathe and vertical milling to fabricate the required component. The fabrication of the component should cover various lathe operations like straight turning, facing, thread cutting, parting off etc., and operations using indexing mechanism on vertical milling. CNC centre: Fundamentals of CNC programming using G and M code; setting and operations of job using CNC lathe or milling, tool reference, work reference, tool offset, tool radius compensation to fabricate the component with a designed profile on Al/Al-alloy plate. 3D printing (Fused Filament Fabrication): (2 weeks) Create the model, select appropriate slicing and path for fabrication of a 3D job by layer deposition (additive manufacturing approach) using polymeric material. Demonstration on pattern fabrication using 3D printing.
Learning Outcome	· This course would enable the students to develop the concept of design, fabrication (subtractive and additive) for various engineering applications. Fabrication of components and assemble them. · The practical skill and hands on experience for various fabrication methods from bulk, sheet metal using conventional as well as CNC machines.
Assessment Method	Fabrication of components in each of the shops required for assembly of the given part; submission of reports for each shop, and quiz assessment.

Text and Reference books:

Hajra Choudhury, HazraChoudhary and Nirjhar Roy, 2007, Elements of Workshop Technology, vol. I,Mediapromoters and Publishers Pvt. Ltd.
W A J Chapman, Workshop Technology, 1998, Part -1, 1st South Asian Edition, Viva Book Pvt Ltd.
N. Rao, 2009, Manufacturing Technology, Vol.1, 3rd Ed., Tata McGraw Hill Publishing Company.
Adithan, B.S. Pabla, 2012, CNC machines, New Age International Publishers

1.5

ME1202/ME1102

Course Number	ME1102/ME1202
Course Number	Engineering Mechanics
L-T-P-C	3-1-0-4
Pre-requisites	Nil
Semester	Spring
Learning Mode	Lectures
Learning Objectives	Complies with PLOs 1, 4 · The objective of this first course in mechanics is to enable engineering students to analyze basic mechanics problems and apply vector-based approach to solve them.
Course Outline	1. Rigid body statics: Equivalent force system. Equations of equilibrium, Free body diagram, Reaction, Static indeterminacy. 2. Structures: 2D truss, Method of joints, Method of section. Beam, Frame, types of loading and supports, axial force, Bending moment, Shear force and Torque Diagrams for a member. 3. Friction: Dry friction (static and kinetic), wedge friction, disk friction (thrust bearing), belt friction, square threaded screw, journal bearings, Wheel friction, Rolling resistance. 4. Centroid and Moment of Inertia 5. Introduction to stress and strain: Definition of Stress, Normal and shear Stress. Relation between stress and strain, Cauchy formula. Stress in an axially loaded member and stress due to torsion in axisymmetric section
Learning Outcomes:	Following learning outcomes are expected after going through this course. · Learn and apply general mathematical and computer skills to solve basic mechanics problems. · Apply the vector-based approach to solve mechanics problems.
Assessment Method	Mid semester examination, End semester examination, Class test/Quiz, Tutorials

Reference Books

Shames, Engineering Mechanics: Statics and dynamics, 4th Ed, PHI, 2002.
P. Beer and E. R. Johnston, Vector Mechanics for Engineers, Vol I - Statics, 3rd Ed, Tata McGraw Hill, 2000.
L. Meriam and L. G. Kraige, Engineering Mechanics, Vol I - Statics, 5th Ed, John Wiley, 2002.
P. Popov, Engineering Mechanics of Solids, 2nd Ed, PHI, 1998.
P. Beer and E. R. Johnston, J.T. Dewolf, and D.F. Mazurek, Mechanics of Materials, 6th Ed, McGraw Hill Education (India) Pvt. Ltd., 2012.

IK1201

Indian Knowledge System (IKS)

TOTAL

22.5

Design and Analysis of Algorithms

Sl. No.

Subject Code

SEMESTER III

MA2101

Course Number	MA2101 (Core)
Course Credit (L-T-P-C)	3-0-2-4
Course Title	Design and Analysis of Algorithms
Learning Mode	Lectures and lab
Learning Objectives	To understand basic algorithm design techniques through solving different type of computational problems
Course Description	This course is meant to introduce the basic algorithm design techniques. It will also introduce few data structures and the notion of NP-Completeness.
Course Content	Model of Computations: RAM Model of computation, uniform cost model, logarithmic cost model. Complexity Analysis: Big O, omega, theta notations, solving recurrence relations Recurrence relations, Divide and conquer relations, Solving of recurrences by iteration method and substitution method, Master theorem, Binary search algorithm, Merger sort, Quick sort, Strassen’s matrix multiplication method. Greedy strategy, Huffman coding algorithm, Graph traversal – BFS, DFS; MST - Kruskal’s algorithm, Data structures of disjoint sets , Prim’s algorithm; Shortest Path Algorithms - Dijkstra’s and Bellman-Ford algorithms, Warshall’s and Floyd’s algorithms; Knapsack problem. Introduction to dynamic programming, Principle of optimality, Optimal binary search trees, Matrix-chain multiplication, Longest common subsequence. Introduction to computability, Reducibility, Polynomial-time verification, NP-completeness, NP-complete problems.
Learning Outcome	Students will be able to design efficient algorithms for different computational problems or will be able to show theoretically that the problem may not be solved in polynomial time.
Assessment Method	Quiz /Assignment/ MSE / ESE

Text Books:

“Introduction to Algorithms” by T. H. Cormen, C. E. Leiserson, R. L. Rivest and C. Stein, Prentice Hall India.

Reference books:

“Data Structures and Algorithms in C++” by M. A. Weiss, Addison-Wesley
“Algorithm Design” by J. Kleinberg and Eva Tardos, Pearson Education

“The Design and Analysis of Computer Algorithms” by A. Aho, J. E. Hopcroft and J. D. Ullman, Addison-Wesley.

MA2102

Probability and Stochastic Processes

Course Number	MA2102 (Core)
Course Credit (L-T-P-C)	3-1-0-4
Course Title	Probability and Stochastic Processes
Learning Mode	Lectures and Tutorials
Learning Objectives	This particular course on probability theory and random processes aims at the undergraduate students to learn about basic properties random variables and their properties. It also covers essential theoretical concepts of random processes which are useful in many fields of practical study.
Course Description	This course is designed to cover basic concepts of probability theory. Particularly properties of random variables like mean, variance, and moment generating functions, quantiles and other important summary of information will be discussed. We also discuss joint distribution of random variables. Probability distributions of transformed random variables will also be discussed. Illustrative discussion on central limit theorems will also be presented. We further discuss basic properties of random processes and also present their classification into different types of processes. We cover both discrete and continuous time Markov chains and study various properties.
Course Content	Axiomatic construction of the theory of probability, independence, conditional probability, and basic formulae, random variables, probability distributions, functions of random variables; Standard univariate discrete and continuous distributions and their properties, mathematical expectations, moments, moment generating function, characteristic functions; Random vectors, multivariate distributions, marginal and conditional distributions, conditional expectations; Modes of convergence of sequences of random variables, laws of large numbers, central limit theorems. Definition and classification of random processes, discrete-time Markov chains, Poisson process, continuous-time Markov chains, renewal and semi-Markov processes, stationary processes, Gaussian process, Brownian motion, filtrations and martingales, stopping times and optimal stopping.
Learning Outcome	(1) Students attending this course will become familiar with different probability laws and properties. (2) This course enables students to get acquaintance with various discrete and continuous probability distributions. Also enable to compute different probabilities for such distributions. Computation of expectations, variance, quantiles and other probabilistic quantities. (3) Learn to compute joint probability distributions, conditional and marginal probability distributions and related properties. (4) Become familiar with the concepts of covariance and correlation. (5) Approximate a distribution using central limit theorem (6) Distribution of transformed random variables (7) Basic concepts of random processes. (8) Poisson processes (9) Markov Chains
Assessment Method	Quiz /Assignment/ MSE / ESE

Text Books:

Papoulis and S. Unnikrishna Pillai: Probabilities, Random Variables and Stochastic Processes, 4th Edition, Tata McGraw-Hill, 2002.

P. G. Hoel, S. C. Port and C. J. Stone: Introduction to Probability Theory, Universal Book Stall, 2000.

MA2103

Optimization Techniques

Course Number	MA2103 (Core)
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Optimization Techniques
Learning Mode	Lectures
Learning Objectives	The objective of the course is to train student about the modeling of linear programming problems and its dual and various algorithms to solve these problems
Course Description	Optimization technique, as a basic subject for undergraduate students, provides the initial knowledge of various models of linear programming problems and different algorithms to solve such problems with its applications in various problems arising in economics, science and engineering.
Course Content	Linear programming: Introduction and Problem formulation, Geometrical aspects of LPP, Graphical solutions, Linear programming in standard form, Simplex, Big M and Two-Phase Methods, Revised simplex method, Special cases of LPP. Duality theory: Dual simplex method, Sensitivity analysis of LP problem, Integer programming problems: Branch and bound method, Gomory cutting plane method for all integer and for mixed integer LPP. Theory of games: saddle point, linear programming formulation of matrix games, two-person zero-sum games. Computational complexity of the Simplex algorithm, Karmarkar's algorithm for LPP. Line search methods for unconstrained non linear optimization, gradient descent, Newton method, conjugate gradient method. Acquaintance to softwares to solve optimization problems.
Learning Outcome	On successful completion of the course, students should be able to: 1. Understand the terminology and basic concepts of various kinds of linear programming problems 2. model several linear programming problems and its dual 3. Develop the understanding of about different solution methods to solve linear Programing problem. 4. Apply and differentiate the need and importance of various algorithms to solve linear programing problems 5. employ programming languages to solve linear programing problems
Assessment Method	Quiz /Assignment/ MSE / ESE

Text Books:

Hamdy Taha, Operations Research: An Introduction, Eighth edition, PHI, New Delhi (2007).
S. Bazaraa, J. J. Jarvis and H. D. Sherali, Linear Programming and Network Flows, 3rd Edition, Wiley (2004).

Reference Books:

G. Luenberger, Linear and Nonlinear Programming, 2nd Edition, Kluwer, (2003).
A. Zenios (editor), Financial Optimization, Cambridge University Press (2002).
S. Hiller, G. J. Lieberman, Introduction to Operations Research, Eighth edition, McGraw Hill (2006).
Chandra, Jayadeva, Aparna Mehra, Numerical Optimization with Applications, Narosa Publishing House (2009).

A. Ravindran, D.T. Phillips, J.J. Solberg, Operation Research, John Wiley and Sons, New York (2005).

MA2104

Algebra

Course Number	MA2104 (Core)
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Algebra
Learning Mode	Lectures
Learning Objectives	The aim of this course is to learn about groups, subgroups, quotient groups, homomorphisms and Sylow’s theorems. Further, it also covers basic properties of rings, ideals, integral domain, ED, PID, UFD which are useful in many branches of mathematics.
Course Description	We will begin by studying the basic concepts of subgroups, homomorphisms and quotient groups with many examples. We then study group actions, and prove the Class equation and the Sylow’ theorems. They are in turn used to prove the structure theorem for finite abelian groups and to discuss the classification of groups of small order. Further, we define rings and discuss ideals, quotient rings. We then discuss the important classes of commutative rings, irreducibility in general and specifically in the context of polynomial rings.
Course Content	Groups, subgroups, normal subgroups, permutation groups, cyclic groups, dihedral groups, matrix groups. Homomorphisms, quotient groups, Isomorphisms. Cayley's theorem, groups acting on sets, Sylow’s theorems (without proof) and applications, direct products, finitely generated abelian groups, Structure Theorem for finite abelian groups. Rings, fields, integral domain, basic properties of rings, units, ideals, homomorphisms, quotient rings, prime and maximal ideals, fields of fractions, Euclidean domains, principal ideal domains and unique factorization domains, polynomial rings.
Learning Outcome	Students will learn basics of abstract algebra and they will be able to take advance courses in Algebra, Number Theory, Cryptography etc. This course will also build foundation for research in mathematical sciences and computer sciences.
Assessment Method	Quiz /Assignment/ MSE / ESE

Text Books:

Dummit and R. Foote, Abstract Algebra, 3rd edition, Wiley, 2004.
A. Gallian, Contemporary Abstract Algebra, 4th ed., Narosa, 1999.

Reference Books:

Artin, Algebra, Prentice Hall of India, 1994.
N. Herstein, Topics in Algebra, Wiley, 2006.
R. Nagpaul and S. K. Jain, Topics in Applied Abstract Algebra, Amer. Math. Soc., First Indian Edition, 2010.
B. Fraleigh, A First Course in Abstract Algebra Paperback, Addison-wesley 1967.

Paul B. Garrett, Abstract Algebra, Chapman and Hall/CRC, 1st edition, 2007.

MA2105

Discrete Mathematics

Course Number	MA2105 (Core)
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Discrete Mathematics
Learning Mode	Lectures
Learning Objectives	To learn formal mathematical way of writing through mathematical logic and different counting techniques through examples
Course Description	This course is meant to introduce different counting techniques. It also covers introductory graph theory and Boolean algebra.
Course Content	Mathematical Logic and Proofs: Propositional logic and equivalences, Predicate and Quantifiers, Introduction to Proofs, Proof methods Sets, Relations and Functions: Relations and their properties, Closure of Relations, Order Relations, Equivalence relations, POSets and Lattices Counting Techniques: Permutations and Combinations, Binomial coefficients, Pigeonhole principle, Double counting, Principle of Inclusion-Exclusion, Recurrence relations and its solution, Divide and Conquer, Generating functions. Graph Theory: Basic definitions, Trees, Connectivity, Spanning trees, Shortest Path Problems, Eulerian and Hamiltonian graphs, Planar graphs, Graph Coloring Boolean Algebra: Boolean functions, Logic gates, Simplification of Boolean Functions, Boolean Circuits
Learning Outcome	Students will be accustomed with the formal mathematical way of writing. They will also be able to apply counting techniques to different problems. Using graph theory, they will be able to model different real- life problems as well.
Assessment Method	Quiz /Assignment/ MSE / ESE

Text Books:

Discrete Mathematics and Its Applications by K. H. Rosen, Tata McGraw-Hill
Discrete Mathematics by C Liu

Reference Books:

Basic Techniques of Combinatorial Theory by D. I. A. Cohen, John Wiley & Sons
Introduction to Graph Theory by D. B. West, Pearson Prentice Hall
A Walk Through Combinatorics by Miklos Bona, 4th Edition, World Scientific
Invitation to Discrete Mathematics by J. Matousek and J. Nesetril, Oxford University Press
Enumerative Combinatorics Vol-I by Richard P. Stanley, Cambridge University Press

HS21XX

HSS Elective - I

TOTAL

Introduction to Machine Learning

Sl. No.

Subject Code

SEMESTER IV

MA2201

Course Number	MA2201 (Core)
Course Credit (L-T-P-C)	2-0-2-3
Course Title	Introduction to Machine Learning
Learning Mode	Lectures and Lab
Learning Objectives	In this subject, the students will be trained with the fundamentals of Machine Learning concepts along with the knowledge of mathematical tools that are required to grasp those skills.
Course Description	Introduction to Machine Learning, as a basic subject for undergraduate students, provides the initial knowledge of Machine Learning with its applications in various Mathematical and Statistical problems.
Course Content	Regression: Least Squares, Goodness of Fit, Bias-Variance Trade Off, Linear, Polynomial and Logistic Regression, Classification: Binary and Multinomial Classification, Naive Bayes Classifier, Neural Networks, K-Nearest Neighbors, Support Vector Machine, Decision Trees, Unsupervised Learning: PCA, K-means clustering, Hiracrhieal Clustering, Density based Clustering. LAB: problems based on theory lectures using R/Python.
Learning Outcome	On successful completion of the course, students should be able to: 1. Analyse the role of mathematical tools in Machine Learning. 2. Understand the terminology and basic concepts of Machine Learning 3. Differentiate and apply the Supervised and Unsupervised Learning models.
Assessment Method	Quiz /Assignment/ MSE / ESE

Text Books:

Tom Mitchell. Machine Learning. First Edition, McGraw- Hill, 1997
Duda, P. Hart and D. Stork, PatternClassification, Wiley

Reference Books:

Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, An Introduction to Statistical Learning with Applications in R
Ethen Alpaydin, Introduction to Machine Learning, 2nd edition.
Machine Learning: An Applied Mathematics Introduction, Panda Ohana Publishing, 2019

MA2202

Real Analysis and Measure Theory

Course Number	MA2202 (Core)
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Real Analysis and Measure Theory
Learning Mode	Lectures
Learning Objectives	Students will understand the concept of sequences and series of real numbers as well as functions. Students also learn various concepts associated with Lebesgue measure and understand the need for Lebesgue integration. Theorems related to Lebesgue integration are discussed to highlight the applications of Lebesgue integration.
Course Description	This course discusses details of sequences and series of real numbers and real valued functions. Distinction between pointwise convergence and uniform convergence shall be covered in detail and how uniform convergence leads to some important theorems. The concept of Lebesgue integration is introduced by discussing the basics of outer measure, measurable sets and measurable functions. Various interesting theorems associated with the Lebesgue integral shall be covered in detail highlighting their applications.
Course Content	Sequence and series of real numbers and tests for convergence, Cauchy sequences, Cauchy criterion for convergence, bounded and monotonic sequences, absolute continuity and uniform continuity. Sequences and Series of real valued functions and uniform convergence, Power series. Sigma Algebra, Lebesgue outer measure, Measurable sets, Measure space, Complete measure space, Lebesgue measure on R, Properties of Lebesgue measure. Lebesgue Integration, the integration of non-negative functions, Measurable functions, Fatou's Lemma, Integrable functions and their properties, Lebesgue's dominated convergence theorem (without proof).
Learning Outcome	On successful completion of the course, students should be able to: 1. Differentiate between uniform convergence and point wise convergence. 2. Differentiate between continuity and uniform continuity. 3. Evaluate the Lebesgue integral of a measurable function.
Assessment Method	Quiz /Assignment/ MSE / ESE

Text Books:

Rudin, Principles of Mathematical Analysis, McGraw-Hill, 1976.
G de Barra, Measure Theory and Integration, New Delhi New Age International,2003

Reference Books:

M. Apostol, Mathematical Analysis, Narosa Publishing House, 2002.
Frank Jones , Lebesgue Integration On Euclidean Space (Revised Ed.) (Jones And Bartlett Books In Mathematics) 2000.
Stein and Shakarchi, Real Analysis, Measure Theory, Integration, and Hilbert Spaces (Princeton Lectures in Analysis), Overseas (May 2005).
Ross, Elementary Analysis: The Theory of Calculus, Springer, 2004.

MA2203

Numerical Linear Algebra

Course Number	MA2203 (Core)
Course Credit (L-T-P-C)	3-0-2-4
Course Title	Numerical Linear Algebra
Learning Mode	Lectures and Labs
Learning Objectives	This course answers the fundamental question of choice of suitable matrix computation method for various kind of problems required linear solvers. Hands-on experience with all the methods covered is the most crucial part of this course. All the topics discussed in this course would be accompanied with parallel practical session to reinforce the learning outcome of the course.
Course Description	Due to increasing complexity in the real world scenarios and recent advances in the area of data science, understanding of numerical linear algebra and large scale matrix computations has become essential for engineers.
Course Content	Review of basic concepts from linear Algebra; direct methods for solving linear systems; vector and matrix norms; condition numbers; least squares problems; iterative methods for solving linear systems - Jacobi, Gauss Seidel, SOR and their convergence; projection methods - general projection method, steepest descent, MR Iteration, RNSD method and their convergence; orthogonalization; singular value decomposition; numerical computation of eigenvalues and eigenvectors; Introduction to Krylov subspace methods - Arnoldi’s method, GMRES method, Conjugate gradient algorithm, Lanczos Algorithm and convergence check for Krylov subspace methods, Preconditioned CG, ILU preconditioner.
Learning Outcome	On completion of the module, students will be able to · do some key matrix factorizations of a matrix, · identify suitable technique to solve linear systems of equations, least squares problems, and the eigenvalue problem.
Assessment Method	Quiz /Assignment/ MSE / ESE

Text Books:

Iterative Methods for Sparse Linear Systems (Textbook), Yousef Saad, SIAM 2003
Matrix Computations (Textbook), Gene H. Golub, Charles, Van Loan, John Hopkins University Press, 1996

Reference Books:

Matrix iterative Analysis, R. S. Varga, Prentice Hall 1962
Introduction to matrix computation, Gilbert W. Stewart, Academic Press 1973
Numerical Linear Algebra, L.N. Trefethen, D. Bau, SIAM, 1997
Fundamentals of Matrix Computations, Watkins, Wiley-Interscience, 2010
Applied numerical linear algebra, Demmel, James W., Vol. 56. SIAM, 1997

MA2204

Computer Architecture and Organization

Course Number	MA2204 (Core)
Course Credit (L-T-P-C)	3-0-3-4.5
Course Title	Computer Architecture and Organization
Learning Mode	Lectures and Lab
Learning Objectives	1. Understand the CPU architecture including registers, instruction execution cycle, addressing modes, and instruction set. 2. Explore CPU control unit design, memory organization, peripheral devices, and their characteristics, while also becoming familiar with assembly language programming.
Course Description	This course covers the fundamentals of CPU architecture, including registers, instruction execution cycle, and addressing modes. It delves into CPU control unit design, memory organization, cache memory, and peripheral devices. Practical aspects include assembly language programming and case studies on instruction sets of common CPUs.
Course Content	CPU - registers, instruction execution cycle, RTL interpretation of instructions, addressing modes, instruction set. Case study - instruction sets of some common CPUs; Assembly language programming for some processor; Data representation: signed number representation, fixed and floating-point representations, character representation. Computer arithmetic - integer addition and subtraction, ripple carry adder, carry look-ahead adder, etc. multiplication – shift-and-add, Booth multiplier, carry save multiplier, etc. Division - non-restoring and restoring techniques, floating point arithmetic; CPU control unit design: hardwired and micro-programmed design approaches, Case study - design of a simple hypothetical CPU; Pipelining: Basic concepts of pipelining, throughput and speedup, pipeline hazards; Memory organization: Memory interleaving, concept of hierarchical memory organization, cache memory, cache size vs block size, mapping functions, replacement algorithms, write policy; Peripheral devices and their characteristics: Input-output subsystems, I/O transfers - program controlled, interrupt driven and DMA, privileged and non-privileged instructions, software interrupts and exceptions. Programs and processes - role of interrupts in process state transitions. Familiarization with assembly language programming; Synthesis/design of simple data paths and controllers, processor design using HDL like verilog/vhdl; Interfacing - DAC, ADC, keyboard-display modules, etc. Development kits as well as Microprocessors/PCs may be used for the laboratory, along with design/simulation tools as and when necessary.
Learning Outcome	1. Develop a comprehensive understanding of CPU architecture, memory organization, and peripheral devices, along with proficiency in assembly language programming. 2. Apply theoretical concepts to design and analyze simple hypothetical CPUs
Assessment Method	Quiz /Assignment/ MSE / ESE

Text Books:

Stalling W, “Computer Organization and Architecture ”, Pearson Eduction India. 2008
Tanenbaum, A.S, “Structured Computer Organization”, Prentice-Hall. 1994

Reference Books:

D V Hall, Microprocessors and Interfacing, TMH, 1995
Barry B, The Intel Microprocessors 8086/8088, 80186/80188, 80286, 80386, 80486, Pentium, and Pentium Pro Processor Architecture, Programming, and Interfacing, Prentice Hall India, 2005
Patterson, D.A., and Hennessy, J.L. , “Computer Organization and Design: The Hardware/Software Interface”, Morgan Kaufmann Publishers, 4th Edition, Inc.2005
Patterson, D.A., and Hennessy, J.L. , “Computer Architecture : A Quantitative Approach ”, Morgan Kaufmann Publishers, 4th Edition, Inc.2005
Hamacher, V.C., Vranesic, Z.G., and Zaky, S.G., “Computer Organization”, 5/e. McGraw-Hill. 2008

4.5

MA2205

Database Management Systems

Course Number	MA2205 (Core)
Course Credit (L-T-P-C)	3-0-3-4.5
Course Title	Database Management Systems
Learning Mode	Lectures and Lab
Learning Objectives	Develop a comprehensive understanding of database system architecture, data models, etc.
Course Description	This course covers database system architecture, data models, relational query languages, relational database design, query processing and optimization, storage strategies, transaction processing, and recent trends in database systems.
Course Content	Database system architecture: Data Abstraction, Data Independence, Data Definition and Data Manipulation Languages; Data models: Entity-relationship, network, relational and object oriented data models, integrity constraints and data manipulation operations; Relational query languages: Relational algebra, tuple and domain relational calculus, SQL and QBE; Relational database design: Domain and data dependency, Armstrongs axioms, normal forms, dependency preservation, lossless design; Schema refinement; Query processing and optimization: Evaluation of relational algebra expressions, query equivalence, join strategies, query optimization algorithms; Storage strategies: Indices, B-trees, hashing; Transaction processing: Recovery and concurrency control, locking and timestamp based schedulers, multiversion and optimistic Concurrency Control schemes; Recent Trends: XML Data, XML Schema, JSON etc. Database schema design, database creation, SQL programming and report generation using a commercial RDBMS like ORACLE/SYBASE/DB2/SQL-Server/INFORMIX. Students are to be exposed to front end development tools, ODBC and CORBA calls from application Programs, internet based access to databases and database administration.
Learning Outcome	Students will develop a comprehensive understanding of database systems, including their architecture, design principles, query processing techniques, transaction management, and emerging trends in data management technologies
Assessment Method	Quiz /Assignment/ MSE / ESE

Text Books:

A Silberschatz, H Korth and S Sudarshan, Database System Concepts, 6th Ed., McGraw-Hill, 2010.
H Garcia-Molina, JD Ullman and Widom, Database Systems: The Complete Book, 2nd Ed., Prentice-Hall, 2008.

Reference Books:

R Elmasri, S Navathe, Fundamentals of Database Systems, 6th edition, Addison-Wesley, 2010.
R Ramakrishnan, J Gehrke, Database Management Systems, 3rd Ed., McGraw-Hill, 2002.

4.5

XX22PQ

IDE - I

TOTAL

Ordinary and Partial Differential Equations

Sl. No.

Subject Code

SEMESTER V

MA3101

Course Number	MA3101 (Core)
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Ordinary and Partial Differential Equations
Learning Mode	Lectures
Learning Objectives	To get expose to the ordinary differential equations. To understand the theory and qualitative properties of solutions of differential equations. The course will also introduce students to partial differential equations and methods of solutions of some basic partial differential equations.
Course Description	This course is meant to introduce the basic properties and solutions of both the ordinary and partial differential equations.
Course Content	ODE: Review of ODEs, IVPs and existence and uniqueness theorems, System of ODEs: Phase plane, critical point, stability, Oscillation and Comparison theorems for second order linear equations and applications, Self-adjoint Eigenvalue problems on a finite interval, BVPs and Sturm Liouville Problems, Green’s function. PDE: Introduction to PDE and the classification of PDEs (Linear, Nonlinear, Quasi Linear), Lagrange’s and Charpit’s Method, Second order PDEs and Their Classification, Method of Separation of Variables, Method of Characteristics, D’Alembert Solution, Duhamel’s principle. Maximum Principle and existence theorems, Fourier series, Fourier Transform, Laplace Transform and their application to solve ODEs and PDEs.
Learning Outcome	Students will be able to identify properties of solutions of the ODEs even when explicit solutions are not possible or feasible. The solutions methods for the PDEs will be explicitly introduced.
Assessment Method	Quiz /Assignment/ MSE / ESE

Text Books:

Earl A. Coddington, Norman Levinson, Theory of Ordinary Differential Equations, Tata McGraw Hill Education Private Limited, New Delhi, 1972.
Ian Sneddon, Elements of Partial Differential Equations, McGraw-Hill International Editions, 1957.

Reference Books:

Mark A. Pinsky, Partial Differential Equations and Boundary-Value Problems with Applications, American Mathematical Society, 2013.
Myint U. Tyn, Lokenath Debnath, Linear Partial Differential Equations for Scientists and Engineers, Birkhauser, 4th Edition.
Amarnath, An Elementary Course in Partial Differential Equations, Narosa, 2nd Edition.

MA3102

Complex Analysis

Course Number	MA3102 (Core)
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Complex Analysis
Learning Mode	Lectures
Learning Objectives	The objective of the course is to train student about the fundamental properties of complex valued functions
Course Description	Complex Analysis is a basic course for undergraduate student and is intended to discuss about important Mathematical properties of complex valued functions and enables students to solve some real-life problem.
Course Content	Limit, Continuity, Differentiability, Analytic functions, Cauchy-Riemann Equations, Harmonic Functions, Reflection Principle, Elementary Functions, Branch point and Branch Cut, Contour Integration, Cauchy-Goursat Theorem- Simply and Multiply Connected Domains, Cauchy Integral Formula, Liouville's Theorem and the Fundamental Theorem of Algebra, Morera’s Theorem, Maximum Modulus Principle, Taylor Series, Laurent Series, Classification of Singularities, Cauchy's Residue Theorem, Residues at Poles, Zeros of Analytic Functions, Zeros and Poles, Behavior Near Isolated Singular Points, Evaluation of Improper Integrals, Jordan's Lemma, Definite integrals involving Sines and Cosines, Argument Principle, Rouche's Theorem, Bilinear Transformations, Conformal Mapping.
Learning Outcome	On successful completion of the course, students should be able to: 1. Analyse the geometric behaviours of different kind of complex valued functions and use them to solve some real life problems. 2. Behaviour of complex valued function near singular points. 3. Use of Branch cut to solve difficult definite integrals.
Assessment Method	Quiz /Assignment/ MSE / ESE

Text Books:

Complex Variables and Applications: James Ward Brown and Ruel V. Churchill, 8th Edition, McGraw Hills.
Lars Ahlfors, Complex Analysis, McGraw Hill Education; Third edition (1 July 2017).

Reference Books:

Fischer, Wolfgang, Lieb, IngoA Course in Complex Analysis, Springer-Verlag, (2012).
Joseph L. Taylor, Complex Variables - American Mathematical Society, 2011.
Edward C. Titchmarsh, The Theory of Functions, Oxford University Press; 2 edition, 1976.
Stein and Shakarchi, Complex Analysis, Overseas (1 January 2006).

MA3103

Theory of Computation

Course Number	MA3103 (Core)
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Theory of Computation
Learning Mode	Lectures
Learning Objectives	To understand how efficiently a computational problem can be solved on a model of computation using algorithm.
Course Description	This course is meant to introduce the fundamental but abstract areas of theoretical computer science.
Course Content	Basic definitions, deterministic and non-deterministic finite automata. Regular Languages, regular operations, Regular Expressions, Equivalence of DFA, NFA, Nonregular Languages and pumping lemma. Context-Free Languages: Context-Free Grammars, Chomsky Normal Form, Pushdown Automata. Noncontext-Free Languages and pumping lemma, Deterministic Context-Free Languages Turing Machines: Definition of TM and its variants, Decidability, Reducibility. Complexity Theory: Time complexity and Space Complexity.
Learning Outcome	Students will have strong theoretical foundation to identify which computational problems are solvable which helps them to learn other areas of computer science like compiler, artificial intelligence, natural language processing and many more.
Assessment Method	Quiz /Assignment/ MSE / ESE

Text Books:

Introduction to the Theory of Computation, by Michael Sipser,
Computational Complexity, by Christos H. Papadimitriou, Addison-Wesley publishers.

Reference Books:

Computational Complexity: A Modern Approach, by Sanjeev Arora and Boaz Barak.

MA3104

Computer Networks

Course Number	MA3104 (Core)
Course Credit (L-T-P-C)	3-0-3-4.5
Course Title	Computer Networks
Learning Mode	Lectures and Labs
Learning Objectives	Comprehend the historical development of computer networks and grasp both the theoretical and practical foundations of data communication.
Course Description	This course provides an in-depth exploration of computer networks, covering the evolution, physical layer, medium access control, data link layer, network layer, transport layer, quality of service, and application layer protocols.
Course Content	Evolution of computer networks; Physical Layer; transmission media and impairments, switching systems Medium Access Control Sublayer: Channel allocation Problem, multiple access protocols, Ethernet Data link layer: Framing, HDLC, PPP, sliding window protocols, error detection and correction Network Layer: Internet addressing, IP, ARP, ICMP, CIDR, routing algorithms (RIP, OSPF, BGP); Transport Layer: UDP, TCP, flow control, congestion control; Introduction to quality of service; Application Layer: DNS, Web, email, authentication, encryption. Simulation experiments for protocol performance, configuring, testing and measuring network devices and parameters/policies; network management experiments; Exercises in network programming.
Learning Outcome	Students will develop a comprehensive understanding of computer networks.
Assessment Method	Quiz /Assignment/ MSE / ESE

Text Books:

Peterson & Davie, Computer Networks, A Systems Approach: 5th Edition
William Stallings, Data and Computer Communication, Prentice-Hall.
AS Tanenbaum, DJ Wetherall, Computer Networks, 5th Ed., Prentice-Hall, 2010.

Reference Books:

LL Peterson, BS Davie, Computer Networks: A Systems Approach, 5th Ed., Morgan-Kauffman, 2011.
JF Kurose, KW Ross, Computer Networking: A Top-Down Approach, 5th Ed., Addison-Wesley, 2009.

4.5

MA3105

Operating Systems

Course Number	MA3105 (Core)
Course Credit (L-T-P-C)	3-0-3-4.5
Course Title	Operating Systems
Learning Mode	Lectures and Labs
Learning Objectives	Gain a comprehensive understanding of operating system fundamentals.
Course Description	This course covers essential concepts in operating systems, including process management, concurrency, memory management, file systems, secondary storage, and advanced topics like distributed systems, security, and real-time systems, with practical examples drawn from Linux, Windows NT/7/8.
Course Content	Process Management: process; thread; scheduling. Concurrency: mutual exclusion; synchronization; semaphores; monitors; Deadlocks: characterization; prevention; avoidance; detection. Memory Management: allocation; hardware support; paging; segmentation. Virtual Memory: demand paging; replacement; allocation; thrashing. File Systems and Implementation. Secondary Storage: disk structure; I/O management; device drivers; disk scheduling; disk management. (Linux will be used as a running example, while examples will draw also from Windows NT/7/8.); Advanced Topics: Distributed Systems. Security. Real-Time Systems. Programming assignments to build different parts of an OS kernel.
Learning Outcome	Students will develop a comprehensive understanding of operating system principles and mechanisms, enabling them to design, implement, and manage efficient and reliable computer systems.
Assessment Method	Quiz /Assignment/ MSE / ESE

Text Books:

Silberschatz, P. B. Galvin and G. Gagne, Operating System Concepts, 8th Ed, John Wiley & Sons, 2010.
S. Tenenbaum, Modern Operating Systems, 2nd Ed, Prentice Hall of India, 2001.

Reference Books:

M. Deitel, P. J. Deitel and D. R. Choﬀness, Operating Systems, 3rd Ed, Prentice Hall, 2004.
Stallings, Operating Systems: Internal and Design Principles, 5th Ed, Prentice Hall, 2005.
J. Bach, The Design of the UNIX Operating System, Prentice Hall of India, 1994.

4.5

XX31PQ

IDE - II

TOTAL

Number Theory and Cryptography

Sl. No.

Subject Code

SEMESTER VI

MA3201

Course Number	MA3201 (Core)
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Number Theory and Cryptography
Learning Mode	Lectures
Learning Objectives	Readers of this course will be well-equipped with basic concepts of numbers, their properties, and some of the standard results which are fundamental to any branch of mathematics. The course will study further properties and some advanced concept which has a lot of application in Cryptography.
Course Description	This course introduces divisibility in integers and some knowledge of the arithmetic of congruences. The prime numbers are the building blocks of all natural numbers. The interplay between the multiplicative and additive properties of numbers and their uses in quadratic residues is particularly interesting. A few applications of these topics of number theory to modern cryptography are also introduced.
Course Content	Integers, mathematical induction, divisibility in integers, basic algebra of infinitude of primes, Prime number theorem, Fundamental theorem of arithmetic, Dirichlet's theorem (without proof). Arithmetic functions, Mobius inversion formula, Structure of units modulo n, Euler's phi function. Primitive roots and indices, group of units. Congruences, Fermat’s theorem and Euler’s theorem, Wilson's theorem, linear congruences, Simultaneous linear congruences, Chinese Remainder Theorem, Simultaneous non-linear congruences. Quadratic residues, law of quadratic reciprocity, binary quadratics forms, Fermat's two square theorem (without proof). Algorithm to solve quadratic equations in Zm. Finite fields: construction and examples, factorizations of polynomials over finite fields, algorithm to determine irreducible polynomials of degree n over Zm. Introduction to classical cryptosystems, DES-security and generalizations, Prime number generation. Public key cryptosystem (RSA).
Learning Outcome	On successful completion of the course, students should be able to: 1. Understand the importance of integers; 2. Understand other basic courses of mathematics, like Algebra, Topology, Calculus, Analysis, Geometry and Combinatorics; 3. Help to understand the basic techniques of Cryptography (the techniques for protecting information from unauthorized access) & Coding Theory and Information Theory (the study of the transfer of information securely) and make able to develop some new techniques too.
Assessment Method	Quiz /Assignment/ MSE / ESE

Text Books:

David M. Burton, Elementary Number Theory, 6th Edition, McGrow Hill Higher Education, 2007.
Koblitz…

Reference Books:

Kenneth H. Rosen: Elementary Number Theory, 6th edition, Pearson, 2010.
W. Adams and L.J. Goldstein, Introduction to the Theory of Numbers, 3rd ed., Wiley Eastern, 1972.
Baker, A Concise Introduction to the Theory of Numbers, Cambridge University Press, Cambridge, 1984.
Niven and H.S. Zuckerman, An Introduction to the Theory of Numbers, 5th Ed., Wiley, New York, 2008.
Thomas Koshy, Elementary Number Theory with Applications, 2nd Edition, Academic Press, 2007.

MA3202

Numerical Methods

Course Number	MA3202 (Core)
Course Credit (L-T-P-C)	3-0-2-4
Course Title	Numerical Methods
Learning Mode	Lectures and Labs
Learning Objectives	In this subject, the students will be trained with the basic available numerical methods which are required to solve applied models. This objective is required for anyone who wanted to work on computational areas.
Course Description	This course will highlight the root finding approximation methods which are required for solving system of differential equations. In addition, the basic convergence criteria for solving ODE and PDEs will be also explained in addition to the numerical algorithms.
Course Content	Bisection Method, Fixed Point Iteration, Secant Method, Solution of Nonlinear System based on Newton Raphson Method, Sufficient condition for convergence of Nonlinear systems, Interpolation (Lagrange’s formula, Newton’s forward and backward method, central difference, divided difference, sterling’s formula), Integration (Trapezoidal, Simpson’s 1/3^rd rule, Simpson’s 3/8^th rule, Quadrature Methods) and Differentiation. Single step methods (Euler method and Runge Kutta Method), Multi-step methods for IVPs (Adam-Bashforth, Adam-Moulton methods), Finite difference methods for BVPs (2^nd order scalar case only), Finite difference methods for parabolic, hyperbolic and elliptic PDEs
Learning Outcome	Students will be able to know the Mathematics behind the basic numerical algorithms
Assessment Method	Quiz /Assignment/ MSE / ESE

Text Books:

K. Jain, S.R.K. Iyenger, RK Jain, Numerical Methods, For Scientific and Engineering Computation, New Age Publisher.
E. Atkinson, An Introduction to Numerical Analysis, John Wiley & Sons.

Reference Books:

Michael T. Heath, Scientific Computing, An Introductory Survey, Tata McGraw Hill.
Endre Suli and David F. Mayers, An Introduction to Numerical Analysis, Cambridge Univ Press.
A Theoretical Introduction to Numerical Analysis, 1st Edition, By Victor S. Ryaben'kii, Semyon V. Tsynkov, Chapman and Hall/CRC.
Sastry…

MA3203

Mathematical Statistics

Course Number	MA3203 (Core)
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Mathematical Statistics
Learning Mode	Lectures
Learning Objectives	This course on mathematical statistics is aimed at the undergraduate students who are interested to learn basic concepts of statistics via mathematical approach. It gives essential background to students who further wish to learn statistics at advanced level.
Course Description	This course is designed to cover various important methods of statistical inference. Order statistics and their join distributions are considered. Various properties of order statistics will be discussed. Then sampling from normal distribution will be discussed. Further different types of estimation problems will be described and illustrated. In this regard point and interval estimation problems will be demonstrated. Both classical and Bayesian methods of estimation will be discussed. Towards the end problem of testing will be covered.
Course Content	Ordered Statistics, probability distributions of Sample Range, Minimum and Maximum order Statistics. Random Sampling, Sampling distributions: Chi-square, T, F distributions. Point Estimation: Sufficiency, Factorization theorem, Consistency, Moment method of estimation, Unbiased Estimation, Minimum Variance Unbiased Estimator and their properties, Rao-Cramer lower bound, Rao-Blackwellization, Fisher Information, Maximum Likelihood Estimator and properties, Criteria for evaluating estimators: Mean squared error. Interval Estimation: Coverage Probabilities, Confidence level, Sample size determination, Shortest Length interval, Pivotal quantities, interval estimators for various distributions. Testing of Hypotheses: Null and Alternative Hypotheses, Simple hypothesis, Composite hypothesis, Test Statistic, Critical region, Error Probabilities, Power Function, Level of Significance, Neyman-Pearson Lemma, One and Two Sided Tests for Mean, Variance and Proportions, One and Two Sample T-Test, Pooled T-Test, Paired T-Test, Chi-Square Test, Contingency Table Test, Maximum Likelihood Test, Duality between Confidence Intervals. Bayesian Estimation: Prior and Posterior Distributions, Quadratic Loss Function, Posterior Mean, Bayes Estimates for well Known Distributions (Normal, Gamma, Exponential, Binomial, Poisson, Beta etc.)
Learning Outcome	Students will become familiar with following topics: (1) Distribution properties of order statistics (2) Desirable properties point estimators like unbiasedness, efficiency, consistency etc. (3) Mean squared error computations (4) Coverage probabilities (5) Posterior distributions (6) Error probabilities and most powerful tests (7) Chi-square test
Assessment Method	Quiz /Assignment/ MSE / ESE

Text Books:

Mathematical Statistics with applications, Kandethody M. Ramachandran, Chris P. Tsokos, Academic Press, 2009
Probability and Statistics in Engineering, William W. Hines, Douglas C. Montgomery, David M. Goldsman, Connie M. Borror, John Wiley & Sons; 4th Edition Edition, 2003.

MA3204

Convex Optimization

Course Number	MA3204 (Core)
Course Credit (L-T-P-C)	3-0-2-4
Course Title	Convex Optimization
Learning Mode	Lectures and Labs
Learning Objectives	The objective of the course is to train student about the modeling of convex programming problems and various classical and numerical optimization techniques and algorithms to solve these problems
Course Description	Convex Optimization, as a basic subject for undergraduate students, provides the knowledge of various models of nonlinear convex optimization problems and different algorithms to solve such problems with its applications in various problems arising in economics, science and engineering.
Course Content	Introduction to nonlinear programming, Convex Sets, Convex Functions and their properties. Unconstrained optimization of functions of several variables: Necessary and Sufficient conditions. Numerical methods for unconstrained optimization: Newton, LM method, and Quasi Newton methods (DFP and BFGS methods) Constrained optimization of functions of several variables, Lagrange Multiplier method, Karush-Kuhn-Tucker theory, Constraint Qualifications, Convex optimization, Interior point methods for inequality constrained optimization, Merit functions for constrained minimization, logarithmic barrier function for inequality constraints, A basic barrier-function algorithm, Perturbed optimality conditions. Quadratic optimization: Wolfe method, Beale’s Method, applications of quadratic programs in some domains like portfolio optimization and support vector machines, etc. Practice of optimization algorithms using Software.
Learning Outcome	On successful completion of the course, students should be able to: 1. Understand the terminology and basic concepts of various kinds of convex optimization problems 3. Develop the understanding about different solution methods to solve convex Programing problem. 4. Apply and differentiate the need and importance of various algorithms to solve convex programing problems 5. Employ programming languages to solve convex programing problems 6. Model and solve several problems arising in science and engineering as a convex optimization problem
Assessment Method	Quiz /Assignment/ MSE / ESE

Text Books:

Edwin K. P. Chong and Stanislaw H. Zak: An Introduction to optimization, 4th Edition, John Wiley & Sons, New York, (2013).
Boyd and L. Vandenberghe: Convex Optimization, Cambridge University Press, New York, (2004).

Reference Books:

L. Mangsarian: Nonlinear Programming, SIAM, (1994).
G. Luenberger, Linear and Nonlinear Programming, 2nd Edition, Kluwer, (2003).
S. Bazaraa, H.D. Sherali and C.M. Shetty: Nonlinear Programming: Theory and Algorithms, John Wiley and Sons, New Jersey, (2006).
S. Rao: Engineering Optimization: Theory and Practice, John Wiley & Sons, (2009).
Nocedal and S. J. Wright, Numerical Optimization, Springer Verlag, (1999).
P. Bertsekas, Dynamic programming and Optimal Control, Athena Scientific, Belmont, 4th Edition, (2012).

MA3205

Functional Analysis

Course Number	MA3205 (Core)
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Functional Analysis
Learning Mode	Lectures
Learning Objectives	The objective of the course is to train student about the advanced concepts of metric space, normed linear spaces, Banach Space, inner product spaces, Hilbert Spaces, orthogonal sets and Lp spaces.
Course Description	This is an advanced course for undergraduate student and is intended to discuss about important Mathematical properties of functional analysis.
Course Content	Metric spaces, Open sets, Closed sets, Continuous functions, Completeness, Cantor intersection theorem, Baire category theorem, totally boundedness, Finite intersection property. Normed spaces, Banach spaces, Properties of Banach spaces, Lp-spaces, Holder's inequality, Minkowski's inequality. Linear operators, Bounded linear operators, fixed point theorems, functionals on Banach spaces, Dual space. Inner product space, Hilbert spaces, Properties of inner product spaces, Orthogonal complements and direct sums, Orthonormal sets and sequences, Total orthonormal sets.
Learning Outcome	On successful completion of the course, students should be able to: 1. Validate the properties of a metric space. 2. Find the norm of a bounded linear operator. 3. Construct orthogonal basis for Hilbert space.
Assessment Method	Quiz /Assignment/ MSE / ESE

Text Books:

Erwin Kreyszig, Introductory Functional Analysis With Applications, John Wiley & Sons 1978.
Rudin, Principles of Mathematical Analysis, McGraw-Hill, 1976.

Reference Books:

Stein and Shakarchi, Functional Analysis: Introduction to Further Topics in Analysis: 04 (Princeton Lectures in Analysis), Overseas (1 January 2011).
T. Nair, Functional Analysis: A First Course, PHI Pvt. Ltd, 2004.
V. Limaye, Functional Analysis, 2nd ed., New Age International, New Delhi, 1996.
F. Simmons, Introduction to Topology and Modern Analysis, McGraw-Hill Inc. 1983.

MA3206

Artificial Intelligence

Course Number	MA3206 (Core)
Course Credit (L-T-P-C)	3 – 0 – 2 – 4
Course Title	Artificial Intelligence
Learning Mode	Lectures and Labs
Learning Objectives	This course aims to impart a deep understanding of theoretical AI concepts while equipping students for research and industry applications in artificial intelligence.
Course Description	This course offers a comprehensive overview of artificial intelligence, covering its foundation, history, and modern advancements, with a focus on uncertainty theory, learning algorithms, decision trees, neural networks, reinforcement learning, and practical machine learning applications for images and language.
Course Content	Introduction to Artificial Intelligence: Foundation, History, State-of-the-Art, Definition of AI: Thinking Vs Active and Humanly Vs Rationally, Example Tasks, Phases of AI; Uncertainty Theory and Learning: Acting under Uncertainty, Uncertain Knowledge, Bayesian Networks, Hidden Markov Models, Bayesian Learning, Bayesian Parameter Learning, Hidden Variables, The EM Algorithm for Unsupervised Learning; Learning Theory for Decision Tree: Types of Learning, Classification using Learning, Decision Tree for Discrete Input/Output Variable, Entropy, Information Gain, Overfitting, Decision Tree Pruning, Significance Test for Pruning, Extending Decision Tree for Continuous Input/Output Variable, Generalizability in Evaluation of the Learning Models, Cross-validation, Regularization, Scaling. Computational Learning Theory: PAC, Regression Using Linear Model, Artificial Neural Network: Structure, Perceptron, Non-Linearity, Multi-layer Architecture, Forward Propagation, Backward Propagation; Reinforcement Learning: Active Learning, Passive Learning, Adaptive Dynamic Programming, Generalization, Application of Reinforcement Learning to Game Playing; Study of Practical Machine Learning for Images and Languages.
Learning Outcome	Students will gain a solid understanding of AI principles and techniques, enabling them to apply theoretical knowledge to real-world problems in research and industry settings.
Assessment Method	Quiz /Assignment/ MSE / ESE

Text Books:

Russell, Stuart, and Peter Norvig. "Artificial intelligence: a modern approach.", Prentice Hall Pearson.
Rich, K Knight, SB Nair. “Artificial intelligence” third edition, Tata McGraw-Hill.
Dick, Stephanie. "Artificial intelligence" MIT Press.

Reference books:

Nilsson, Nils J. “Principles of artificial intelligence.” Springer Science & Business Media2.
Witten Ian H., Eibe Frank, Mark A. Hall, and Christopher J. Pal. “Data Mining: Practical machine learning tools and techniques”. Morgan Kaufmann.
Cohen, Paul R., and Edward A. Feigenbaum, eds. The Handbook of Artificial Intelligence: Volume 3. Vol. 3. Butterworth-Heinemann.
Trevor Hastie, R. Tibshirani, J . Friedman. “The Elements of Statistical Learning” Second Edition, Springer.

TOTAL

Sl. No.	Subject Code	SEMESTER VII	L	T	P	C
1.	HS41XX	HSS Elective - II	3	0	0	3
2.	XX41PQ	IDE - III	3	0	0	3
3.	MA41XX	Departmental Elective – I	3	0	0	3
4.	MA41XX	Departmental Elective – II	3	0	0	3
5.	MA4198	Summer Internship*	0	0	12	3
6.	MA4199	Project – I	0	0	12	6
TOTAL						21

* For specific cases of internship after 6^th Semester, the performance evaluation would be made on joining the VII^th Semester and graded accordingly in the VII^th Semester:

Note :

a) (i) Summer internship (*) period of at least 60 days’ (8 weeks) duration begins in the intervening vacation between semester VI and VII that may be done in industry / R&D / Academic Institutions including IIT Patna. The evaluation would comprise combined grading based on host supervisor evaluation, project internship report after plagiarism check and seminar presentation at the Department (DAPC to coordinate) with equal weightage of each of the three components stated herein.

a) (ii) Further, on return from internship, students will be evaluated for internship work through combined grading based on host supervisor evaluation, project internship report after plagiarism check, and presentation evaluation by the parent department with equal weightage of each component.

b) (i) In the VII^th semester, students can opt for a semester long internship on recommendation of the DAPC and approval of the Competent Authority.

b) (ii) On approval of semester long internship, at the maximum two courses (properly mapped/aligned syllabus) at par with institute electives may be opted from NPTEL and / or SWAYAM and the other two more should be done at the institute through course overloading in any other semester (either before or after the internship) and/or during following summer semester.

b) (iii) The candidates opting two courses from NPTEL and / or SWAYAM would be required to appear in the examination at the Institute as scheduled in the Academic Calendar.

Cryptography and Network Security

Department Elective I

Sl. No.

Course Code

Department Elective I

MA4101

Advanced Algorithms

Course Number	MA4101 (DE)
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Advanced Algorithms
Learning Mode	Lectures
Learning Objectives	To learn few advanced algorithms and also to learn how to deal with the computational problems which are NP-Complete
Course Description	This course introduces some advanced algorithms along with algorithm designing techniques in randomized and approximation algorithms.
Course Outline	Graph algorithms: Maximum bipartite matching, Maximum weighted bipartite matching, Matching in general graphs. Fast Fourier transformation and its applications. String Matching: Rabin-Karp algorithm, Knuth-Morris-Pratt algorithm. Randomized algorithm: Randomized Min-Cut algorithm, Coupon collector’s problem, Median finding, Randomized quick sort, Markov-Chebyshev, Chernoff bound, Load balancing, Hashing revisited, Probabilistic methods: Basic counting, Expectation arguments, Sample and Modify, Application of Lovasz Local Lemma Introduction to approximation algorithms.
Learning Outcome	Students will learn few advanced algorithms and also learn how to design randomized algorithms, approximation algorithms for different computational problems.
Assessment Method	Quiz /Assignment/ Project / MSE / ESE

Text Books:

Algorithm Design By Jon Kleinberg, Éva Tardos, Pearson Education
The Design of Approximation Algorithms By David P. Williamson, David B. Shmoys, Cambridge University Press
Probability and Computing: Randomization and Probabilistic Techniques in Algorithms and Data Analysis By Michael Mitzenmacher, Eli Upfal , Cambridge University Press

Reference Books:

Design and Analysis of Algorithms: A Contemporary Perspective By Sandeep Sen and Amit Kumar, Cambridge University Press
Algorithms By Sanjoy Dasgupta, Christos H. Papadimitriou, Umesh Virkumar Vazirani, McGraw-Hill Higher Education, Pearson Education

MA4102

Course Number	MA4102 (DE)
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Cryptography and Network Security
Learning Mode	Lectures
Learning Objectives	The objective of the course is to present an introduction to Cryptography, with an emphasis on how to protect information security from unauthorized users and is to understand the basics of Network vulnerability and Security Protection.
Course Description	The aim of this course is to introduce the student to the areas of cryptography and cryptanalysis. This course develops a basic understanding of the algorithms used to protect users online and to understand some of the design choices behind these algorithms.
Course Outline	Security goals and attacks, Cryptography and cryptanalysis basics, Mathematics behind cryptography, Traditional and modern symmetric-key ciphers, DES, AES, Asymmetric-key ciphers, One-way function, Trapdoor one-way function, RSA cryptosystem, Elgamal Cryptosystem, Diffie-Hellman key exchange algorithm, Cryptographic hash function, Message authentication, Digital signature, RSA digital signature, IPSec, SSL/TLS, PGP and Email security
Learning Outcome	Students will be familiar with the significance of information security in the digital era. Also, they can identify various threats and vulnerabilities in networking.
Assessment Method	Quiz /Assignment/ Project / MSE / ESE

Text Books:

Cryptography and Network Security by Behrouz A. Forouzan and Debdeep Mukhopadhyay, Second edition, Tata McGraw Hill, 2011.
Cryptography and Network Security Principles and practice by W. Stallings, 5/e, Pearson Education Asia, 2012.

Reference Books:

Cryptography: Theory and Practice by Stinson. D., third edition, Chapman &Hall/CRC, 2010.
Elementary Number Theory with applications by Thomas Koshy, Elsevier India, 2005.
Research papers

MA4103

Rings and Modules

Course Number	MA4103 (DE)
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Rings and Modules
Learning Mode	Lectures/ Tutorials
Learning Objectives	Readers of this course will be well-equipped with basic concepts of Rings & Modules which are prerequisites to the courses on Fields and Galois Theory, Coding Theory, Cryptography, Homological Algebra, Noncommutative Algebra, Algebraic Geometry, and advanced courses on Analysis.
Course Description	It gives a foundation for further studies in algebra by discussing several classes of rings and modules. This course includes structure theorems for modules over PID, Artinian, and Noetherian rings and modules, and their radicals.
Course Outline	Rings, Ring of Endomorphisms, Ideal, Prime Ideals, Maximal Ideal, Principal Ideal Domain, Nilpotent Ideal, Nil Ideal, UFD, Field of Fractions. Modules, submodules, quotient modules and module homomorphisms, Generation of modules, direct sums and free modules, simple modules Finitely generated modules over principal ideal domains. Ascending Chain Condition and Descending Chain Condition, Artinian and Noetherian rings and modules, Hilbert basis theorem, Primary decomposition of ideals in Noetherian rings. Radicals: Nil radical, Jacobson radical and prime radical, the relation of radicals in the case of Artinian and Noetherian rings.
Learning Outcome	On successful completion of the course, students should be able to: 1. Understand, apply and analyze the notion of rings, ideals, and modules in related concepts required for advanced courses and research in Algebra. 2. Familiar with the key properties and examples of Artinian and Noetherian rings and modules and their generalization; 3. Decide whether a given ring or module, or a class of rings or modules, is Noetherian/Artinian, by applying the characterizations discussed in the course; 4. Able to use this concept for research in Information Circuits (Coding Theory, Cryptography, Image Processing, etc.
Assessment Method	Quiz /Assignment/ Project / MSE / ESE

Text Books:

Musili, Introduction to Rings and Modules, Narosa Pub. House, New Delhi, Sec. Edition, 2001.
A. Beachy, Introduction to Rings and Modules, London Math. Soc., Cam. Univ. Press, 2004.

Reference Books:

F. Atiyah and I. G. Macdonald, Introduction to Commutative Algebra, Addison Wesley, 1969.
R. Goodearl and Jr. R. B. Warfield, An Introduction to Noncommutative Noetherian Rings. 2nd ed. Cambridge University Press; 2004.
S. Dummit and R. M. Foote, Abstract Algebra, 2nd Ed., John Wiley, 2002.
Jacobson, Basic Algebra I and II, 2nd Ed., W. H. Freeman, 1985 and 1989.
Lang, Algebra, 3rd Ed., Springer (India), 2004.

Department Elective II

Sl. No.

Course Code

Department Elective II

MA4104

Introduction to Coding Theory

Course Number	MA4104 (DE)
Course Credit (L-T-P-C)	2 – 0 – 2 – 3
Course Title	Deep Learning
Learning Mode	Lectures and Labs
Learning Objectives	Gain expertise in artificial neural networks, covering fundamentals, feedforward and deep neural networks, convolutional networks, recurrent neural networks, and popular deep learning architectures, to proficiently tackle pattern recognition tasks and real-world challenges.
Course Description	Explore the foundations and applications of deep learning, covering artificial neural networks, convolutional networks, recurrent neural networks, and popular architectures like VAE, and GANs for solving real-world tasks.
Course Outline	Basics of artificial neural networks (ANN); Feedforward neural networks: Pattern classification using perceprton, Multilayer feedforward neural networks, Backpropagation learning, Normalization; Deep neural networks (DNNs): Difficulty of training DNNs, Optimization for training DNNs, Optimization methods for neural networks (AdaGrad, RMSProp, Adam etc.), Regularization methods. Convolutional Networks (CNNs): Introduction to CNNs – convolution, pooling, Deep CNNs, Deep CNN architectures (AlexNet, VGG, GoogLeNet, ResNet), Other Recent CNN architectures. Recurrent neural networks (RNNs), Long Short Term Memory (LSTM), Other Recent Sequential Networks; Some popular Architectures/concepts in Deep Learning: Object Detection and Localization, Siamese Networks, Autoencoders & VAE, Generative Adversarial Networks (GANs), Other Recent Topics.
Learning Outcome	Students will acquire a thorough grasp of both foundational and advanced concepts in deep learning, along with practical proficiency in utilizing these methods to address real-world challenges.
Assessment Method	Quiz /Assignment/ Project / MSE / ESE

Text Books:

Deep learning, Ian Goodfellow, Yoshua Bengio and Aaron Courville, MIT Press, 2016.

Reference Books:

Haykin, Neural Networks and Learning Machines , Prentice Hall of India, 2010
Satish Kumar, Neural Networks - A Class Room Approach, Second Edition, Tata McGraw-Hill, 2013
M. Bishop, Pattern Recognition and Machine Learning, Springer, 2006

MA4105

Fields and Galois Theory

Course Number	MA4105 (DE)
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Fields and Galois Theory
Learning Mode	Lectures
Learning Objectives	To get exposed to the classical journey of solving polynomial equations, the theoretical ways to look at it, particularly when numerical methods have its limitations.
Course Description	This course will cover the basics of field theory and its extensions from the perspective of the existence of solutions of polynomial equations.
Course Content	Review of Rings, Ring homomorphisms, Ideals, prime and maximal ideals Fields, basic theory of field extensions: field automorphisms, Algebraic extension, splitting fields, algebraic closure, separable and inseparable extensions, finite fields, cyclotomic polynomials, normal extensions. Galois extension, Galois group of a field extension, The fundamental theorem of Galois theory, Galois closure, theory of symmetric polynomials, the fundamental theorem of Algebra, solvable extensions, radical extensions, solution of polynomial equations by radicals, insolvability of the quintic, transcendence of e and pi
Learning Outcome	The students will know algebraically closed field, splitting fields, Fundamental theorems of Algebra and of Galois theory. Students will understand why it is not possible to have a formula for solving a polynomial equation of degree five.
Assessment Method	Quiz /Assignment/ Project / MSE / ESE

Text Books:

S. Dummit and R. M. Foote, Abstract Algebra, John Wiley & sons, Inc., 2nd Edition, 1999.
Stewart: Galois Theory, Academic Press, edition 1989.

Reference Books:

Emil Artin: Galois Theory, University of Notre Dame Press, 1971.
Lang: Algebra, III Edition, Springer, 2004.

MA4106

Mathematical Finance

Course Number	MA4106 (DE)
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Mathematical Finance
Learning Mode	Lectures
Learning Objectives	The main objective of the course is to introduce the students to the broader area of mathematical finance from a theoretical and computational perspective.
Course Description	Mathematical Finance, as an interdisciplinary subject, focuses on relations between fundamentals of Mathematics and concepts of financial markets along with the other economic activities.
Course Outline	Financial markets and instruments, risk-free and risky assets; Interest rates, present and future values of cash flows, term structure of interest rates, spot rate, forward rate; Bonds, bond pricing, yields, duration, term structure of interest rates; Asset pricing models, no-arbitrage principle; Cox-Ross-Rubinstein binomial model, geometric Brownian motion model; Financial derivatives, Forward and futures contracts and their pricing, hedging strategies using futures, interest rate and index futures; Swaps and its valuation, interest rate swaps, currency swaps; Options, general properties of options, trading strategies involving options; Discrete time pricing of European and American derivative securities by replication; Continuous time pricing of European and American derivate securities by risk-neutral valuation.
Learning Outcome	On successful completion of the course, students should be able to: 1. Understand the fundamentals of quantitative finance. 2. Grasp the concept of time value of money and interest rates. 3. Comprehend ideas of pricing through the application of basic mathematical concepts.
Assessment Method	Quiz /Assignment/ Project / MSE / ESE

Text Books:

Capinski and T. Zastawniak, Mathematics for Finance: An Introduction to Financial Engineering, 2nd Edition, Springer, 2010.
Higham, Introduction to Financial Option Valuation: Mathematics, Stochastic and Computation, Cambridge University Press, 2004.

Reference Books:

C. Hull, Options, Futures and Other Derivatives, 10th Edition, Pearson, 2018.
Cvitanic and F. Zapatero, Introduction to the Economics and Mathematics of Financial Markets, Prentice-Hall of India, 2007.

Department Elective III

Sl. No.

Course Code

Department Elective III

MA4201

Topology

Course Number	MA4201 (DE)
Course Credit (L-T-P-C)	3 – 0 – 0 – 3
Course Title	Topology
Learning Mode	Lectures and Tutorials
Learning Objectives	The main objectives of this course is to lay a foundation for general topology. Students will learn how to generalize concepts from the realm of real numbers to arbitrary sets with some structure. The course will help students for future study in geometry or analysis.
Course Description	This course serves to lay the foundations for general topology. It begins with defining topological spaces, its basis, subspace topology, order topology, product and box topology. The core of the subject includes limit points, properties of functions on topological spaces, metric spaces, connectedness, compactness, countability and separation axioms.
Course Content	Definition and examples of topological spaces (including metric spaces), Open and closed sets, Subspaces and relative topology, Closure and interior, Accumulation points, Dense sets, Neighborhoods, Boundary, Bases and sub-bases. Construction of Topological spaces from known spaces. Product spaces, Cone and Suspension construction. Identification spaces. Neighborhood systems. Nets and Filters. Continuous functions and homeomorphism, Quotient topology, First and second countability and separability, Lindelöf spaces. The separation axioms T0, T1, T2, T3,T3_1/2 , and T4; their characterizations and basic properties. Urysohn’s lemma, Urysohn’s metrization theorem, Tietze’s extension theorem. Compactness. Basic properties of compactness. Compactness and the finite intersection property, Local compactness, One-point compactification. Connected spaces and their basic properties. Connectedness of the real line. Components, Locally connected spaces. Tychonoff 's theorem
Learning Outcome	(1) Students will learn the concepts of general topology. (2) Students will appreciate the art of abstraction by relating the course with real analysis. (3) They will learn to extend the notions of open and closed sets, limit points, closure, connected and compact sets from the set of real numbers to general topological spaces. (4) They will learn the separation and countability axioms which will help them differentiate between the structural properties of spaces. (5) This course will enhance the research appetite of students through some deep ideas through Tychonoff theorem and the Titze extension theorem.
Assessment Method	Quiz /Assignment/ Project / MSE / ESE

Text Books:

A. Armstrong, Basic Topology, Springer, 2014.
R. Munkres, Topology, 2nd Edition, Pearson International, 2000.
D. Joshi, Introduction to General Topology, New Age International, 2000.

Reference Books:

F. Simmons, Introduction to Topology and Modern Analysis, McGraw-Hill, 1963.

J. L. Kelley, General Topology, Van Nostrand, 1995.

MA4206

Control Theory

Course Number	MA4206 (DE)
Course Credit	3-0-0-3
Course Title	Control Theory
Learning Mode	Lectures
Learning Objectives	The objective of the course is to train student about the fundamental principles of control theory to analyze and design control systems.
Course Description	Control theory is a basic course for undergraduate student and is intended to discuss about important mathematical properties of control systems and enables students to solve some optimal control problems.
Course Outline	Mathematical models of control systems, State space representation, Autonomous and non-autonomous systems, State transition matrix, Solution of linear dynamical system. Transfer function, Realization, Controllability, Kalman theorem, Controllability Grammian, Control computation using Grammian matrix, Observability, Duality theorems, Discrete control systems, Controllability and Observability results for discrete systems. Companion form, Feedback control, State observer, Liapunov stability, Stability analysis for linear systems, Liapunov theorems for stability and instability for nonlinear systems, Stability analysis through Linearization, Routh criterion, Nyquist criterion, Stabilizability and detachability. State feedback of multivariable system, Riccatti equation, Introduction to Calculus of variation, Euler- Hamiltonian equations, Computation of optimal control for linear systems.
Learning Outcome	By the end of the course, students will be able to describe the fundamental components of control systems, including controllability, observability and stability. They should be able to explain how these components interact to achieve desired system behavior.
Assessment Method	Quiz /Assignment/ Project / MSE / ESE

Text Books:

Barnett, Introduction to Mathematical Control Theory, Clarendon press Oxford 1975
V. Dukkipati, Control Systems, Narosa 2005
J. Nagrath and M. Gopal, Control System Engineering, New Age international 2001.
Datta, Numerical Methods for Linear Control Systems, Academic press Elsevier, 2004.

MA4207

Finite Element Analysis

Course Number	MA4207 (DE)
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Finite Element Analysis
Learning Mode	Lectures
Learning Objectives	In this course, the students will be trained with the knowledge of finite element methods and its mathematical analysis for solving ODE/PDEs.
Course Description	This course will be providing computational algorithms for solving non dimensionalized mathematical models and their mathematical analysis.
Course Outline	Polynomial approximations and interpolation errors. Piecewise linear basis functions, Poincare inequality. Construction of finite element spaces. Distributions. Triangular finite elements. Computation of finite element solutions and their convergence analysis. Variational formulation for elliptic boundary value problems in one and two dimensions. Galerkin orthogonality. Parabolic initial and boundary value problems: Semi-discrete and fully discrete (forward and backward Euler in time) schemes, Convergence analysis. Stiffness matrix and computational algorithms.
Learning Outcome	On successful completion of the course, students should be able to: 1. know the methodology of finite element approach 2. write algorithms for solving one and two dimensional ODE/PDEs by using finite element approach 3. understand on how to solve engineering problems by using finite element
Assessment Method	Quiz /Assignment/ Project / MSE / ESE

Text Books:

Suli and D. F. Mayers, An Introduction to Numerical Analysis, Cambridge Univ. Press, 2003.
C. Brenner and R. Scott, The Mathematical Theory of Finite Element Methods, Springer, 2008.
Suli, Lecture Notes on Finite Element Methods for Partial Differential Equations, University of Oxford, 2020.

Reference Books:

Johnson, Numerical solutions of Partial Differential Equations by Finite Element Methods, Cambridge Univ. Press, 2009.
Philippe G. Ciarlet, The Finite Element Method for Elliptic Problems, SIAM, 2002

MA4208

Course Number	MA4208 (DE)
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Introduction to Coding Theory
Learning Mode	Lectures/ Tutorials
Learning Objectives	Readers of this course will be well-equipped with the application of the basics of mathematics, specially, Algebra, Number Theory and Probability Theory in Information Theory.
Course Description	It gives a foundation for further studies in information communications. This course includes different codes such as binary codes, Hamming codes, linear codes (cyclic codes in detail), and nonlinear codes, with different bounds by using mathematical tools, which are essential to understand an information communication system.
Course Content	Polynomial rings over fields, Extension of fields, Computation in GF(q), n-th roots of unity, Vector space over finite fields. Error Detection, correction and decoding. Linear block codes: Hamming weight, Generator and Parity-check matrix Encoding and Decoding of linear codes, Bounds: Sphere-covering bound, Gilbert-Varshamov bound, Hamming bound, Singleton bound. Hamming codes, Simplex codes, Golay codes, First and Second order Reed-Muller codes. Nonlinear codes: Hadamard codes, Preparata codes, Kerdock codes, Nordstorm-Robinson code, Weight distribution of codes. The structure of cyclic codes, roots of cyclic codes, Decoding of cyclic codes, Burst-error-correcting codes, Quadratic residue codes, BCH codes, Reed-Solomon (RS) codes, GRS codes, Convolutional codes, LDPC codes, Turbo codes.
Learning Outcome	On successful completion of the course, students should be able to: 1. Understand the primary information communication circuits; 2. Able to understand the importance of better codes in communication channels; 3. Help to develop some MDS, and better new codes using the concept of number theory and algebra; 4. Capable of analyzing the capacity of a code based on studied bounds and results.
Assessment Method	Quiz /Assignment/ Project / MSE / ESE

Text Books:

Raymond Hill, A First Course in Coding Theory (Oxford Applied Mathematics and Computing Science Series), Clarendon Press, 1986.
Ron Roth, Introduction to Coding Theory, Cambridge University Press, 2006.

Reference Books:

H. van Lint, Introduction to Coding Theory, Springer, 1999.
San Ling and Chaoping Xing, Coding Theory: A First Course. Cambridge University Press, 2004.

MA4209

Portfolio Theory and Risk Management

Course Number	MA4209 (DE)
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Portfolio Theory and Risk Management
Learning Mode	Lectures
Learning Objectives	The goal of this course are two-folds, namely design of portfolios and the identification as well as risk management of such portfolios.
Course Description	Portfolio theory involves the usage of techniques of probability theory and statistics in the design and analysis of a financial portfolios (such as mutual funds). On the other hand, risk management involves tools from Mathematics and Statistics in the identification of financial risks to portfolios.
Course Outline	Return and risk of a portfolio, mean-variance portfolio theory, efficient frontier, Capital Asset Pricing Model, Arbitrage Pricing Theory; Utility theory, risk attitude of investors; Non-mean-variance portfolio theory, safety first models, semi-deviation, stochastic dominance; Bond portfolios, duration and convexity of a bond. Fundamentals of financial risk management, credit risk, market risk, operational risk, Basel and Solvency regulations; Market risk, Value-at-Risk (VaR), computation of VaR, coherent measures of risk.
Learning Outcome	On successful completion of the course, students should be able to: 1. Understand the fundamentals of portfolio theory from asset picking to asset allocation and performance analysis of the portfolio. 2. Identification and quantification of risk of financial portfolios using mathematical and statistical tools. 3. Determination of robust techniques to mitigate the identified financial risks.
Assessment Method	Quiz /Assignment/ Project / MSE / ESE

Text Books:

P. Chakrabarty and A. Kanaujiya, Mathematical Portfolio Theory and Analysis, 1st Edition Birkshauser, 2023.
Roncalli, Handbook of Financial Risk Management, CRC Press, 2020

Reference Books:

C. Francis and D. Kim, Modern Portfolio Theory: Foundations, Analysis, and New Developments, 1st Edition, Wiley, 2013.
C. Hull, Risk Management and Financial Institutions, 4th Edition, Wiley, 2016.

Department Elective IV

Sl. No.

Course Code

Department Elective IV

MA4210

Differential Geometry

Course Number	MA4210 (DE)
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Differential Geometry
Learning Mode	Lectures
Learning Objectives	Same as Learning Outcome
Course Description	It is a basic course in classical differential geometry of curves and surfaces.
Course Content	Curve theory: Regular curves, arc-length parametrization, curvature, torsion, Frenet formula, isoperimetric inequality Surface theory: Regular surfaces, Curvatures: Gauss and Mean, Surfaces of revolution, Constant mean curvature surfaces: minimal surfaces, Weierstrass-Enneper representation, Geodesics: The geodesic equations, Isometries and conformal maps. The Gauss-Bonnet theorem.
Learning Outcome	At the end of this course, students will learn: - to compute curvature and torsion of curves - to compute Gauss and mean curvature of surfaces - to compute the complex representation formula for a minimal surface given in isothermal parametrization. - the relation between Euler characteristic of a surface and the Gaussian curvature of a surface through Gauss-Bonnet theorem.
Assessment Method	Quiz /Assignment/ Project / MSE / ESE

Text Books:

Oprea, John., Differential Geometry and its Applications, The Mathematical Association of America, second edition (2007)

Reference Books:

do Carmo, Manfred P., Differential Geometry of Curves and Surfaces, Prentice Hall (1976)
Bar, Christian, Elementary Differential Geometry, Cambridge University Press. (2010)

Millman, Richard S. and Parker, George D., Elements of Differential Geometry, Prentice Hall-Inc. (1977

MA4211

Introduction to Mathematical Biology

Course Number	MA4211 (DE)
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Introduction to Mathematical Biology
Learning Mode	Lectures
Learning Objectives	To learn application of Mathematics in Biology. To comprehend mathematical analysis and to correlate the outcome of mathematical system into biological system. To learn and understand the bridge between mathematical and biological worlds.
Course Description	This course is meant to expose the candidate to mathematical modeling biological systems and then apply it to various systems and analyse these models and obtain biological inferences from models.
Course Outline	Motivation. Introduction to biological systems and their mathematical representation. Basic mathematical tools such as Linearization, qualitative solution of difference and differential equations, stability, nonlinear dynamics. Mathematical modeling in ecology: Single species models (continuous and discrete), multispecies models: Prey-predator models, Competition models, cooperation models, harvesting in population, fisheries models, optimal harvest. Mathematical modeling of infectious diseases: Introduction to disease modeling, compartmental models, Basic models- SI, SIS, SIR, SIRS etc. Models with demography, Vaccination models, Ross Malaria Model. Mathematical models in cellular biology such as HIV in vivo dynamics, Models in immunology. Stochastic models. Parameter estimation.
Learning Outcome	Students will be able to apply the mathematical knowledge on a biological system, analyse it and interpret it in terms of the biological systems.
Assessment Method	Quiz /Assignment/ Project / MSE / ESE

Text Books:

Kot, Elements of Mathematical Ecology, Cambridge University Press, 2012.
Y. Li, An introduction to mathematical modeling of infectious diseases, Springer, 2018.

Reference Books:

J.D. Murray, Mathematical Biology Vol I & II, Springer, 2001

MA4212

Statistical Decision Theory

Course Number	MA4212 (DE)
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Statistical Decision Theory
Learning Mode	Lectures
Learning Objectives	This course provides an in-depth content for understanding useful fundamentals concepts of statistical decision theory. Several inference problems under this framework will be discussed.
Course Description	Most of inference problems are described under faily general setup of statistical decision theory. Efficient estimation procedures will be discussed under different parametric restrictions.
Course Outline	Decision theoretic estimation problems, Classical risk functions, Bayes risk, Bayes and minimax estimators, Admissible Bayes estimators, essentially complete class rules, minimal complete class, illustrations, Ancillarity, UMVUE, truncated parameter space estimation problems, equivariance of decision rules, location-scale groups of transformations, minimum risk equivariant estimators, highest posterior density intervals.
Learning Outcome	Students will learn basic concepts of statistical decision problems with a focus on deriving efficient estimators for various parametric functions.
Assessment Method	Quiz /Assignment/ Project / MSE / ESE

Text Books:

1. A. M. Mood, F. A. Graybill, D. C. Boes. Introduction to the Theory of Statistics, Tata McGraw-Hill, Third Edition, 2017. Reference Books: 1. E. L. Lehmann, Theory of Point Estimation, Springer, Second Edition, 1998.2. G. Casella and R.L. Berger. Statistical Inference, Duxbury Advanced Series, 2007.

MA4213

Applied Computational Techniques

Course Number	MA4213 (DE)
Course Credit (L-T-P-C)	3 – 0 – 0 – 3
Course Title	Applied Computational Techniques
Learning Mode	Lectures
Learning Objectives	In this subject, the students will be informed about numerical analysis and computational schemes for solving ordinary and partial differential equations.
Course Description	This course involves basic parts of computing approaches involving numerical analysis and how to solve differential equations
Course Outline	Introduction to floating point arithmetic, Machine precision, Approximation errors, Truncation and roundoff errors, Condition number of function Generation of finite differences schemes by interpolating data, Finite-difference schemes for various derivatives, Smoothness, Rate of accuracy Introduction of system of linear IVP's and BVP's, Euler's Explicit and Implicit Method, Runge-Kutta Methods, Linear Multistep Methods, Nonlinear Two-Point BVPs and its discretization. PDEs, Initial and Boundary Conditions, Finite difference method for elliptic PDE. Approximations of parabolic and hyperbolic PDEs by FTCS and BTCS, Crank-Nicolson schemes, ADI methods, Lax Friedrich method, Upwind scheme; CFL conditions. Consistency, Stability analysis by matrix method and/or von Neumann analysis, Convergence by Lax's equivalence theorem.
Learning Outcome	Through this course, students will learn the basic ideas of computations and their convergence analysis. They will also learn solving differential equations numerically.
Assessment Method	Quiz /Assignment/ Project / MSE / ESE

Text Books:

Heath, Scientific Computing, ‎ McGraw-Hill Education; 2nd edition, 2001.
Morton and D. F. Mayers, Numerical Solution of Partial Differential Equations, Cambridge University Press, 2nd Edn., 2005.
Li, Y. Chen, Computational Partial Differential Equations Using MATLAB, 2020

Reference Books:

C. Strikwerda, Finite Difference Schemes and Partial Differential Equations, SIAM, 2004
K. Jain, S. R. K. Iyengar, R. K. Jain, Numerical Methods For Scientific And Engineering Computation, New Age International, 2019

Department Elective V

Sl. No.

Course Code

Department Elective V

MA4214

Deep Learning for Computer Vision

Course Number	MA4214 (DE)
Course Credit (L-T-P-C)	2 – 0 – 2 – 3
Course Title	Deep Learning for Computer Vision
Learning Mode	Lectures and Labs
Learning Objectives	This is an advanced course on Computer Vision. This will enable the students to learn concepts of image processing, computer vision and utilize these techniques to implement vision algorithms efficiently for use in research or industry.
Course Description	This course provides a comprehensive exploration of computer vision fundamentals, covering image formation, deep learning techniques, and advanced topics such as object detection, segmentation, and 3D computer vision.
Course Outline	Introduction and Overview: Course Overview and Motivation; Introduction to Image Formation, Capture and Representation; Linear Filtering, Correlation, Convolution; Visual Features and Representations: Edge, Blobs, Corner Detection; SIFT, SURF; HoG, LBP; Review of Deep Learning (DL): Multi-layer Perceptrons, Backpropagation, CNN, RNN, Transfomer, AE, VAE, GANs, Diffusion Models etc. Deep Learning for Computer Vision; Image Classification and Action/Activity Recognition; Object Detection; Segmentation: FCN, SegNet, U-Net, Other Recent Models; Visualizing CNN features, DeepDream, Style Transfer. DL for Pose Estimation, Optical Flow, Object Tracking, Depth Estimation, Image Matching, Image Editing, Image Inpainting, and Image Super-resolution; 3D computer vision: 3D scene understanding and segmentation, 3D shape synthesis; Other Recent Topics.
Learning Outcome	At the end of the course, the students will be able to: 1. Implement fundamental image processing techniques required for computer vision 2. Understand Image formation process 3. Develop computer vision applications
Assessment Method	Quiz /Assignment/ Project / MSE / ESE

Text Books:

David A. Forsyth and Jean Ponce, Computer Vision: A Modern Approach, 2nd edition, Pearson, 2012.
Ian Goodfellow, Yoshua Bengio, Aaron Courville, Deep Learning, 2016
Michael Nielsen, Neural Networks and Deep Learning, 2016

Reference Books:

Yoshua Bengio, Learning Deep Architectures for AI, 2009
Richard Szeliski, Computer Vision: Algorithms and Applications, 2010.
Simon Prince, Computer Vision: Models, Learning, and Inference, 2012.

MA4212

MA4215

Discrete Differential Geometry

Course Number	MA4215 (DE)
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Discrete Differential Geometry
Learning Mode	Lectures
Learning Objectives	Same as Learning Outcome
Course Description	The aim of this course is to discretize. The classical differential geometric object. This course also finds its application in design, graphics and architectural engineering.
Course Content	Brief introduction to Differential geometry of curves and surfaces (smooth). Exterior calculus: Vectors and 1-forms, differential forms and the wedge product, differential operators and Stoke’s theorem. Curvature of discrete surfaces: Vector area, Area gradient, Volume gradient, Gauss-Bonnet theorem. The Laplacian: Discretization via finite element method and via discrete exterior calculus.
Learning Outcome	At the end of this course, students should be able to: -discretize classical geometric objects such as curves, surfaces. -discretize the Laplacian operator
Assessment Method	Quiz /Assignment/ Project / MSE / ESE

Text Books:

Discrete Differential Geometry: An applied introduction, Notes by Keenan Crane, available at https://www.cs.cmu.edu/~kmcrane/Projects/DDG/paper.pdf (2023)

Reference Books:

Bobenko, Alexander , Schroder, P., Sullivan, John M., and Ziegler, Gu¨nter M. (2008), Discrete differential geometry. Birkhauser Verlag AG.
Discrete Integrable Geometry and Physics, Oxford lecture series in mathematics and its applications 16, edited by A. I. Bobenko and R. Seiler, Clarendon Press (1999).
Bobenko, Alexander and Yuri B. Suris (2008), ”Discrete Differential Geometry (integrable structure)”, American Mathematical Society

MA4216

Integral Equations and Calculus of Variations

Course Number	MA4216 (DE)
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Integral Equations and Calculus of Variations
Learning Mode	Lectures
Learning Objectives	In this subject, the students will learn the mathematical methods for solving integral equations and integro differential equations and their convergence analysis.
Course Description	This course is on the existing approaches for solving integral equations and integro differential equations with their convergence analysis.
Course Content	Introduction of Integral Equation, Correlation between integral and differential Equations, Classification of integral equations - Volterra and Fredholm equations, Green's function. Iterative methods for solving equations of the second kind, Neumann series and Fredholm theory, Singular integral equations. Calculus of Variation: Variational problem with functionals containing first order derivatives and Euler equations. Variational problem with moving boundaries. Boundaries with constraints. Higher order necessary conditions, Existence of solutions of variational problem
Learning Outcome	Main focus will be on how to solve integral equations and integro differential equations and their convergence analysis
Assessment Method	Quiz /Assignment/ Project / MSE / ESE

Text Books:

S. Gupta, Calculus of variations with applications, PHI Learning Pvt Ltd, 2017.
N. Mandal, A Chakrabarti, Applied singular integral equations, CRC Press, 2011.
Lokenath Debnath and D. Bhatta, Integral Transform and their Applications, Taylor & Francis Group, 2002.

Reference Books:

Ram P Kanwal, Linear Integral Equations, Birkhauser Boston, 2013
Peter Linz, Analytical and numerical methods for Volterra equations, SIAM, 1985.

IDE - I (Available to students other than Dept. of M&C)

Sl. No.

Code

Course Name

MA2206

Introduction To Numerical Methods

Course Number	MA2206 (IDE)
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Introduction To Numerical Methods
Learning Mode	Lectures
Learning Objectives	To learn basics of computation, errors, and how to manage error during computation.
Course Description	Course starts with definition of number representation and errors. It focuses on solutions of nonlinear equations, system of nonlinear equations, quadrature, finite differences, their applications to solve ODEs and PDEs.
Course Content	Number Representation and Errors: Numerical Errors; Floating Point Representation; Finite Single and Double Precision Differences; Machine Epsilon; Significant Digits. Numerical Methods for Solving Nonlinear Equations: Method of Bisection, Secant Method, False Position, Newton‐Raphson's Method, Multidimensional Newton's Method, Fixed Point Method and their convergence. Numerical Methods for Solving System of Linear Equations: Norms; Condition Numbers, Forward Gaussian Elimination and Backward Substitution; Gauss‐Jordan Elimination; FGE with Partial Pivoting and Row Scaling; LU Decomposition; Iterative Methods: Jacobi, Gauss Siedal; Power method and QR method for Eigen Value and Eigen vector. Interpolation and Curve Fitting: Introduction to Interpolation; Calculus of Finite Differences; Finite Difference and Divided Difference Tables; Newton‐Gregory Polynomial Form; Lagrange Polynomial Interpolation; Theoretical Errors in Interpolation; Spline Interpolation; Approximation by Least Square Method. Numerical Differentiation and Integration: Discrete Approximation of Derivatives: Forward, Backward and Central Finite Difference Forms, Numerical Integration, Simple Newton‐Cotes Rules: Trapezoidal and Simpson's (1/3) Rules; Gaussian Quadrature Rules: Gauss‐Legendre, Gauss‐Laguerre, Gauss‐Hermite, Gauss‐Chebychev. Numerical Solution of ODE & PDE: Euler's Method for Numerical Solution of ODE; Modified Euler's Method; Runge‐Kutta Method (RK2, RK4), Error estimate; Multistep Methods: Predictor‐Corrector method, Adams‐Moulton Method; Boundary Value Problems and Shooting Method; finite difference methods, numerical solutions of elliptic, parabolic, and hyperbolic partial differential equations. Exposure to software package MATLAB.
Learning Outcome	Students should be able to write Program in MATLAB and solve some real life problems based the techniques learned during the course.
Assessment Method	Quiz /Assignment/ Project / MSE / ESE

Text Books:

D. Conte and C. de Boor, Elementary Numerical Analysis ‐ An Algorithmic Approach, McGraw‐Hill, 2005.

Reference Books:

Stoer and R. Bulirsch, Introduction to Numerical Analysis, 2nd Edition, Texts in Applied Mathematics, Vol. 12, Springer Verlag, 2002.
D. Hoffman, Numerical Methods for Engineers and Scientists, McGraw‐Hill, 2001.
E. Atkinson, Numerical Analysis, John Wiley, Low Price Edition (2004).

MA2207

Complex Analysis

Course Number	MA2207 (IDE)
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Complex Analysis
Learning Mode	Lectures
Learning Objectives	This course involves necessity of complex analysis over basic real analysis and its use to approximate indefinite integrals and for other purposes.
Course Description	This course mainly involves theory and applications of complex analysis with several mathematical examples.
Course Outline	Complex Analysis: Complex numbers, Geometric representation, Applications of complex numbers in geometry, Powers and roots of complex numbers. Functions of a complex variable: Limit, Continuity, Differentiability, Analytic functions, Cauchy-Riemann equations, Laplace equation, Harmonic functions, Harmonic conjugates. Elementary Analytic functions (polynomials, exponential function, trigonometric functions), Complex logarithm function, Branches and Branch cuts of multiple valued functions. Complex integration, Cauchy's integral theorem, Cauchy's integral formula. Liouville’s Theorem and Maximum-Modulus theorem, Power series and convergence, Taylor series and Laurent series. Zeros, Singularities and its classifications, Residues, Rouches theorem (without proof), Argument principle (without proof), Residue theorem and its applications to evaluating real integrals and improper integrals. Conformal mappings, Mobius transformation, Schwarz-Christoffel transformation.
Learning Outcome	Upon the finishing of this course, students will be able to incline on higher mathematics and to obtain analytical understanding. It will also help them to move towards research.
Assessment Method	Quiz /Assignment/ Project / MSE / ESE

Text Books:

1. R. V. Churchill and J. W. Brown, Complex Variables and Applications, 5th Edition, McGraw-Hill, 2013.2. S. Ponnusamy, Foundations of Complex Analysis. Narosa, 2011.Reference Books:1. J. H. Mathews and R. W. Howell, Complex Analysis for Mathematics and Engineering, 3rd Edition, Narosa, 2011.2. A. R. Shastri, Basic Complex Analysis of One Variable, Laxmi, 2011

Simon, Basic Complex Analysis, A Comprehensive Course in Analysis. Part 2A, AMS

IDE - II (Available to students other than Dept. of M&C)

Sl. No.

Code

Course Name

MA3106

An Introduction to Computational Commutative Algebra

Course Number	MA3106 (IDE)
Course Credit (L-T-P-C)	3-0-0-3
Course Title	An Introduction to Computational Commutative Algebra
Learning Mode	Lectures
Learning Objectives	To expose students with the basic computational techniques in Commutative Algebra and its applications in engineering problems
Course Description	This course covers the classical theory of Grobner basis and some of its first applications.
Course Content	Ring, Ideals, Ring homorphisms, polynomial rings, Unique factorization, polynomials and affine space, affine varieties, parametrization of affine varieties, monomial ordering: Lexicographic order, graded lex order, graded rev lex order, inverse lexicographic order etc, division algorithm for polynomials in n variables, monomial ideals, Dickson’s Lemma, Hilbert basis theorem, Grobner bases and its properties, Buchberger’s algorithm, reduced Grobner basis, Applications of Grobner basis: Ideal description problem, Ideal membership problem, Solving polynomial equations, Implicitization problem, integer programming problem.
Learning Outcome	Students will learn the basic theory of Grobner basis, Hilbert basis theorem, a division algorithm for polynomials in n variables etc. students will be exposed to various applications of Grobner basis in engineering and math problems.
Assessment Method	Quiz /Assignment/ Project / MSE / ESE

Text Books:

David A Cox, John Little and Donal O’Shea, , Ideals, Varieties and Algorithms, An introduction to computational Algebraic Geometry and Commutative Algebra, Fourth Addition, Springer Undergraduate texts in Mathematics
Martin Kreuzer and Lorenzo Robbiano, Computational Commutative Algebra 1, first edition, Springer Berlin, Heidelberg

Reference Books:

David Eisenbud, Commutative Algebra with a view towards Algebraic Geometry, Springer-Verlag New York (1995).

David S. Dummit and Richard M. Foote, Abstract Algebra, third edition, Wiley Publication, 2011.

MA3107

Partial Differential Equations

Course Number	MA3107 (IDE)
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Partial Differential Equations
Learning Mode	Lectures
Learning Objectives	To understand the basic concepts of the Partial Differential Equations, how they arise and what are the main methods to solve them. In addition to build conceptual understanding of properties of the solutions.
Course Description	The course will introduce the students about Fourier Series and Fourier Transform and further introduce them to the first and second order partial differential equations and their solutions.
Course Outline	Fourier series: Fourier Integral, Fourier series of 2π periodic functions, Fourier series of odd and even functions, Half-range series, Convergence of Fourier series, Gibb’s phenomenon, Differentiation and Integration of Fourier series, Complex form of Fourier series. Fourier Transformation: Fourier Integral Theorem, Fourier Transforms, Properties of Fourier Transform, Convolution and its physical interpretation, Statement of Fubini’s theorem, Convolution theorems, Inversion theorem. Partial Differential Equations: Introduction and motivation, basic concepts, Linear and quasi-linear first order PDE, Lagrange’s Method of solution and its geometrical interpretation, compatibility condition, Charpits method, special types of first order equations. Derivations of Heat and Wave equations in one-dimension and interpretation of different types of conditions. Second order PDE and classification of second order semi-linear PDE, Canonical form. Cauchy problems. D’ Alemberts formula and Duhamel’s principle for one dimensional wave equation, Fourier series method for IBV problem for wave and heat equation in 1-D, rectangular region. Uniqueness of solutions for IBVPs for heat and wave equations. Laplace and Poisson equations, Maximum principle with application, Fourier series method for Laplace equation in two and three dimensions. Fourier transform method to solve PDEs.
Learning Outcome	The students will be able to understand what are PDEs and how to find their solutions, when they exist. They will also understand tools to find these solutions for standard cases.
Assessment Method	Quiz /Assignment/ Project / MSE / ESE

Text Books:

1. K. Sankara Rao, Introduction to Partial Differential Equations, 2nd Edition, 2005.2. I. N. Sneddon, Elements of Partial Differential Equations, McGraw-Hill, 1957. Reference Books:1. E. Kreyszig, Advanced Engineering Mathematics, 5th / 8th Edition, Wiley Eastern / John Wiley, 1983/1999

IDE - III (Available to students other than Dept. of M&C)

Sl. No.

Code

Course Name

MA4112

Number Theory and Algebra

Course Number	MA4112 (IDE)
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Number Theory and Algebra
Learning Mode	Lectures
Learning Objectives	This course aims to help the students: (1) well-equipped with basic concepts of numbers, their properties, and some of the standard results that are fundamental to any branch of mathematics; (2) gain a comprehensive understanding of algebraic structures as groups and rings; (3) help to understand the advanced algebraic structures and their applications; (4) properties of these topics and some advanced concept have a lot of applications in Cryptography, Coding Theory, Networking etc
Course Description	It covers basic topics of number theory, groups and rings. Besides the many examples of groups, rings, this course includes applications of Sylow’s theorems, Isomorphism theorems for groups and rings, Euclidean domain, UFD, quotient fields, and finite field extensions with several examples. On the other hand, this course also presents quadratic residue and Gauss quadratic reciprocity law and its applications.
Course Content	Number Theory: Divisibility, primes, fundamental theorem of arithmetic. Congruences, solution of congruences, Euler's Theorem, Fermat's Little Theorem, Wilson's Theorem, Chinese remainder theorem, primitive roots and power residues. Arithmetical functions (Φ(n), μ(n), d(n), σ(n)). Quadratic residues, quadratic reciprocity. Diophantine equations. Semigroups, groups, subgroups, normal subgroups, homomorphisms, quotient groups, isomorphisms. Examples: group of integers modulo m, permutation groups, cyclic groups, dihedral groups, matrix groups. Sylow's theorems (without proof) and applications. Basic properties of rings, units, ideals, homomorphisms, Isomorphism theorems, quotient rings, prime and maximal ideals, fields of fractions, Euclidean domains, principal ideal domains and unique factorization domains, polynomial rings.
Learning Outcome	On successful completion of the course, students should be able to: 1. Understand the importance of integers and their properties; 2. Understand, apply, and analyze the notion of groups, rings, and ideals in related concepts required for advanced courses; 3. Familiar with the basic properties and examples of different notions of algebra and their generalization; 4. Help to understand the basic techniques of Cryptography (the techniques for protecting information from unauthorized access) & Coding Theory and Information Theory (the study of the transfer of information securely) and make able to develop some new techniques too.
Assessment Method	Quiz /Assignment/ Project / MSE / ESE

Text Books:

N. Herstein: Topics in Algebra, Wiley, 2006.
David M. Burton: Elementary Number Theory, 6th Edition, McGrow Hill Higher Education, 2007.

Reference Books:

S. Dummit and R.M. Foote: Abstract Algebra, Wiley, 1999.
Niven, H.S. Zuckerman, H.L. Montgomery: An introduction to the theory of numbers, Wiley, 2000
H. Hardy, E.M. Wright: An introduction to the theory of numbers, OUP, 2008.
M. Apostol: Introduction to Analytic Number Theory, Springer, UTM, 1998.

MA4113

Theory of Relativity

Course Number	MA4113 (IDE)
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Theory of Relativity
Learning Mode	Lectures
Learning Objectives	The students would learn the singularity theorems of Hawking and Penrose, the positive mass theorem, and the theorems on black hole uniqueness and black hole thermodynamics.
Course Description	To introduce the students to some of the most important mathematical results of general relativity.
Course Content	Minkowski spacetime, Penrose diagrams, The Schwarzschild solution, Causality, Singularity theorems: Geodesic congruences, Hawking’s Singukarity theorem, Penrose’s singularity theorem, Cauchy Problem: Klein-Gordon equation, Maxwell’s equation, Einstein’s equations, Mass in General relativity: Komar mass, Field theory, Einstein-Hilbert action, Gravitational waves, Positive mass theorem, Penrose inequality, Black holes: The Kerr solution, Black hole thermodynamics and Hawking radiation.
Learning Outcome	The students would learn the singularity theorems of Hawking and Penrose, the positive mass theorem, and the theorems on black hole uniqueness and black hole thermodynamics.
Assessment Method	Quiz /Assignment/ Project / MSE / ESE

Text Books:

Jose Natario, An introduction to Mathematical Relativity, Latin American Mathematics Series, Springer (2021)

Reference Books:

Robert M. Wald, General Relativity, The University of Chicago Press (1984)

Mechanical Engineering

Program Learning Objectives:	Program Learning Outcomes:
Program Goal 1: Apply basic knowledge of engineering principles to solve technical problems applied to mechanical systems, stress and strain analysis of structures, design of machine elements, control systems to achieve desirable performance and to assess life of mechanical components.	Program Learning Outcome 1: The students should be able to apply the principles of Kinematics and Dynamics of Mechanisms, mechanics of solid, system dynamics and control to the engineering problems of societal relevance.
Program Goal 2: To impart the ability to model and analyse pertinent transport phenomena based on the fundamental conservations laws of thermodynamics and fluid mechanics.	Program Learning Outcome 2: Upon completion of the course, students will possess the capability to design and implement mathematical models and simulation tools specifically tailored to address complex mechanical engineering issues within crucial domains such as energy and the environment.
Program Goal 3: The graduates will be possessing the knowledge of concepts and practices of material removal, material forming, material joining, additive manufacturing-based processes, identify damage and failure of material to meet the present and future demands of the industry.	Program Learning Outcome 3: The students should gain the knowledge of the behaviour and processing of engineering materials through different conventional and state-of-the-art material subtractive and additive based processes.
Program Goal 4: To train the graduates with adequate engineering knowledge to develop skills for solving multi-disciplinary problems and achieving optimal results.	Program Learning Outcome 4: The graduates will be able to embrace leadership and collaborative roles for societal, environmental and economic enterprise.

Sl. No.

Subject Code

SEMESTER I

MA1101

Course Number	MA1101
Course Credit (L-T-P-C)	3-1-0-4
Course Title	Calculus and Linear Algebra
Learning Mode	Lectures and Tutorials
Learning Objectives	To provide the essential knowledge of basic tools of Differential Calculus, Integral Calculus, Vector spaces and Matrix Algebra.
Course Description	This course provides a foundation for Calculus and Linear Algebra. Topics related to properties of single and two variable functions along with their applications will be discussed. In addition fundamentals of linear algebra and matrix theory with applications will also be discussed.
Course Content	Differential Calculus (12 Lectures): Limit and continuity of one variable function (including ε-δ definition). Limit, continuity and differentiability of functions of two variables, Tangent plane and normal, Change of variables, chain rule, Jacobians, Taylor’s Theorem for two variables, Extrema of functions of two or more variables, Lagrange’s method of undetermined multipliers. Integral Calculus (10 Lectures): Riemann integral for one variable functions, Double and Triple integrals, Change of order of integration. Change of variables, Applications of Multiple integrals such as surface area and volume. Vector Spaces (12 Lectures): Vector spaces (over the field of real numbers), subspaces, spanning set, linear independence, basis and dimension. Linear transformations, range and null space, rank-nullity theorem, matrix of a linear transformation. Matrix Algebra (8 Lectures): Elementary operations and their use in getting the rank, inverse of a matrix and solution of linear simultaneous equations, Orthogonal, symmetric, skew-symmetric, Hermitian, skew-Hermitian, normal and unitary matrices and their elementary properties, Eigenvalues and Eigenvectors of a matrix, Cayley-Hamilton theorem, Diagonalization of a matrix.
Learning Outcome	Students completing this course will be able to: 1. Understand various properties of functions such as limit, continuity and differentiability. 2. Learn about integrations in various dimension and their applications. 3. learn about the concept of basis and dimension of a vector space. 4. define Linear Transformations and compute the domain, range, kernel, rank, and nullity of a linear transformation. 5. compute the inverse of an invertible matrix. 6. solve the system of linear equations. 7. Apply linear algebra concepts to model, solve, and analyze real-world problems.
Assessment Method	Quiz /Assignment/ MSE / ESE

Textbooks:

Thomas, G. B., Hass, J., Heil, C. and Weir M. D., “Thomas’ Calculus”, 14th Ed., Pearson Education, 2018
Kreyszig, E., “Advanced Engineering Mathematics”, 10th Ed., Wiley India Pvt. Ltd, 2015

Reference Books:

Jain, R. K. and Iyenger, S. R. K., “Advanced Engineering Mathematics”, 5^th, Narosa Publishing House, 2017
Axler, S., “Linear Algebra Done Right”, 3rd Ed., Springer Nature, 2015
Strang, G., “Linear Algebra and Its Applications” 4th Ed., Cengage India Private Limited, 2005

4.0

CS1101

Course Number	CS1101
Course Credit	3-0-3-4.5
Course Title	Foundations of Programming
Learning Mode	Offline
Learning Objectives	· To understand the fundamental concepts of programming · To develop the basic problem-solving skills by designing algorithms and implementing them. · To learn about various data types, control statements, functions, arrays, pointers, and file handling. · To achieve proficiency in debugging and testing a C program
Course Description	This introductory course provides a solid foundation in programming principles and techniques. Designed for students with little to no prior programming experience, it covers fundamental concepts such as variables, data types, control structures, functions, and basic data structures. Students will learn to write, debug, and execute programs using a high-level programming language. Emphasis is placed on developing problem-solving skills, logical thinking, and the ability to write clear and efficient code. By the end of the course, students will be equipped with the essential skills needed to pursue more advanced studies in computer science and software development.
Course Outline	Introduction and Programming basics, Expressions Control and Iterative statements, Functions, Arrays, Recursion vs. Iteration Pointers, 2D-Array with pointers, Structures, String, Dynamic memory allocation, File handling, Contemporary programming languages, and applications Practical component: Lab to be conducted on a 3-hour slot weekly. It will be conducted with the theory course so the topics for problems given in the lab are already initiated in the theory class.
Learning Outcome	· Understanding of Basic Syntax and Structure in C language · Proficiency in Data Types, Operators, and Control Structures · Function Implementation and learn to use them appropriately · Efficient Use of Arrays and Strings · Pointer Utilization · Ability to perform dynamic memory allocation and deallocation using malloc (), calloc (), realloc (), and free () functions. · Structured data management with structures and unions · Exposure of file Handling · Learning debugging and error Handling
Assessment Method	Internal (Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Reading

Knuth, Donald E. The art of computer programming, volume 4A: combinatorial algorithms, part 1. Pearson Education India, 2011.
P.J. Deitel and H.M. Deitel, C How To Program, Pearson Education (7th Edition)
Brian W. Kernighan and Dennis M. Ritchie, The C Programming Language, Prentice−Hall
A. Kelley and I. Pohl, A Book on C, Pearson Education (4th Edition)
K. N. King, C PROGRAMMING A Modern Approach, W. W. Norton & Company

4.5

PH1101/PH1201

Course Number	PH1101/PH1201
Course Credit	3-1-3-5.5
Course Title	Physics
Learning Mode	Lectures and Tutorials
Learning Objectives	Complies with Program Goals 1 and 2
Course Description	This course deals with fundamentals in Classical mechanics, Waves and Oscillations and Quantum Mechanics. As a prerequisite, the mathematical preliminaries such as coordinate systems, vector calculus etc will be discussed in the beginning.
Course Outline	Orthogonal coordinate systems (Plane polar, Spherical, Cylindrical), concept of generalised coordinates, generalised velocity and phase space for a mechanical system, Introduction to vector operators, Gradient, divergence, curl and Laplacian in different co-ordinate systems. Central force problem and its applications. Rigid body rotation, vector nature of angular velocity, Finding the principal axes, Euler's equations; Gyroscopic motion and its application; Accelerated frame of reference, Fictitious forces. Potential energy and concept of equilibrium, Lennard-Jones and double-well potentials, Small oscillations, Harmonic oscillator, damped and forced oscillations, resonance and its different examples, oscillator states in phase space, coupled oscillations, normal modes, longitudinal and transverse waves, wave equation, plane waves, examples two- and three-dimensional waves. Michelson-Morley experiment, Lorentz transformation, Postulates of special theory of relativity, Time dilation and length contraction, Applications of special theory of relativity.
Learning Outcome	Complies with PLO 1a, 2a, 3a
Assessment Method	Quiz, Assignments and Exams

Suggested Readings:

Textbooks:

Engineering Mechanics, M. K. Harbola, 2^nd ed., Cengage, 2012
D. Kleppner and R. J. Kolenkow, An introduction to Mechanics, Tata McGraw-Hill, New Delhi, 2000.
I. G. Main, Oscillations and Waves
H. G. Pain, The Physics of Vibrations and Waves, 1968
Frank S. Crawford, Berkeley Physics Course Vol 3: Waves and Oscillations, McGraw Hill, 1966.

References:

R. P. Feynman, R. B. Leighton and M. Sands, The Feynman Lecture in Physics, Vol I, Narosa Publishing House, New Delhi, 2009.
David Morin, Introduction to Classical Mechanics, Cambridge University Press, NY, 2007.

3. P. C. Deshmukh, Foundations of Classical Mechanics, Cambridge University Press, 2019

5.5

CE1101/CE1201

Course code	CE1101/CE1201
Course Credit (L-T-P-C)	1-0-3-2.5
Course Title	Engineering Graphics
Learning Mode	Lectures and Practical
Learning Objectives	Complies with PLO-1a 1. The course on engineering drawing is designed to introduce the fundamentals of technical drawing as an important form of conveying information. 2. Apply principles of engineering visualization and projection theory to prepare engineering drawings, using conventional and modern drawing tools. 3. Practice drawing orthographic projections, isometric views, and sectional views, of simple and combined solids in different orientations.
Course Description	This course will introduce drawing as a tool to represent a complex three-dimensional object on two-dimensional paper through methods of projections. The course explains the use of different drafting tools and the importance of conventions for uniformity and standardization of the interpretation of the drawings.
Course Outline	Fundamental of engineering drawing, line types, dimensioning, and scales. Conic sections: ellipse, parabola, hyperbola; cycloidal curves. Principle of projection, method of projection, orthographic projection, plane of projection, first angle of projection, Projection of points, lines, planes and solids. Section of solids: Sectional views of simple solids- prism, pyramid, cylinder, cone, sphere; the true shape of the section. Methods of development, development of surfaces. Isometric projections: construction of isometric view of solids and combination of solids from orthographic projections. Introduction to AutoCad and solving isometric problems.
Learning Outcome	After attending this course, the following outcomes are expected: a) The student will understand the basic concepts of engineering drawing. b) The student will be able to use basic drafting tools, drawing instruments, and sheets. c) The student will be able to represent three-dimensional simple and combined solid objects on two-dimensional paper. d) The student will be able to visualize and interpret the orientation of simple and combine solid objects.
Assessment Method	Laboratory Assignments (30%), Mid-semester examination (25%) and End-semester examination (45%).

Suggested Readings:

Textbooks:

N.D. Bhatt, Engineering Drawing, Charotar Publishing House.
Agrawal & Agrawal, Engineering Drawing, McGraw Hill.
Jolhe, Engineering Drawing.

References:

Engineering Drawing and Design by David Madsen

2.5

EE1101/EE1201

Course Number	EE1101/EE1201
Course Credit	3-0-3-4.5
Course Title	Electrical Sciences
Learning Mode	Lectures and Experiments
Learning Objectives	Complies with Program goals 1, 2 and 3
Course Description	The course is designed to meet the requirements of all B. Tech programmes. The course aims at giving an overview of the entire electrical engineering domain from the concepts of circuits, devices, digital systems and magnetic circuits.
Course Outline	Circuit Analysis Techniques, Circuit elements, Simple RL and RC Circuits, Kirchoff’s law, Nodal Analysis, Mesh Analysis, Linearity and Superposition, Source Transformations, Thevenin’s and Norton’s Theorems, Time Domain Response of RC, RL and RLC circuits, Sinusoidal Forcing Function, Phasor Relationship for R, L and C, Impedance and Admittance, Instantaneous power, Real, reactive power and power factor. Semiconductor Diode, Zener Diode, Rectifier Circuits, Clipper, Clamper, UJT, Bipolar Junction Transistors, MOSFET, Transistor Biasing, Transistor Small Signal Analysis, Transistor Amplifier and their types, Operational Amplifiers, Op-amp Equivalent Circuit, Practical Op-amp Circuits, Power Opamp, DC Offset, Constant Gain Multiplier, Voltage Summing, Voltage Buffer, Controlled Sources, Instrumentation Amplifier, Active Filters and Oscillators. Number Systems, Logic Gates, Boolean Theorem, Algebraic Simplification, K-map, Combinatorial Circuits, Encoder, Decoder, Combinatorial Circuit Design, Introduction to Sequential Circuits. Magnetic Circuits, Mutually Coupled Circuits, Transformers, Equivalent Circuit and Performance, Analysis of Three-Phase Circuits, Power measurement in three phase system, Electromechanical Energy Conversion, Introduction to Rotating Machines (DC and AC Machines). Laboratory: Experiments to verify Circuit Theorems; Experiments using diodes and bipolar junction transistor (BJT): design and analysis of half -wave and full-wave rectifiers, clipping and clamping circuits and Zener diode characteristics and its regulators, BJT characteristics (CE, CB and CC) and BJT amplifiers; Experiment on MOSFET characteristics (CS, CG, and CD), parameter extraction and amplifier; Experiments using operational amplifiers (op-amps): summing amplifier, comparator, precision rectifier, Astable and Monostable Multivibrators and oscillators; Experiments using logic gates: combinational circuits such as staircase switch, majority detector, equality detector, multiplexer and demultiplexer; Experiments using flip-flops: sequential circuits such as non-overlapping pulse generator, ripple counter, synchronous counter, pulse counter and numerical display; Power Measurement by two Wattmeter method; Open and Short Circuit Tests of Transformer.
Learning Outcomes	Complies with PLO 1a, 2a and 3a
Assessment Method	Quiz, Assignments and Exams

Texts/References

C. K. Alexander, M. N. O. Sadiku, Fundamentals of Electric Circuits, 3rd Edition, McGraw-Hill, 2008.
W. H. Hayt and J. E. Kemmerly, Engineering Circuit Analysis, McGraw-Hill, 1993.
R. L. Boylestad and L. Nashelsky, Electronic Devices and Circuit Theory, 6th Edition, PHI, 2001.
M. M. Mano, M. D. Ciletti, Digital Design, 4th Edition, Pearson Education, 2008.
Floyd, Jain, Digital Fundamentals, 8th Edition, Pearson.
David V. Kerns, Jr. J. David Irwin, Essentials of Electrical and Computer Engineering, Pearson, 2004.
Donald A Neamen, Electronic Circuits; analysis and Design, 3rd Edition, Tata McGraw-Hill Publishing Company Limited.
Adel S. Sedra, Kenneth C. Smith, Microelectronic Circuits, 5th Edition, Oxford University Press, 2004.
A. E. Fitzgerald, C. Kingsley Jr., S. D. Umans, Electric Machinery, 6th Edition, Tata McGraw-Hill, 2003.
D. P. Kothari, I. J. Nagrath, Electric Machines, 3rd Edition, McGraw-Hill, 2004.
Del Toro, Vincent. "Principles of electrical engineering." (No Title) (1972).

4.5

HS1101

Course Number	HS1101
Course Credit	L-T-P-W: 2-0-1-2.5
Course Title	English for Professionals
Learning Mode	Offline
Learning Objectives	This course aims to help the students (a) attain proficiency in written English through the construction of grammatically correct sentences, utilization of subject-verb agreement principles, mastery of various tenses, and effective deployment of active and passive voice to ensure coherent and impactful written expression; (b) enhance oral communication skills by honing public speaking abilities, acquiring strategies to deliver persuasive presentations, and cultivating a polished telephone etiquette, enabling confident and articulate verbal communication; (c) foster active listening capabilities by recognizing different types of listening, and applying proven methods and strategies to improve active listening skills; (d) strengthen reading skills, including comprehension, interpretation, and critical analysis, to grasp diverse written materials and derive meaning from various types of texts encountered in academic and professional contexts; (e) develop adeptness in written communication for business purposes, encompassing the understanding of essential writing elements, mastery of appropriate writing styles thereby enhancing prospects for successful job interviews and subsequent professional endeavors.
Course Description	This academic course on communication skills aims to equip students with fluency in spoken and written English for effective expression in both academic and professional settings. By focusing on essential communication principles and providing practical experiences, students develop clarity, precision, and confidence in their communication. Through interactive discussions and exercises, students enhance critical thinking and adaptability in diverse contexts. Upon completion, students will excel in formal presentations, group discussions, and persuasive writing, enhancing their overall communication proficiency.
Course Outline	Unit I: Introduction to professional communication – LSRW - Phonetics and phonology Sounds in English Language – production and articulation – rhythm and intonation – connected speech - Basic Grammar and Advanced Vocabulary Sounds in English Language – production and articulation – rhythm and intonation – connected speech – persuading and negotiating – brevity and clarity in language. Unit II: Characteristics of Technical Communication: Types of communication and forms of communication - Formal and informal communication Verbal and non-Verbal Communication – Communication barriers and remedies Intercultural communication – neutral language Unit III: Comprehension and Composition – summarization, precis writing Business Letter Writing CV/ Resume – E-Communication Unit IV: Statement of Purpose, Writing Project Reports, Writing research proposal, writing abstracts, developing presentations, interviews – combating nervousness Tutorial: Listening Exercises, Speaking Practice (GDs, and Presentations), and Writing Practice Learning Outcome Attain proficiency in written English, enabling the construction of grammatically correct sentences and coherent written expression through the use of appropriate grammar, tenses, and voice. Enhance oral communication skills, including public speaking, persuasive presentation, and polished telephone etiquette, fostering confident and articulate verbal expression. Cultivate active listening abilities, recognizing different listening types, overcoming obstacles, and employing strategies for attentive and effective communication. Develop proficient written communication skills for business purposes, demonstrating understanding of essential writing elements, appropriate styles, and the creation of reports, notices, agendas, and minutes that effectively convey information.
Assessment Method	Class test + Quiz = 20%; Mid-semester = 25%; Assignment = 15%; End semester = 40%

Suggested Reading

Balzotti, Jon. Technical Communication: A Design-Centric Approach. Routledge, 2022.
Kaul, Asha, Business Communication. PHI Learning Pvt. Ltd. 2009
Laplante, Phillip A. Technical Writing: A Practical Guide for Engineers, Scientists, and Nontechnical Professionals. CRC Press, 2018.
Lawson, Celeste, et al. Communication Skills for Business Professionals, Second Edition. CUP, 2019.
Sharon Gerson and Steven Gerson. Technical Writing: Process and Product (8th Edition), London: Longman, 2013
Rentz, Kathryn, Marie E. Flatley & Paula Lentz. Lesikar’s Business Communication Connecting in a Digital world, McGraw-Hill, Irwin.2012
Allan & Barbara Pease. The Definitive Book of Body Language, New York, Bantam,2004
Jones, Daniel. The Pronunciation of English, New Delhi, Universal Book Stall.2010
Savage, Alice. Effective Academic Writing. OUP. 2014
Swan and Alter. Oxford English grammar course. OUP. 201

2.5

TOTAL

23.5

Probability Theory and Ordinary Differential Equations

Sl. No.

Subject Code

SEMESTER II

MA1201

Course Number	MA1201
Course Credit (L-T-P-C)	3-1-0-4
Course Title	Probability Theory and Ordinary Differential Equations
Learning Mode	Lectures and Tutorials
Learning Objectives	To introduce the basic concepts of probability, statistics, and Differential equations.
Course Description	This course aims to cover basic concepts of probability, statistics and ordinary differential equations. In particular, popular distributions, random sampling, various estimators and hypothesis testing will be discussed. Students will also get exposure to the linear ordinary differential equations and their solution techniques.
Course Content	Probability (12 Lectures): Random variables and their probability distributions, Cumulative distribution functions, Expectation and Variance, probability inequalities, Binomial, Poisson, Geometric, negative binomial distributions, Uniform, Exponential, beta, Gamma, Normal and lognormal distributions. Statistics (10 Lectures): Random sampling, sampling distributions, Parameter estimation, Point estimation, unbiased estimators, maximum likelihood estimation, Confidence intervals for normal mean, Simple and composite hypothesis, Type I and Type II errors, Hypothesis testing for normal mean. Ordinary Differential Equations (20 Lectures): First order ordinary differential equations, exactness and integrating factors, Picard's iteration, Ordinary linear differential equations of n-th order, solutions of homogeneous and non-homogeneous equations (Method of variation of parameters). Systems of ordinary differential equations, Power series methods for solutions of ordinary differential equations. Legendre equation and Legendre polynomials, Bessel equation and Bessel functions.
Learning Outcome	Students will get exposure and understanding of: Random variables and their probability distributions Understand popular distributions and their properties Sampling, estimation and hypothesis testing Solution of ordinary differential equations Solution of system of ordinary differential equations Special functions arising as power series solutions of ordinary differential equations
Assessment Method	Quiz /Assignment/ MSE / ESE

Text Books:

Hogg, R. V., Mckean, J. and Craig, A. T., “Introduction to Mathematical Statistics”, 8th Ed., Pearson Education India, 2021
M. Ross “An introduction to Probability Models, Academic Press INC, 11th edition.
Miller, I. and Miller, M., “John E. Freund's Mathematical Statistics with Applications”, 8th Ed., Pearson Education India, 2013
L. Ross, Differential equations, 3rd Edition, Wiley, 1984
E. Boyce and R. C. Di Prima, Elementary Differential equations and Boundary Value Problems, 7th Edition, Wiley, 2001.

CS1201

Course Number	CS1201
Course Credit	3-0-3-4.5
Course Title	Data Structure
Learning Mode	Offline
Learning Objectives	· Understand the principles and concepts of data structures and their importance in computer science. · Learn to implement various data structures and understand how different algorithms works. · Develop problem-solving skills by applying appropriate data structures to different computational problems. · Achieving proficiency in designing efficient algorithms.
Course Description	This course provides a comprehensive study of data structures and their applications in computer science. It focuses on the implementation, analysis, and use of various data structures such as arrays, linked lists, stacks, queues, trees, and graphs. Through theoretical concepts and practical programming exercises, this course aims to develop problem-solving and algorithmic thinking skills essential for advanced topics in computer science and software development.
Course Outline	· Introduction to Data Structure, · Time and space requirements, Asymptotic notations · Abstraction and Abstract data types · Linear Data Structure: stack, queue, list, and linked structure · Unfolding the recursion · Tree, Binary Tree, traversal · Search and Sorting, · Graph, traversal, MST, Shortest distance · Balanced Tree Practical component: Lab to be conducted on a 3-hour slot weekly. It will be conducted with the theory course so the topics for problems given in the lab are already initiated in the theory class.
Learning Outcome	Understand Data Structure Fundamentals Implement Basic Data Structures using a programming language Analyse and Apply Algorithms Design and Analyse Tree Structures Understand the usage of graph and its related algorithms Design and Implement Sorting and Searching Algorithms Debug and Optimize Code
Assessment Method	Internal (Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Reading

Alfred V. Aho, John E. Hopcroft, Jeffrey D. Ullman, Data Structures and Algorithms, Published by Addison-Wesley
Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest and Clifford Stein., Introduction to Algorithms,
Mark Allen Weiss, Data Structures and Algorithm Analysis in Java
Robert Sedgewick and Kevin Wayne, Algorithms
Narasimha Karumanchi, Data Structures and Algorithms Made Easy

4.5

CH1201/CH1101

Course Number	CH1201/CH1101
Course Credit	3-1-3-5.5
Course Title	Chemistry
Learning Mode	Offline
Learning Objectives	The course aims to lay a foundation for all three branches of chemistry, viz. Organic, Inorganic, and Physical Chemistry. The course aims to nurture knowledge to appreciate the interface of chemistry with other science and Engineering branches by combining theoretical concepts and experimental studies.
Course Description	This course introduces basic organic chemistry, inorganic chemistry and Physical chemistry to understand fundamental laws that governs various reactions, reaction rates, equilibrium, and their applications in daily life through relevant experimentation.
Course Outline	Module 1: Thermodynamics: The fundamental definition and concept, the zeroth and first law. Work, heat, energy and enthalpies. Second law: entropy, free energy and chemical potential. Change of Phase. Third law. Chemical equilibrium. Conductance of solutions, Kohlrausch’s law-ionic mobilities, Basic Electrochemistry. Module 2: Coordination chemistry: Crystal field theory and consequences color, magnetism, J.T distortion. Bioinorganic chemistry: Trace elements in biology, heme and non-heme oxygen carriers, haemoglobin and myoglobin; Organometallic chemistry. Module 3: Stereo and regio-chemistry of organic compounds, conformational analysis and conformers, Molecules devoid of point chirality (allenes and biphenyls); Significance of chirality in living systems, organic photochemistry, Modern techniques in structural elucidation of compounds (UV–Vis, IR, NMR). Module 4 (Lab Component): Experiments based on redox and complexometric titrations; synthesis and characterization of inorganic complexes and nanomaterials; synthesis and characterization of organic compounds; experiments based on chromatography; experiments based on pH and conductivity measurement; experiment related to chemical kinetics and spectroscopy.
Learning Outcome	Students will be able to 1. identify organic and inorganic molecules and relate them to daily life applications through experiments. 2. understand important hypothesis, laws and their derivations to intercept physical phenomenon of chemical reactions and apply them in hands-on experiments. 3. understand the importance of organic and inorganic molecules in our body and environment. 4. know important analytical techniques to intercept chemical entity. 5. approach organic and inorganic synthesis as a skillset for drug manufacturing, calculate limiting reagents and yields, use various analytical tools to characterize organic compounds, interpret and ascertain data related to Physical chemistry aspects and know laboratory safety measures, risk factors and scientific report writing skills.
Assessment Method	Theory: 20% Quiz and assignment, 30% Mid sem and 50% End semester exams for theory part (4 credits). Lab: 60% lab report, lab performance and assignment, 20% End semester exam for practical part, 20% viva/quiz (1.5 credits). Overall Weightage: Theory (70%), Lab (30%).

Suggested Reading:

Text books:

Vogel's Qualitative Inorganic Analysis, G. Svehla, 7^th Edition, Revised, Prentice Hall, 1996.
A. J. Elias, S. S. Manoharan and H. Raj, "Experiments in General Chemistry", Universities Press (India) Pvt. Ltd., 1997.
A. J. Elias, A Collection of Interesting General Chemistry Experiments, revised edition, Universities Press (India) Pvt. Ltd., 2007.
F. Albert Cotton, G. Wilkinson, C. A. Murillo, M. Bochmann, Advanced Inorganic Chemistry - 6^th Edition New Delhi: Wiley India, 2008.
K. Mukkanti, Practical Engineering Chemistry, B.S. Publications, Hyderabad, 2009.
Shriver and Atkins inorganic chemistry / Peter Atkins, Tina Overton, Jonathan Rourke, Mark Weller, Fraser Armstrong-5^th Edition – Oxford: UOP. 2012.
Atkins’ Physical Chemistry, Peter Atkins, Julio de Paula, James Keeler, Oxford University Press, 11^th Edition 2017.
K. L. Kapoor, A Textbook of Physical Chemistry, Vol: 1, 2 (6^th Edition, 2019), Vol: 3 (5^th Edition, 2020) MaGraw Hill.
G. R. Chatwal, S. K. Anand, Instrumental Methods of Chemical Analysis, 5^th Edition, Himalaya Publications, 2023.

5.5

ME1201/ME1101

Course Number	ME1201/ME1101
Course Credit	0-0-3-1.5
Course Title	Mechanical Fabrication
Learning Mode	Fabrication work – hands on fabrication work in Workshop
Learning Objectives	Complies with PLOs 3-4. · This course aims to develop the concepts and skills of various mechanical fabrication methods. · Fabrication of metallic and non-metallic components, fabrication using bulk and sheet metals, subtractive and additive manufacturing methods, and assemble the parts
Course Description	This course is designed to fulfil the need of hand on experience about various approaches (conventional and CNC, subtractive and additive) of mechanical fabrication approaches. Prerequisite: NIL
Course Outline	The jobs for various shops should be planned such that they are the parts of an assembled item. The student groups will fabricate different parts in various shops which will involve some amount of their creativeness/input particularly in design and/or planning. Various components as required for the assembled part can be made using the following shops: Sheet Metal Working: Development, sheet cutting and fabrication of designated job using sheet metal (ferrous/nonferrous); Joining of required portions by soldering, in case part is desired to be made leak proof. Pattern Making and Foundry: Making of suitable pattern (wood); making of sand mould, melting of non-ferrous metal/alloy (Al or Al alloys), pouring, solidification. Observation/identification of various defects appeared on the component. Joining: Butt/lap/corner joint job fabrication as required of low carbon steel plates; weld quality inspection by dye-penetration test (non-destructive testing approach)of the component made. Demonstration of semi-automatic Gas Metal Arc welding (GMAW). Conventional machining: Operations on lathe and vertical milling to fabricate the required component. The fabrication of the component should cover various lathe operations like straight turning, facing, thread cutting, parting off etc., and operations using indexing mechanism on vertical milling. CNC centre: Fundamentals of CNC programming using G and M code; setting and operations of job using CNC lathe or milling, tool reference, work reference, tool offset, tool radius compensation to fabricate the component with a designed profile on Al/Al-alloy plate. 3D printing (Fused Filament Fabrication): (2 weeks) Create the model, select appropriate slicing and path for fabrication of a 3D job by layer deposition (additive manufacturing approach) using polymeric material. Demonstration on pattern fabrication using 3D printing.
Learning Outcome	· This course would enable the students to develop the concept of design, fabrication (subtractive and additive) for various engineering applications. Fabrication of components and assemble them. · The practical skill and hands on experience for various fabrication methods from bulk, sheet metal using conventional as well as CNC machines.
Assessment Method	Fabrication of components in each of the shops required for assembly of the given part; submission of reports for each shop, and quiz assessment.

Text and Reference books:

Hajra Choudhury, HazraChoudhary and Nirjhar Roy, 2007, Elements of Workshop Technology, vol. I,Mediapromoters and Publishers Pvt. Ltd.
W A J Chapman, Workshop Technology, 1998, Part -1, 1st South Asian Edition, Viva Book Pvt Ltd.
N. Rao, 2009, Manufacturing Technology, Vol.1, 3rd Ed., Tata McGraw Hill Publishing Company.
Adithan, B.S. Pabla, 2012, CNC machines, New Age International Publishers

1.5

ME1202/ME1102

Course Number	ME1202/ ME1102
Course Number	Engineering Mechanics
L-T-P-C	3-1-0-4
Pre-requisites	Nil
Semester	Spring
Learning Mode	Lectures
Learning Objectives	Complies with PLOs 1, 4 · The objective of this first course in mechanics is to enable engineering students to analyze basic mechanics problems and apply vector-based approach to solve them.
Course Outline	1. Rigid body statics: Equivalent force system. Equations of equilibrium, Free body diagram, Reaction, Static indeterminacy. 2. Structures: 2D truss, Method of joints, Method of section. Beam, Frame, types of loading and supports, axial force, Bending moment, Shear force and Torque Diagrams for a member. 3. Friction: Dry friction (static and kinetic), wedge friction, disk friction (thrust bearing), belt friction, square threaded screw, journal bearings, Wheel friction, Rolling resistance. 4. Centroid and Moment of Inertia 5. Introduction to stress and strain: Definition of Stress, Normal and shear Stress. Relation between stress and strain, Cauchy formula. Stress in an axially loaded member and stress due to torsion in axisymmetric section
Learning Outcomes:	Following learning outcomes are expected after going through this course. Learn and apply general mathematical and computer skills to solve basic mechanics problems. Apply the vector-based approach to solve mechanics problems.
Assessment Method	Mid semester examination, End semester examination, Class test/Quiz, Tutorials

Reference Books

Shames, Engineering Mechanics: Statics and dynamics, 4th Ed, PHI, 2002.
P. Beer and E. R. Johnston, Vector Mechanics for Engineers, Vol I - Statics, 3rd Ed, Tata McGraw Hill, 2000.
L. Meriam and L. G. Kraige, Engineering Mechanics, Vol I - Statics, 5th Ed, John Wiley, 2002.
P. Popov, Engineering Mechanics of Solids, 2nd Ed, PHI, 1998.
P. Beer and E. R. Johnston, J.T. Dewolf, and D.F. Mazurek, Mechanics of Materials, 6th Ed, McGraw Hill Education (India) Pvt. Ltd., 2012.

IK1201

Indian Knowledge System (IKS)

TOTAL

22.5

Sl. No.

Subject Code

SEMESTER III

ME2101

Dynamics

Course Name				Dynamics
Course Number				ME2101
L-T-P-C				3- 1- 0- 4
Pre-requisites				Nil
Semester				Third
Learning Mode				Lectures
Course Learning Objectives
			Complies with PLOs 1 and 4. The objective of this course is to introduce students to the fundamental principles and methods of dynamics. Students will be introduced to specific problems on modelling of engineering systems using principles of dynamics. Some of the exercise problems will be solved using computer based programs.
Course Content
		1. Kinematics of Particles: Rectilinear motion, curvilinear motion rectangular, normal, tangential, polar, cylindrical, spherical (coordinates), relative and constrained motion, space curvilinear motion. 2. Kinetics of Particles: Force, mass and acceleration, work and energy, impulse and momentum, impact. Introduction to central force motion. 3. Kinetics of a system of particles, 4. Center of Gravity and Moment of Inertia: First and second moment of mass, radius of gyration, parallel axis theorem, product of inertia, rotation of axes and principal moment of inertia, thin plates, composite bodies. 5. Potential energy, impulse-momentum and associated conservation principles, Euler equations of motion and its application. 6. Introduction to Variational principles, Lagrange’s equation, Hamilton’s principle. 7. Equation of motion in Eulerian angles. 8. Vibration of a single spring-mass-dashpot system: Free and forced vibration, damping resonance, magnification factor, amplitude and phase plot for a harmonically excited single degree of freedom system. Linear Stability (Infinitesimal Stability)
Learning Outcomes		Following learning outcomes are expected after going through this course. a) Learn and apply general mathematical and computer skills to solve dynamics problems. b) Application of Newton’s laws of motion, work energy principles, and momentum conservation principles in various coordinate systems for single particles, system of particles, and rigid bodies. c) c) Introductory understanding of vibration of simple mechanical systems.
Assessment Method		Mid Semester Examination, End Semester examination, Class test & quiz, Assignment, Class Performance and Viva
Texts and References
	1. I. H. Shames, Engineering Mechanics: Statics and dynamics, 4th Ed, PHI, 2002. 2. F. P. Beer and E. R. Johnston, Vector Mechanics for Engineers, Vol II - Dynamics, 3rd Ed, Tata McGraw Hill, 2000. 3. J. L. Meriam and L. G. Kraige, Engineering Mechanics, Vol II - Dynamics, 5th Ed, John Wiley, 2002. 4. L. Meirovitch, Methods of analytical dynamics, Dover Publication, 2007.

ME2102

Thermodynamics

Course Name				Thermodynamics
Course Number				ME2102
L-T-P-C				3- 1- 0- 4
Pre-requisites				Nil
Semester				Third
Learning Mode				Lectures
Course Learning Objectives
			Complies with PLOs 2 and 4. 1. To develop the basic understanding of classical thermodynamics and principles of engineering applications 2. To develop skills to formulate and analyze thermodynamic problems involving control volumes and control masses
Course Content
		Thermodynamic systems: Macroscopic and microscopic view, system and control volume, states and properties, processes; Properties of pure substances and steam: Phase changes, steam tables and Mollier diagram, Heat and work; Zeroth law; First law: for systems and control volumes, enthalpy, Applications of first law: closed and open systems, SSSF, USUF, Second law: Carnot cycle, entropy, corollaries of the second law; Applications of second law: closed and open systems, vapor compression and Rankine cycle; irreversibility, availability, exergy; Thermodynamic relations; Properties of mixtures of ideal gases; Third law of thermodynamics; Introduction to psychrometry
Learning Outcomes		The course has been designed to achieve the following outcomes: 1. Understanding of the basic concepts of engineering thermodynamics. 2. Understanding of the thermodynamic properties of pure substances at different states. 3. Acquire basic knowledge about thermodynamic cycles (a) to produce mechanical power from heat, and (b) to keep a place cool and comfortable. 4. Analyse thermodynamic processes for maximum feasible efficiency. 5. Select an engineering approach to problem-solving based on the properties of substances and the laws of thermodynamics.
Assessment Method		Mid Semester Examination, End Semester examination, Class test & quiz, Assignment, Class Performance and Viva
Texts and References
	Textbook: 1. C Borgnakke& R E Sonntag, Fundamentals of Thermodynamics, 7^th Edition, John Wiley, 2009. 2. Y. A. Cengel and M. A. Boles, Thermodynamics: An Engineering Approach, 7^th Edition, Tata McGraw Hill, 2017. 3. P. K. Nag, Engineering Thermodynamics, Fifth Edition, McGraw Hill Education, 2013

ME2103

Course Name					Fluid Mechanics
Course Number					ME2103
L-T-P-C					3-1-2-5
Pre-requisites					Nil
Semester					Third
Learning Mode					Lectures and Practical
Course Learning objectives
				Complies with PLOs 2 and 4. 1. To develop the basic understanding of fluid statics and dynamics 2. To develop analytical skills to deal with various types of fluid flow problems 3. Laboratory sessions are designed for developing experimental skills
Course Content
			Introduction: Definition and classification of fluids, Fluid as a continuum, Properties of fluids, Dimensional Analysis and Similitude: Buckingham-pi theorem, Similarities-geometric, kinematic and dynamic. Fluid Statics: Pascal’s Law, Submerged surfaces Buoyancy and Stability , Stability of submerged bodies, Fluid in a Rigid Body Motion, Fluid Kinematics: Lagrangian and Eulerian Approaches, Flow lines, Features of fluid Motion, Potential flows: stream and velocity potential function, basic flows, doublet, Blunt body, flow past a stationary and rotating cylinders. Conservation Equations: Reynolds Transport Theorem, Integral and differential equations for mass, momentum and energy conservation. Steady Incompressible Viscous Flows: Flow between infinite parallel plates, Couette Flow, Hagen-Poiseuille Flow, Losses in a pipe, Pipe networks, Boundary layer flow: Prandtl boundary layer equations, Blasius Solution Von Karman Momentum Integral Equation, Boundary layer separation, etc., Turbulent Flows: character of turbulence, Reynolds-averaged Navier-Stokes equation, Anatomy of turbulent boundary layer, Prandtl mixing length model. Introduction to Compressible Flows: Velocity of sound, Effect of Mach number on flow compressibility
List of experiments
		1. Stability of floating bodies 2. Centre of pressure 3. PIV measurements (DST-FIST facility: No.SR/FST/ET-II/2018/240(C)) 4. Reynolds Experiment 5. Bernoulli’s apparatus 6. Wind tunnel experiments 7. Venturimeter and orificemeter 8. Pitot-tube 9. Losses in pipe 10. Notch/Weir
Learning Outcomes		1. Students should be able to demonstrate the knowledge of fluids, flow behavior, and flow system design 2. Students should be able to apply the fluid flow concepts on practical systems and provide solution to the problems associated with them
Assessment Method		Mid Semester Examination, End Semester examination, Class test & quiz, Assignment, Class Performance and Viva, Practical Exam
Texts and References
	Textbook: 1. F. M. White, 2016, Fluid Mechanics, 8th Ed, McGraw-Hill. 2. B. R. Munson, D. F. Young and T. H. Okhiishi, 2002, Fundamentals of Fluid Mechanics, 4th Ed, John Wiley, 3. M. K. Khan, 2015, Fluid Mechanics and Machinery, Oxford University Press. References: 1. Cengel and Cimbala, 2019, Fluid Mechanics: Fundamentals and Applications, 4^th Edition, McGraw-Hill. 2. R. W. Fox, A.T. McDonald and J.W. Mitchell, 2020, Introduction to Fluid Mechanics, 10th Ed, Wiley. 3. V. Streeter, E. B. Wylie, and K.W. Bedford, 2017, Fluid Mechanics, 9^th Edition, McGraw-Hill. 4. Irwing Shames, 2002, Mechanics of Fluids, 4th Ed., McGraw-Hill. 5. P. Kundu, I. M. Cohen, and D.R. Dowling, 2015, Fluid Mechanics, 6^th Ed., Elsevier. 6. J.A. Fay, 2008, Introduction to Fluid Mechanics, PHI Learning Pvt Ltd., New Delhi 7. Sawan S. Sinha, 2024, Fundamentals of Fluid Mechanics, Ane Books Pvt. Ltd.

ME2104

Engineering Materials

Course Name					Engineering Materials
Course Number					ME2104
L-T-P-C					3-0-2-4
Pre-requisites					Nil
Semester					Third
Learning Mode					Lectures and Practical
Course Learning Objectives
				Complies with PLOs 1, 3 and 4. 1. Introduce the fundamental science and engineering of materials. 2. Introduce the standard testing procedures to evaluate the mechanical properties of materials. 3. Approaches to alter the mechanical properties of materials and evaluate its performance.
Course Content
			Crystal imperfections: point defects, line defects, surface defects. Characteristics of dislocations, generation of dislocations. Bonds in solids and characteristics of Metallic bonding, Deformation mechanisms and Strengthening mechanisms in structural materials. Phase diagrams: Principles and various types of phase diagrams, Iron carbon phase diagrams. Principles of solidification: Structural evaluation during solidification of metals and alloys. Heat treatment of steels and CCT diagrams: Pearlitic, martensitic, bainitic transformation in steel during heat treatment. Hot working and cold working of metals: recovery, re-crystallization and grain growth, Fracture, Fatigue and creep phenomenon in metallic materials. General classifications, properties and applications of alloy steels, tool steels, stainless steels, cast irons, Nonferrous materials like copper base alloys, aluminum base alloys, Nickel base alloys, etc., Non-metals/New materials: composites, ceramics, polymers, 2D materials/structural materials, electronic materials, etc.
List of experiments
		Strength of materials: Tensile testing of steel, hardness, torsion, and impact testing. Metallography: Microscopic techniques, determination of volume fraction of different phases in material including metals, estimation of grain sizes, study of heat affected regions in welded steel specimen.
Learning Outcomes		1. Students will be able to understand fundamental reason for the choice of engineering materials for various application. 2. Students will be able to suggest appropriate method to improve the mechanical properties of materials as per the requirements. 3. 3. Student will be able to choose the appropriate materials as well as testing method for engineering application.
Assessment Method		Mid Semester Examination, End Semester examination, Class test & quiz, Assignment, Class Performance and Viva, Practical Exam
Texts and References
	Textbook: 1. William D. Callister, Material science and Engineering and Introduction, Wiley, 2006. 2. V. Raghavan, Materials Science and Engineering, Fifth Edition, Prentice Hall Of India, 2008. 3. G. E. Dieter, Mechanical Metallurgy, McGraw Hill, 1988. 4. W. F. Smith, Materials Science and Engineering (SIE), Tata-McGraw Hill, 2008. References: · AVNER, Introduction to Physical Metallurgy, Tata-McGraw Hill, 2008.

HS21XX

HSS Elective - I

TOTAL

Kinematics and Dynamics of Mechanisms

Sl. No.

Subject Code

SEMESTER IV

ME2201

Course Name					Kinematics and Dynamics of Machines
Course Number					ME2201
L-T-P-C					3- 1- 2- 5
Pre-requisites					Dynamics
Semester					Fourth
Learning Mode					Lectures and Practical
Course Learning Objectives
				Complies with PLOs 1 and 4. The objectives of this course are to cover the kinematics and dynamics of planar single degree-of-freedom mechanisms. Specifically, this course will introduce students to the graphical and analytical techniques used for analysis and design of planar mechanism. A semester long course project will be assigned to enable students to apply learned theoretical concepts to real life problems. A side objective of this course will be to introduce MATLAB as a computer tool to solve analysis equations.
Course Content
			1. Introduction and course policies 2. Degrees of freedom, elements of kinematic chains, Kutzbach, Gruebler, Grashof’s criterion 3. Graphical method of kinematic (displacement, velocity and acceleration) analysis of planar mechanisms 4. Analytical and computer-aided method of kinematic analysis of planar and spatial mechanisms 5. Synthesis of mechanisms 6. Special mechanisms: steering, Hooke’s joint 7. Introduction to Cams, classification, terminology of Cams, Design and synthesis of cams by analytical and graphical methods 8. Different gear trains, applications of gear in gear boxes 9. Static and dynamic force analysis, friction in joints 10. Balancing of reciprocating and rotating machines, Gyroscope
List of experiments
		a) Learn and apply general mathematical and computer skills to kinematics and dynamics analysis of machine elements including linkages, cams, and gears, within the general machine design context. b) Apply the theoretical principles to a real-life problem using computer tools. c) Application of MATLAB software to solve kinematics and dynamics problems.
Learning Outcomes		1. Learn and apply geometrical, analytical and computer skills to kinematics and dynamics analysis of machine elements including linkages, cams, and gears, within the general machine design context. 2. Apply the theoretical principles to a real-life problem using mechanism.
Assessment Method		Mid Semester Examination, End Semester examination, Class test & quiz, Assignment, Class Performance and Viva, Practical Exam
Texts and References
	1. J. E. Shigley and J.J. Uicker, Theory of Machines and Mechanisms, McGraw Hill, 1995 2. A. K. Mallik, A. Ghosh, G. Dittrich, Kinematic analysis and synthesis of Mechanisms, CRC, 1994. 3. A. G. Erdman and G. N. Sandor, Mechanism Design, Analysis and Synthesis Volume 1, PHI, Inc., 1997. 2. J. S. Rao and R. V. Dukkipati, Mechanism and Machine Theory, New Age International, 1992. 3. S. S. Rattan, Theory of Machines, Tata McGraw Hill, 1993. 4. T. Bevan. Theory of Machines, CBS Publishers and Distributors, 1984

ME2202

Heat and Mass Transfer


Course Name					Heat and Mass Transfer
Course Number					ME2202
L-T-P-C					3-1-2-5
Pre-requisites					Thermodynamics and Fluid Mechanics, or equivalent
Semester					Fourth
Learning Mode					Lectures and Practical
Course Learning objectives
				Complies with PLOs 2 and 4. 1. The student should internalize the meaning of the terminology and physical principles associated with heat and mass transfer processes. 2. The student should be able to delineate pertinent transport phenomena for any process or system involving heat or mass transfer. 3. The student should be able to use requisite inputs for computing heat transfer rates and/or material temperatures. 4. The student should be able to develop representative models of real processes and systems and draw conclusions concerning process/system design or performance analysis. 5. The student should become familiar with design of heat transfer experiments and concerning measurement techniques.
Course Content
			Modes of heat transfer: Conduction: One-dimensional steady conduction, resistance network analogy, fins, two- and three-dimensional steady conduction, one-dimensional unsteady conduction, semi-infinite solids. Convection: fundamentals, order of magnitude analysis of momentum and energy equations, hydrodynamic and thermal boundary layers, dimensional analysis, free and forced convection, external and internal flows. Heat exchangers: LMTD and є-NTU methods. Radiation: Stefan Boltzmann law, Planck’s law, emissivity and absorptivity, radiant exchange between black surfaces, view factors, network analysis. Phase change heat transfer: Boiling and condensation. Mass transfer: molecular diffusion, Fick’s law, binary species
List of experiments
		1. Measurement thermal conductivity different materials using composite wall apparatus 2. Determination of the heat transfer coefficient during Forced Convection 3. Determination of the heat transfer coefficient during Natural Convection 4. Determination of Thermal Conductivity of Liquid 5. Phase change heat transfer: (a) Pool boiling 6. Phase change heat transfer: (b) Condensation 7. Performance evaluation of double pipe heat exchanger (a) parallel flow (b) counter flow 8. Performance evaluation of shell-and-tube heat exchanger 9. Emissivity measurement 10. Heat Pipe Demonstration
Learning Outcomes		1. The student should be able to develop representative models of real processes and systems and draw conclusions concerning process/system design or performance analysis. 2. The student should be able to design heat transfer experiments using suitable measurement techniques
Assessment Method		Mid Semester Examination, End Semester examination, Class test & quiz, Assignment, Class Performance and Viva, Practical Exam
Texts and References
	Textbook: 1. Bergman, Theodore L., Frank P. Incropera, David P. DeWitt, and Adrienne S. Lavine. Fundamentals of heat and mass transfer. 7^th Edition, John Wiley & Sons, 2011. 2. J.P. Holman, Heat Transfer, 8^th Edition, McGraw Hill, 1997. References: 1. M.N. Ozisik, Heat Transfer – A basic approach, McGraw Hill, 1985.Bejan, Convection Heat Transfer, 2^nd Edition, Interscience, 1994. 2. Bejan, Convection Heat Transfer, 2nd Edition, Interscience, 1994. 3. Y. A. Cengel and Afshin J. Ghajar, Heat and Mass Transfer, 5^th Edition, McGraw-Hill, New Delhi, 2020.

ME2203

Mechanics of Solids

Course Name				Mechanics of Solids
Course Number				ME2203
L-T-P-C				3- 1- 0- 4
Pre-requisites				Engineering Mechanics (ME102)
Semester				Fourth
Learning				Lectures
Course Learning Objectives
			Complies with PLOs 1 and 4. The objective of this course is to introduce students to the advanced principles and methods of solid mechanics. Design exercises help students to apply theoretical knowledge to practical problems.
Course Content
		1. Stress as a tensor: stress at point, Cauchy stress tensor, equilibrium equations, analysis of deformation and definition of strain components, compatibility relations: One-to-one deformation mapping, invertibility of deformation gradient, compatibility. 2. Constitutive relations, Theory of failures for isotropic materials. 3. Some properties of Stress and Strain Tensor: Principal stresses and strains, stress and strain invariants. Uniqueness of solution. Plane stress and plane strain problems, Airy's stress function. 4. 2-D problems in polar coordinates: Thin and thick-walled cylinder, Rotating disks and cylinders. 5. Torsion of circular bar, Torsion of non-circular bars: Saint Venant's semi-inverse method, Prandtl stress function. Elliptical and triangular shaft, shaft with cutout, rectangular shaft, hollow shafts, thin tubes narrow rectangular shaft. Membrane analogy. 6. Symmetrical bending, Advanced problem in beam bending: Unsymmetrical bending: pure bending of prismatic and composite beams. Curved beam. Bending of beam with thin profile section - shear flow, determination of shear center. 7. Elastic stability: Buckling of mechanisms, Buckling of straight and bent Beam columns. 8. Energy Methods: Strain energy due to axial, torsion, bending and transverse shear. Comparison of strain energies due to bending and shear. Castigliano’s theorem, reciprocity theorem etc. 9. Contact Stresses: Geometry of contact surface, methods of computing contact stress, deflection of bodies in point contact and line contact with normal load. 10. Stress Concentration: Plate with circular hole. 11. Introduction to plate theory (Kirchhoff's theory).
Learning Outcomes		· Develop the analytical skill to calculate stress and strain in an element using suitable theoretical techniques. · Understand different failure theories to predict the failure of solids under multiaxial loading conditions.
Assessment Method		Mid Semester Examination, End Semester examination, Class test & quiz, Assignment, Class Performance and Viva
Texts and References
	1. S. Timoshenko, Strength of Materials – Parts I and Part II, 3 Ed., CBS Publishers and Distributers, 2004. 2. L.S. Srinath, Advanced Mechanics of Solids, Tata McGraw Hill, 2009. 3. E.P. Popov, Engineering Mechanics of Solids, 2nd Ed, PHI, 1998. 4. F. P. Beer and E. R. Johnston, J.T. Dewolf, and D.F. Mazurek, Mechanics of Materials, 6th Ed, McGraw Hill Education (India) Pvt. Ltd., 2012. 5. Y.C. Fung, Foundations of Solid Mechanics, Prentice-Hall, 1965. 6. S. C. Crandall, N. C. Dahl, and T. J. Lardner, An Introduction to the Mechanics of Solids, 2e, McGraw Hill, 1999. 7. S. P. Timoshenko and J. N. Goodier, Theory of Elasticity, 3e, McGraw Hill International, 1970.

ME2204

Mechanical Measurements and Instrumentation

Course Name				Mechanical Measurements and Instrumentation
Course Number				ME2204
L-T-P-C				3-0-2-4
Pre-requisites				Nil
Semester				fourth
Learning Mode				Lecture & Practical
Learning Objectives Course Learning Objectives: Complies with PLOs 1, 2 and 3. After completion of this course the student should be able to: • Recognize different sensors and measurement Methodology in Measurement Systems. • Should be able to apply measurement Fundamentals in innovative way to apply in varieties of systems. Project Based Lab a) select and apply appropriate design methodology. b) generate a variety of conceptual instruments. c) demonstration of feasibility of the conceptual design with special emphasis on Mechanical Systems
Course Content
			Fundamental of Measurement: Elements of a generalized measurement system, standards, and types of signals. Static performance characteristics, Dynamic performance, instrument types - zero, first and second order instruments, transfer function representation, system response to standard input signals - step, ramp, impulse, and frequency response. Treatment of uncertainties: Error classification, systematic and random errors, statistical analysis of data, propagation and expression of uncertainties. Measurement of various physical quantities: Linear and angular displacement, velocity, force, torque, strain, pressure, flow rate and temperature. Transfer functions of some standard measuring devices. Metrology: measurement of angles, threads, surface finish, inspection of straightness, flatness and alignment, gear testing, digital readouts, coordinate measuring machine. Data Acquisition and processing: Digital methods, digitization, signal conditioning, interfacing, standard methods of data analysis – quantities obtainable from time series. Fourier spectra, DFT, FFT. Data acquisition parameters - sampling rate, Nyquist sampling frequency, aliasing & leakage errors. Internet of Things: Signal recovery, data transmission, IOT components.
List of experiments
		Linear and Angular Measurements using Vernier, Micrometer, Screw Gauge, Filler gauge, Radius gauge, combination set, Angle measurement using Sine bar, slip gauge and Dial gauge & Error calculation, Thread and Gear tooth measurement, Surface roughness measurement, Use of Sensor kits, Force measurement using dynamometer. Temperature measurement and calibration of thermocouple, Shaft alignment test, Use of accelerometer, Measurements using slip gauge/balls/roller set; Go-NoGo, Telescopic gauge, Depth gauge, Measurements using CMM, Roundness, Scan, C-t-C Distance etc., Nano indentation experiment(DST-FIST facility: No.SR/FST/ET-II/2018/240(C)) Image Processing and visualization using High speed camera. Statistical analysis of measurements in the experiments.
Learning Outcomes		Students after covering this course. (i) Understand the methods of measurement, selection of measuring instruments and standards of measurement. (ii) Identify and learn to use various measuring instruments. (iii) Ability to explain tolerance, limits of size, fits, geometric and position tolerances and gauge design. (iv) Recommend the Quality Control Techniques and Statistical Tools appropriately. (v) Ability to analyze the collected data (vi) Develop an ability of problem solving and decision making by identifying and analyzing the cause for variation and recommend suitable corrective actions for quality improvement
Assessment Method		Class test & quiz, Class Performance and Viva, Practical Exam
Texts and References
	Textbooks 1. E. O. Deobelin, Measurement Systems - Application and Design, Tata McGraw-Hill, 1990. 2. Beckwith T. G., Marangoni, R. D., and Lienhard, J. H., MechanicalmMeasurements, 6e, Addison Wesley, 2020 2. J. Bentley, Principles of measurement systems, 4e, 2004 3. Sudip Misra, Anandarup Mukherjee, Arijit Roy, Introduction to IoT, 2021, Cambridge University Press. 4. E. Doebelin, D. Manik, Measurement Systems, ‎6th edition, McGraw Hill Education; 2017 5. B. C. Nakra and K. K. Chaudhry, Instrumentation Measurement and Analysis, 4^th Edition, 2016 Reference 1. Figiola, R.S. and Beasley, D.E., Theory and design for mechanical measurements, 6e, John Wiley, 2015. 2. Dally, Riley, and McConnell, Instrumentation for engineering measurements, 2e, John Wiley & Sons, 2010. 3. Doebelin E.O., Engineering Experimentation: Planning, Execution, Reporting, McGraw-Hill, 1995. 4. Jain R.K., Engineering Metrology, 21e, Khanna Publishers, New Delhi, 1997

XX22PQ

IDE-I

TOTAL

Data Analytics and Machine Learning Tools for Engineers

Sl. No.

Subject Code

SEMESTER V

ME3101

Course Name	Data Analytics and Machine Learning Tools for Engineers
Course Number	ME 3101
L-T-P-C	1-2-1-3.5
Pre-requisites	Mechanical Measurements and Instrumentation
Semester	Fifth
Learning Mode	Lecture and Practical
Course objectives
Complies with PLO 4. 1. To expose students to the implementation of data analysis strategies and tools used therein 2. To expose students to the basics of modern machine learning tools for mechanical engineering applications
Course Content
Data Analytics: Data: Vectors and Arrays, managing data, Statistical Visualization of data, Evaluating Data: Central Tendency, Measure of dispersion Distributions: Normal (Gaussian and Poisson) Distribution, Exponential Distribution, Weibull Distribution, Chi-square, Distribution Fitting, Confidence interval Random Variates: Pseudorandom, Uniform and Normal, Quasi-Random Sequence Halton Regression: Linear regression models, Fitting linear models to data, Evaluating the fit Optimization tools: Specifying the objective function, specifying constraints, selecting optimization methodology, evaluating results, global optimization tools Analysis of experimental data: quality of measurement, types of errors, error propagation Machine Learning: Fundamentals of Machine Learning, Supervised learning techniques, Overfitting/Confronting overfitting, Classification and Regression, Neural Networks, Training of Multi-Layer Neural Network, Neural Network and Classifications, Deep learning, Convolutional Neural Network, Introduction to unsupervised learning techniques, K-means clustering, K-nearest neighbours, Case-Studies showing use of Machine Learning in Mechanical Engineering such as Acoustics, CFD, Robotics, Metrology
Learning Outcome By the end of this course, mechanical engineering undergraduate students should be able to: · Appreciate the use of data analytics and machine learning tools to solve mechanical engineering problems wherein analytical solutions are difficult to obtain · Appreciate what is involved in developing models for a given data set · Understand a wide variety of learning algorithms · Understand how to evaluate models generated from data Apply the models learnt to relevant mechanical engineering problems, optimize the models learned, and report on the expected accuracy that can be achieved by applying the models
Assessment Method Mid Semester Examination, End Semester examination, Class test & quiz, Assignment, Class Performance and Viva
Texts and References
1. Introduction to the Theory of Statistics by A.M. Mood, F.A. Graybill and D.C. Boes, 2017 2. Statistics and Machine Learning Toolbox, User Guide, MATLAB R2021b 3. MATLAB Deep Learning with Machine Learning, Neural Network and Artificial Intelligence by Phil Klim, Apress 2017 4. Deep Learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville, MIT Press, 2016 5. Christopher Bishop. Pattern Recognition and Machine Learning. ISBN 0387310738, 2010.

3.5

ME3102

Design of Machine Elements

Course Name					Design of Machine Elements
Course Number					ME3102
L-T-P-C					3- 0- 3- 4.5
Pre-requisites					Mechanics of Solids
Semester					Fifth
Learning Mode					Lectures and Practical
Course objectives
				Complies with PLOs 1 and 4. 1. To develop the basic understanding of machine design criteria 2. To develop analytical skills to deal with various types of machine element design problems. 3. Laboratory sessions are designed for developing software and experimental skills
Course Content
			Limits, fits, and tolerances, Principles of mechanical design; Factor of safety, strength, rigidity, fracture, wear, and material considerations; Stress concentrations; Design for fatigue; Design of bolted, and welded joints; Shafts; Keys; Clutches; Brakes; Springs; Gears; bearing and lubrication.
List of experiments/Laboratory Session
		1. Machine Drawing: Assembly and Part drawings, Solid modeling etc. 2. Design of gear box and sub-components (shafts, bearings, bolts, housing, coupling, etc.); 3. IC engine components; Screw jack; Shaft coupling; 4. Computer Aided Design 5. Two Tribology experiments
Learning Outcomes		1. Develop analytical and computer skills to design a simple engineering element 2. Understand the static and dynamic failure principles of solid and apply them in engineering element design
Assessment Method		Mid Semester Examination, End Semester examination, Class test & quiz, Assignment, Class Performance and Viva, Practical Exam
Texts and References
	1. J. E. Shigley, Mechanical Engineering Design, McGraw Hill, 1989. 2. Design Data, PSG Tech, Coimbatore, 1995 3. M. F. Spotts, Design of Machine Elements, 6th ed., Prentice Hall, 1985 4. A. H. Burr and J. B. Cheatham, Mechanical Analysis and Design, 2nd ed., Prentice Hall,1997. 5. Machine Drawing by N D Bhatt

4.5

ME3103

Manufacturing Technology- I

Course Name					Manufacturing Technology - I
Course Number					ME3103
L-T-P-C					3-0-2-4
Pre-requisites					Nil
Semester					Fifth
Learning Mode					Lectures & Practical
Course Learning objectives
				Complies with PLOs 3 and 4. This course aims to impart (a) the fundamental aspects of casting, welding, forming processes and powder metallurgy (b) to train the students with the analytical, practical, and problem-solving skills related to above manufacturing processes.
Course Content
			Module 1: Foundry Moulding materials and their requirements: types, composition and properties of molding sand, sand testing; Patterns: types of patterns, pattern allowances; Casting processes: sand casting, shell moulding, sodium silicate moulding, no bake moulding, gravity die, pressure die casting, investment casting, centrifugal casting, continuous casting, thin roll casting, plaster moulding, ceramic shell moulding; Solidification of casting: nucleation, grain growth, flow properties of molten metal, mechanism of heat transfer, phase change, solidification of binary alloy, directional and progressive solidification; Gating and risering systems: casting terminology, design of flask, sprue, runner and gating system, type of gate, time of solidification, chill and chaplet, CFR; Casting defects and their remedies. Module 2: Joining processes Physics, principle of operation and process parameters: Fusion welding (MMAW, MIG, TIG, SAW, power characteristics, seam, spot, projection, electroslag, Thermit and gas welding), Solid-state welding (adhesive, diffusion, friction, ultrasonic and explosive welding), Solid-liquid state welding (brazing and soldering), Unconventional welding (EBW, LBW etc.); Relative advantages and limitations of joining processes; Welding defects, inspection and testing. Module 3: Fundamentals of metal forming Introduction to plastic deformation of materials and related properties; various bulk deformation processes (forging, drawing, extrusion, rolling, swaging); load analysis of various bulk deformation processes by slab method; forming defects; sheet metal working (blanking & punching, bending, deep drawing, spinning, load analysis); Module 4: Powder metallurgy Basic principles, powder properties and production, blending and mixing, compaction, sintering, post-sintering treatment, shape factor and aspect ratio, advantages and limitations of the process, applications.
List of experiments
		1. Foundry: Testing of Moulding sand and Core sand, Preparation of one casting (Aluminum or cast iron), Testing’s (Destructive and Non-destructive) 2. Joining: Tungsten inert gas welding, Metal Inert Gas welding, and Friction stir welding, Determination of weld thermal cycle, cooling rate, Mechanical and Microstructural characterization of welds 3. Metal Forming: Estimation of force in Deep drawing, Extrusion, Open die forging 4. Powder Metallurgy: Metal powders preparation, Evaluate Green Density as well as Strength Characteristics (hardness) of Cold-compacted and sintered (Conventional) powder, Data Analysis, Destructive and Non-destructive tests
Learning Outcomes		1. The main objective of the course is to make the student familiar with the importance of manufacturing sciences in the day-to-day life, and to study the basic manufacturing processes like casting, metal forming, welding, and powder metallurgy. 2. To trained the graduates with the analytical, practical and problem-solving skills related to the conventional manufacturing processes.
Assessment Method		Mid Semester Examination, End Semester examination, Class test & quiz, Assignment, Class Performance and Viva
Texts and References
	Textbook: 1. Fundamental of Modem Manufacturing: Materials, Processes and Systems, Mikell P.Groover 2. Fundamental of Manufacturing, G. K. Lal & S. K. Choudhury 3. Materials &Processes in Manufacturing, E. P. DeGarmo, J. T. Black and Kohser 4. Manufacturing Engineering &Technology, S. Kalpakjian, S.R. Schmid

ME3104

Engineering Software Laboratory

Course Name			Engineering Software Laboratory
Course Number			ME3104
L-T-P-C			1-0-3-2.5
Pre-requisites			Nil
Semester			Fifth
Learning Mode			Lectures and Practical
Course Learning Objectives: Complies with PLOs 1-4. Exposure to industrial software used in Mechanical Engineering practices.
Course Content
		CAD: 2D and 3D geometric transformation, Curves and surfaces in CAD FEM: Solid model creation, different types of elements, chunking of model, meshing, mesh quality, different kinds of analysis: static, dynamic, transient, thermal, electromagnetic, acoustics, sub-structuring and condensation, Error and convergence. CFD: Different types of CFD techniques, various stages of CFD techniques (i) preprocessor: governing equations, boundary conditions, grid generation, different discretization techniques (ii) processor: solution schemes, different solvers (iii) post-processing: analysis of results, validation, grid independent studies etc. Developing codes using commercial software for solving few problems of laminar and turbulent flow with heat transfer applications. Engineering softwares related to CAD/CAM, FEM, CFD, with both GUI and script like languages, are to be used for laboratory assignments.
Learning outcomes		At the end of the course, students will be able to use the industrial software for simulating industrial and research problems related to solid and fluid mechanics. A mature understanding of various numerical techniques and their advantages and disadvantages will develop with respect to the software used in the class.
Assessment Method		Class test & quiz, Assignment (hands-on exercises using software), Class Performance and Viva, Practical Exam
Texts and References
	Textbook: 1. J. N. Reddy, “An Introduction to Finite Element Methods”, 3rd Ed., Tata McGraw-Hill, 2005. 2. D. F. Rogers and J. A. Adams, “Mathematical Elements for Computer Graphics”, McGraw-Hill, 1990 3. M. Groover and E. Zimmers, “CAD/CAM: Computer-Aided Design and Manufacturing”, Pearson Education, 2009. 4. J. D. Anderson, “Computational Fluid Dynamics”, McGraw-Hill Inc. (1995).

2.5

ME3105

Numerical Methods for Engineers

Course Name	Numerical Methods for Engineers
Course Number	ME3105
L-T-P-C	3-0-0-3
Pre-requisites	Nil
Semester	Fifth
Learning Mode	Lectures
Course objectives
Complies with PLOs 1-4. 1. To expose students to a range of topics related to solving mechanical engineering problems using computational techniques. 2. To expose students to the basics of numerical methods for solving governing equations related to engineering problems. 3. To utilize software tools for solving numerical problems related to this course
Course Content
1. Introduction & Approximation: Motivation and Application, Accuracy and precision; Truncation and round-off errors; Binary Number System; Error propagation 2. Linear Systems and Equations: Direct Methods Matrix representation; Cramer’s rule; Gauss Elimination; Matrix Inversion; LU Decomposition; 3. Linear Systems and Equations: Indirect Methods Iterative Methods; Relaxation Methods; Eigen Values 4. Algebraic Equations: Introduction to Algebraic Equations, Bracketing methods: Bisection, Reguli-Falsi; Algebraic Equations: Open Methods, Secant; Fixed point iteration; Newton-Raphson; Multivariate Newton’s method 5. Numerical Differentiation: Numerical differentiation; error analysis; higher order formulae 6. Numerical Integration: Trapezoidal rules; Simpson’s rules; Gauss Quadrature 7. Regression: Linear regression; Least squares; Total Least Squares 8. Interpolation and Curve Fitting: Interpolation; Newton’s Difference Formulae; Cubic Splines 9. ODEs: Initial Value Problems: Introduction to ODE-IVP, Euler’s methods; Runge-Kutta methods; Predictor-corrector methods. 10. ODE-IVP (Part-2) Extension to multi-variable systems; Adaptive step size; Stiff ODEs 11. ODEs: Boundary Value Problems: Shooting method; Finite differences; Over/Under Relaxation (SOR)
Learning Outcomes: By the end of this course, mechanical engineering undergraduate students should be able to: · Understand how to apply numerical methods to solve problems related to mechanical engineering using software’s. · Solve ordinary differential equations (ODEs) and partial differential equations (PDEs) using numerical methods. · Solve problems and write programs related to engineering problems with respect to mechanical engineering. · Find roots of equations
Assessment Method Mid Semester Examination, End Semester examination, Class test & quiz, Assignment, Class Performance and Viva
Texts and References
1. Numerical Methods in Engineering: M. Salvadori. 2. Applied Numerical Methods: B. Carnahan. 3. Applied Numerical Analysis: C.F. Gerald and P.O. Wheatley. 4. Numerical Mathematics & Computing: W. Cheney and D. Kincaid. 5. Applied Partial Differential Equations: Paul DuChateau and David Zachmann. 6. Partial Differential Equations for Scientists and Engineers: Stanley J. Farlow. 7. Numerical Methods for Partial Differential Equations: William F. Ames. 8. Numerical Methods for Elliptic and Parabolic Partial Differential Equations: John R Levison, Peter, Knabner, Lutz Angermann. 9. Numerical Methods for Engineers by Steven Chapra, and Raymond Canale.

XX31PQ

IDE-II

TOTAL

20.5

Applied Thermodynamics and Turbomachinery

Sl. No.

Subject Code

SEMESTER VI

ME3201

Course Name					Applied Thermodynamics and Turbomachinery
Course Number					ME3201
L-T-P-C					3- 1- 2- 5
Pre-requisites					Thermodynamics and Fluid Mechanics, or equivalent
Semester					Sixth
Learning Mode					Lectures and practical
Course Learning objectives
				Complies with PLOs 2 and 4. 1. To develop a good understanding of the various power and refrigeration cycles, 2. To understand basic fundamentals of turbomachinery and their working principles and thermodynamic design 3. To develop knowledge on designing different components of power and refrigeration cycles
Course Content
			Vapour power cycles: Rankine cycle, reheat cycle, regenerative cycle, cogeneration; Steam turbine: impulse and reaction stage, degree of reaction, velocity triangle, velocity and pressure compounding, efficiencies, Steam nozzles. Refrigeration cycles: Properties of Refrigerants, Carnot refrigeration cycle, vapor compression cycle, Psychrometry. Gas power cycles: Gas turbine cycle, intercooling, reheating, regeneration, closed cycles, optimal performance of various cycles, combined gas and steam cycles; Axial-flow gas turbine; Jet propulsion: turbojet, turbofan. I.C. Engines: Classification - SI, CI, two-stroke, four-stroke etc., operating characteristics - mean effective pressure, torque and power, efficiencies, specific fuel consumption etc., air standard cycles - Otto, Diesel and dual, real air-fuel engine cycles, combustion in S.I. and C.I. engines, Air and fuel injection system, engine emissions. Compressors: Reciprocating Air Compressors, Centrifugal and Axial-flow compressors. Fluid Machines: Pelton-wheel, Francis and Kaplan turbines, centrifugal and reciprocating pumps.

List of experiments
		1. Impact of jet 2. Performance of Pelton turbine 3. Performance of Axial Flow turbine 4. Performance of Francis turbine 5. Performance evaluation of centrifugal pump 6. Performance evaluation of reciprocating pump 7. Refrigeration test rig 8. Air conditioning test rig 9. Performance of 4-stroke petrol & diesel engine 10. Exhaust gas analyzer
Learning Outcomes		1. Students will be able to think critically for solving relevant practical problems 2. Students will develop analytical skills for designing different components of gas and refrigerant cycles 3. Students will be able to come up with innovative ideas on applications of existing thermodynamics cycles
Assessment Method		Mid Semester Examination, End Semester examination, Class test & quiz, Assignment, Class Performance and Viva, Practical Exam
Texts and References
	Textbook: 1. M MEl Wakil, Power Plant Technology, McGraw Hill Education, 1e, 2017. 2. P K Nag, Powerplant Engineering, Tata McGraw Hill, 4e, 2017. 3. H I H Saravanamuttoo, G F C Rogers and H. Cohen, Gas Turbine Theory 7e, Pearson, 2019 4. W WPulkrabek, Engineering Fundamentals of the Internal Combustion Engine, PHI, 2002. 5. T. D. Eastop and A. McConkey, 2009, Applied Thermodynamics for Engineering Technologists, 5^th Ed. References: 1. G. F.C. Rogers and Y R Mayhew, 2009, Engineering Thermodynamics Work and Heat Transfer, 4^th Ed., Pearson Education. 2. M J Moran and H N Shapiro, Fundamentals of Engineering Thermodynamics 6e, John Wiley, 2007. 3. Arora C P, Refrigeration and Air Conditioning, McGraw Hill, 4e, 2021 4. C R Fergusan and A T Kirkpatrick, Internal Combustion Engines: Applied Thermosciences, 3e, John Wiley & Sons, 2016.

ME3202

System Dynamics and Control

Course Name				System Dynamics and Control
Course Number				ME3202
L-T-P-C				3-1-2-5
Pre-requisites				Dynamics (ME 207)
Semester				Six
Learning Mode				Lectures and Practical
Course Learning Objectives: Complies with PLOs 1 and 4. 1. The objective of this course is to introduce students to the theory and techniques for system dynamics and control so as to ensure the system design achieves desirable properties (e.g., stability, performance). 2. The course will introduce students to mathematical modeling of linear time invariant dynamic systems. In particular, the course will cover multi-degree of freedom systems with multiple components. The response of these systems to inputs and initial conditions will be analyzed. 3. Systems obtained as interconnections (e.g., feedback) of two or more other systems will be covered. The course will also introduce the students to the concepts of stability. Various techniques for determination of stability will be covered. 4. Techniques of controller design are also covered in this course. The course comprises complementary laboratory and tutorial sessions.
Course Content
			Fundamental of System- zero, first and second order system, application to free vibration, Frequency and time domain response. Transfer function- application to SDOF forced vibration, whirling of rotating shaft and critical speeds of shafts, vibration isolation, Transfer functions of some standard motion sensor like accelerometer, seismometer and velocity pick up. Feedback System- Block diagram and signal flow representation, state space model. Introduction to PID controller, Application to common control system. Stability and analysis of Dynamical System- Routh-Hurwitz stability criterion, relative stability, Root-locus method, Bode diagrams, Nyquist stability criterion, PI, PD, and PID controllers; Lead, lag, and lag-lead compensators, Application to common engineering problems. Introduction to Passive two and multi-DOF system- normal mode vibration, coordinate coupling, forced harmonic vibration, vibration absorber, flexibility matrix, stiffness matrix, reciprocity theorem, eigenvalues and eigenvectors, orthogonal properties of eigenvectors, modal matrix, Normal mode summation. Introduction to State Space Control: Controllability, observability and design.
List of experiments
		(1) Cantilever Beam damping estimation (2) Cantilever Beam system identification (3) Air Track mass spring vibratory system (4) Matlab primer (5) Dynamics and Control of magnetic levitation system (6) System Identification of Black box (7) Control of servomotor (8) Control of inverted pendulum (9) NI data acquisition via a few basic sensors like a potentiometer, optical encoder, and strain gauge (10) Matlab control toolbox and simulink (11) Programmable Logic Controller Ladder Logic
Learning Outcomes		After completing this course, the students will be able to 1. develop mathematical models of single and multi degree of freedom dynamic systems, 2. determine stability of a given linear time-invariant dynamical system, 3. design feedback PID control systems, 4. appreciate practical aspects of dynamics and control via laboratory experiments on sensors and instrumentation.
Assessment Method		Mid Semester Examination, End Semester examination, Class test & quiz, Assignment, Class Performance and Viva, Practical Exam
Texts and References
	1. W. T. Thomsom and Dahleh, M. D., Theory of Vibration with Applications, 5th ed., Pearson Education, 1999. 2. Doebelin E.O., Measurement systems- Applications and Design, 4e, Tata McGraw-Hill, 1990 3. K Ogata, Modern Control Engineering, 4th ed, Pearson Education Asia, 2002. 4. B C Kuo and F. Golnaraghi, Automatic Control Systems, 8th ed, John Wiley (students ed.), 2002. 5. M Gopal, Control Systems: Principles and Design, 2nd ed, TMH, 2002. 6. M Gopal, Modern Control System Theory, 2nd ed., New Age International, 1993. 7. R. C. Dorf and R. H. Bishop, Modern Control Systems, 8th ed., Addison Wesley, 1998. 8. P. Belanger, Control Engineering: Amodern approach, Saunders College Publishing, 1995.

ME3203

Manufacturing Technology -II

Course Name				Manufacturing Technology - II
Course Number				ME3203
L-T-P-C				3-0-3-4.5
Pre-requisites				Nil
Semester				Sixth
Learning Mode				Lectures and Practical
Course Learning objectives Complies with PLOs 3 and 4. 1. Introduce the fundamental science and engineering of conventional and non-conventional machining processes. 2. Introduce the standard testing procedures to evaluate the machining performance.
Course Content
			Module-I: Fundamentals of metal cutting Geometry of single point cutting tool (ORS, ASA etc.); orthogonal cutting; mechanism of chip formation; Analytical and experimental determination of cutting forces (Merchant’s circle diagram); cutting temperature (causes, effect, assessment and control); machinability; tool materials; failure of cutting tools and tool life; economics of metal cutting Module-II: Machine tools Generatrix and directrix; classification of machine tools; setting and operations on machines: lathe, shaper, planer, milling, drilling, broaching, slotting, grinding, gear cutting machines; mechanism: thread cutting, pawl and ratchet wheel, quick return, indexing etc.; Finishing: honing, lapping; CNC machine tools Module-III: Tooling Principle of location and clamping; principles of design of jigs and fixtures Module-IV: Unconventional machining USM, AJM, AWJM, ECM, EDM, LBM, EBM: principle of operation, process parameters, material removal rate, advantages and limitations. Module-V: Manufacturing with plastic materials Properties of plastics; plastic materials; processing technology: extrusion, injection moulding, blow moulding, thermoforming, etc, 3D printing of polymers and plastic materials

List of experiments
		Fabrication of single point cutting tool, Resharpening of drill Bit, Fabrication of helical gear, Experimental determination of cutting forces in turning, with or without cutting fluid, Experimental determination of cutting temperatures in turning with or without cutting fluid, CAD/CAM – Creo Manufacturing Module/CNC milling, Effect of USM parameters on Material removal rate(MRR), Surface roughness (SR) and Dimensional Accuracy (Taper, overcut), Effect of EDM parameters on Material removal rate(MRR), Surface roughness (SR) and Dimensional Accuracy (Taper, overcut), Experimentation on WEDM/Surface grinding , 3D printing.
Learning Outcomes		1. Students will be able to understand the fundamental reason for the choice of machining processes for making various product 2. Students will be able to choose the appropriate machining process, operation for building engineering components economically. 3. Students will be able to characterize the machining performance of materials 4. Student will be able to choose the appropriate machine tool do get a job done.
Assessment Method		Mid Semester Examination, End Semester examination, Class test & quiz, Assignment, Class Performance and Viva, Practical Exam
Texts and References
	Textbook: 1. M. C. Shaw, Metal Cutting, Tata McGraw Hill, New Delhi, 2004. 2. S. Kalpakjain, S. R. Schmid, Manufacturing Processes for Engineering Materials, fifth edition, Pearson. 3. A. Ghosh and A. K. Malik, Manufacturing Science, East West Press, 2010. 4. P.N Rao, Manufacturing Technology, 4e, volume 1, McGraw Hill Education. References: 1. G. Boothroyd and W. A. Knight, Fundamentals of Machining and Machine Tools, CRC-Taylor and Francis, 2006.

4.5

ME3204

Industrial Engineering and Operations Research

Course Name
Course Number
L-T-P-C
Pre-requisites
Semester
Learning Mode
Course Learning Objectives Complies with PLO 4. The objectives are to produce graduates who: Contribute to the success of companies through effective problem solving. Design, develop, implement, and improve integrated systems that include people, materials, information, equipment, and environments. 1. To impart knowledge in concept and tools of OR 2. To understand mathematical models used in Operations Research 3. To apply these techniques constructively to make effective business decisions
		Introduction: history, method, Organisation: Theory, Principle, structure Product Design and Development: Principles of product design, tolerance design; Quality and cost considerations; Product life cycle; Standardization, simplification, diversification Engineering Economy and Costing: Elementary cost accounting and methods of depreciation; Break-even analysis; elasticity of demand, break even analysis. Job evaluation: methods, wage payments plan, incentive scheme Production planning and control: Forecasting techniques – causal and time series models, moving average, exponential smoothing, trend and seasonality;Aggregate production planning;Master production scheduling; MRP, MRP-II, JIT, CIM and ERP; Routing, scheduling and priority dispatching; Push and pull production systems,concepts of Lean and JIT manufacturing systems; Inventory – functions, costs, classifications, deterministic inventory models- Objective, type (ABC and VED analysis), EOQ and EPQ (case study), quantity discount; Perpetual and periodic inventory control systems Work System Design: Taylor’s scientific management, Gilbreths’s contributions; Productivity – concepts and measurements; Method study, Micro-motion study, Principles of motion economy; Work measurement – cycle time, learning curve, time study, Work sampling, charting technique, PMTS; Ergonomics- Objective, History, system components, Type (physical, cognitive, work environment, operational safety health).; Job evaluation and merit rating. Facility Design: Facility location factors and evaluation of alternate locations; Types of plant layout and their evaluation, layout planning and design, line balancing, Chart and diagram: process analysis, operation chart, process chart, flow diagram, activity chart, Assembly line balancing; Reliability and Maintenance: Reliability, availability and maintainability; Distribution of failure and repair times; Determination of MTBF and MTTR, Reliability models; Determination of system reliability; Preventive and predictive maintenance and replacement, Total productive maintenance. Quality engineering: Quality objectives, quality dimension, Quality control – Quality Assurance Quality costs, Quality loss function, Quality gurus and their philosophies, control charts for variables and attributes, Process capability studies, Six sigma; Total quality management; Quality assurance and certification - ISO 9000, ISO14000, SQC and SPC Operation Research: Introduction, Linear Programming: Graphical, Simplex, Dual Simplex, Sensitivity analysis, Transportation, Assignment, Integer Programming: Branch and Bound technique, Network Model: PERT and CPM, Spanning Tree (Prism and Kruskal algorithm), Markovian queuing models
Learning Outcomes		1. An ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics. 2. Ability to design, develop, implement, and improve integrated systems that include people, materials, information, equipment and energy. 3. An ability to function effectively on a team whose members together provide leadership, create a collaborative and inclusive environment, establish goals, plan tasks, and meet objectives 4. An ability to apply engineering design to produce solutions that meet specified needs with consideration of public health, safety, and welfare, as well as global, cultural, social, environmental, and economic factors. 5. Identify and develop operational research models from the verbal description of the real system 6. Understand the mathematical tools that are needed to solve optimisation problems. 7. Use mathematical software to solve the proposed models. 8. Develop a report that describes the model and the solving technique, analyze the results and propose recommendations in language understandable to the decision-making processes in Management Engineering.
Assessment method		Mid Semester Examination, End Semester examination, Class test & quiz, Assignment, Class Performance and Viva
Texts and References
	Textbook: 1. S L Narasimhan, D W McLeavey, P J Billington, Production, Planning and Inventory Control, Prentice Hall, New Edition 2. N V S Raju, Industrial Engineering and Management, CENAGE , New Edition 3. A Muhlemann, J Oakland and K Lockyer, Productions and Operations Management, Macmillan, New Edition 4. H A Taha, Operations Research - An Introduction, Prentice Hall of India, New Edition References: 1. J K Sharma, Operations Research, Macmillan, New Edition 2. O. P Khana, Industrial Engineering, Dhanpat Rai, New Edition 3. J L Riggs, Production Systems: Planning, Analysis and Control, Wiley, New Edition

ME3205

Technical Writing and Presentations

Course Name	Technical Writing and Presentations
Course Number	ME3205
L-T-P-C	0-0-4-2
Pre-requisites	Nil
Semester	Sixth
Learning Mode	Practical
Course objectives
Complies with PLO 4. 1. To train students for technical presentation which includes making PPT slides and verbal communication during presentations. 2. To train students for technical writing which includes writing an abstract, extended abstracts, and full paper.
Course Content
Module 1: Technical Writing Writing an abstract o Standard formats and templates o Writing effective titles Writing an extended abstract o Standard formats and templates o Writing effective titles, abstracts, introductions, and conclusions o Organizing content with headings and subheadings o Referencing and citation standards o Writing drafts o Techniques for clear and concise writing o Avoiding common pitfalls in technical writing o Editing for grammar, style, and accuracy Module 2: Technical Presentations Preparing for Technical Presentations o Audience analysis for presentations o Structuring a technical presentation o Designing effective presentation slides Presentation Delivery o Public speaking skills for technical presentations o Handling questions and feedback o Strategies for engaging the audience Module 3: Technical Writing on a specialized scientific Topic o Students select a specific topic write abstract and further extended abstract on the same topic. o Abstract and extended abstracts are evaluated and students are provided with comments and suggestions for improvement of the write-up. Module 4: Technical presentation on a specialized scientific Topic o Students prepare a presentation on a specialized topic and present in the class. o Based on the presentation, students are evaluated and advised for improving in slide preparation as well as delivery.
Learning Outcomes: By the end of this course, the student should be able to: Understand the principles of technical writing and its various forms. Develop and organize technical documents effectively. Master the use of visuals and data in technical communication. Create professional presentations tailored to technical content. Present technical information clearly and confidently to diverse audiences. Review and edit technical documents for clarity, coherence, and correctness
Assessment Method Ongoing Evaluation for each section through the semester: Abstract and Extended Abstract; and Technical Presentations
Texts and References
Books: · "Technical Communication" by Mike Markel and Stuart A. Selber · "The Elements of Technical Writing" by Gary Blake and Robert W. Bly · "Writing and Speaking in the Technology Professions: A Practical Guide" by David F. Beer and David A. McMurrey Online Resources: · Purdue OWL: Technical Writing · IEEE Author Center · Society for Technical Communication (STC) website

TOTAL

20.5

	Sl. No.	Subject Code	SEMESTER VII	L	T	P	C
	1.	ME41XX	Departmental Elective-I	3	0	0	3
	2.	ME41XX	Departmental Elective- II	3	0	0	3
	3.	XX41PQ	IDE-III	3	0	0	3
	4.	HS41PQ	HSS Elective-II	3	0	0	3
	5.	ME4198	Summer Internship*	0	0	12	3
	6.	ME4199	Project – I	0	0	12	6
	TOTAL			12	0	24	21
* For specific cases of internship after 6^th Semester, the performance evaluation would be made on joining the VII^th Semester and graded accordingly in the VII^th Semester: Note: a) (i) Summer internship () period of at least 60 days’ (8 weeks) duration begins in the intervening vacation between semester VI and VII that may be done in industry / R&D / Academic Institutions including IIT Patna. The evaluation would comprise combined grading based on host supervisor evaluation, project internship report after plagiarism check and seminar presentation at the Department (DAPC to coordinate)* with equal weightage of each of the three components stated herein. a) (ii) Further, on return from internship, students will be evaluated for internship work through combined grading based on host supervisor evaluation, project internship report after plagiarism check, and presentation evaluation by the parent department with equal weightage of each component. b) (i) In the VII^th semester, students can opt for a semester long internship on recommendation of the DAPC and approval of the Competent Authority. b) (ii) On approval of semester long internship, at the maximum two courses (properly mapped/aligned syllabus) at par with institute electives may be opted from NPTEL and / or SWAYAM and the other two more should be done at the institute through course overloading in any other semester (either before or after the internship) and/or during following summer semester. b) (iii) The candidates opting two courses from NPTEL and / or SWAYAM would be required to appear in the examination at the Institute as scheduled in the Academic Calendar.

Tribology and Surface Engineering

Department Electives - I

Sl. No.

Subject Code

Department Electives - I

ME4101

Course Number	ME4101
Course Credit	L-T-P-C : 3-0-0-3
Course Title	Tribology and Surface Engineering
Learning Mode	Lectures
Learning Objectives	Complies with PLOs 1 and 4 After attending the class, the students will be able to understand 1. The primary cause of friction and wear in various tribological contact 2. The importance of lubrication and regimes of lubrication in engineering surfaces 3. The use of surface treatment and suitable coatings for the improvement of tribological characteristic 4. The need for different characterization techniques to evaluate the performance of engineering surfaces.
Course Description	This course is designed to understand theories of friction, wear, and lubrication, model basic tribological processes, and understand the influence of surface engineering on tribological contact. Prerequisite: NIL
Course Outline	Introduction – Significance of tribology, history of tribology, Economic Benefits, Interdisciplinary Approach, Need of surface engineering. Surface characteristics – Topography and microstructure of surfaces, Origin of roughness, Measurement of surface characteristics, Roughness parameters, Mechanics of solid surfaces. Friction – Laws of friction, Adhesion theory, Abrasion theory, Stick-slip motion, Rolling friction, Tribological tests. Wear – Adhesive Wear, Abrasive Wear, Delamination Wear, Fretting Wear, Erosive Wear, Corrosive Wear, Oxidative Wear, Wear Mechanism Maps. Lubrication and Lubricants – Boundary Lubrication, Mixed Lubrication, Elasto-Hydrodynamic Lubrication, Hydrodynamic Lubrication, Types and Properties of Lubricants, Lubricants Additives. Applications/ Case study – Sliding contacts, Rolling contacts, Bearing design, Selection of surface treatment/ soft or hard coatings/ surface textures
Learning Outcome	Develop an understanding of the characteristics of tribological contact of moving engineering components and ways to prevent failure or increase the life of such components.
Assessment Method	Assignments, Quiz, Mid-semester and End-semester exams
Suggested Readings: Text Books: [1] R.D. Arnell, P.B. Davies, J. Halling, T.L. Whomes, Tribology: principles and design applications, Macmillan Education Ltd, First edition 1991. [2] B. Bhushan, Principles and Applications of Tribology, John Wiley, second edition, 2013. [3] A. Cameron, Basic Lubrication Theory, E. Horwood, Halsted Press, 1976. [4] I. Hutchings, P. Shipway, Tribology: friction and wear of engineering materials, Butterworth-heinemann, 2nd Edition, 2017. [5] G. Stachowiak, A.W. Batchelor, Engineering tribology, Butterworth-heinemann, Fourth edition, 2013. [6] B. J. Hamrock, B. O. Jacobson, S. R. Schmid, Fundamentals of Machine Elements, McGraw-Hill Inc., 1998. [7] K. S. Edwards, R. B. McKee, Fundamentals of Mechanical Component Design, McGraw-Hill Inc., 1991.

ME4102

Basics of Computational Fluid Dynamics

Course Name	Basics of Computational Fluid Dynamics
Course Number	ME4102
L-T-P-C	3-0-0-3
Pre-requisites	Undergraduate Fluid Mechanics and Heat Transfer course
Learning Mode	Class room lecture
Course objectives
Complies with PLOs 2 and 4 · This course is designed to fulfil the basic concepts of computational fluid dynamics. The course first discusses the general background required for understanding the various numerical methods or discretization techniques involved in CFD. It is followed by a detailed understanding of the two of the popular discretization methods – Finite Difference Method (FDM) and Finite Volume Method (FVM).
Course Content
Concept of Computational Fluid Dynamics: Different techniques of solving fluid dynamics problems, their merits and demerits, governing equations of fluid dynamics and boundary conditions, classification of partial differential equations and their physical behavior, Navier-Stokes equations for Newtonian fluid flow, computational fluid dynamics (CFD) techniques, different steps in CFD techniques, criteria and essentialities of good CFD techniques. Finite Difference Method (FDM): Application of FDM to model problems, steady and unsteady problems, implicit and explicit approaches, errors and stability analysis, direct and iterative solvers. Finite Volume Method (FVM): FVM for diffusion, convection-diffusion problem, different discretization schemes, FVM for unsteady problems. SIMPLE family FVM for solving Navier-Stokes equation
Learning Outcomes: After attending this course, the following outcomes are expected: 1. Ability to classify the partial differential equations involved in fluid mechanics and heat flow and understanding of their physical behaviour. 2. Ability to write CFD codes for the various algorithms covered in this course.
Assessment Method · Quiz, mid and end semester examinations, Coding Assignments, Viva
Texts and References
Text Books: 1. J. D. Anderson, “Computational Fluid Dynamics”, McGraw-Hill Inc. (New Edition). 2. S. V. Patankar, “Numerical Heat Transfer and Fluid Flow”, Hemisphere Pub. (New Edition) 3. D. A. Anderson, J. C. Tannehill and R. H. Pletcher, “Computational Fluid Mechanics And Heat Transfer”, Hemisphere Pub. (New Edition) 4. M. Peric and J. H. Ferziger, “Computational Methods for Fluid Dynamics”, Springer (New Edition). 5. H. K. Versteeg and W. Malalaskera, “An Introduction to Computational Fluid Dynamics”, Dorling Kindersley (India) Pvt. Ltd. (New Edition). Reference Books: 1. C. Hirsch, “Numerical Computation of Internal and External Flows”, ButterworthHeinemann, (New Edition). 2. K. Muralidhar, and T. Sundarajan, “Computational Fluid Flow and Heat Transfer”, Narosa (New Edition) 3. A. Sharma, “Introduction to Computational Fluid Dynamics Development, Application and Analysis”, Ane Books, 1st edition 2016

ME4103

Industrial Automation

Course Name	Industrial Automation
Course Number	ME4103
L-T-P-C	3-0-0-3
Pre-requisites	Nil
Learning Mode	Class room lecture
Course objectives
Complies with PLOs 3 and 4 · To gain fundamental principles of industrial automation approaches. · To understand the various pneumatic, hydraulic actuators, valves, sensors. · To gain concept of pneumatic, hydraulic and electo-pneumatic/-hydraulic circuit design for different activities/operations. · To gain concepts of automatic transfer lines, assembly systems.
Course Content
Fundamental concepts and types of automation, Various automation strategies. Introduction to Pneumatics and Hydraulics, Electro-pneumatic, and Electro-hydraulic devices: Basic elements of Pneumatics/Hydraulics and Electro-pneumatic/-hydraulic systems, construction and working of pneumatic/hydraulic cylinders and actuators, their mounting and operations, Pneumatic and hydraulic valves for flow, pressure control, direction control valves, Solenoid valves, Gates, Feedback systems; Pneumatic and hydraulic element symbols. Circuit design of pneumatic/hydraulic, electro-pneumatic systems for various sequence of operations. Control circuits for various applications like clamping, releasing, counting, stopping, safety and similar operations. Flexible manufacturing systems: Automatic transfer, feeding, orientation devices. Various automatic transfer machines, Automated transfer lines with and without buffer storage, Automatic storage and retrieval systems, Group technology.
Learning Outcomes: By the end of this course, undergraduate students should be able to: · explain the working of various pneumatic and hydraulic components, · select the suitable devices for designing pneumatic and hydraulic systems required for automated operations, · design the pneumatic/hydraulic circuits and understand the working of such system, · understand the automation in manufacturing and assembly operations.
Assessment Method · Quiz, Assignments, Mid and End semester examinations
Texts and References
Text Books: [1] Groover, M. P., Automation, Production System & Computer Integrated Manufacturing, Pearson Education Asia (2004). [2] Majumdar, S. R., Pneumatic Systems, McGraw Hill (2005). Reference Books: [1] Nakra, B. C., Automatic Control, New Age International (2005). [2] Morriss, S. B., Automataed Manufacturing Systems, McGraw Hill (2006).

Department Electives - II

Sl. No.

Subject Code

Department Electives - II

ME4104

Vehicle Dynamics

Course Name	Vehicle Dynamics
Course Number	ME4104
L-T-P-C	3-0-0-3
Pre-requisites	Engineering Mechanics/Dynamics or equivalent course
Learning Mode	Class room lecture
Course objectives
Complies with PLOs 1 and 4 By the end of this course, undergraduate students should be able to: · Understand rigid body dynamics analysis of wheeled vehicle system. · Develop models for handling and stability of vehicle.
Course Content
1. Introduction to vehicle dynamics: Vehicle coordinate systems; loads on axles of a parked car and an accelerating car. Acceleration performance: Power-limited acceleration, traction-limited acceleration. 2. Tire models: Tire construction and terminology and mechanics of force generation; 3. Aerodynamic effects on a vehicle: Mechanics of airflow around the vehicle 4. Braking performance: Equations for braking for a vehicle with constant deceleration and deceleration with wind-resistance 5. Steering systems and cornering: Geometry of steering linkage, steering geometry error; steering system models 6. Suspension and ride: Suspension types—solid axle suspensions, independent suspensions; suspension geometry; roll center analysis; active suspension systems; 7. Vehicle rider excitation and comfort; 8. Roll-over: Quasi-static roll-over of rigid vehicle and suspended vehicle; transient roll-over, yaw-roll model, tripping, use of standards for design.
Learning Outcomes: · Mathematical modeling of the vehicle dynamic system with integrations of various subsystems · Understanding of the stability, rider comfort and rollover limits of the vehicle. · Use of simulation tools for developing the analytical model
Assessment Method · Quiz, mid and end semester examinations
Texts and References
Text Books: 1. T.D. Gillespie, “Fundamental of Vehicle Dynamics”, SAE Press (1995). 2. J.Y. Wong, “Theory of Ground Vehicles”, 4th Edition, John Wiley & Sons (2008). 3. Reza N. Jazar, “Vehicle Dynamics: Theory and Application”, 1st Edition, Springer (2008). 4. R. Rajamani, “Vehicle Dynamics and Control”, Springer (2006). 5. H. Baruh, Analytical Dynamics, McGraw-Hill, 1999. Reference Books: 1. G. Genta, “Motor Vehicle Dynamics”, World Scientific Pub. Co. Inc. (1997). 2. H.B. Pacejka, “Tyre and Vehicle Dynamics”, SAE International and Elsevier (2005). 3. Dean Karnopp, “Vehicle Stability”, Marcel Dekker (2004). 4. U. Kiencke and L. Nielsen, “Automotive Control System”, Springer-Verlag, Berlin. 5. M. Abe and W. Manning, “Vehicle Handling Dynamics: Theory and Application”, 1st Edition, Elsevier (2009).

ME4105

Mathematical Modelling of Computer Aided Design

Course Number	ME4105
Course Credit	L-T-P-C: 3-0-0-3
Course Title	Mathematical Modelling of Computer Aided Design
Learning Mode	Classroom mode
Learning Objectives	Complies with PLOs 1, 3 and 4 By the end of this course, students will be able to: 1. Understand the mathematical concepts underlying CAD. 2. Apply mathematical techniques to model geometric entities. 3. Develop algorithms for geometric modelling. 4. Analyze and solve geometric problems using numerical methods.
Course Description	Implement mathematical models in CAD software. This course explores the mathematical foundations and algorithms used in computer-aided design (CAD). Students will learn about various mathematical techniques and their applications in creating, analyzing, and manipulating geometric models. The course covers topics such as curves, surfaces, solid modelling, transformations, and numerical methods. Prerequisite: NIL
Course Outline	Introduction to Mathematical Modelling in CAD: Overview of CAD and its applications, Importance of mathematical modelling in CAD, Introduction to geometric modelling Coordinate Systems and Transformations: Cartesian and polar coordinate systems, Homogeneous coordinates, Affine transformations (translation, scaling, rotation), Composite transformations Curves in CAD: Parametric representation of curves, Polynomial curves, Bezier curves, B-splines and NURBS Surface Modelling: Parametric representation of surfaces, Bezier surfaces, B-spline surfaces, Surface-surface intersections Solid Modelling: Solid representation schemes (CSG, B-rep), Boolean operations on solids, Boundary representation (B-rep), Euler operators Geometric Interrogation: Curve and surface fitting, Intersection algorithms, Distance and angle calculations, Surface evaluation Numerical Methods in CAD: Numerical integration and differentiation, Root-finding algorithms (Newton-Raphson method), Numerical solutions of linear systems, Optimization techniques Advanced Topics in Curve and Surface Modelling: Subdivision surfaces, Implicit surfaces, Mesh generation and processing, Curve and surface smoothing Computer Graphics in CAD: Basics of computer graphics, Rasterization and rendering, Shading and lighting models, Visualization of geometric models
Learning Outcome	This course would enable the students to understand the mathematical concepts underlying CAD to apply mathematical techniques to model geometric entities and to develop algorithms for geometric modelling ·
Assessment Method	Mid Semester Examination, End Semester examination, Class test & quiz, Assignment, Mini Project
Text Books: [1] "Mathematical Elements for Computer Graphics" by David F. Rogers and J. Alan Adams [2] "Curves and Surfaces for Computer-Aided Geometric Design" by Gerald Farin [3] "Geometric Modeling" by Michael E. Mortenson [4] "Numerical Methods for Engineers" by Steven C. Chapra and Raymond P. Canale

ME4106

Energy Engineering

Course Number	ME4106
Course Credit	L-T-P-C: 3-0-0-3
Course Title	Energy Engineering
Pre-requisite	Thermodynamics
Learning Mode	Lectures
Learning Objectives	Complies with PLOs 2 and 4 The objective of this course is, · To impart the knowledge of various sources of conventional and nonconventional energy. · To impart the knowledge of working principle of different types of power plants and their conversion efficiency. · To develop skill in renewable and non-renewable energy technology. To design and analyze energy systems, considering sustainability and economic factors.
Course Description	This course is designed to provide the concepts of various energy sources, energy conversion principles, power plants.
Course Outline	Conventional Energy Sources: Hydel, Steam, Gas turbine, Diesel and Nuclear Power Plant, Layout, function of different components and types, Energy and Exergy analyses of power plants. Power plant Economics. Non-conventional or Renewable energy sources: Solar energy, application of solar energy, Wind, Ocean, Geothermal, Biomass Energies, Energy Conversion Principles and types. Energy and Exergy analyses of non-conventional/renewable energy conversion units. Carbon footprint.
Learning Outcome	Following learning outcomes are expected after going through this course. · Will be able to understand various sources of conventional and nonconventional energy. · Will be able to select appropriate and efficient power plant based on the availability of energy sources. · Will be able to design and analyse various energy conversion systems considering sustainability and economic factors.
Assessment Method	Mid Semester Examination (25%), End Semester examination (35%), Class test & quiz (30%), Assignment (10%)
Suggested Readings: 1. PK Nag, Power Plant Engineering, Tata McGraw Hill, 5th Ed. 2012. 2. M.M.El. Wakil, Power Plant Techniques, McGraw Hill, New York, 1985. Sukathme S.P., Solar Energy Principles of Thermal Collection and Storage, 2nd Ed., TMC New Delhi,1984. John R. Lamarsh and Anthony J. Baratta, "Introduction to Nuclear Engineering", Prentice Hall, 2001. Elmer E. Lewis, "Fundamentals of Nuclear Reactor Physics", Academic Press Inc., 2008. Houghton E.L., Carruthers, Aerodynamics for Engineering students, Butterworth-Hinemann Ltd., 2006.

Department Electives - III

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Subject Code

Department Electives - III

ME4201

Refrigeration and Cryogenics

Course Name	Finite Element Method
Course Number	ME4201
L-T-P-C	3-0-0-3
Pre-requisites	Elementary calculus and matrix algebra
Learning Mode	Class room lecture
Course objectives
Complies with PLOs 1 and 4 · To provide the concepts of the finite element method and its applications to a wide range of engineering problems.
Course Content
1. Basic Concepts: Introduction, weak formulations, weighted residual methods, linear and bilinear Forms, variational formulations, weighted residual, collocation, subdomain, least square and Galerkin’s method 2. One Dimensional Problems: Second-order differential equations in one dimension, Basis steps, discretization, element equations, linear and quadratic shape functions, assembly, local and global stiffness matrix and its properties, boundary conditions, penalty approach, multipoint constraints, applications to solid mechanics, heat and fluid mechanics problems, axisymmetric problems 3. Trusses, Beams and Frames: Plane truss, local and global coordinate systems, stress calculations, temperature effect on truss members, Euler Bernoulli beam element, Hermite cubic spline functions, frame element, solution of practical problems. 4. Eigen Value and Time dependent problems: Formulation, FEM models, semidiscrete FEM models, Time approximation schemes, Applications, problems. 5. Two Dimensional Problems: Single variables in 2-D, triangular and rectangular elements, constant strain triangle, isoparametric formulation, higher order elements, six node triangle, nine node quadrilateral, master elements, modelling considerations, numerical integration, approximations errors, convergence and accuracy computer implementation. 6. Scalar Field Problems: Torsion, heat transfer, heat transfer in thin fins, potential flow problems, axisymmetric problems, impositions of essential BCs 7. Elasticity and Viscous Incompressible flows Problems: Review of equations of elasticity, stress-strain and strain-displacement relations, plane stress and plane strain problems, velocity pressure formulation, LMM and PM model, examples
Learning Outcomes: By the end of this course, undergraduate students should be able to: Develop a stiffness/conductivity vector for a given partial differential equation. Apply engineering FEM principles to solve and evaluate primary variables such as displacement, temperature, velocity, voltage, etc and secondary variables stress and heat. · Analyze and design engineering problems using FEM-based methods.
Assessment Method · Quiz, Project, mid and end semester examinations
Texts and References
Text Books and Reference Books: 1. Reddy, J.N., “An Introduction to Finite Element Methods”, 3^rd Ed., Tata McGraw-Hill.20005 [Text Book] 2. Zienkiewicz, O. C. “The Finite Element Method, 3^rd Edition, Tata McGraw-Hill. 2002 3. Cook, K.D., Malkus, D.S. and Plesha, M.E., “Concept and Applications of Finite Element Analysis”, 3^th Ed., John Wiley and Sons. 1989 4. Rao, S.S., “The Finite Element Method in Engineering”, 4^th Ed., Elsevier Science. 2005 5. Reddy, J.N. and Gartling, D.K “The Finite Element Method in Heat Transfer and Fluid Dynamics”, 2^rd Ed., CRC Press. 2005

ME4202

Course Number	ME4202
Course Credit	L-T-P-C: 3-0-0-3
Course Title	Refrigeration and Cryogenics
Learning Mode	Lectures
Learning Objectives	Complies with PLOs 2 and 4 Students will be able to: · Comprehend the nomenclature of refrigerants, their physical, chemical, thermodynamic requirements and the environmental concerns, · analyse various types of refrigeration systems · design different components of vapour compression refrigeration system · understand the introductory knowledge of Cryogenic Engineering. · analyse the Liquefaction process, gas separation process, thermophysical and mechanical properties of materials at cryogenic temperature.
Course Description	This course is designed to impart the necessary knowledge of the processes and components involved in refrigeration and cryogenic systems.
Course Outline	*Refrigeration* Refrigerants: Classification and nomenclature, desirable properties of refrigerants, common refrigerants, environmental issues-Ozone depletion and global warming Refrigeration systems: Vapour compression, vapour absorption and air refrigeration system, Thermo- electric refrigeration, Cryogenics. Capacity control techniques: Hot gas by-pass scheme, Cylinder loading scheme, suction gas throttling scheme *Cryogenics* Introduction to Cryogenics and its applications Properties of cryogens: T-s diagram of a cryogenic fluid, Properties of cryogenic fluids: Argon, Nitrogen, Oxygen, Neon, Hydrogen (ortho/para), Helium (He³ and He⁴), Liquid He-I and He-II (superfluid He) and its applications. Gas Liquefaction Systems: Basics of refrigeration/Liquefaction, Production of low temperatures, Ideal thermodynamic temperature cycle, Various liquefaction cycles. J-T expansion of real gas, adiabatic expansion, Ideal thermodynamic cycle. Linde-Hampson system. Gas Separation, storage, transportation: Basics of gas separation, Ideal gas separation system, Principles of gas separation. Introduction to Cryocoolers: Cryocoolers classification and basics, Applications, Stirling cryocooler, Comparison of GM, Stirling and Pulse tube cryocooler. Introduction to Cryogenic Insulations and Vacuum Technology.
Learning Outcome	The course training will enable students to achieve the learning objectives: · Selection of an appropriate refrigerant for a given application taking into account the physical, chemical, and thermodynamic requirements and the environmental concerns · Analysis of various refrigeration and air conditioning systems, · do thermodynamic analysis of different liquefaction plants and choose a suitable method of liquefaction · display new contemporary methods and tools to carry out thermo-physical and mechanical investigations, analysis, and processing of refrigeration and cryogenic equipment.
Assessment Method	Mid Semester Examination, End Semester examination, Assignments, Quiz, and Seminar
Text books: 1. Arora C.P., 2005. Refrigeration and Air Conditioning, Tata McGraw-Hill Publishing Company Limited, New Delhi. 2. Thomas M. Flynn, “Cryogenic Engineering”, second edition, CRC press, New York (2005). Ref. Books: 3. Dossat R.J., 2008. Principles of Refrigeration, Pearson Education (Singapore) Pte. Ltd. 4. Stoecker W., 1982. Refrigeration and Air Conditioning, Tata McGraw-Hill Publishing Company Limited, New Delhi. 5. Ameen A., 2006. Refrigeration and Air Conditioning, Prentice Hall of India Private Limited, New Delhi. 6. Randall F. Barron, “Cryogenics Systems”, Second Edition, Oxford University Press, New York (1985). 7. G.M Walker. “Cryocooler- Part 1: Fundamentals” Plenum Press, New York (1983). 8. G.M Walker. “Cryocooler- Part 2” Plenum Press, New York (1983). 9. Mamata Mukhopadhyay, “Fundamentals of Cryogenic Engineering”, PHI Learning Pvt. Ltd, New Delhi (2010).

ME4203

Mechanics, Processing and failure of Composite Materials

Course Number	ME4203
Course Credit	L-T-P-Cr : 3-0-0-3
Course Title	Mechanics, Processing and failure of Composite Materials
Prerequisite:	Knowledge of solid mechanics or equivalent course
Learning Mode	Lectures
Learning Objectives	Complies with PLOs 1, 3 and 4 This course aims to: (1) to understand the manufacturing processes of reinforcement fibers and matrices for composites, (2) to understand the concept of tailored design philosophy (3) Explain the behavior of constituents in the composite materials (4) Develop the student’s skills in understanding the different manufacturing methods available for composite material.(5)Illuminate the knowledge and analysis skills in applying basic laws in mechanics to the composite materials.(6) use failure theories for multiaxial loading to determine the composite survivability.
Course Description	This course is designed to fulfil (1) Knowledge on classification of matrix, reinforcement and type of composite material (2) Mechanics of continuous fiber composite lamina, composite properties evaluation using micro mechanics, mechanics of laminate and hybrid laminate (3) Fabrication techniques (4) To gather knowledge on failure theories of composite laminate
Course Outline	Module 1: Introduction to Composites: Basic Definitions and Classification of Composites, Classification based on Matrix Material, Classification based on reinforcements, Advantages and Limitations Module 2: Basic constituent materials in Composites: Fibers/Reinforcement Materials, Matrix Materials, Fiber reinforced Polymer (FRP) Laminated composites, Lamina & Laminate Lay-up, Ply-orientation definition Module 3: Micromechanics: Rule of mixture, Properties of matrix and reinforcement material, Micro mechanics relationship, Determination of strength, stiffness, Mechanics of load-transfer, Prediction of elastic constants, environmental effect and hygro-thermal effect Module 4: Mechanics of Laminae: Behaviour of a Laminae , Stress-Strain relations for Anisotropic and Orthotropic cases, indicial notation and tensorial representations in Elasticity, Plane Stress (Isotropic and Orthotropic cases) Transformation relations Module 5: Mechanics of Laminated Composites: Kirchhoff’s Plate Theory, Classical Laminated Plate Theory, Stress-resultants, forces and moments, bending, buckling and vibration, environmental effect and hygro-thermal effect, Laminate Stiffness and ABD Matrices, Special Classification, Symmetric-Anti-symmetric- Non-symmetric laminates. Module 6: Strength and Failure theories: Maximum stress theory, Maximum Strain Theory , Tsai-Hill Theory , Tsai-Wu Theory, Comparison of Failure Theories Module 7: Manufacturing Processes: Hand Lay-up, Autoclave curing, Differential scanning calorimeter (DSC), Wet Lay-up and Spray-up, Vacuum bagging, Pressure bagging, Filament winding, Pultrusion, Resin Transfer Molding (RTM), Compression molding, Recycling of Composites, Hybrid Composite
Learning Outcome	Upon completion of this course the student will be able to: 1. Explain the mechanical behavior of layered composites compared to isotropic materials. 2. Apply constitutive equations of composite materials and understand mechanical behavior at micro and macro levels. Identify and explain the fundamental properties of composite materials; Determine stresses and strains relation in composites materials. 3. Identify and explain laminate conventions and stacking sequence 4. Identify and explain the fundamentals of the classical lamination theory (CLT); 5. Identify and explain the main manufacturing processes of composite products 6. Identify failure mode of composite material and hence take appropriate approach to design and fabricate composite for practical application
Assessment Method	Mid Semester Examination (30%), End Semester examination (50%), Class test & quiz (15%), Assignment (5%)
Texts Books 1. M.W. Hyer, Stress Analysis of Fiber Reinforced Composite Materials, DEStech Publications Inc, Update Edition 2008. 2. R.M. Jones, Mechanics of Composite Materials, 2nd edition, CRC Press, 2015 3. J N Reddy and A V Krishna Moorty, Composite Structures: Testing, Analysis and Design, Springer-Verlag Berlin and Heidelberg GmbH & Co. K, 1993 4. F.L. Matthews, G.A.O. Davies, D. Hitchings and C. Scouts, Finite Element Modeling of Composite Materials and Structures, Woodhead Publishing, 2000. Reference Books: 1. Kaw, Mechanics of Composite Materials, 2nd edition, CRC Press, 2006 2. M. Mukhopadhyay, Mechanics of Composite Materials and Structures, Universities Press, 2005 3. Gay and S. Hoa, Composite Materials: Design and Applications, 2nd edition, CRC Press, 2007 4. I.M. Daniel and O.Ishai, Engineering Mechanics of Composite Materials, 2nd edition, Oxford University Press, USA, 2005. 5. B.D. Agarwal and L.J. Broutman, Analysis and Performance of Fiber Composites, John Wiley and Sons, 2006. 6. R.F. Gibson, Principles of Composite Material Mechanics, 3rd edition, CRC Press, 2011.

Department Electives - IV

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Department Electives - IV

ME4204

Mechanical Characterization of Materials

Course Name	Mechanical Characterization of Materials
Course Number	ME4204
L-T-P-C	3-0-0-3
Pre-requisites	Solid Mechanics
Learning Mode	Class room lecture
Course objectives
Complies with PLOs 1, 3 and 4 · Impart a thorough understanding of the mechanical behaviour of materials under various conditions. · Teach students how to interpret the results of mechanical tests. Apply this knowledge to solve real-world engineering problems.
Course Content
1. Introduction Fundamentals of elastic and plastic deformation, Role of dislocations, twinning, and slip in plastic deformation, Strengthening mechanisms in alloys, Influence of temperature, strain rate, and environment on plastic deformation, Application of mechanical properties in engineering design 2. Monotonic Tests Tensile, compression, shear, and torsion tests, Bend test and notch tensile test, Macro, micro, and nano hardness tests, Wear testing, Hydrogen embrittlement evaluation 3. Fatigue Low cycle fatigue, high cycle fatigue, and giga cycle fatigue, Concept of endurance limit, Basquin and Coffin-Manson laws, strain energy density laws for life prediction, Cyclic stress-strain curve analysis, Cyclic hardening/softening, Notch fatigue, Thermo-mechanical fatigue, , Rolling contact fatigue, Effect of hydrogen embrittlement on fatigue, Influence of defects on fatigue 4. Fracture Stress concentration factor and stress intensity factor, Griffith theory, Basics of linear elastic and elastoplastic fracture mechanics, Impact toughness and ductile to brittle transition, Fracture toughness and concepts of K_1c and J_1c, Fatigue Crack Growth Rate (FCGR), and Paris law, Short crack growth and concept of K_th 5. Creep Creep and creep crack growth, Stress relaxation tests, Creep-fatigue interaction, 6. Sheet Metal Forming Concept of planar anisotropy, Forming limit diagram, Hole expansion ratio, Spring back, r-ratio and deep drawing ratio.
Learning Outcomes: By the end of this course, undergraduate students should be able to: · Demonstrate a comprehensive understanding of various advanced mechanical properties. · Interpret various mechanical tests · Apply knowledge of advanced mechanical properties to solve real-world engineering problems and enhance material performance.
Assessment Method · Quiz, mid and end-semester examinations
Texts and References
Text Books: 1. George E. Dieter, Mechanical Metallurgy, McGraw Hill Education, 3rd Edition, 1 July 2017. 2. S. Suresh, Fatigue of Materials, Cambridge University Press, 2nd edition, June 2012. 3. T.L. Anderson, Fracture Mechanics: Fundamentals and Applications, CRC Press, 4TH EDN, 2017 4. M.N. Shetty, Dislocation and mechanical behaviour of materials, PHI, 2013. Reference Books: 1. Prashant Kumar, Elements of Fracture Mechanics, McGraw Hill Education, 2017. 2. J. Schijve, Fatigue of Structures and Materials, Springer, 2nd ed. 2009. 3. Bruno C. De Cooman and Kip O. Findley, Introduction to the Mechanical Behavior of Steel, Association for Iron & Steel Technology, 30 Nov 2017.

ME4205

Internal Combustion Engines

Course Name	Internal Combustion Engines
Course Number	ME4205
L-T-P-C	3-0-0-3
Pre-requisites	Basic and Applied Thermodynamics
Learning Mode	Class room lecturer
Course objectives
Complies with PLOs 2 and 4 · To understand the fundamental Principles of IC engines. · To explore recent advancements in combustion technologies · To analyze the impact of alternative fuels on engine performance and emissions · To investigate strategies for improving engine efficiency and reducing environmental impact. · To understand the generation of undesirable exhaust emissions · To understand the Optical diagnostics in I C Engines · To examine the integration of hybrid and electrification technologies with I C engines
Course Content
1. Introduction: Basic Introduction to SI and CI engine, Engine Performance Parameters. 2. Conventional fuels & Alternative fuels: Energy Scenario, Transport Fuel, Petroleum Based Liquid Fuel and Their Characteristics, Straight vegetable oils, Biodiesels, Emulsified Fuels, Methanol, Ethanol, and higher versions of alcohols. Gaseous fuels include CNG, LPG, LNG, DME, hydrogen, and ammonia. 3. Combustion in SI and CI Engines: Combustion in SI engines, Flame Propagation, Stages of Combustion in SI engines, Combustion in CI engine, Stages of CI engine combustion. Knocking in SI and CI engine, Comparison of knocking in SI & CI engine, Factors Affecting Detonations. Stoichiometric Combustion of Fuels, Adiabatic Flame Temperature. Combustion chambers in SI and CI engines, Important Factors Considered in Combustion Chamber Design, Modern developments in IC Engines such as EGR, MPFI, GDI, HCCI and Turbocharging. 4. Engine Ignition cooling and Lubrication system Different Ignition Systems and Working, Components of battery Ignition System, parameters affecting Engine Heat Transfer, Engine Friction and Types, Factors affecting Mechanical Friction, Lubrication and its mechanism, Different Lubrication System 5. Fuel Injection System Electronic Fuel in Injection (EFI) System, Components of an EFI system, Fuel Injectors, Types of Injection, Electronic control of engines, Requirement of Diesel Injection System, Types of Injection system for CI engine, Fuel Pump, Nozzles. Importance of ECU. 6. Measurement and Testing of Engine Performance Parameters: Measurement of Speed, Fuel Consumption Measurement, Volumetric type flowmeters, Measurement of Air consumption, Types of the dynamometer, Measurement of Brake Power, Frictional Power, and Indicated Power, Endurance test of I C Engine as per Indian standard 7. Air Pollution and its Control Exhaust Emissions, Effect of Various Parameter on Exhaust Emissions, Exhaust Emissions from SI and CI Engines. Exhaust gas measurement techniques (NDIR, FID, Chemiluminescence, Smoke opacimeter), Principle and working of emission reduction technologies. Indian emission standards for SI and CI engines. Comparison between US, European and Bharat stage emission standards 8. Optical Diagnostics in IC Engines: Spray and combustion measurements in the optical engine and constant volume combustion chamber, Application of optical techniques such as High-speed imaging, Schlieren imaging, PIV, PLIF, Diffused back Illumination (DBI), Phase Doppler Anemometry (PDA), Combustion Luminosity Imaging, etc. 9. Hybrid and Electric vehicles History of electric vehicles, Vehicle Power Plant and Transmission Characteristics, Basic architecture of Hybrid Drive trains, Power flow in HEVs. Electric and Hybrid Electric Drivetrains, Energy Storage Requirements in Hybrid and Electric Vehicles, Battery Thermal Management System.
Learning Outcomes: By the end of this course, mechanical engineering undergraduate students should be able to: · Students should deeply understand advanced concepts in Internal Combustion Engines. · Understand the application of alternative fuels in I.C. Engine and their implications for future engine design and operation. · Students should be able to identify and explain the function of various engine components and systems, such as fuel injection systems, ignition systems, and exhaust after-treatment systems. · Understand the advanced techniques for reducing emissions from I.C. engines. · Understand the concepts of optical diagnostic techniques in I.C. Engine and use them in real-life experiments. · Understand the technologies of hybrid and electric vehicles.
Assessment Method · Quiz, Seminar, mid and semester examinations
Texts and References
Text Books: 1. IC Engine Fundamentals: John B. Heywood, 2^nd Edition, Mc Graw Hill, 2018 2. Fundamentals of IC Engines: P. W. Gill and James Smith, Fourth Revised Edition, Oxford IBH, 1959 3. Modern Electric, Hybrid Electric and Fuel Cell Vehicles: Fundamentals, Theory and Design Lino Guzzella and Antonio Sciarretta, , CRC Press, 2nd Edition, 2009 4. Electric Vehicle Technology Explained: James Larminie and John Lowry, Wiley, 1st Edition, 2003 Reference Books: 5. Introduction to Internal Combustion Engines: Richard Stone, SAE Inc., 1999 6. IC Engines Combustion and Emissions, B. P. Pundir, Narosa Publications, 2010 7. IC Engine Fundamentals: V. Ganesan, Tata Mc Graw Hill 8. The Internal combustion Engine in theory and practice: C F Taylor,2^nd Edition, MIT Press, Cambridge, 1985. 9. Hydrogen Fuel for Surface Transportation: Joseph Norbeck, SAE Publications, 1996.

ME4206

Micro-manufacturing

Course Name	Micro-manufacturing
Course Number	ME4206
L-T-P-C	3-0-0-3
Pre-requisites	Nil
Learning Mode	Class room lecturer
Course objectives
Complies with PLOs 3 and 4 · To acquire knowledge about the need and fundamental principle of micro-manufacturing. · To gain knowledge of various micro-machining techniques that uses conventional and non-conventional material removal approached. · To be familiar with micro-fabrication techniques. · To understand the metrology aspects of micro-manufactured components.
Course Content
Introduction to micro-manufacturing: definition, need/importance, applications. Size effect. Classification of micro-manufacturing processes. Micro-machining processes: Micro-milling Tools and micro-milling technique, Micro-drilling and Macro-drilling Technique, diamond micro-machining and grinding, ultrasonic micro- machining, micro-EDM, laser beam micro-machining, micro-ECM, electron beam micro- machining, focused ion-beam techniques, abrasive micro-finishing techniques. Micro-fabrication using deposition techniques such as epitaxial, sputtering, chemical vapor deposition (CVD) techniques and Lithography (LIGA) based techniques. Sensors and actuators for micro-manufacturing. Metrology for micro- manufacturing.
Learning Outcomes: By the end of this course, undergraduate students should be able to: · Realize the importance and suitability of micro-manufacturing techniques. · select the suitable micro-manufacturing process based on the need and requirements of the components, · analyse and decide the viable micro-machining or micro-fabrication technique for specific requirements, · assess the quality of the fabricated micro-scale products.
Assessment Method · Quiz, Assignments, Mid and End semester examinations
Texts and References
Text Books: [1] V. K. Jain, Introduction to Micromachining, Narosa Publishing House, 2010. Reference Books: [1] M.J. Madou, Fundamentals of Microfabrication, 2^nd Edn, CRC Press, 2009. [2] M. Adithan, Micromanufacturing: Materials, Processes, and Technology, Atlantic Publishers, 2019.

Department Electives - V

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Subject Code

Department Electives - V

ME4207

Energy Methods and Variational Principles in Applied Mechanics

Course Name	Energy Methods and Variational Principles in Applied Mechanics
Course Number	ME4207
L-T-P-C	3-0-0-3
Pre-requisites	Mechanics of Solids
Semester	Spring
Learning Mode	Lectures
Intended Audience
BTech Final Year (Mechanical Engineering)
Course Description
· This course leverages fundamental theorems from variational calculus and solid mechanics to derive equations of mechanics using energy and variational principles. It covers the formulation and solution of initial, boundary, and eigenvalue problems through direct variational methods.
Course Objectives
Complies with PLOs 1 and 4 · Formulating the governing equations using variational principles for static bodies such as: bars, beams and plates. · Solving problems in applied mechanics using the principle of minimum total potential energy, principle of minimum total complementary potential energy, principle of virtual work, and principle of complementary virtual work. · Formulating and solving initial, boundary and eigen-value problems using Rayleigh-Ritz or Galerkin method. · Applying Hamilton’s principle and Lagrange equations to obtain equations of motion.
Course Content
1. Introduction and Mathematical Preliminaries Introduction to role of energy methods; historical perspective; introduction to tensor; tensor operation; properties of tensors; invariants, eigenvalues and eigenvectors of second order tensors; tensor fields; differentiation of tensors; Divergence and Stokes theorem; displacement field; deformation gradient; small strain tensor; Cauchy stress tensor, state of stress; conservation of linear and angular momentum; constitutive relation for linear elastic solids. 2. Introduction to Variational Calculus Variational operator; concept of a functional; extremum principles; functionals of one independent variable; functional of two independent variables; Euler equations. 3. Fundamentals of Energy Methods Concepts of work and energy; strain energy; virtual work principles; principle of total potential energy and complementary potential energy; Betti’s and Maxwell’s reciprocity theorems. 4. Energy Methods for the Static Analysis Analysis of longitudinal bars; Euler-Bernoulli beams and plates under static loading conditions using variational principles; separation of natural and essential boundary conditions; introduction to Ritz and Galerkin methods. 5. Energy Methods for the Dynamics Analysis Hamiltonian principle for particles, rigid bodies and continuum of least action; Euler-Lagrange equation; dynamics of deformable bodies: longitudinal vibration of rod, transverse vibration of strings and Euler-Bernoulli beams.
Learning Outcomes: · Able to understand various concepts of energy and variational principles. · Able to derive governing equations for mechanical systems. · Able to understand other relevant courses easily.
Assessment Method Mid semester examination, End semester examination, Class test/Quiz, Assignments
Reference Books
Textbook: 1. Reddy, J.N., Energy Principles and Variational Methods in Applied Mechanics, 3^rdEd., John Wiley and Sons, Inc., 2017. Suggested Books: 1. Berdichevsky, V.L., Variational Principles of Continuum Mechanics-I: Fundamentals, 1st Ed., Springer, 2009. 2. Berdichevsky, V.L., Variational Principles of Continuum Mechanics-II: Applications, 1st Ed., Springer, 2009. 3. Shames, I.H., and Dym, C.L., Energy and Finite Element Methods in Structural Mechanics, 1st Ed. New Age International Publishers, 1991

ME4208

Failure Analysis of Engineering Materials

Course Name	Failure Analysis of Engineering Materials
Course Number	ME4208
L-T-P-C	3-0-0-3
Pre-requisites	Nil
Learning Mode	Lectures
Course objectives
Complies with PLOs 1, 3 and 4 · Provide a foundational understanding of the fundamental causes of material failure. Introduce students to general procedures and methodologies for conducting failure analysis.
Course Content
1. Key Sources of Failure Design deficiencies, Material and processing faults, Improper service conditions, Residual stresses 2. Tools for Failure Analysis Fault tree diagram, Failure mode and effects analysis (FMEA), Weibull distribution, Pareto diagram 3. Common Practices in Failure Analysis: Defining objectives for analysis, Collecting background data relevant to the failure, Selecting and handling samples appropriately, Cleaning and preserving fractured surfaces for examination, Identifying failure modes through thorough analysis, Applying systematic approaches to failure investigation, Determining root causes of failure with precision, Following standardised reporting practices 4. Examination of Fractured Components: Conducting initial examination of fractured surfaces, Using appropriate equipment for preliminary analysis, Preserving records of failure for detailed investigation 5. Identification of failure modes: Classifying failure modes, Identifying specific characteristics of each mode, Distinguishing between different types of fractures, Analysing factors influencing fracture modes and defects 6. Analysis of Failure Causes: Physical observation, Chemical analysis, Optical microscopic examination, Utilisation of scanning electron microscope (SEM) and X-ray diffraction 7. Applying Fracture Mechanics in Failure Analysis: Fracture toughness KIc, JIC, and CTOD , Impact toughness and ductile to brittle transition Fatigue crack growth rate behaviour, Remaining life assessment 8. Case Studies: Failure analysis of different components, such as rail, spring, shaft, automobile chassis and wheel, pressure vessels and pipelines.
Learning Outcomes: By the end of this course, undergraduate students should be able to: · Understand the fundamental causes of material failure. · Apply tools for systematic failure analysis. · Perform detailed examination and classification of failure modes. · Analyse failure and apply findings to real-world case studies.
Assessment Method · Quiz, mid and end-semester examinations
Texts and References
Text Books: 1. A. K. Das, Metallurgy of Failure Analysis, Special Indian Edition, 1997, Tata McGraw- Hill. 2. Richard W. Hertzberg, Deformation and Fracture Mechanics of Engineering Materials, John Wiley & Sons Inc, 5th Edition, 2012. Reference Books: 3. Robert H. and Bhadeshia H. H.K.D.H., Steels: Microstructure and Properties, 3rd Edition, 1995, Butterworth-Heinemann. 4. W. T. Becker, and R. J. Shipley, Metals Handbook, Failure Analysis and Prevention, Volume 11, 2002, ASM International. 5. Metals Handbook, Fractography, Volume 12,1992, ASM International. 6. Prashant Kumar, Elements of Fracture Mechanics, McGraw Hill Education, 2017. 7. George E. Dieter, Mechanical Metallurgy, McGraw Hill Education, 3rd Edition, 1 July 2017. 8. S. Suresh, Fatigue of Materials, Cambridge University Press, 2nd edition, June 2012. 9. J. Schijve, Fatigue of Structures and Materials, Springer, 2nd ed. 2009.

ME4209

Hydraulic Machines

Course Name	Hydraulic Machines
Course Number	ME4209
L-T-P-C	3- 0- 0- 3
Pre-requisites	Nil
Learning Mode	Lectures
Course objectives
Complies with PLOs 2 and 4 · To gain fundamental principles behind the working of various hydraulic machines · To analyse the problems involving hydraulic turbines and pumps · To understand the performance characteristics of different hydraulic machines
Course Content
1. Introduction Classification of hydraulic machines- turbines and pumps, heads and efficiencies, the impact of jet on stationary and moving flat and curved vanes, the fundamental equation of hydraulic machines 2. Hydraulic turbines Classification of turbines-impulse and reaction Impulse turbine: Pelton turbine-components, governing mechanism, velocity triangles, Reaction turbine-Francis, Kaplan/Propeller-components, draft tube, governing mechanism, velocity triangles Performance characteristics: Main characteristics, operating characteristics, and Muschel characteristics 3. Hydraulic pumps Classification of pumps-rotodynamic and positive displacement pumps Rotodynamic pumps: centrifugal pumps-components, velocity triangles, cavitation, net positive suction head (NPSH), role of dimensional analysis and similitude, heads, and efficiencies, performance characteristics-main and operating characteristics Positive displacement pumps: reciprocating pump- components, air vessels, slip, effect of piston acceleration and effect of friction. 4. Miscellaneous fluid machines: Hydraulic crane, hydraulic ram, fluid coupling, torque converter, etc.
Learning Outcomes: By the end of this course, undergraduate students should be able to: · demonstrate a comprehensive understanding of various hydraulic machines · analyse the velocity triangles to evaluate the output and efficiency of hydraulic machines · analyse the performance characteristics of turbines and pumps · understand the working of miscellaneous fluid machines such as cranes, rams, torque converters
Assessment Method · Assignments, quizzes, seminar, mid-semester and end-semester examinations
Texts and References
Text Books: 1. Jagdish Lal, Hydraulic Machines Including Fluidics, Metropolitan Book Co. Ltd, 2016. 2. Terry Wright and Phillip Gerhart, Fluid Machinery Application, Selection, and Design, Second Edition, CRC Press, 2010. Reference Books: 1. S. Pati, Fluid Mechanics and Hydraulic Machines, McGraw Hill, 2012. 2. K Subramanya, Fluid Mechanics and Hydraulic Machines-Problems and Solution, 2nd Edition, McGraw Hill, 2018.

Interdisciplinary Elective (IDE) Courses for B. Tech. (Available to students other than Dept. of ME)

Sl. No.

Subject Code

Subject Name

ME2205

Manufacturing Processes for Metallic Materials

Course Name	Manufacturing Processes for Metallic Materials
Course Number	ME2205
L-T-P-C	3-0-0-3
Pre-requisites	Nil
Learning Mode	Class room lecture
Course objectives
· To gain fundamental principles of manufacturing processes · To understand the various approaches of manufacturing processes namely machining, casting, forming, welding, powder metallurgy for metallic materials. · To understand the different key process parameters involved in such processes and their role.
Course Content
1. Machining: Fundamental of material removal processes, single-point cutting operations, cutting tool and tool materials, force and power consumption, tool life, basics of multi-point cutting like drilling, milling etc. 2. Casting: Sand casting processes, various elements and requisites of sand casting processes, defects in casting, concept of permanent casting processes. 3. Forming: Hot and cold forming operations, Fundamentals of forging, rolling, drawing, extrusion, basics of different sheet metal forming operations, their relative advantages and disadvantages, applications. 4. Welding: Arc welding – fundamentals, power source characteristics, Gas welding, Resistance welding, Soldering, Brazing. Welding defects. 5. Other manufacturing processes for metallic materials: Introduction to Powder metallurgy, introduction to additive manufacturing. 6. Process suitability and applications: Relative comparison about process capability, product quality, application of various manufacturing processes.
Learning Outcomes: By the end of this course, undergraduate students should be able to: · demonstrate a comprehensive understanding of various manufacturing processes. · apply engineering principles to suitably select the manufacturing process for a desired application. · identify and explain the influence of various controlling process parameters and adopt the viable approach to fabricate the products. · understand the technologies advanced needed to enhance the process applicability.
Assessment Method · Quiz, mid and end semester examinations
Texts and References
Text Books: 1. S. Kalpakjian and S. R. Schmid, Manufacturing Processes for Engineering Materials, Prentice Hall, 2003. 2. A. Ghosh and A. K. Mallik, Manufacturing Science, Wiley Eastern, 2010 3. M. P. Groover, Introduction to Manufacturing Processes, Wiley, 2011 Reference Books: 1. P. N. Rao, Manufacturing Technology – Vol I: Foundry, Forming and Welding, Tata McGraw Hill, 2017. 2. P. N. Rao, Manufacturing Technology – Vol II: Metal Cutting and Machine Tools, Tata McGraw Hill, 2018. 3. Introduction to Manufacturing Processes, J.A. Schey, 3nd edition. McGraw Hill, 2000

ME3106

Automotive Technology

Course Name	Automotive Technology
Course Number	ME3106
L-T-P-C	3-0-0-3
Pre-requisites	Nil
Learning Mode	Class room lecturer
Course objectives
· To gain fundamental knowledge of automobile · To explore recent advancements in automotive technologies · To understand the components of automobile systems such as chassis, engine, transmission, brakes, clutches, electrical systems, steering system, wheel and tyre etc. · To understand the testing, maintenance and fault diagnosis in engine, power transmission devices etc. · To study the hybrid and electric vehicle technologies
Course Content
1. Introduction: Automobile classification and specification, Automobile chassis: General layout, types of layout and its arrangement, Body construction type and materials, Functional requirements of vehicle body, Body trim and fittings. 2. Power Transmission systems: Engines: I.C. Engine Construction and Components. Engine Cooling and Lubrication System, Fuel Supply System for petrol and diesel Engine, alternative fuels, Ignition System, Engine Testing, Engine Emissions Clutch: Constructional features and working of single plate, multi plate, semi centrifugal and centrifugal clutch, Calculation of surface area and number of driving and driven plates. Transmission gear box: sliding mesh, constant mesh, synchromesh gearboxes and four wheel drive. Propeller shaft and Final drive: Propeller shaft, universal joints, Hotchkiss & Torque tube Drives, front drive shaft types and their construction and working, Differential gearbox, rear axle. Automatic Transmission and CVT, Fault and diagnosis of the power transmission system. 3. Axle, Suspension and Steering System: Axle: Classification, types of front axle, Construction, Components and their functions, types of rear axle and application. Suspension: Principle, Types of suspension systems, Functional requirements of suspension systems, types and Constructional features of Front Suspension and Rear suspension system, Spring types, Rubber and Air suspensions, Factors affecting design and selection; Analysis of Suspension system: Mobility, kinematic/graphical analysis, Roll centre analysis and force analysis. Steering System: Steering Layout, types of steering gears, steering linkages, steering mechanism, definitions, and significance of camber, caster king, pin inclination, toe in and toe out on turn. Measurement and adjustment of various steering system layouts, steering ratio, under steering and over steering, power-assisted steering, steering geometry, wheel alignment, and diagnosis of fault. 4. Brake system: Components and configurations, Fundamentals of braking: braking distance, braking efficiency, weight transfer, wheel skidding, Brake proportioning and adhesion utilization, Hydraulic brake system, Power assisted brakes, ABS and EBD: Working principles, Features and advantages, Fault and diagnosis 5. Wheel and Tyres: Types of wheels, types of tyres, tyre construction, constituents of tyre, tyre tread pattern, tyre pressure and wear, tyre properties, tyre size, tyre maintenance. 6. Electrical, Electronics and Safety systems: Engine control Unit, Monitoring and Instrumentation, Safety interlocks and alarms, Lamps, Lighting and other circuits, fuel gauge, temperature gauge, wiper, speedometer and odometer. Active and Passive Safety systems. 7. Hybrid and Electric vehicles: Layout and components of electric vehicles, Vehicle Power Plant and Transmission Characteristics, Basic architecture of Hybrid Drive trains, Power flow in HEVs.
Learning Outcomes: By the end of this course, undergraduate students should be able to: · Demonstrate a comprehensive understanding of automotive systems such as engines, transmission, suspension, braking, and electrical systems. · Apply engineering principles to design automotive components and systems, considering factors such as performance, efficiency, safety, and manufacturability · Students should be able to identify and explain the function of various engine components and systems, such as fuel injection, ignition, and exhaust after-treatment systems. · Analyze and solve engineering problems related to vehicle design, performance, and maintenance. · Understand the technologies of hybrid and electric vehicles.
Assessment Method · Quiz, mid and end semester examinations
Texts and References
Text Books: 1. Automotive Mechanics by William H. Crouse, Donald L. Anglin, Tata Mc Graw Hill Publication 2. IC Engine Fundamentals: John B. Heywood, 2 ^nd Edition, Mc Graw Hill, 2018 3. Fundamentals of IC Engines: P. W. Gill and James Smith, Fourth Revised Edition, Oxford IBH, 1959 4. Automotive Vehicle Technology by Heinz Heisler, Butterworth-Heinemann Ltd; 2nd edition (17 July 2002) 5. The Automotive Chassis by Jornsen Reimpell, Helmut Stoll, Jurgen W. Betzler, SAE International, 2nd edition (2001). Reference Books: 1. Automobile Engineering Vol- I & II by Dr. Kirpal Singh, Standard Pub.& Dist. 2. Automobile Technology by Dr. N.K.Giri, Khanna Publisher 3. Automobile Engineering by G.B.S.Narang, Khanna Publisher

ME4103

Nonlinear Dynamics and Chaos

Course Number	ME4103
Course Credit	L-T-P-C: 3-0-0-3
Course Title	Nonlinear Dynamics and Chaos
Pre-requisite	NIL
Learning Mode	Lectures
Learning Objectives	The objective of this course is, · To impart the ability of solving different nonlinear systems through analytical approach · To impart the ability of solving different nonlinear systems through numerical approach as well · To impart the ability of analyzing nonlinear systems through fixed points, phase portrait, linear and nonlinear stability approaches. · To impart the ability of analyzing chaotic systems by identifying Lyapunov exponent, Poincare Map, Fractal dimension, Information dimension and other appropriate dimensions · To impart the ability of identifying Chaos, Hyper Chaos and Nonlinearity in systems and to impart the ability to deal with them across the discipline of science and engineering.
Course Description	This course is designed to fulfil the requirement of systems per se considering the inevitable nonlinearity in the system, which is usually ignored in analyzing system dynamics. Chaos and Hyper Chaos are frequently observed in systems and in general unattended.
Course Outline	Introduction: Linear vs. nonlinear behavior, Example across a broad spectrum of Science and Engineering. First-order continuous time nonlinear systems: Autonomous systems: Equilibrium points, linear systems, invariant sets, linearization, phase diagrams and velocity fields, behavior dependence on parameters, bifurcations of equilibria (saddle-node, pitchfork and transcritical), implicit function theorem. Non-autonomous systems. Second and higher order continuous time nonlinear Systems: conservative/non-conservative systems: Phase plane analysis, equilibrium points, linearization, stability, periodic orbits and saddle points, potential function and phase portrait, parameter-dependent conservative systems, local bifurcations, examples of global bifurcations, effect of dissipative forces. Perturbation method, Poincare-Lindstedt method, Harmonic balance and Fourier series for periodic solutions. Averaging methods, Multiple time-scale techniques, Continuation Method. Discrete time Dynamical Systems: One dimensional map, Cobweb plot, bifurcation diagram, two dimensional map, bifurcation diagram, Poincare map, Chaos, Lyapunov exponent, strange attractors Delay in continuous and discrete time dynamical Systems: Stability and Bifurcation analysis. Chaos in piecewise linear time delay system, Synchronization of Chaos. Feedback. Hamiltonian Chaos: Perturbed Hamiltonian system and separatrix chaos, Chirikov Standard Map, KAM theory Chaos Control- PID control, Nonlinear Control Fractals- Fractal Dimensions, Cantor Set, Julia set, Mandelbrot set, Hausdroff dimension, Information dimension, Kaplan-Yorke dimension. Analysis of experimentally obtained data. Experimental Class Room Demonstration: For class room demonstration magnetic pendulum is developed by the instructor. A few others will be developed by students as per their interest and to be demonstrated.
Learning Outcome	Following learning outcomes are expected after going through this course. · Will be able to solve nonlinear system of equations both analytically and numerically. · Will be able to apply the method of multiple scale, perturbation method, harmonic balance for solving a set of nonlinear differential equations. · Will be able obtain the interpretation of nonlinear system behavior over the linear system behavior. · Will be able to identify the Chaos in engineering system and will be able to quantify through various measures. · Will be able to derive and analyze nonlinear system behavior.
Assessment Method	Mid Semester Examination (25%), End Semester examination (35%), Class test & quiz (30%), Assignment (10%)
Suggested Readings: 1. Jordan, D. W. and Smith, P.: Nonlinear Ordinary Differential equations, 4^th Edition, Clarendon Press, Oxford, 2007 ed. 2. Nayfeh, A. H and Balachandran, B.: Applied Nonlinear Dynamics: Analytical, Computational and Experimental Methods, Wiley, 2008 ed. 3. Strogatz, S. H. : Nonlinear Dynamics And Chaos: With Applications To Physics, Biology,Chemistry, And Engineering, Westview Press, 2001 ed. 4. Moon, F. C.: Chaotic Vibrations- An introduction for Applied Scientist and Engineers, Wiley-VCH, 2004 ed. 1. Sprott, J. C.: Chaos and Time Series Analysis, Oxford University Press, 2003 ed

Minor in Thermal Engineering

Sl. No.

Subject Code

Subject Name

ME2102

Thermodynamics

Course Name				Thermodynamics
Course Number				ME2102
L-T-P-C				3- 1- 0- 4
Pre-requisites				Nil
Semester				Third
Learning Mode				Lectures
Course Learning Objectives
			Complies with PLOs 2 and 4. 1. To develop the basic understanding of classical thermodynamics and principles of engineering applications 2. To develop skills to formulate and analyze thermodynamic problems involving control volumes and control masses
Course Content
		Thermodynamic systems: Macroscopic and microscopic view, system and control volume, states and properties, processes; Properties of pure substances and steam: Phase changes, steam tables and Mollier diagram, Heat and work; Zeroth law; First law: for systems and control volumes, enthalpy, Applications of first law: closed and open systems, SSSF, USUF, Second law: Carnot cycle, entropy, corollaries of the second law; Applications of second law: closed and open systems, vapor compression and Rankine cycle; irreversibility, availability, exergy; Thermodynamic relations; Properties of mixtures of ideal gases; Third law of thermodynamics; Introduction to psychrometry
Learning Outcomes		The course has been designed to achieve the following outcomes: 1. Understanding of the basic concepts of engineering thermodynamics. 2. Understanding of the thermodynamic properties of pure substances at different states. 3. Acquire basic knowledge about thermodynamic cycles (a) to produce mechanical power from heat, and (b) to keep a place cool and comfortable. 4. Analyse thermodynamic processes for maximum feasible efficiency. 5. Select an engineering approach to problem-solving based on the properties of substances and the laws of thermodynamics.
Assessment Method		Mid Semester Examination, End Semester examination, Class test & quiz, Assignment, Class Performance and Viva
Texts and References
	Textbook: 1. C Borgnakke& R E Sonntag, Fundamentals of Thermodynamics, 7^th Edition, John Wiley, 2009. 2. Y. A. Cengel and M. A. Boles, Thermodynamics: An Engineering Approach, 7^th Edition, Tata McGraw Hill, 2017. 3. P. K. Nag, Engineering Thermodynamics, Fifth Edition, McGraw Hill Education, 2013

ME2202

Heat and Mass Transfer


Course Name					Heat and Mass Transfer
Course Number					ME2202
L-T-P-C					3-1-2-5
Pre-requisites					Thermodynamics and Fluid Mechanics, or equivalent
Semester					Fourth
Learning Mode					Lectures and Practical
Course Learning objectives
				Complies with PLOs 2 and 4. 1. The student should internalize the meaning of the terminology and physical principles associated with heat and mass transfer processes. 2. The student should be able to delineate pertinent transport phenomena for any process or system involving heat or mass transfer. 3. The student should be able to use requisite inputs for computing heat transfer rates and/or material temperatures. 4. The student should be able to develop representative models of real processes and systems and draw conclusions concerning process/system design or performance analysis. 5. The student should become familiar with design of heat transfer experiments and concerning measurement techniques.
Course Content
			Modes of heat transfer: Conduction: One-dimensional steady conduction, resistance network analogy, fins, two- and three-dimensional steady conduction, one-dimensional unsteady conduction, semi-infinite solids. Convection: fundamentals, order of magnitude analysis of momentum and energy equations, hydrodynamic and thermal boundary layers, dimensional analysis, free and forced convection, external and internal flows. Heat exchangers: LMTD and є-NTU methods. Radiation: Stefan Boltzmann law, Planck’s law, emissivity and absorptivity, radiant exchange between black surfaces, view factors, network analysis. Phase change heat transfer: Boiling and condensation. Mass transfer: molecular diffusion, Fick’s law, binary species
List of experiments
		1. Measurement thermal conductivity different materials using composite wall apparatus 2. Determination of the heat transfer coefficient during Forced Convection 3. Determination of the heat transfer coefficient during Natural Convection 4. Determination of Thermal Conductivity of Liquid 5. Phase change heat transfer: (a) Pool boiling 6. Phase change heat transfer: (b) Condensation 7. Performance evaluation of double pipe heat exchanger (a) parallel flow (b) counter flow 8. Performance evaluation of shell-and-tube heat exchanger 9. Emissivity measurement 10. Heat Pipe Demonstration
Learning Outcomes		1. The student should be able to develop representative models of real processes and systems and draw conclusions concerning process/system design or performance analysis. 2. The student should be able to design heat transfer experiments using suitable measurement techniques
Assessment Method		Mid Semester Examination, End Semester examination, Class test & quiz, Assignment, Class Performance and Viva, Practical Exam
Texts and References
	Textbook: 1. Bergman, Theodore L., Frank P. Incropera, David P. DeWitt, and Adrienne S. Lavine. Fundamentals of heat and mass transfer. 7^th Edition, John Wiley & Sons, 2011. 2. J.P. Holman, Heat Transfer, 8^th Edition, McGraw Hill, 1997. References: 1. M.N. Ozisik, Heat Transfer – A basic approach, McGraw Hill, 1985.Bejan, Convection Heat Transfer, 2^nd Edition, Interscience, 1994. 2. Bejan, Convection Heat Transfer, 2nd Edition, Interscience, 1994. 3. Y. A. Cengel and Afshin J. Ghajar, Heat and Mass Transfer, 5^th Edition, McGraw-Hill, New Delhi, 2020.

ME3104

Engineering Software Laboratory

Course Name			Engineering Software Laboratory
Course Number			ME3104
L-T-P-C			1-0-3-2.5
Pre-requisites			Nil
Semester			Fifth
Learning Mode			Lectures and Practical
Course Learning Objectives: Complies with PLOs 1-4. Exposure to industrial software used in Mechanical Engineering practices.
Course Content
		CAD : 2D and 3D geometric transformation, Curves and surfaces in CAD FEM: Solid model creation, different types of elements, chunking of model, meshing, mesh quality, different kinds of analysis: static, dynamic, transient, thermal, electromagnetic, acoustics, sub-structuring and condensation, Error and convergence. CFD: Different types of CFD techniques, various stages of CFD techniques (i) preprocessor: governing equations, boundary conditions, grid generation, different discretization techniques (ii) processor: solution schemes, different solvers (iii) post-processing: analysis of results, validation, grid independent studies etc. Developing codes using commercial software for solving few problems of laminar and turbulent flow with heat transfer applications. Engineering softwares related to CAD/CAM, FEM, CFD, with both GUI and script like languages, are to be used for laboratory assignments.
Learning outcomes		At the end of the course, students will be able to use the industrial software for simulating industrial and research problems related to solid and fluid mechanics. A mature understanding of various numerical techniques and their advantages and disadvantages will develop with respect to the software used in the class.
Assessment Method		Class test & quiz, Assignment (hands-on exercises using software), Class Performance and Viva, Practical Exam
Texts and References
	Textbook: 1. J. N. Reddy, “An Introduction to Finite Element Methods”, 3rd Ed., Tata McGraw-Hill, 2005. 2. D. F. Rogers and J. A. Adams, “Mathematical Elements for Computer Graphics”, McGraw-Hill, 1990 3. M. Groover and E. Zimmers, “CAD/CAM: Computer-Aided Design and Manufacturing”, Pearson Education, 2009. 4. J. D. Anderson, “Computational Fluid Dynamics”, McGraw-Hill Inc. (1995).

2.5

ME3201

Applied Thermodynamics and Turbomachinery

Course Name					Applied Thermodynamics and Turbomachinery
Course Number					ME3201
L-T-P-C					3- 1- 2- 5
Pre-requisites					Thermodynamics and Fluid Mechanics, or equivalent
Semester					Sixth
Learning Mode					Lectures and practical
Course Learning objectives
				Complies with PLOs 2 and 4. 1. To develop a good understanding of the various power and refrigeration cycles, 2. To understand basic fundamentals of turbomachinery and their working principles and thermodynamic design 3. To develop knowledge on designing different components of power and refrigeration cycles
Course Content
			Vapour power cycles: Rankine cycle, reheat cycle, regenerative cycle, cogeneration; Steam turbine: impulse and reaction stage, degree of reaction, velocity triangle, velocity and pressure compounding, efficiencies, Steam nozzles. Refrigeration cycles: Properties of Refrigerants, Carnot refrigeration cycle, vapor compression cycle, Psychrometry. Gas power cycles: Gas turbine cycle, intercooling, reheating, regeneration, closed cycles, optimal performance of various cycles, combined gas and steam cycles; Axial-flow gas turbine; Jet propulsion: turbojet, turbofan. I.C. Engines: Classification - SI, CI, two-stroke, four-stroke etc., operating characteristics - mean effective pressure, torque and power, efficiencies, specific fuel consumption etc., air standard cycles - Otto, Diesel and dual, real air-fuel engine cycles, combustion in S.I. and C.I. engines, Air and fuel injection system, engine emissions. Compressors: Reciprocating Air Compressors, Centrifugal and Axial-flow compressors. Fluid Machines: Pelton-wheel, Francis and Kaplan turbines, centrifugal and reciprocating pumps.

List of experiments
		1. Impact of jet 2. Performance of Pelton turbine 3. Performance of Axial Flow turbine 4. Performance of Francis turbine 5. Performance evaluation of centrifugal pump 6. Performance evaluation of reciprocating pump 7. Refrigeration test rig 8. Air conditioning test rig 9. Performance of 4-stroke petrol & diesel engine 10. Exhaust gas analyzer
Learning Outcomes		1. Students will be able to think critically for solving relevant practical problems 2. Students will develop analytical skills for designing different components of gas and refrigerant cycles 3. Students will be able to come up with innovative ideas on applications of existing thermodynamics cycles
Assessment Method		Mid Semester Examination, End Semester examination, Class test & quiz, Assignment, Class Performance and Viva, Practical Exam
Texts and References
	Textbook: 1. M MEl Wakil, Power Plant Technology, McGraw Hill Education, 1e, 2017. 2. P K Nag, Powerplant Engineering, Tata McGraw Hill, 4e, 2017. 3. H I H Saravanamuttoo, G F C Rogers and H. Cohen, Gas Turbine Theory 7e, Pearson, 2019 4. W WPulkrabek, Engineering Fundamentals of the Internal Combustion Engine, PHI, 2002. 5. T. D. Eastop and A. McConkey, 2009, Applied Thermodynamics for Engineering Technologists, 5^th Ed. References: 1. G. F.C. Rogers and Y R Mayhew, 2009, Engineering Thermodynamics Work and Heat Transfer, 4^th Ed., Pearson Education. 2. M J Moran and H N Shapiro, Fundamentals of Engineering Thermodynamics 6e, John Wiley, 2007. 3. Arora C P, Refrigeration and Air Conditioning, McGraw Hill, 4e, 2021 4. C R Fergusan and A T Kirkpatrick, Internal Combustion Engines: Applied Thermosciences, 3e, John Wiley & Sons, 2016.

Metallurgical and Materials Engineering

Program Learning Objectives:	Program Learning Outcomes:
Program Goal 1: The B.Tech program in Metallurgical and Materials Engineering aims to equip graduates with the necessary knowledge, skills, and values to succeed in professional careers related to metallurgical and materials engineering.	Program Learning Outcome 1a: Upon successful completion of the B.Tech program in Metallurgical and Materials Engineering, graduates will be able to identify, formulate, and analyse complex engineering problems related to metallurgical and materials engineering. Program Learning Outcome 1b: Students will be able to understand the science behind the functioning mechanism of metals, ceramics, polymers and glass
· Program Goal 2: Apply fundamental principles of science and engineering to solve complex problems in metallurgical and materials engineering and cultivate critical thinking and problem-solving skills in students to address real-world challenges in the metallurgy and materials domain.	Program Learning Outcome 2: Student will be able to apply research-based knowledge and methodologies, including experimental design, data analysis, and interpretation, to investigate complex problems in metallurgical and material engineering. Graduates will be capable to carry out research work in their area of interest either in academic area or in industry.
· Program Goal 3: Expose the students to the scientific and engineering concepts on metals, ceramics, polymer and composites and apply engineering principles to design, develop, and improve materials and processes for specific applications.	· Program Learning Outcome 3a: Students will be well versed with the concepts of microscopic analysis, characterization techniques, metallurgical testing, polymer synthesis & analysis, nano & electro ceramics, plasma-coating and flash sintering, mineral beneficiation & process metallurgy. · Program Learning Outcome 3b: Students will be able to design and develop new engineering materials with desired properties based on demands of various engineering sectors.
Program Goal 4: To impart hand-on exposure to modern laboratory equipment through structured laboratory experiments.	Program Learning Outcome 4a: Students will be able to correlate the theoretical concepts with the experiments and will be ready to apply the experimental knowledge in industries. Program Learning Outcome 4b: Students will be ready for quality control, higher studies and research work in the domain of metallurgical and materials engineering.
Program Goal 5: To inculcate research aptitude in the students and prepare the students to be industry-ready after the completion of their B. Tech. programme.	· Program Learning Outcome 5: Students will be able to design solutions for complex engineering problems related to materials, considering public health, safety, cultural, societal, and environmental factors. In addition, apply ethical principles and commit to professional ethics and social responsibility as a metallurgical and materials engineer. Graduate will be able to launch start-ups as entrepreneur to create job opportunities in the country.

Sl. No.

Subject Code

SEMESTER I

MA1101

Course Number	MA1101
Course Credit (L-T-P-C)	3-1-0-4
Course Title	Calculus and Linear Algebra
Learning Mode	Lectures and Tutorials
Learning Objectives	To provide the essential knowledge of basic tools of Differential Calculus, Integral Calculus, Vector spaces and Matrix Algebra.
Course Description	This course provides a foundation for Calculus and Linear Algebra. Topics related to properties of single and two variable functions along with their applications will be discussed. In addition fundamentals of linear algebra and matrix theory with applications will also be discussed.
Course Content	Differential Calculus (12 Lectures): Limit and continuity of one variable function (including ε-δ definition). Limit, continuity and differentiability of functions of two variables, Tangent plane and normal, Change of variables, chain rule, Jacobians, Taylor’s Theorem for two variables, Extrema of functions of two or more variables, Lagrange’s method of undetermined multipliers. Integral Calculus (10 Lectures): Riemann integral for one variable functions, Double and Triple integrals, Change of order of integration. Change of variables, Applications of Multiple integrals such as surface area and volume. Vector Spaces (12 Lectures): Vector spaces (over the field of real numbers), subspaces, spanning set, linear independence, basis and dimension. Linear transformations, range and null space, rank-nullity theorem, matrix of a linear transformation. Matrix Algebra (8 Lectures): Elementary operations and their use in getting the rank, inverse of a matrix and solution of linear simultaneous equations, Orthogonal, symmetric, skew-symmetric, Hermitian, skew-Hermitian, normal and unitary matrices and their elementary properties, Eigenvalues and Eigenvectors of a matrix, Cayley-Hamilton theorem, Diagonalization of a matrix.
Learning Outcome	Students completing this course will be able to: 1. Understand various properties of functions such as limit, continuity and differentiability. 2. Learn about integrations in various dimension and their applications. 3. learn about the concept of basis and dimension of a vector space. 4. define Linear Transformations and compute the domain, range, kernel, rank, and nullity of a linear transformation. 5. compute the inverse of an invertible matrix. 6. solve the system of linear equations. 7. Apply linear algebra concepts to model, solve, and analyze real-world problems.
Assessment Method	Quiz /Assignment/ MSE / ESE

Textbooks:

Thomas, G. B., Hass, J., Heil, C. and Weir M. D., “Thomas’ Calculus”, 14th Ed., Pearson Education, 2018
Kreyszig, E., “Advanced Engineering Mathematics”, 10th Ed., Wiley India Pvt. Ltd, 2015

Reference Books:

Jain, R. K. and Iyenger, S. R. K., “Advanced Engineering Mathematics”, 5^th, Narosa Publishing House, 2017
Axler, S., “Linear Algebra Done Right”, 3rd Ed., Springer Nature, 2015
Strang, G., “Linear Algebra and Its Applications” 4th Ed., Cengage India Private Limited, 2005

4.0

CS1101

Course Number	CS1101
Course Credit	3-0-3-4.5
Course Title	Foundations of Programming
Learning Mode	Offline
Learning Objectives	· To understand the fundamental concepts of programming · To develop the basic problem-solving skills by designing algorithms and implementing them. · To learn about various data types, control statements, functions, arrays, pointers, and file handling. · To achieve proficiency in debugging and testing a C program
Course Description	This introductory course provides a solid foundation in programming principles and techniques. Designed for students with little to no prior programming experience, it covers fundamental concepts such as variables, data types, control structures, functions, and basic data structures. Students will learn to write, debug, and execute programs using a high-level programming language. Emphasis is placed on developing problem-solving skills, logical thinking, and the ability to write clear and efficient code. By the end of the course, students will be equipped with the essential skills needed to pursue more advanced studies in computer science and software development.
Course Outline	Introduction and Programming basics, Expressions Control and Iterative statements, Functions, Arrays, Recursion vs. Iteration Pointers, 2D-Array with pointers, Structures, String, Dynamic memory allocation, File handling, Contemporary programming languages, and applications Practical component: Lab to be conducted on a 3-hour slot weekly. It will be conducted with the theory course so the topics for problems given in the lab are already initiated in the theory class.
Learning Outcome	· Understanding of Basic Syntax and Structure in C language · Proficiency in Data Types, Operators, and Control Structures · Function Implementation and learn to use them appropriately · Efficient Use of Arrays and Strings · Pointer Utilization · Ability to perform dynamic memory allocation and deallocation using malloc (), calloc (), realloc (), and free () functions. · Structured data management with structures and unions · Exposure of file Handling · Learning debugging and error Handling
Assessment Method	Internal (Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Reading

Knuth, Donald E. The art of computer programming, volume 4A: combinatorial algorithms, part 1. Pearson Education India, 2011.
P.J. Deitel and H.M. Deitel, C How To Program, Pearson Education (7th Edition)
Brian W. Kernighan and Dennis M. Ritchie, The C Programming Language, Prentice−Hall
A. Kelley and I. Pohl, A Book on C, Pearson Education (4th Edition)
K. N. King, C PROGRAMMING A Modern Approach, W. W. Norton & Company

4.5

PH1101/ PH1201

Course Number	PH1101/PH1201
Course Credit	3-1-3-5.5
Course Title	Physics
Learning Mode	Lectures and Tutorials
Learning Objectives	Complies with Program Goals 1 and 2
Course Description	This course deals with fundamentals in Classical mechanics, Waves and Oscillations and Quantum Mechanics. As a prerequisite, the mathematical preliminaries such as coordinate systems, vector calculus etc will be discussed in the beginning.
Course Outline	Orthogonal coordinate systems (Plane polar, Spherical, Cylindrical), concept of generalised coordinates, generalised velocity and phase space for a mechanical system, Introduction to vector operators, Gradient, divergence, curl and Laplacian in different co-ordinate systems. Central force problem and its applications. Rigid body rotation, vector nature of angular velocity, Finding the principal axes, Euler's equations; Gyroscopic motion and its application; Accelerated frame of reference, Fictitious forces. Potential energy and concept of equilibrium, Lennard-Jones and double-well potentials, Small oscillations, Harmonic oscillator, damped and forced oscillations, resonance and its different examples, oscillator states in phase space, coupled oscillations, normal modes, longitudinal and transverse waves, wave equation, plane waves, examples two- and three-dimensional waves. Michelson-Morley experiment, Lorentz transformation, Postulates of special theory of relativity, Time dilation and length contraction, Applications of special theory of relativity.
Learning Outcome	Complies with PLO 1a, 2a, 3a
Assessment Method	Quiz, Assignments and Exams

Suggested Readings:

Textbooks:

Engineering Mechanics, M. K. Harbola, 2^nd ed., Cengage, 2012
D. Kleppner and R. J. Kolenkow, An introduction to Mechanics, Tata McGraw-Hill, New Delhi, 2000.
I. G. Main, Oscillations and Waves
H. G. Pain, The Physics of Vibrations and Waves, 1968
Frank S. Crawford, Berkeley Physics Course Vol 3: Waves and Oscillations, McGraw Hill, 1966.

References:

R. P. Feynman, R. B. Leighton and M. Sands, The Feynman Lecture in Physics, Vol I, Narosa Publishing House, New Delhi, 2009.
David Morin, Introduction to Classical Mechanics, Cambridge University Press, NY, 2007.

3. P. C. Deshmukh, Foundations of Classical Mechanics, Cambridge University Press, 2019

5.5

CE1101/ CE1201

Course code	CE1101/CE1201
Course Credit (L-T-P-C)	1-0-3-2.5
Course Title	Engineering Graphics
Learning Mode	Lectures and Practical
Learning Objectives	Complies with PLO-1a 1. The course on engineering drawing is designed to introduce the fundamentals of technical drawing as an important form of conveying information. 2. Apply principles of engineering visualization and projection theory to prepare engineering drawings, using conventional and modern drawing tools. 3. Practice drawing orthographic projections, isometric views, and sectional views, of simple and combined solids in different orientations.
Course Description	This course will introduce drawing as a tool to represent a complex three-dimensional object on two-dimensional paper through methods of projections. The course explains the use of different drafting tools and the importance of conventions for uniformity and standardization of the interpretation of the drawings.
Course Outline	Fundamental of engineering drawing, line types, dimensioning, and scales. Conic sections: ellipse, parabola, hyperbola; cycloidal curves. Principle of projection, method of projection, orthographic projection, plane of projection, first angle of projection, Projection of points, lines, planes and solids. Section of solids: Sectional views of simple solids- prism, pyramid, cylinder, cone, sphere; the true shape of the section. Methods of development, development of surfaces. Isometric projections: construction of isometric view of solids and combination of solids from orthographic projections. Introduction to AutoCad and solving isometric problems.
Learning Outcome	After attending this course, the following outcomes are expected: a) The student will understand the basic concepts of engineering drawing. b) The student will be able to use basic drafting tools, drawing instruments, and sheets. c) The student will be able to represent three-dimensional simple and combined solid objects on two-dimensional paper. d) The student will be able to visualize and interpret the orientation of simple and combine solid objects.
Assessment Method	Laboratory Assignments (30%), Mid-semester examination (25%) and End-semester examination (45%).

Suggested Readings:

Textbooks:

D. Bhatt, Engineering Drawing, Charotar Publishing House.
Agrawal & Agrawal, Engineering Drawing, McGraw Hill.
Jolhe, Engineering Drawing.

References:

Engineering Drawing and Design by David Madsen

2.5

EE1101/ EE1201

Course Number	EE1101/EE1201
Course Credit	3-0-3-4.5
Course Title	Electrical Sciences
Learning Mode	Lectures and Experiments
Learning Objectives	Complies with Program goals 1, 2 and 3
Course Description	The course is designed to meet the requirements of all B. Tech programmes. The course aims at giving an overview of the entire electrical engineering domain from the concepts of circuits, devices, digital systems and magnetic circuits.
Course Outline	Circuit Analysis Techniques, Circuit elements, Simple RL and RC Circuits, Kirchoff’s law, Nodal Analysis, Mesh Analysis, Linearity and Superposition, Source Transformations, Thevenin’s and Norton’s Theorems, Time Domain Response of RC, RL and RLC circuits, Sinusoidal Forcing Function, Phasor Relationship for R, L and C, Impedance and Admittance, Instantaneous power, Real, reactive power and power factor. Semiconductor Diode, Zener Diode, Rectifier Circuits, Clipper, Clamper, UJT, Bipolar Junction Transistors, MOSFET, Transistor Biasing, Transistor Small Signal Analysis, Transistor Amplifier and their types, Operational Amplifiers, Op-amp Equivalent Circuit, Practical Op-amp Circuits, Power Opamp, DC Offset, Constant Gain Multiplier, Voltage Summing, Voltage Buffer, Controlled Sources, Instrumentation Amplifier, Active Filters and Oscillators. Number Systems, Logic Gates, Boolean Theorem, Algebraic Simplification, K-map, Combinatorial Circuits, Encoder, Decoder, Combinatorial Circuit Design, Introduction to Sequential Circuits. Magnetic Circuits, Mutually Coupled Circuits, Transformers, Equivalent Circuit and Performance, Analysis of Three-Phase Circuits, Power measurement in three phase system, Electromechanical Energy Conversion, Introduction to Rotating Machines (DC and AC Machines). Laboratory: Experiments to verify Circuit Theorems; Experiments using diodes and bipolar junction transistor (BJT): design and analysis of half -wave and full-wave rectifiers, clipping and clamping circuits and Zener diode characteristics and its regulators, BJT characteristics (CE, CB and CC) and BJT amplifiers; Experiment on MOSFET characteristics (CS, CG, and CD), parameter extraction and amplifier; Experiments using operational amplifiers (op-amps): summing amplifier, comparator, precision rectifier, Astable and Monostable Multivibrators and oscillators; Experiments using logic gates: combinational circuits such as staircase switch, majority detector, equality detector, multiplexer and demultiplexer; Experiments using flip-flops: sequential circuits such as non-overlapping pulse generator, ripple counter, synchronous counter, pulse counter and numerical display; Power Measurement by two Wattmeter method; Open and Short Circuit Tests of Transformer.
Learning Outcomes	Complies with PLO 1a, 2a and 3a
Assessment Method	Quiz, Assignments and Exams

Texts/References

K. Alexander, M. N. O. Sadiku, Fundamentals of Electric Circuits, 3rd Edition, McGraw-Hill, 2008.
H. Hayt and J. E. Kemmerly, Engineering Circuit Analysis, McGraw-Hill, 1993.
L. Boylestad and L. Nashelsky, Electronic Devices and Circuit Theory, 6th Edition, PHI, 2001.
M. Mano, M. D. Ciletti, Digital Design, 4th Edition, Pearson Education, 2008.
Floyd, Jain, Digital Fundamentals, 8th Edition, Pearson.
David V. Kerns, Jr. J. David Irwin, Essentials of Electrical and Computer Engineering, Pearson, 2004.
Donald A Neamen, Electronic Circuits; analysis and Design, 3rd Edition, Tata McGraw-Hill Publishing Company Limited.
Adel S. Sedra, Kenneth C. Smith, Microelectronic Circuits, 5th Edition, Oxford University Press, 2004.
E. Fitzgerald, C. Kingsley Jr., S. D. Umans, Electric Machinery, 6th Edition, Tata McGraw-Hill, 2003.
P. Kothari, I. J. Nagrath, Electric Machines, 3rd Edition, McGraw-Hill, 2004.
Del Toro, Vincent. "Principles of electrical engineering." (No Title) (1972).

4.5

HS1101

Course Number	HS1101
Course Credit	L-T-P-W: 2-0-1-2.5
Course Title	English for Professionals
Learning Mode	Offline
Learning Objectives	This course aims to help the students (a) attain proficiency in written English through the construction of grammatically correct sentences, utilization of subject-verb agreement principles, mastery of various tenses, and effective deployment of active and passive voice to ensure coherent and impactful written expression; (b) enhance oral communication skills by honing public speaking abilities, acquiring strategies to deliver persuasive presentations, and cultivating a polished telephone etiquette, enabling confident and articulate verbal communication; (c) foster active listening capabilities by recognizing different types of listening, and applying proven methods and strategies to improve active listening skills; (d) strengthen reading skills, including comprehension, interpretation, and critical analysis, to grasp diverse written materials and derive meaning from various types of texts encountered in academic and professional contexts; (e) develop adeptness in written communication for business purposes, encompassing the understanding of essential writing elements, mastery of appropriate writing styles thereby enhancing prospects for successful job interviews and subsequent professional endeavors.
Course Description	This academic course on communication skills aims to equip students with fluency in spoken and written English for effective expression in both academic and professional settings. By focusing on essential communication principles and providing practical experiences, students develop clarity, precision, and confidence in their communication. Through interactive discussions and exercises, students enhance critical thinking and adaptability in diverse contexts. Upon completion, students will excel in formal presentations, group discussions, and persuasive writing, enhancing their overall communication proficiency.
Course Outline	Unit I: Introduction to professional communication – LSRW - Phonetics and phonology Sounds in English Language – production and articulation – rhythm and intonation – connected speech - Basic Grammar and Advanced Vocabulary Sounds in English Language – production and articulation – rhythm and intonation – connected speech – persuading and negotiating – brevity and clarity in language. Unit II: Characteristics of Technical Communication: Types of communication and forms of communication - Formal and informal communication Verbal and non-Verbal Communication – Communication barriers and remedies Intercultural communication – neutral language Unit III: Comprehension and Composition – summarization, precis writing Business Letter Writing CV/ Resume – E-Communication Unit IV: Statement of Purpose, Writing Project Reports, Writing research proposal, writing abstracts, developing presentations, interviews – combating nervousness Tutorial: Listening Exercises, Speaking Practice (GDs, and Presentations), and Writing Practice Learning Outcome · Attain proficiency in written English, enabling the construction of grammatically correct sentences and coherent written expression through the use of appropriate grammar, tenses, and voice. · Enhance oral communication skills, including public speaking, persuasive presentation, and polished telephone etiquette, fostering confident and articulate verbal expression. · Cultivate active listening abilities, recognizing different listening types, overcoming obstacles, and employing strategies for attentive and effective communication. · Develop proficient written communication skills for business purposes, demonstrating understanding of essential writing elements, appropriate styles, and the creation of reports, notices, agendas, and minutes that effectively convey information.
Assessment Method	Class test + Quiz = 20%; Mid-semester = 25%; Assignment = 15%; End semester = 40%

Suggested Reading

Balzotti, Jon. Technical Communication: A Design-Centric Approach. Routledge, 2022.
Kaul, Asha, Business Communication. PHI Learning Pvt. Ltd. 2009
Laplante, Phillip A. Technical Writing: A Practical Guide for Engineers, Scientists, and Nontechnical Professionals. CRC Press, 2018.
Lawson, Celeste, et al. Communication Skills for Business Professionals, Second Edition. CUP, 2019.
Sharon Gerson and Steven Gerson. Technical Writing: Process and Product (8th Edition), London: Longman, 2013
Rentz, Kathryn, Marie E. Flatley & Paula Lentz. Lesikar’s Business Communication Connecting in a Digital world, McGraw-Hill, Irwin.2012
Allan & Barbara Pease. The Definitive Book of Body Language, New York, Bantam,2004
Jones, Daniel. The Pronunciation of English, New Delhi, Universal Book Stall.2010
Savage, Alice. Effective Academic Writing. OUP. 2014
Swan and Alter. Oxford English grammar course. OUP. 201

2.5

TOTAL

23.5

Probability Theory and Ordinary Differential Equations

Sl. No.

Subject Code

SEMESTER II

MA1201

Course Number	MA1201
Course Credit (L-T-P-C)	3-1-0-4
Course Title	Probability Theory and Ordinary Differential Equations
Learning Mode	Lectures and Tutorials
Learning Objectives	To introduce the basic concepts of probability, statistics, and Differential equations.
Course Description	This course aims to cover basic concepts of probability, statistics and ordinary differential equations. In particular, popular distributions, random sampling, various estimators and hypothesis testing will be discussed. Students will also get exposure to the linear ordinary differential equations and their solution techniques.
Course Content	Probability (12 Lectures): Random variables and their probability distributions, Cumulative distribution functions, Expectation and Variance, probability inequalities, Binomial, Poisson, Geometric, negative binomial distributions, Uniform, Exponential, beta, Gamma, Normal and lognormal distributions. Statistics (10 Lectures): Random sampling, sampling distributions, Parameter estimation, Point estimation, unbiased estimators, maximum likelihood estimation, Confidence intervals for normal mean, Simple and composite hypothesis, Type I and Type II errors, Hypothesis testing for normal mean. Ordinary Differential Equations (20 Lectures): First order ordinary differential equations, exactness and integrating factors, Picard's iteration, Ordinary linear differential equations of n-th order, solutions of homogeneous and non-homogeneous equations (Method of variation of parameters). Systems of ordinary differential equations, Power series methods for solutions of ordinary differential equations. Legendre equation and Legendre polynomials, Bessel equation and Bessel functions.
Learning Outcome	Students will get exposure and understanding of: 1. Random variables and their probability distributions 2. Understand popular distributions and their properties 3. Sampling, estimation and hypothesis testing 4. Solution of ordinary differential equations 5. Solution of system of ordinary differential equations 6. Special functions arising as power series solutions of ordinary differential equations
Assessment Method	Quiz /Assignment/ MSE / ESE

Text Books:

Hogg, R. V., Mckean, J. and Craig, A. T., “Introduction to Mathematical Statistics”, 8th Ed., Pearson Education India, 2021
M. Ross “An introduction to Probability Models, Academic Press INC, 11th edition.
Miller, I. and Miller, M., “John E. Freund's Mathematical Statistics with Applications”, 8th Ed., Pearson Education India, 2013
L. Ross, Differential equations, 3rd Edition, Wiley, 1984
E. Boyce and R. C. Di Prima, Elementary Differential equations and Boundary Value Problems, 7th Edition, Wiley, 2001.

CS1201

Course Number	CS1201
Course Credit	3-0-3-4.5
Course Title	Data Structure
Learning Mode	Offline
Learning Objectives	· Understand the principles and concepts of data structures and their importance in computer science. · Learn to implement various data structures and understand how different algorithms works. · Develop problem-solving skills by applying appropriate data structures to different computational problems. · Achieving proficiency in designing efficient algorithms.
Course Description	This course provides a comprehensive study of data structures and their applications in computer science. It focuses on the implementation, analysis, and use of various data structures such as arrays, linked lists, stacks, queues, trees, and graphs. Through theoretical concepts and practical programming exercises, this course aims to develop problem-solving and algorithmic thinking skills essential for advanced topics in computer science and software development.
Course Outline	· Introduction to Data Structure, · Time and space requirements, Asymptotic notations · Abstraction and Abstract data types · Linear Data Structure: stack, queue, list, and linked structure · Unfolding the recursion · Tree, Binary Tree, traversal · Search and Sorting, · Graph, traversal, MST, Shortest distance · Balanced Tree Practical component: Lab to be conducted on a 3-hour slot weekly. It will be conducted with the theory course so the topics for problems given in the lab are already initiated in the theory class.
Learning Outcome	· Understand Data Structure Fundamentals · Implement Basic Data Structures using a programming language · Analyse and Apply Algorithms · Design and Analyse Tree Structures · Understand the usage of graph and its related algorithms · Design and Implement Sorting and Searching Algorithms · Debug and Optimize Code
Assessment Method	Internal (Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Reading

Alfred V. Aho, John E. Hopcroft, Jeffrey D. Ullman, Data Structures and Algorithms, Published by Addison-Wesley
Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest and Clifford Stein., Introduction to Algorithms,
Mark Allen Weiss, Data Structures and Algorithm Analysis in Java
Robert Sedgewick and Kevin Wayne, Algorithms
Narasimha Karumanchi, Data Structures and Algorithms Made Easy

4.5

CH1201/ CH1101

Course Number	CH1201/CH1101
Course Credit	3-1-3-5.5
Course Title	Chemistry
Learning Mode	Offline
Learning Objectives	The course aims to lay a foundation for all three branches of chemistry, viz. Organic, Inorganic, and Physical Chemistry. The course aims to nurture knowledge to appreciate the interface of chemistry with other science and Engineering branches by combining theoretical concepts and experimental studies.
Course Description	This course introduces basic organic chemistry, inorganic chemistry and Physical chemistry to understand fundamental laws that governs various reactions, reaction rates, equilibrium, and their applications in daily life through relevant experimentation.
Course Outline	Module 1: Thermodynamics: The fundamental definition and concept, the zeroth and first law. Work, heat, energy and enthalpies. Second law: entropy, free energy and chemical potential. Change of Phase. Third law. Chemical equilibrium. Conductance of solutions, Kohlrausch’s law-ionic mobilities, Basic Electrochemistry. Module 2: Coordination chemistry: Crystal field theory and consequences color, magnetism, J.T distortion. Bioinorganic chemistry: Trace elements in biology, heme and non-heme oxygen carriers, haemoglobin and myoglobin; Organometallic chemistry. Module 3: Stereo and regio-chemistry of organic compounds, conformational analysis and conformers, Molecules devoid of point chirality (allenes and biphenyls); Significance of chirality in living systems, organic photochemistry, Modern techniques in structural elucidation of compounds (UV–Vis, IR, NMR). Module 4 (Lab Component): Experiments based on redox and complexometric titrations; synthesis and characterization of inorganic complexes and nanomaterials; synthesis and characterization of organic compounds; experiments based on chromatography; experiments based on pH and conductivity measurement; experiment related to chemical kinetics and spectroscopy.
Learning Outcome	Students will be able to 1. identify organic and inorganic molecules and relate them to daily life applications through experiments. 2. understand important hypothesis, laws and their derivations to intercept physical phenomenon of chemical reactions and apply them in hands-on experiments. 3. understand the importance of organic and inorganic molecules in our body and environment. 4. know important analytical techniques to intercept chemical entity. 5. approach organic and inorganic synthesis as a skillset for drug manufacturing, calculate limiting reagents and yields, use various analytical tools to characterize organic compounds, interpret and ascertain data related to Physical chemistry aspects and know laboratory safety measures, risk factors and scientific report writing skills.
Assessment Method	Theory: 20% Quiz and assignment, 30% Mid sem and 50% End semester exams for theory part (4 credits). Lab: 60% lab report, lab performance and assignment, 20% End semester exam for practical part, 20% viva/quiz (1.5 credits). Overall Weightage: Theory (70%), Lab (30%).

Suggested Reading:

Text books:

Vogel's Qualitative Inorganic Analysis, G. Svehla, 7^th Edition, Revised, Prentice Hall, 1996.
J. Elias, S. S. Manoharan and H. Raj, "Experiments in General Chemistry", Universities Press (India) Pvt. Ltd., 1997.

A. J. Elias, A Collection of Interesting General Chemistry Experiments, revised edition, Universities Press (India) Pvt. Ltd., 2007.

Albert Cotton, G. Wilkinson, C. A. Murillo, M. Bochmann, Advanced Inorganic Chemistry - 6^th Edition New Delhi: Wiley India, 2008.
Mukkanti, Practical Engineering Chemistry, B.S. Publications, Hyderabad, 2009.
Shriver and Atkins inorganic chemistry / Peter Atkins, Tina Overton, Jonathan Rourke, Mark Weller, Fraser Armstrong-5^th Edition – Oxford: UOP. 2012.
Atkins’ Physical Chemistry, Peter Atkins, Julio de Paula, James Keeler, Oxford University Press, 11^th Edition 2017.
L. Kapoor, A Textbook of Physical Chemistry, Vol: 1, 2 (6^th Edition, 2019), Vol: 3 (5^th Edition, 2020) MaGraw Hill.
R. Chatwal, S. K. Anand, Instrumental Methods of Chemical Analysis, 5^th Edition, Himalaya Publications, 2023.

5.5

ME1201/ ME1101

Course Number	ME1201/ME1101
Course Credit	0-0-3-1.5
Course Title	Mechanical Fabrication
Learning Mode	Fabrication work – hands on fabrication work in Workshop
Learning Objectives	Complies with PLOs 3-4. · This course aims to develop the concepts and skills of various mechanical fabrication methods. · Fabrication of metallic and non-metallic components, fabrication using bulk and sheet metals, subtractive and additive manufacturing methods, and assemble the parts
Course Description	This course is designed to fulfil the need of hand on experience about various approaches (conventional and CNC, subtractive and additive) of mechanical fabrication approaches. Prerequisite: NIL
Course Outline	The jobs for various shops should be planned such that they are the parts of an assembled item. The student groups will fabricate different parts in various shops which will involve some amount of their creativeness/input particularly in design and/or planning. Various components as required for the assembled part can be made using the following shops: Sheet Metal Working: Development, sheet cutting and fabrication of designated job using sheet metal (ferrous/nonferrous); Joining of required portions by soldering, in case part is desired to be made leak proof. Pattern Making and Foundry: Making of suitable pattern (wood); making of sand mould, melting of non-ferrous metal/alloy (Al or Al alloys), pouring, solidification. Observation/identification of various defects appeared on the component. Joining: Butt/lap/corner joint job fabrication as required of low carbon steel plates; weld quality inspection by dye-penetration test (non-destructive testing approach)of the component made. Demonstration of semi-automatic Gas Metal Arc welding (GMAW). Conventional machining: Operations on lathe and vertical milling to fabricate the required component. The fabrication of the component should cover various lathe operations like straight turning, facing, thread cutting, parting off etc., and operations using indexing mechanism on vertical milling. CNC centre: Fundamentals of CNC programming using G and M code; setting and operations of job using CNC lathe or milling, tool reference, work reference, tool offset, tool radius compensation to fabricate the component with a designed profile on Al/Al-alloy plate. 3D printing (Fused Filament Fabrication): (2 weeks) Create the model, select appropriate slicing and path for fabrication of a 3D job by layer deposition (additive manufacturing approach) using polymeric material. Demonstration on pattern fabrication using 3D printing.
Learning Outcome	· This course would enable the students to develop the concept of design, fabrication (subtractive and additive) for various engineering applications. Fabrication of components and assemble them. · The practical skill and hands on experience for various fabrication methods from bulk, sheet metal using conventional as well as CNC machines.
Assessment Method	Fabrication of components in each of the shops required for assembly of the given part; submission of reports for each shop, and quiz assessment.

Text and Reference books:

Hajra Choudhury, HazraChoudhary and Nirjhar Roy, 2007, Elements of Workshop Technology, vol. I,Mediapromoters and Publishers Pvt. Ltd.
W A J Chapman, Workshop Technology, 1998, Part -1, 1st South Asian Edition, Viva Book Pvt Ltd.
N. Rao, 2009, Manufacturing Technology, Vol.1, 3rd Ed., Tata McGraw Hill Publishing Company.
Adithan, B.S. Pabla, 2012, CNC machines, New Age International Publishers

1.5

ME1202/ ME1102

Course Number	ME1202/ ME1102
Course Number	Engineering Mechanics
L-T-P-C	3-1-0-4
Pre-requisites	Nil
Semester	Spring
Learning Mode	Lectures
Learning Objectives	Complies with PLOs 1, 4 · The objective of this first course in mechanics is to enable engineering students to analyze basic mechanics problems and apply vector-based approach to solve them.
Course Outline	1. Rigid body statics: Equivalent force system. Equations of equilibrium, Free body diagram, Reaction, Static indeterminacy. 2. Structures: 2D truss, Method of joints, Method of section. Beam, Frame, types of loading and supports, axial force, Bending moment, Shear force and Torque Diagrams for a member. 3. Friction: Dry friction (static and kinetic), wedge friction, disk friction (thrust bearing), belt friction, square threaded screw, journal bearings, Wheel friction, Rolling resistance. 4. Centroid and Moment of Inertia 5. Introduction to stress and strain: Definition of Stress, Normal and shear Stress. Relation between stress and strain, Cauchy formula. Stress in an axially loaded member and stress due to torsion in axisymmetric section
Learning Outcomes:	Following learning outcomes are expected after going through this course. · Learn and apply general mathematical and computer skills to solve basic mechanics problems. · Apply the vector-based approach to solve mechanics problems.
Assessment Method	Mid semester examination, End semester examination, Class test/Quiz, Tutorials

Reference Books

Shames, Engineering Mechanics: Statics and dynamics, 4th Ed, PHI, 2002.
P. Beer and E. R. Johnston, Vector Mechanics for Engineers, Vol I - Statics, 3rd Ed, Tata McGraw Hill, 2000.
L. Meriam and L. G. Kraige, Engineering Mechanics, Vol I - Statics, 5th Ed, John Wiley, 2002.
P. Popov, Engineering Mechanics of Solids, 2nd Ed, PHI, 1998.
P. Beer and E. R. Johnston, J.T. Dewolf, and D.F. Mazurek, Mechanics of Materials, 6th Ed, McGraw Hill Education (India) Pvt. Ltd., 2012.

IK1201

Indian Knowledge System (IKS)

TOTAL

22.5

Introduction to Metallurgical and Materials Engineering

Sl. No.

Subject Code

SEMESTER III

MM2101

Course Number	MM2101
Course Credit (L-T-P-C)	3-0-0 (3 AIU Credits)
Course Title	Introduction to Metallurgical and Materials Engineering
Learning Mode	Lectures
Prerequisite	None
Learning Objectives	To understand the theoretical description of crystal and bonding in solids and the atomic arrangement and defects in crystalline materials. To understand the structure-property correlation in materials.
Course Description	A foundational course delving into the interrelationship between microstructure, properties, and processing, providing understanding of the behaviour of various materials and their applications.
Course Content	Bonding in solids: Concept of energy versus interatomic separation for atoms, bonding in solids, primary interatomic bonding, secondary bonding. Properties of differently bonded solids. Property of materials in relation to crystal symmetry. Tensors. Structure of crystalline solids: Basic idea of lattice, crystalline and non-crystalline materials, unit cell, crystal systems, indexing planes and directions, Miller indices, coordination number, packing of atoms, voids, elements of symmetry. Defects in solids: Point, linear, planar and volume defects, equilibrium concentration of vacancies, Types of dislocations, Burgers vectors, slip systems, grain boundaries, twin and stacking faults. Mechanical properties of materials: Concept of stress and strain, Hooks law, elastic and plastic deformation, tensile properties, hardness. Structure-property correlation: Introduction to ceramic, polymer and composite – processing, structure, properties and applications.
Learning Outcome	Upon completion of this course the student will be able to: Identify the properties of material with respect to their crystal structure and bonding Correlate the influence of defects on material properties. Correlate the structure of crystalline materials with their properties.
Assessment Method	Assignments, Quizzes, Mid-semester examination, End-semester examination.

Text Books:

Materials Science and Engineering, an Introduction: William D. Callister, 7^th, John Wiley and Sons, 2007.
Materials Science and Engineering: V. Raghavan, 6^th, Prentice Hall India, 2015.

Reference Books:

Physical Foundation of Materials Science: Günter Gottstein, Springer, 2004.
An Introduction to Metallurgy: Sir Alan Cottrell, 2^nd, Universities Press, 2000.

MM2102

Mineral Processing and Process Metallurgy

Course Number	MM2102
Course Credit (L-T-P-C)	3-0-3 (4.5 AIU Credits)
Course Title	Mineral Processing and Process Metallurgy
Learning Mode	Lectures and Practical
Prerequisite	None
Learning Objectives	To understand various mineral processing techniques for metal extraction. To understand process technology for mineral beneficiation, extraction and refining of metals, especially non-ferrous metals.
Course Description	This course covers mineral beneficiation, separation techniques, and key metallurgical principles, focusing on thermodynamics, kinetics, and metallurgical processes.
Course Content	Mineral Engineering: Minerals of economic importance, laws of comminution, Principles of separation technologies (Gravity Separation, Froth Floatation, magnetic separation & Electrostatic separation) Beneficiation efficiency ratios and two product mass balance equation. Principles of Process Metallurgy: Thermodynamics (Free energy, Ellingham diagram, Predominance area Diagram), Kinetics (Rate laws, Order of reactions, solubility of gases in metal, Arrhenius Equation). Pyrometallurgy: Principles of drying, calcination, roasting, smelting (including flash smelting); Extraction of Fe, Cu, Pb, Ni, Mg, Zn, Ti. Hydrometallurgy: Theory of leaching, leaching techniques (bacterial leaching, Pressure leaching), leaching solvents, solvent extraction, Ion exchange, Cementation process, Examples (Bayer’s process for Alumina and Sherritt-Gorden process for Cu, Ni, Co), rare metals. Electrometallurgy: Principles of electrolysis, Faraday’s law of Electrolysis, Electro winning & electrorefining, Electrolysis of Fused salt (extraction of aluminium through Hall-Heroult process), Electrolysis of aqueous salt (extraction magnesium from sea-water through Dow’s process). Refining of metals: Principles & Techniques of refining: Selective dissolution, Liquation, zone refining, chemical and electrochemical method.
Learning Outcome	Upon completion of this course the student will be able to: Apply appropriate knowledge for: Mineral Beneficiation (comminution and separation; extraction and refining of metals). Appreciate the importance of scientific concepts for mineral beneficiation, extraction of non-ferrous metals from ores including their refinement.
Assessment Method	Assignments, Quizzes, Mid-semester examination, End-semester examination.

Text Books:

Mineral Processing Technology: B.A. Wills, 8^th, Butterworth Heinemann, Elsevier, 2015
Extraction of Nonferrous metals: H.S. Ray, R. Sridhar & K.P. Abraham, Affiliated East-West Press, 2018
Principles of extractive metallurgy: H.S. Ray & A. Ghosh, New Age International Publishers, 3^rd Edition, 2019

Reference Books:

Chemical Metallurgy: J.J. Moore, 2^nd Edition, Elsevier, 1990

4.5

MM2103

Thermodynamics and Phase Equilibria

Course Number	MM2103
Course Credit (L-T-P-C)	3-0-3 (4.5 AIU Credits)
Course Title	Thermodynamics and Phase Equilibria
Learning Mode	Lectures and Practical
Prerequisite	None
Learning Objectives	To understand how thermodynamics is fundamental to the study of materials engineering and apply the thermodynamics for engineering problem solving. To understand the stability criteria of various systems (vapour-solid, solid liquid, liquid-vapour) under consideration.
Course Description	The course provides basic understanding of different laws of thermodynamics, enthalpy, entropy, Gibbs free energy, solution (Ideal and Regular) etc. The course will help to plot and understand phase diagram, which are essential for developing new materials.
Course Content	Introduction to Thermodynamics: Concept of state, reversible and irreversible processes, path and state functions, extensive and intensive properties, kinetic theory of gases. First Law of Thermodynamics: Internal energy, enthalpy, constant volume and pressure process, isothermal and adiabatic process and heat capacity. Second Law of Thermodynamics: Equilibrium, entropy, most probable microstate, statistical concepts of entropy, Thermodynamical functions, Maxwell’s relations, Gibbs-Helmholtz stability. Third Law of Thermodynamics: Gibbs free energy vs temperature and Gibbs free energy vs. pressure, Clausius-Clapeyron equation, P-T diagram. Thermodynamic stability of materials. Ellingham diagram and its importance, application of electrochemical series. The behaviour of solutions, Phase equilibria, and phase diagram: Ideal solution, Gibb’s-Duhem equation, Raoults, and Henry’s law, the activity of a component, concept of chemical potential, regular solutions, free energy-composition diagrams for ideal and regular solutions and its relation to phase diagram, Gibbs phase rule, eutectic and eutectoid, peritectic and peritectoid diagrams. Ternary phase diagrams. Binodal and spinodal decomposition in metals, ceramics and polymers.
Learning Outcome	Upon completion of this course, the student will be able to: Understand the practical implication of laws of thermodynamics. Apply the laws of thermodynamics to solve common industrial important reactions. Appreciate the implications of various systems in metallurgical/allied industry.
Assessment Method	Assignments, Quizzes, Mid-semester examination, End-semester examination.

Text Books:

Introduction to Metallurgical Thermodynamics: David R. Gaskell, McGraw Hill, 4^th, 2009.
The laws of thermodynamics, P. Atkins, Oxford University Press. 2010
Phase Transformation: Porter and Easterling.

Reference Books:

Physical Chemistry of Metals: L. Darken and R.W. Gurry, McGraw-Hill, 1953.

Thermodynamics of Solids: Richard A. Swalin, 2^nd Ed., Wiley, 1972.

4.5

MM2104

Transport Phenomena

Course Number	MM2104
Course Credit (L-T-P-C)	3-1-0 (4 AIU Credits)
Course Title	Transport Phenomena
Learning Mode	Lecture and Tutorial
Prerequisite	None
Learning Objectives	To develop fundamental concepts governing the transport of momentum, energy and mass. To demonstrate the common mathematical formulation of transport problems to the students.
Course Description	This course introduces dimensional analysis, fluid mechanics, heat and mass transfer, and reaction kinetics, emphasizing transport phenomena and practical engineering applications.
Course Content	Dimensional Analysis: Introduction; Dimensions and Units; Buckingham's π theorem. Momentum Transfer: Fluid Properties and fluid as a Continuum; Viscosity; Dimensional Formula and Units of Viscosity; Effect of Temperature and Pressure on Viscosity; Laminar flow and Turbulent flow; Flow: Rate and continuity equation; Losses in Pipes; Head loss due to friction; Flow measurement; Flow past immersed objects, packed & fluidized beds. Heat Transfer: Modes of Heat Transfer: Introduction to conduction, convection, and radiation; Conduction: Heat transfer through a wall, Composite walls with materials in series, Composite walls with materials in parallel, Multidimensional heat transfer problems; Convection: Types of Convection, Film heat transfer coefficients, Newton's Law of Cooling; Radiation: Black body radiation; Law: Stefan-Boltzmann, Kirchhoff's Law; Radiation Properties: Emissivity, Receiving Properties; Radiation heat transfer; Factors affecting: Thermal conductivity of gases, liquids, solid metals and alloys; Heat transfer with change of phases: solidification, melting problems. Mass transfer: Diffusion; Laws of diffusion; Fick's first law of diffusion; Fick's second law of diffusion; Factors affecting Mass transfer coefficient k, Parameters affecting convective mass transfer; Application of dimensionless analysis; Homogenization of alloys; Formation of surface layers. Introduction to kinetics: Basic kinetic laws, order of reactions, rate constant, elementary and complex reactions, rate limiting steps and Arrhenius equations, theories of reaction rates - simple collision theory, activated complex theory.
Learning Outcome	Upon completion of the course, students will be able to: Estimate transport properties such as viscosity, conductivity and diffusivity. Develop the conservative equations of laws of momentum, energy and mass
Assessment Method	Assignments, Quizzes, Mid-semester examination, End-semester examination.

Text Books:

Fundamentals of Heat and Mass Transfer; 5^th Edition; F.P. Incropera and D.P. DeWitt, 2006; Wiley India.
Transport Phenomena; 2^nd Edition; R. Byron Bird, Warren E. Stewart, Edwin N. Lightfoot; 2021; John Wiley & Sons, Inc.
Fundamentals of Momentum, Heat, and Mass Transfer; 4^th Edition; Welty, James, Charles E. Wicks, R. E. Wilson, and Gregory L. Rorrer; 2000; New York: John Wiley and Sons Inc.,
Kinetics of Materials: R.W. Balluffi, S.M. Allen, and W.C. Carter, Wiley, 2005.

Reference Books:

Fundamental of Transport Phenomena and Metallurgical Process Modeling; Sujay Kumar Dutta; 2022; Springer
Transport Processes and Separation Process Principles; 4^th Edition; C. J. Geankoplis; PHI Learning Private Limited., New Delhi.
Transport Phenomena in Materials Processing; D. R. Poirier, G. H. Geiger; 2016; Springer International Publishing.

MM2105

Fundamentals of Polymer Science and Technology

Course Number	MM2105
Course Credit (L-T-P-C)	3-0-0 (3 credits AIU Credits)
Course Title	Fundamentals of Polymer Science and Technology
Learning Mode	Lecture
Prerequisite	None
Learning Objectives	To understand the structure-property correlation in polymers and develop knowledge of the mechanics underlying various polymerization techniques and polymer reactions. To understand the variables controlling the physical characteristics of polymers.
Course Description	This course will educate the students on the subject of polymers that constitute one of the most important materials used presently. The course will include fundamentals of synthesis, characterization, properties and also include discussion on the applications of polymers.
Course Content	Basic concepts: Molecular forces, chemical bonding, Configuration, Conformation, tacticity, molecular weight studies, molecular weight distribution, transitions in polymers, viscoelasticity, types of macromolecules, classification of polymers. Structure and property relationships: Amorphous and crystalline nature of polymers, factors affecting crystallization and melting, glassy state and glass transition temperature and factors influencing the glass transition temperature. Polymerization techniques: General features of chain growth polymerization - initiators, generation of initiators, free radical, anionic and cationic polymerization, ring opening polymerization, general features of step growth polymerization - mechanism of step growth polymerization, coordination polymerization, kinetics of addition, condensation and coordination polymerization, copolymerization mechanism and kinetics, homogeneous polymerization techniques- bulk, solution, heterogeneous polymerization techniques- emulsion, suspension, solid phase polymerization. Polymer solutions: Thermodynamics of polymer solutions, solution properties of polymers, solubility parameter, polymer chains' conformation in polymer solutions: Flory-Krigbaum and Flory-Huggins theories, solution viscosity, osmotic pressure, molecular size and molecular weight. Testing and characterization: End group analysis, colligative property measurement, light scattering, ultra-centrifugation, viscosity methods, gel permeation chromatography, IR, NMR, XRD, microscopy, thermal characterization, rheology/viscoelasticity, Mechanical properties testing - tensile, flexural, compressive, abrasion, endurance, fatigue, hardness, tear, resilience, impact and toughness. Advanced polymerization techniques: ATRP, RAFT.
Learning Outcome	Upon completion of this course the student will be able to: Recognize how polymers' structures and properties relate to one another. Choose appropriate polymerization techniques for polymer synthesis Select suitable characterization methods to characterize the polymers
Assessment Method	Assignments, Quizzes, Mid-semester examination, End-semester examination.

Text Books:

F.W. Billmeyer, Textbook of polymer science, 3^rd ed., John Wiley & Sons, Asia, New Delhi, 1994.
J. Young and P. A. Lovell, Introduction to Polymers, 2nd ed., CRC Press (Taylor and Francis Group) 2004.

Reference Books:

Fundamental of Transport Phenomena and Metallurgical Process Modeling; Sujay Kumar Dutta; 2022; Springer
Transport Processes and Separation Process Principles; 4^th Edition; C. J. Geankoplis; PHI Learning Private Limited., New Delhi.
Transport Phenomena in Materials Processing; D. R. Poirier, G. H. Geiger; 2016; Springer International Publishing.
V.R. Gowariker, N.V. Viswanathan, J. Sreedhar, Polymer Science, New Age International, 2010.

HS21XX

HSS Elective - I

TOTAL

Techniques of Materials Characterization - I

Sl. No.

Subject Code

SEMESTER IV

MM2201

Iron and Steel Making

Course Number	MM2201
Course Credit (L-T-P-C)	3-1-0 (4 credits AIU Credits)
Course Title	Iron and Steel Making
Learning Mode	Lecture and tutorial
Prerequisite	None
Learning Objectives	To understand the scientific principles for the production of iron and steel and process technology of ironmaking, steelmaking and continuous casting To introduce the emerging trends in iron and steelmaking technologies
Course Description	The course aims to instill in the students a scientific understanding of the iron and steel manufacturing process, from ore extraction to the final product, including its historical milestones.
Course Content	Ironmaking: Routes of modern steel making (BF-BOF, DRI-EAF), Thermodynamics of Ironmaking, Burden preparation (sintering, pelletization, coke making), Blast furnace Ironmaking (Design, operation, reactions and zones, direct & indirect reduction, burden distribution, Auxiliary fuel injection, RAFT calculations, RIST Diagram, Aerodynamics, development trends). Alternate routes of ironmaking: Sponge ironmaking, Smelting Reduction. Steelmaking Principles of Steelmaking: Basic thermodynamics & Kinetics of steelmaking. Primary Steelmaking: LD steelmaking converter, design, reactions, operations, refractories, development trends like Post combustion & slag splashing; EAF steelmaking. Secondary steelmaking: Ladle metallurgy, vacuum degassing, Inclusion refining. Casting of steel: Ingot Vs Continuous Casting, Continuous casting (Tundish Metallurgy, defects in CC products), neat net shape casting etc. Future trends: Clean steel & Hydrogen-assisted steelmaking.
Learning Outcome	Upon completion of this course the student will be able to: Appreciate the complexities in the production of iron and steel Apply the acquired knowledge to various processes like BF ironmaking, BOF steelmaking, Casting, EAF steelmaking etc Appreciate the latest green iron and steelmaking techniques
Assessment Method	Assignments, Quizzes, Mid-semester examination, End-semester examination.

Text Books:

F.W. Billmeyer, Textbook of polymer science, 3^rd ed., John Wiley & Sons, Asia, New Delhi, 1994.
J. Young and P. A. Lovell, Introduction to Polymers, 2nd ed., CRC Press (Taylor and Francis Group) 2004.

Reference Books:

Fundamental of Transport Phenomena and Metallurgical Process Modeling; Sujay Kumar Dutta; 2022; Springer
Transport Processes and Separation Process Principles; 4^th Edition; C. J. Geankoplis; PHI Learning Private Limited., New Delhi.
Transport Phenomena in Materials Processing; D. R. Poirier, G. H. Geiger; 2016; Springer International Publishing.
V.R. Gowariker, N.V. Viswanathan, J. Sreedhar, Polymer Science, New Age International, 2010.

MM2202

Course Number	MM2202
Course Credit (L-T-P-C)	3-0-3 (4.5 AIU Credits)
Course Title	Techniques of Materials Characterization - I
Learning Mode	Lecture and Practical
Prerequisite	None
Learning Objectives	To understand how material characterization is of paramount importance to the study of materials science. To understand the strength and weaknesses of different characterization techniques and gain hands-on training on different characterization techniques.
Course Description	The course involves i) the study of the crystal structure of solids. ii) the structural analysis of material at different length scales, such as micro, nano and angstrom levels, using different characterization techniques.
Course Content	Introduction: Importance and the need for materials characterization, crystal system, miller indices, Bravais lattice. Diffraction: Basics of diffraction and interference of light, Young’s double slit experiment, interpretation of diffraction from the single slit and multiple slits. X-ray Diffraction: Generation of X-Rays, X-Ray Diffraction (XRD), Bragg’s Law, Atomic scattering factor, structure factor, indexing of diffraction patterns, selection rules, estimation of peak intensity, phase identification and analysis by XRD, determination of structure and lattice parameters, strain and crystallite size measurements through XRD, effect of temperature on XRD. Reciprocal lattice and Ewald’s sphere. Optical Microscopy: Principles of optical microscopy, magnification, Rayleigh criterion, resolution limitation, Airy disk, depth of focus and field. Electron diffraction: Wave properties of the electron, electron-matter interactions, ring patterns, spot patterns, and Laue zones. Scanning Electron Microscopy: Principle, construction, and operation of Scanning Electron Microscope, SE and BSE imaging modes, Elemental analysis using Energy dispersive analysis of X-rays, sample preparation of different materials for SEM. Transmission electron microscope: Principle, construction, and working of Transmission Electron Microscope (TEM), the origin of contrast: mass-thickness contrast, electron diffraction pattern, Bright field and dark field images, sample preparation.
Learning Outcome	Upon completion of this course, the student will be able to: Understand the working principle and applications of various characterization techniques Choose an appropriate technique to characterize various microstructural aspects Characterize the microstructure of various materials by themselves
Assessment Method	Assignments, Quizzes, Mid-semester examination, End-semester examination.

Text Books:

Elements of X-Ray Diffraction: B.D. Cullity and S.R. Stock, 3rd Ed., Pearson, 2001.
Scanning Electron Microscopy and X-Ray Microanalysis: Joseph Goldstein, Eric Lifshin, Charles E. Lyman, David C. Joy and Patrick Echlin, 3rd Ed., Springer, 2003.

Reference Books:

Transmission Electron Microscopy: A Textbook for Materials Science: David B. Williams and C. Barry Carter, Springer, 2009.
Structure of Materials: An Introduction to Crystallography, Diffraction and Symmetry, Marc De Graef, Michael E. McHenry; 2^nd, Cambridge University Press, 2012.

4.5

MM2203

Phase Transformation and Diffusion

Course Number	MM2203
Course Credit (L-T-P-C)	3-1-0 (4 AIU Credits)
Course Title	Phase Transformation and Diffusion
Learning Mode	Lecture and Tutorial
Prerequisite	None
Learning Objectives	To understand the importance of phase transformation and diffusion in metallurgy To explain different types of phase transformations commonly encountered in metallic systems and understand the role of diffusion in phase transformations
Course Description	This course provides a foundation for understanding the phenomenological and atomistic kinetic process in materials. It provides a basis for the analysis for the evolution of structure during material processing.
Course Content	Fundamentals of Phase Transformations: Introduction to phase transformations, Types of phase transformations, Free energy and chemical potential, Free energy change estimation for phase transformations. Diffusion in Solids: Fick's laws of diffusion, Solution to Fick’s laws, Uphill diffusion and spinodal decomposition, Kirkendall effect. Structure of surfaces and interfaces, Grain boundaries and phase boundaries, Types of interfaces in materials, Energy of surfaces and interfaces, Interface energy and its impact on material properties. Nucleation, Growth Theories, and Kinetics of Phase Transformations: Nucleation theories, Homogeneous nucleation, Heterogeneous nucleation, Growth Theories, thermally activated growth, diffusion-controlled growth, interface controlled growth, coupled growth in eutectoid transformations, discontinuous precipitation, the kinetics of phase transformation, JMAK equation, TTT diagrams, CCT diagrams. Applications and Advanced Phase Transformations: Heat Treatment Processes, Quenching methods: Austempering, Martempering, Annealing, Normalization, Spherodization, and Homogenization, Martensitic transformations, Characteristics of martensitic transformations, Mechanisms and effects on material properties, Applications of TTT and CCT Diagrams, Phase transformations in Polymers and Ceramics, Specifics of phase transformations in polymers, Specifics of phase transformations in ceramics, Practical applications in materials engineering.
Learning Outcome	Upon completion of this course, the student will be able to: Grasp how the microstructure of the alloys is influenced by phase transformations. Acquire a fundamental understanding thermodynamic and kinetics aspects of phase transformation in metals and alloys. Differentiate the diffusion and diffusionless transformations in selected metallic systems.
Assessment Method	Assignments, Quizzes, Mid-semester examination, End-semester examination.

Text Books:

Solid State Phase transformation: V. Raghavan, Prentice Hall India, 1987.
Phase Transformation in Metals and Alloys, D.A. Porter and K. Easterling, 3^rd, CRC Press, 2009.

Reference Books:

Physical Metallurgy Principles, Robert E. Reed-Hill, Affiliated East-West Press, 2008.
Physical Metallurgy, Vijender Singh, Standard Publishers Distributors, 2010.
Introduction to Physical Metallurgy, Sidney H. Avner, Tata McGraw-Hill.

MM2204

Mechanical Behaviour of Materials

Course Number	MM2204
Course Credit (L-T-P-C)	3-0-3 (4.5 AIU Credits)
Course Title	Mechanical Behaviour of Materials
Learning Mode	Lecture and Practical
Prerequisite	None
Learning Objectives	To understand the behaviour of various materials when subjected to various forces/stresses. To understand the performance of metallic system during their service in terms of fatigue, fracture and creep.
Course Description	The course deals with the behaviour of various materials when they are subjected to various mechanical stresses at ambient and at high temperatures.
Course Content	Dislocation theory: Dislocation motion: jogs, kinks, cross-slip, climb, Peierls stress, stress field of dislocation, forces on dislocations, dislocation multiplication, interaction of dislocations with defects, dislocation dissociation, stacking faults. Plasticity: Elements of plasticity, Von Mises and Tresca criterion, Single Crystal slip, Critically resolved shear stress. Tensile testing (engineering and true), Work-hardening, yield point phenomena, necking. Hardness testing. Mechanical behaviour of polymers and ceramics. Strengthening Mechanisms: Strain hardening, solid solution strengthening, Dispersion hardening, grain size strengthening and Hall-Petch relationship, Precipitate hardening. Fracture: Types of fracture, brittle fracture, Griffith’s criteria, fracture in ductile material, fracture toughness, notch effects. Linear elastic fracture mechanics and elasto-plastic fracture mechanics. Ductile to brittle transition. Fatigue: Fatigue testing, S/N curve, low cycle fatigue, structural features, surface effects, mechanisms. Creep: Creep testing, creep curve, creep mechanisms, diffusion creep, dislocation creep, superplasticity.
Learning Outcome	After successfully completing the course, the student will be able to Interpret the deformation behaviour of engineering materials under various loading conditions for various applications. Gain the knowledge of dislocation theory and its correlation to the strengthening mechanisms. Design materials with improved creep, fatigue and fracture properties.
Assessment Method	Assignments, Quizzes, Mid-semester examination, End-semester examination.

Text Books:

Mechanical Metallurgy: G.E. Dieter, 3^rd, McGraw Hill, 2017.

Reference Books:

Mechanical Behavior of Materials: Thomas H. Courtney, 2^nd, Waveland Press Inc., 2005.
Introduction to Dislocations: D. Hull and D.J. Bacon, Butterworth-Heinemann, Elsevier, 2011.
Deformation and Fracture Mechanics: R.W. Hertzberg, R.P. Vinci, J.L. Hertzberg, 5^th, Wiley, 2012.

4.5

MM2205

Welding and Solidification

Course Number	MM2205
Course Credit (L-T-P-C)	3-0-0 (3 credits AIU Credits)
Course Title	Welding and Solidification
Learning Mode	Lecture
Prerequisite	None
Learning Objectives	To know about the relevance of solidification of metals and understand the challenges in joining of metals. To understand the thermodynamics and kinetics of the solidification and welding processes.
Course Description	In this course students explore thermodynamics and kinetics of solidification, metal casting, and various welding processes, with a focus on heat transfer and welding defects.
Course Content	Thermodynamics and kinetics of solidification: Thermodynamics of undercooled melts, nucleation process, kinetics of growth, growth mechanisms: continuous growth, stepwise growth. Solidification of pure metals and alloys: - Role of undercooling and Gibbs-Thomson effect on solidification, solutal undercooling, constitutional undercooling, Mullins-Sekerka instability, cellular and dendritic growth, eutectic growth. single crystal growth techniques, zone refining. Metal casting: Pattern and moulds designing, feeding, gating, risering, melting and casting practices, different types of casting: sand casting, die casting, pressure casting, continuous casting, investment casting, casting defects and repair, Ingot structure: chill zone, columnar zone, equiaxed zone. rate of solidification, heat transfer during solidification, Biot number. Welding: Theory and classification of welding, Heat transfer, fluid flow, and solute distribution during welding, submerged arc welding, gas metal arc welding or MIG/MAG welding, TIG welding, resistance welding. Other joining processes, soldering, brazing, diffusion bonding, problems associated with welding of steels and aluminium alloys, defects in welded joints. Solid state welding technique: Friction welding, friction stir welding.
Learning Outcome	After successfully completing the course, the student will be able to Gain insight about casting and solidification. Understand the difficulties of joining metals and come up with solutions. Appreciate the advancements in the solidification and welding of metals from research and industrial perspectives.
Assessment Method	Assignments, Quizzes, Mid-semester examination, End-semester examination.

Text Books:

Solidification Processing; Fleming, M.C., McGraw-Hill, N.Y., 1974
Science and Engineering of Casting Solidification; Stefanescu, D.M., Kluwar Publications, 2002
Applied Welding Engineering: Process, Codes and Standard; R.Singh,. Elsevier Inc.,2012
Advanced Welding processes, Norrish, J., Woodhead, Woodhead Publishing, 2006
Solidification and Casting, Davies, G.J., John Wiley and Sons, 1973

XX22PQ

IDE-I

TOTAL

Thermomechanical Processing of Metallic Materials

Sl. No.

Subject Code

SEMESTER V

MM3101

Course Number	MM3101
Course Credit (L-T-P-C)	3-0-2 (4 AIU Credits)
Course Title	Thermomechanical Processing of Metallic Materials
Learning Mode	Lecture and Practical
Prerequisite	None
Learning Objectives	To understand the dynamic phenomena occurring at deformation of materials at elevated temperatures and their kinetics. To understand different metal forming technologies and their application in controlling the microstructure for specific structural applications.
Course Description	A course exploring the techniques used to modify the microstructure and properties of metals through the combined application of heat and mechanical deformation.
Course Content	Microstructure: Concept of microstructure and micro structural features, Introduction to texture. Crystal plasticity: Deformation in polycrystals, Concept of dislocations and dislocation structures, slip and twinning. Softening mechanisms: (i) Recovery - mechanism and kinetics, structural changes during recovery. Dislocation migration and annihilation, polygonization, subgrain formation. (ii) Recrystallization - mechanism and kinetics, JMAK model. Particle stimulated nucleation. (iii) Grain growth – mechanism and kinetics. Abnormal grain growth. Hot deformation: Dynamic recovery and dynamic recrystallization. Forming technologies: 1. Classification of forming processes 2. Mechanics of metalworking (slab and uniform energy methods) 3. Concept of flow stress and its determination 4. Temperature in metal working (hot and cold working) 5. Strain rate effects 6. Role of friction and residual stresses 7. Concept of workability 8. Microstructure characterization after cold rolling/working, extrusion and forging Case studies: (i) Production of aluminium beverage cans (ii) Microstructure and texture control in electrical steels (iii) Steel for car body applications (iv) Microstructure control via grain boundary engineering
Learning Outcome	After completion of this course, the student will be able to: Understand the deformation behaviour of metallic materials under hot working conditions. Achieve required properties through microstructure control and apply the knowledge for designing materials for various industrial applications.
Assessment Method	Assignments, Quizzes, Mid-semester examination, End-semester examination.

Text Book:

Thermo-mechanical Processing of Metallic Materials: B. Verlinden, J. Driver, I. Samajdar and R.D. Doherty, Pergamon Materials Science, Elsevier, 2007.

Reference Book:

Recrystallization and Related Annealing Phenomena, F.J. Humphreys and M. Hatherly, 2^nd Eds, Elsevier, 2004.
Metal forming: Mechanics and Metallurgy: W.F. Hosford and R.M. Caddell, 4^th, Cambridge University Press, 2014.

MM3102

Computational Materials Science

Course Number	MM3102
Course Credit (L-T-P-C)	2-1-0 (3 (AIU Credits)
Course Title	Computational Materials Science
Learning Mode	Lecture and Tutorial
Prerequisite	None
Learning Objectives	To introduce students to the field of computational materials science. To learn basic numerical methods to solve ordinary differential equations (ODEs) and partial differential equations (PDEs). Role of ODEs and PDEs to solve problems related to materials science and engineering.
Course Description	This course introduces the students to different computer simulations and modelling techniques to investigate the properties and behavior of materials at various length scales.
Course Content	Statistical analysis: p-value, confidence levelling, regression and curve fitting. Introduction to numerical methods: Numerical methods for solving problems. Error estimation, the accuracy of numerical methods. Role of ODEs and PDEs in solving the problems of the physical world Ordinary differential equations: Euler and Runge-Kutta methods, FFT Partial differential equations: classification, elliptic, parabolic, and hyperbolic PDEs, Dirichlet, Neumann, and mixed boundary value problems, Numerical solution of PDEs: relaxation methods for solving elliptic PDEs, explicit and implicit methods, Calculus of variations and variational techniques for solving PDEs, introduction to Finite element method, method of weighted residuals, weak and Galerkin forms Application of numerical methods to solve problems related to materials science: Diffusion (Carburization), spinodal decomposition, grain growth, solidification, Zener pinning. Coding using modern computer languages.
Learning Outcome	Upon completion of this course, the student will be able to: Understand the importance of modelling and simulation in materials engineering. Learn numerical techniques to solve ordinary and partial differential equations. Understand the numerical approaches employed in modelling and simulation in materials science and engineering.
Assessment Method	Assignments, Quizzes, Mid-semester examination, End-semester examination.

Text Books:

Arfken, G.B., and Weber, H.J., Mathematical Methods for Physicists, Sixth Edition, Academic Press, 2005.
H., Teukolsky, S.A., Vetterling, W.T., and Flannery, B.P., Numerical Recipes in C/FORTRAN – The art of Scheme of Instruction 2016 Page 278
Scientific Computing, Second Edn, Cambridge University Press, 1998.

Reference Books:

Lynch, D.R., Numerical Partial Differential Equations for Environmental Scientists and Engineers – A First Practical Course, Springer, New York, 2005

MM3103

Engineering Polymers

Course Number	MM3103
Course Credit (L-T-P-C)	3-0-2 (4 credits AIU Credits)
Course Title	Engineering Polymers
Learning Mode	Lecture and Practical
Prerequisite	None
Learning Objectives	To support to comprehend the relationship between structure and properties as well as the uses of engineering polymers. To disseminate information about the characteristics and uses of engineering polymers. To comprehend the purposes of various additives, as well as the kinds, mechanisms, and technical specifications needed for their efficient assessment. Testing products for predicting product performance.
Course Description	This course introduces polymers as engineering materials. This course will also cover the various aspects associated with different engineering polymers such as polymerization processes, morphology, crystallinity, thermal transitions, viscoelasticity, structure-property correlation, compounding and applications.
Course Content	Structure property relationship in polymers: The synthesis, characteristics, and uses of thermoplastic engineering polymers include polyesters -PET, PBT, polyacetals, PC, LCPs, modified polyamides, and polyamides. High temperature resistant thermoplastic engineering polymers, such as PTFE, PCTFE, PVDF, PPO, PPS, polysulphones, PEEK, polyimides, polybenzimidazoles, and aromatic polyamides- Synthesis, properties & applications. Thermoset engineering polymers. Blends of engineering polymers. Additives and engineering polymer compounding: fillers, plasticizers, lubricants, colorants, fire retardants, coupling agents, blowing agents, UV stabilizer, antistatic agents, anti-blocking agents, slip and anti-slip agents, processing aids, antioxidants, stabilizers, lubricants, and toughening agents. Engineering polymer processing- Characterization and testing of engineered polymers.
Learning Outcome	At the end of the course the student will be able to Comprehend the significance of engineering polymers. Acquire fundamental knowledge about characteristics of polymers Select appropriate processing, compounding, and additive methods.to create various engineering polymeric compound grades. Will be able to prepare the test sample for various polymer testing operations. Will be able to measure the polymer properties.
Assessment Method	Assignments, Quizzes, Mid-semester examination, End-semester examination.

Text books:

Engineering Plastics Handbook: James M. Margolis, McGraw Hill, 2006.
Plastic Materials: J.A. Brydson, 6th Ed., Elsevier, 1995.

References books:

Industrial Polymers, Specialty Polymers, and Their Applications: Manas Chanda, Salil K. Roy, CRC Press, 2008.
Specialty Plastics: R.W. Dyson, 2nd Ed., Blackie Academic & Professional, 1988.

Modern Plastics Handbook: C.A. Harper, McGraw Hill, 2000.

MM3104

Ceramic Science and Technology

Course Number	MM3104
Course Credit (L-T-P-C)	3-0-2 (4 AIU Credits)
Course Title	Ceramic Science and Technology
Learning Mode	Lecture and Practical
Prerequisite	None
Learning Objectives	To classify ceramic materials and distinguish them from metals and polymers in relation to their properties and behaviours. To understand the structure of ceramic materials in different length scales and role of processing on structure and microstructure. To understand different industrially relevant processing operations for making of ceramic materials.
Course Description	This course provides an introduction to the diverse world of ceramic materials along with their processing details (with an emphasis on sintering) and selected properties.
Course Content	Introduction to Ceramic Science: Bonding in ceramics, Pauling’s rules, Ceramic crystal structures (rocksalt, fluorite, spinel, perovskite), Kröger-Vink notation, defects in ceramic. Defect equilibria, Brouwer diagram. Diffusion in ceramics. Fundamentals of glass science. Ceramic phase diagrams: binary and ternary systems. Physical properties of ceramics (porosity, bulk density, permeability, water absorption, specific gravity) Basics of Ceramic Processing: Synthesis and characterization of ceramic powders. selection of refractory raw materials (natural, synthetic, additives, binders) for specific products. Colloidal processing, rheology of suspensions, ceramic forming methods, and drying. Science of sintering, microstructure development. Properties of ceramics: Fracture behaviour of ceramic materials, The Weibull distribution, Toughening mechanism. Dielectric and piezoelectric ceramics. Applications of ceramics: Traditional ceramics, Abrasives, and high temperature ceramics (refractories and UHTCs). Glass and glass-ceramics.
Learning Outcome	Upon completing of this course, the student will be able to Identify the properties of ceramics with respect to their crystal structure and composition (between oxide and non-oxide). Interpret microstructure property correlation of sintered ceramic materials based on different processing operations. Distinguish different ceramic materials and their utility for diverse industrial applications ranging from traditional to advance ceramic sectors.
Assessment Method	Assignments, Quizzes, Mid-semester examination, End-semester examination.

Textbooks:

Introduction to Ceramics: W.D. Kingery, H.K. Bowen, D.R. Uhlmann, 2^nd, Wiley, 1976.
Ceramic Processing and Sintering: M.N. Rahaman, Marcel Dekker, 1995
Ceramic Materials: Science and Engineering: C. Barry Carter, M. Norton, Springer, 2^nd, 2013.

Reference Books:

Fundamentals of Ceramics: M.W. Barsoum, McGraw Hill, 1997.
Introduction to Ceramics, 2^nd, W. David Kingery, H.K. Bowen, Donald R. Uhlmann, Wiley, 1976.
A Concise Introduction to Ceramics: G.C. Phillips, VNR Publications, 1991.

MM3105

Metallography and Heat Treatment Laboratory

Course Number	MM3105
Course Credit (L-T-P-C)	0-0-2 (1 AIU Credits)
Course Title	Metallography and Heat Treatment Laboratory
Learning Mode	Practical
Prerequisite	None
Learning Objectives	To understand the metallographic sample preparation of metals. To understand the basic microstructural analysis of metals.
Course Description	This lab course focuses on studying the microstructures of metals and alloys to gain insights into their composition and structural characteristics. Additionally, it explores the various types of heat treatments and their impact on properties of materials.
Course Content	Metallographic sample preparation: Sample cutting, mounting, grinding, dry and wet polishing. Etching: chemical etching, thermal etching. Quantification of microstructures: ASTM grain size number, calculating grain size, mean intercept method, Jefferies method, determining volume fraction of phases Microstructure of ferrous alloys: cast iron, 304 stainless steel, pearlitic steel, annealed and deformed steels, microstructure of quenched and tempered steel. Microstructure of non-ferrous alloys: Aluminium alloy, copper and brass microstructure, deformed and recrystallized microstructure. Surface hardening: Carburizing, nitriding of steel, verification of Harris equation. Hardenability test, Jominy end quench test Precipitation hardening in Al alloy: Homogenization, solutionized, quenching and ageing of AA7075, hardness measurement.
Learning Outcome	Upon completing of this course, the student will be able to Understand the microstructure of ferrous and nonferrous alloys. Distinguish the metallographic techniques, microstructure and hardening process different commercially important metal alloys.
Assessment Method	Assignments, Quizzes, Mid-semester examination, End-semester examination.

XX31PQ

IDE-II

TOTAL

Techniques of Materials Characterization – II

Sl. No.

Subject Code

SEMESTER VI

MM3201

Course Number	MM3201
Course Credit (L-T-P-C)	3-0-3 (4.5 AIU Credits)
Course Title	Techniques of Materials Characterization – II
Learning Mode	Lecture and Practical
Prerequisite	None
Learning Objectives	To understand different aspects materials characterization involving spectroscopy, thermal techniques. To learn about non-destructive characterisation techniques. To obtain hands-on training on different characterization techniques.
Course Description	This course provides an introduction to important thermal and spectroscopic characterization techniques along with the basics of powder characterization and powder processing methods.
Course Content	Spectroscopy: Vibrational spectroscopy, Principles of vibrational spectroscopy, Infrared and Raman activity, Fourier transform infrared spectroscopy, Raman spectroscopy, Micro-Raman. XPS, XRF. UV-visible spectroscopy: Beer’s law, Instrumentation, Quantitative analysis. NMR Atomic absorption/ emission spectroscopy: ICP methods for compositional analyses, the difference between ICP-mass spectroscopy and optical/atomic emission methods. Thermal analysis: Instrumentation and principles of techniques used for thermal analysis (DSC, TG-DTA, DMA, EGA), a combined method of thermal analysis and their applications in materials characterization. Particle/grain characterization: Particle size analysis techniques based on light scattering, DLS, gas adsorption (BET), and Gas pycnometer for density measurement. Non-destructive techniques: dye-penetration, ultrasonic, radiography, eddy current, acoustic emission and magnetic particle methods.
Learning Outcome	Upon completion of this course, the student will be able to: Understand the basics of thermal and spectroscopic analysis tools Gain knowledge on the utility of non-destructive characterisation tools and their industrial utility. Gain hands on expertise of thermal, spectroscopic, and non-destructive characterisation techniques.
Assessment Method	Assignments, Quizzes, Mid-semester examination, End-semester examination.

Textbook:

Materials Characterization: Introduction to Microscopic and Spectroscopic Methods; Y. Leng.
Fundamentals of Molecular Spectroscopy; C. N. Banwell and E. M. McCash.
Surface Analysis: The Principal Techniques; J. C. Vickerman, I. Gilmore.

Reference Books:

ASM Handbook: Materials Characterization, ASM International, 2008.
Yang Leng: Materials Characterization-Introduction to Microscopic and Spectroscopic Methods, John Wiley & Sons (Asia) Pte Ltd., 2008.
Robert F. Speyer: Thermal Analysis of Materials, Marcel Dekker Inc., New York, 1994.

Prentice Hall India, 2010.
Heat Treatment of Metals: Vijendra Singh, Standard Publishers Distributors, 2009

4.5

MM3202

Corrosion and Corrosion Prevention

Course Number	MM3202
Course Credit (L-T-P-C)	3-0-2 (4 AIU Credits)
Course Title	Corrosion and Corrosion Prevention
Learning Mode	Lecture and Practical
Prerequisite	None
Learning Objectives	To measure and compare the corrosion rates of two different metals/alloys. Hands-on training on electrochemical characterization techniques.
Course Description	The purpose of the course is to provide undergraduate students a scientific knowledge on corrosion engineering along with hands-on training on corrosion measurements and their interpretations.
Course Content	Introduction to terminologies of corrosion experimentation: Oxidation/reduction electrode potential series, listing of half-cell reactions. Practical implications of Nernst equation, standard reference electrode cells, Electrochemical cells, electrolyte, galvanic series, and galvanic corrosion. Corrosion experiments and rate calculations: Preparation of corrosion test samples per ASTM G1. NACE and ASTM standards for measurements of corrosion rates. Salt spray test methods and standards, stress corrosion cracking tests, immersion corrosion testing per ASTM G3. Corrosion rates calculations from Tafel measurements as per the ASTM G102 standard. DC-experimental testing techniques: Potentiodynamic polarization measurement as per ASTM G5, potentiosatat, galvanostats, cyclic voltammetry, chrono- amperomtery, chrono-potentiometry, potentiodynamic analysis and Tafel extrapolation and linear polarization resistance methods. AC-impedance spectroscopy in corrosion measurements: Assessments related to charge transfer resistance and double layer capacitance from impedance tests. Interpretation of Nyquist and Bode plots. Modelling of impedance data to fit the experimental data. Case studies: Use of AC impedance methods to study the corrosion behaviour of implant alloys.
Learning Outcome	Upon completion of this course, the student will be able to: Correlate the utility of different electrochemical testing techniques. Implement and interpret the data of DC and AC corrosion testing.
Assessment Method	Assignments, Quizzes, Mid-semester examination, End-semester examination.

Text Books:

Corrosion Science and Technology, By David Talbot, James Talbot, CRC Press, 1998

Reference Books:

J. Bundy, J. Dillard, R. Luedemann, Use of A.C. impedance methods to study the corrosion behaviour of implant alloys, Biomaterials, Volume 14, Issue 7, 1993 Pages 529-536.
Harrington, P. van den Driesch, Mechanism and equivalent circuits in electrochemical impedance spectroscopy, Electrochimica Acta, Volume 56, Issue 23, 2011 Pages 8005-8013.
ASTM Corrosion Standards and Electrochemical Measurements in Corrosion Testing, ASTM International.

MM3203

Functional Materials

Course Number	MM3203
Course Credit (L-T-P-C)	3-0-0 (3 AIU Credits)
Course Title	Functional Materials
Learning Mode	Lecture
Prerequisite	None
Learning Objectives	To identify various ranges of functions displayed by materials and correlation of the same with respect to their properties. To understand the fundamental reasons due to which this variety of properties is possible for different materials. To evaluate the efficacy of a particular material with respect to emerging and conventional industrial applications.
Course Description	This course provides general overview on the origins of functional properties of materials, their wide varieties and their usability in different advanced technological applications.
Course Content	Free Electron Theory of Metals: Band theory, classification of materials based on band theory viz. conductors, conductors-classification and properties, factors affecting conductivity/resistivity of conductors, various conducting materials: composition, properties and applications. Resistors: Materials used for heating elements viz. nichrome, kanthal, silicon carbide and molybdenum, their composition, properties and applications Semiconductors: Intrinsic and extrinsic semi-conductors, II-VI, III-V and IV-IV group semiconductors, effects of doping. Magnetic materials: Sources of magnetism-orbital and spin motion of electron, types of magnetism: Dia-, para-, ferro-, ferri- and antiferro-magnetism, domain theory, types of magnetic materials: soft and hard magnetic materials and ferrites. GMR. Ferro-electric, Piezo-electric and Dielectric materials: Principle, materials and their applications; Ferroelectric ceramic materials, Basic Ceramic Dielectric formulation for capacitors. Multi-Layer Capacitors. Super conductivity: BCS theory, Meissner effect, materials, Type I and II superconductors.
Learning Outcome	Upon completing of this course, the student will be able to Identify the properties of metals, ceramics and polymers in relation to different functional properties Understand the fundamental reasons which enable a particular material to display a particular function Classify and distinguish different types of functional properties and correlate the same with relevant industrial applications
Assessment Method	Assignments, Quizzes, Mid-semester examination, End-semester examination.

Text Books:

Introduction to the Electronic Properties of Materials: David C. Jiles, 2nd Ed., CRC Press, 2001.
Electronic Materials Science: Eugene A. Irene, Wiley, 2005.
An Introduction to Electronic Materials for Engineers: Zhengwei Li, Nigel M. Sammes, 2^nd, World Scientific Publishing Company Pvt. Ltd., 2011.

Reference Books:

Electronic Materials and Devices: David K. Ferry, Jonathan P. Bird, Wiley, 2001.
Introduction to Magnetism and Magnetic Materials: David Jiles, 3rd Ed., CRC Press, 2015.

Electroceramics: Materials, Properties, Applications: A.J. Moulson, J.M. Herbert, Wiley, 2003.

MM3204

Non-ferrous Metals and Alloys

Course Number	MM3204
Course Credit (L-T-P-C)	3-0-0 (3 AIU Credits)
Course Title	Non-ferrous Metals and Alloys
Learning Mode	Lecture
Prerequisite	None
Learning Objectives	To understand the different types of nonferrous alloys. To understand the physical and mechanical characteristics of nonferrous alloys.
Course Description	A specialized course covering the properties, applications, and processing of non-ferrous alloys, including aluminum, copper, magnesium, titanium, nickel, cobalt, and refractory metals.
Course Content	Non-ferrous alloys: (i) Classification of aluminium alloys, heat treatable and non-heat-treatable alloys, tempers, grain refiners, phase diagram, heat treatment, properties and applications. (ii) Copper alloys, copper-zinc phase diagram, brass and bronze. Properties and applications (iii) Magnesium and its alloys, properties and applications, corrosion behavior. (iv) Titanium alloys, alpha, beta, alpha-beta alloys, processing characteristics, properties and applications. (v) Nickel and cobalt based alloys and superalloys, properties and applications (vi) Refractory metals-based alloys, intermetallics. Case studies: Materials design and selection for (i) Automobile engine, (ii) turbine blades and (iii) bio-implants.
Learning Outcome	Upon completion of this course, the student will be able to: Gain in-depth knowledge of non-ferrous alloys, their properties and applications. Understand the mechanical processing, heat treatments and corrosion of various nonferrous alloys.
Assessment Method	Assignments, Quizzes, Mid-semester examination, End-semester examination.

Text Books:

Physical Metallurgy and Advanced Materials: R.E. Smallman, A.H.W. Ngan, 7^th, Butterworth Heinemann, Elsevier, 2007.
Physical Metallurgy: Principles and Design: G.N. Haidemenopoulos, CRC Press, 2018.

Reference Books:

Concepts in Physical Metallurgy: A. Lavakumar, Morgan and Claypool Publishers, IOP Science, 2017
Nonferrous Physical Metallurgy: Robert Raudebaugh, Literary Licensing, 2013.
Nickel, Cobalt and their alloys: Joseph R. Davis, ASM International, 2000.

MM3205

Capstone Laboratory

Course Number	MM3205
Course Credit (L-T-P-C)	0-0-4 (2 AIU Credits)
Course Title	Capstone Laboratory
Learning Mode	Practical
Prerequisite	None
Learning Objectives	To allow students to implement and test their designs, integrating theoretical knowledge with practical application. To enhance hands-on learning, reinforce theoretical concepts, and promote creativity.
Course Description	Capstone Laboratory course is designed to integrate and apply knowledge and skills acquired throughout a student's academic program in the Metallurgical and Materials Engineering department.
Course Content	Capstone projects to be decided by the course instructor Few suggested projects are: Environmental Barrier Coatings for Gas Turbine Engines Finite Element Analysis of Metal Forming Metal Extraction from Ores obtained from Indian Mines Development of Aluminium alloys and Study the Effect of Heat Treatment on Properties Ceramic Musical Instrument Making through Traditional Techniques Development of Metal Matrix Composite for High Temperature Application Piezo sensor for Determination of Force Involved in Cricket Shot Demonstrations of few polymerization techniques for synthesis of porous polymers/ smart polymers/ self-healing polymers
Learning Outcome	After doing the laboratory course the student will be able to Apply the knowledge acquired from MME program in real-life situation. Able to understand theoretical concepts more effectively and come up with new ideas.
Assessment Method	Assignments, Quizzes, Mid-semester examination, End-semester examination.

Text Books:

Reference Books:

MM3206

Metals Processing Laboratory

Course Number	MM3206
Course Credit (L-T-P-C)	0-0-3 (1.5 AIU Credits)
Course Title	Metals Processing Laboratory
Learning Mode	Practical
Prerequisite	None
Learning Objectives	To learn various metal casting techniques and identify common casting defects, microstructure of casting. To learn various metal forming techniques including welding. To observe the microstructural changes imparted by various processing techniques.
Course Description	The course covers the ingot casting, casting defects, solidification microstructures, recrystallization, the shape memory effect in Nitinol, and various welding techniques.
Course Content	Ingot casting: Casting design, Melting furnaces, die casting of metal and alloy, shape casting, moiling, refining and pouring. Defects in castings: Physical inspection, hot tear cracks, pores, voids. Solidification microstructures: Dendritic microstructure, grain structure, homogenization, alloying element addition during casting. Recrystallization and annealing: recrystallization in copper and aluminium alloys. Shape memory effect: Reversible phase transition in Nitinol. Welding: arc welding, soldering, friction stir welding, welding microstructures,
Learning Outcome	After doing the laboratory course the student will be able to Understand the different metal casting methods for various applications. Understand various secondary processing techniques including metal forming and joining. Understand the effect of various processes on microstructural evolution.
Assessment Method	Assignments, Quizzes, Mid-semester examination, End-semester examination.

Text Books:

Heat Treatment: Principles and Techniques: T.V. Rajan, C.P. Sharma, Ashok Sharma, 2^nd, Prentice Hall India, 2010.
Heat Treatment of Metals: J.L. Smith, G.M. Russel, S.C. Bhatia, Vol. 1, CBS Publishers, 2008.
Heat Treatment of Metals: Vijendra Singh, Standard Publishers Distributors, 2009.

Reference Books:

Heat treatment of Steel: Hardening, Tempering and Case Hardening: H.R. Badger, Forgotten Books, 2018.

1.5

TOTAL

Sl. No.	Subject Code	SEMESTER VII	L	T	P	C
1.	MM41XX	Departmental Elective - I	3	0	0	3
2.	MM41XX	Departmental Elective - II	3	0	0	3
3.	HS41XX	HSS Elective - II	3	0	0	3
4.	XX41PQ	IDE-III	3	0	0	3
5.	MM4198	Summer Internship*	0	0	12	3
6.	MM4199	Project – I	0	0	12	6
TOTAL			12	0	24	21

Note :

* For specific cases of internship after VI^th Semester, the performance evaluation would be made on joining the VII^th Semester and graded accordingly in the VII^th Semester:

Note :

a) (i) Summer internship (*) period of at least 60 days’ (8 weeks) duration begins in the intervening vacation between Semester VI and VII that may be done in industry / R&D / Academic Institutions including IIT Patna. The evaluation would comprise combined grading based on host supervisor evaluation, project internship report after plagiarism check and seminar presentation at the Department (DAPC to coordinate) with equal weightage of each of the three components stated herein.

a) (ii) Further, on return from internship, students will be evaluated for internship work through combined grading based on host supervisor evaluation, project internship report after plagiarism check, and presentation evaluation by the parent department with equal weightage of each component.

b) (i) In the VII^th semester, students can opt for a semester long internship on recommendation of the DAPC and approval of the Competent Authority.

b) (ii) On approval of semester long internship, at the maximum two courses (properly mapped/aligned syllabus) at par with institute electives may be opted from NPTEL and / or SWAYAM and the other two more should be done at the institute through course overloading in any other semester (either before or after the internship) and/or during following summer semester.
b) (iii) The candidates opting two courses from NPTEL and / or SWAYAM would be required to appear in the examination at the Institute as scheduled in the Academic Calendar.

Departmental Elective - I

Sl. No.

Subject Code

Departmental Elective - I

MM4101

Environmental Sustainability and Industrial Safety

Course Number	MM4101
Course Credit (L-T-P-C)	3-0-0 (3 AIU Credits)
Course Title	Environmental Sustainability and Industrial Safety
Learning Mode	Lecture
Prerequisite
Learning Objectives	The ability to select and use the discipline's knowledge, methods, and cutting-edge instruments to domains widely construed as safety, health, and environmental engineering and technology, as well as fire prevention. To support students in comprehending the core ideas of sustainable development, including strong and weak sustainability, natural capitalism, steady state, and green economies, as well as equality within and between generations and economic, social, and environmental sustainability
Course Description	This course covers essential topics such as environmental regulations, sustainable development goals in mining, ceramic, polymer and metallurgy industries and safety measures in industrial environments.
Course Content	Introduction: Sustainable Development Goals (SDGs) and their concept, environmental concerns in mining, metallurgy, ceramics, and polymers operational standards, safeguarding and managing resources. Industrial waste in materials industries: Industrial wastes from metals, ceramics, plastics and rubber based industries - Identification, characterization and classification. Handling, transportation and storage. Disposal: equipment and processing methods; legal procedures. Recover, recycle, and recycle. Impact of beneficiation process. Industrial safety: Concept of safety, safety by design, safety inspection, accident prevention, Heinrich theory of accident prevention, cost of accident, safety performance monitoring. Safety against fire, chemicals, and acids: detection, prevention, and protection. Health hazards: Common industrial hazards and remedies, engineering controls and personal protective controls, hazard identification and risk assessment- FMEA and HAZOP, QRA. Temporary and cumulative effects in Occupation diseases - silicosis, asbestosis, pneumoconiosis, aluminosis, gas poisoning. Game theory approach to deal pollution. Safety management: Safety guidelines and procedures in the materials sector. Case studies on the mining, blast furnace, iron and steel industries, foundries, hot and cold processing of metals, and blast furnaces. Handling powders and raw ingredients for ceramics. Issues about the health and safety of raw materials used in sanitary napkins, glass, refractory, and cement. Prevention and awareness of associated hazards and illnesses.
Learning Outcome	At the end of the course the student will be able to Apply comprehension of engineering principles to identify, evaluate, and control occupational hazards. Recognize and promise to abide by legislative rules and regulations, as well as contractual duties related to sustainable development, in order to protect occupational health, safety, and the environment in the organization.
Assessment Method	Assignments, Quizzes, Mid-semester examination, End-semester examination.

Text Books:

H. Fulekar, B. Pathak, R.K. Kale, Environment and sustainable development, Springer, 2014.
Petersen, Techniques for safety management - A systems approach, ASSE 1998.

Reference Books:

P. Mahajan, Pollution control in process industries, Tata McGraw Hill Publishing Company, New Delhi, 1993.
Nagaraj, Industrial safety and pollution control handbook, National safety council, 1992.
Michael Karmis, Mine Health and Safety Management, SME, Littleton Co., 2001.
V. Krishnan, Safety in Industry, Jaico Publishing House, 1996, Mine Health and Safety Management SME, Littleton, CO, USA, 2001.

MM4102

Glass Science and Technology

Course Number	MM4102
Course Credit (L-T-P-C)	3-0-0 (3 AIU Credits)
Course Title	Glass Science and Technology
Learning Mode	Lecture
Prerequisite	None
Learning Objectives	To know about the critical role glass plays in day-to-day life and to understand the state-of-art modern and updated Industrial glass production techniques To understand the thermodynamics and kinetics of glass formation and how it influences the structure and property
Course Description	The course explores the fundamental principles underlying glass behaviour and their applications in various industries
Course Content	Glass Science: Nature of the glassy state, Glass formation and the glass transition, Kinetic and thermodynamic criteria for glass formation, glass former, modifier and Intermediate, TTT diagram, phase diagrams in glass manufacture, structural, Thermodynamic, and Kinetic effects on T_g, viscosity of glass, Bridging and Non-Bridging Oxygen. Types of glasses and their chemical compositions, Physical properties of glasses, density, refractive index, thermal expansion and thermal stresses, thermal endurance of glass, toughening of glasses, strength and fracture behavior of glass and its articles, effect of temperature and composition on the physical properties of glasses, durability and corrosion behavior, colored glass. Glass Technology: Glass-making raw materials, addition of cullet to the batch, glass-batch formulation, batch materials handling equipment, reactions amongst the constituents of glass, design of glass tank furnace. temperature modelling for appropriate refractory selection, thermal currents, and flow pattern in the glass tank furnace, refining of glass, defects in glass, bubbles and seeds, cords, stresses, and color inhomogeneity and their remedies, annealing of glasses, measurement of stress/ strain in glass, Float glass, Container glass, Glass Fibre, and fiberglass. Glass-ceramics: Nucleation and crystal growth in glasses, nucleation through micro miscibility, nucleating agents, properties and applications of glass-ceramics.
Learning Outcome	Upon completion of this course, the student will be: Familiar with different types of glass and its application Familiar with industrial glass making and able to solve industrial problems regarding glass processing Able to understand the properties of glass and how it is different from its crystalline counterpart
Assessment Method	Assignments, Quizzes, Mid-semester examination, End-semester examination.

Text Books:

Glass Science and Technology, D.R. Uhlmann, N.J. Kredl (ed), Vol. 1&2, Academic Press, 1990.
Chemistry of Glasses: Amal Paul, Chapman Hall, 1990.

Reference Books:

Fundamentals of Ceramics: M.W. Barsoum, McGraw Hill, 1997.
Introduction to Ceramics, 2nd Ed., W. David Kingery, H.K. Bowen, Donald R. Uhlmann, Wiley,1976.
Hand book of Glass Manufacture: F.V.Tooley, Vol 1 & 2, Ashlee Pub. Co, 1984.

MM4103

Semiconductor Materials and Devices

Course Number	MM4103
Course Credit (L-T-P-C)	3-0-0 (3 AIU Credits)
Course Title	Semiconductor Materials and Devices
Learning Mode	Lecture
Prerequisite
Learning Objectives	To discuss the working and applications of basic semiconductor devices To impart a fundamental knowledge of device fabrication relevant to the semiconductor industry. To enable the students to understand working principle of semiconductor devices such as transistors, diodes, solar cells, and light-emitting devices.
Course Description	This course provides students with foundational knowledge of semiconductor devices, covering essential principles and advanced semiconductor physics. After completion of the course, students will have understanding of semiconductor technology fundamentals, designed and analysed semiconductor devices.
Course Content	Fundamental of Semiconductors: Energy band theory, Sommerfield free electron theory for metals, Brillouin Zone Theory, density of states, Quasi-Fermi levels, Maxwell-Boltzmann distribution, Fermi-Dirac statistics, intrinsic semiconductor, n-type/p-type semiconductor, transport phenomenon of charge carriers, Energy bands in solids, band structure, band diagram of few important semiconductors (Si, Ge, GaAs, GaN), engineering of doping, surface energy of solids, effective mass, Brillouin zone, direct and indirect gaps semiconductor and photovoltaic effect. Fabrication of Semiconductors and devices: Production of single crystal of semiconducting materials, Semiconductor Grade Silicon, metallurgical grade silicon, Lithography, DC/RF magnetron sputtering. Devices and characterizations: Heterostructure p-n junctions, Schottky junctions, Ohmic contacts: Metal-semiconductor junctions, Schottky and Ohmic contacts, Metal-Semiconductor contacts, Metal-insulator-semiconductor structures, tunnel diodes, Gunneffect, p-i-n structures, Zener diode, Bipolar transistors, principle of operation of MOSFETs, characteristics of MOSFET, source-drain/transfer characteristics of MOSFET, introduction to JFETs, MESFETs, and MODFETs. carrier statistics under illumination condition, generation and recombination of carriers, emitting diodes (LED), LEDs, laser-diodes and solar cells, Current-voltage characteristics, capacitance-voltage (CV) and impedance measurements.
Learning Outcome	Upon completion of this course, the student will be able to: Grasp the basics concepts of semiconductor materials such as the energy bands, band gap, charge carrier concentration, transport phenomenon of charge carriers. Describe the fabrication of semiconductors devices Demonstrate the applications of various semiconducting devices such as p-n and Schottky junctions, BJTs and FETs, LEDs and solar cells
Assessment Method	Assignments, Quizzes, Mid-semester examination, End-semester examination.

Text Books:

Semiconductor Devices: Physics and Technology Hardcover – by Simon M. Sze (Author), Ming-Kwei Lee, 2012.
An Introduction to Semiconductor Devices- D. Neamen, McGraw-Hill Education, 2005.
Physics of Semiconductor Devices -S.M. Sze and K. K. Ng, Wiley- Interscience, 3rd edition, 2006.

Reference Books:

Semiconductor Physics: An Introduction. K. Seeger, Springer-Verlag, Berlin, 9^th, 2004.
Electronic Materials and Devices: David K. Ferry, Jonathan P. Bird, Wiley, 2001.
Introduction to the Electronic Properties of Materials: David C. Jiles, 2^nd, CRC Press, 2001.

Departmental Elective - II

Sl. No.

Subject Code

Departmental Elective - II

MM4104

Thin Films

Course Number	MM4104
Course Credit (L-T-P-C)	3-0-0 (3 AIU Credits)
Course Title	Thin Films
Learning Mode	Lecture
Prerequisite	None
Learning Objectives	Understand about the various physical and chemical deposition methods Understand and analyse the characteristics of thin films using different instrumentation technique. Able to understand different types of nucleation theories, growth mechanisms of thin films.
Course Description	This course provides fundamentals of synthesis, nucleation, growth of thin films along with their suitability of applications in diverse technological fields.
Course Content	Basics of surface: Concept of Surface Energy, Surface Thermodynamics, Surface Tension and Surface Energy, Broken Bond Model for Surface Energy of Crystalline Solids (BCC and FCC), Mechanisms for Reduction of Surface Energies, Surface Relaxation, Restructuring and Adsorption. Vacuum components for thin films: Importance of High Vacuum for making thin films, Details of Vacuum Pumps (e.g. Rotary Turbo-molecular and Diffusion Pump), Pressure Gauge regular maintenance for vacuum conditions. Thin film deposition techniques: Physical vapour deposition methods (Glow discharge, RF, Magnetron sputtering), Evaporation (Vacuum, electron beam, ion beam evaporation), Chemical Vapour Deposition Methods (Metal-Organic, Plasma Enhanced, Photochemical etc.), Plasma Technology for Thin Films, Molecular Beam Epitaxy atomic layer deposition. Solution based chemical Techniques: Spray pyrolysis, Electrodeposition, Electroless deposition and plating for large area industrial coating, Sol-gel (spin coating and dip coating) and Langmuir Blodgett techniques for polymer and soft molecules. Fundamental physical and chemical processes: Nucleation and Growth of Thin Films, Structure Zone Model, 3-D island layer by layer growth, thin film Microstructure, orientation and their influence on final properties. Characterization of thin films: In situ characterizations, techniques for physical and structural characterization (thickness, phase, composition, morphology etc.), Highlights of measurements for various functional and chemical properties of thin films. Applications of thin films: Hard Mechanical Thin Coatings, Thin films for Transistors and Semiconductors, Applications of Organic Thin Fims.
Learning Outcome	Upon completing of this course, the student will be able to: Identify various techniques of thin film depositions Classify and distinguish different types of thin film and their properties with relevant industrial applications Understand the nucleation and growth of various thin films during processing.
Assessment Method	Assignments, Quizzes, Mid-semester examination, End-semester examination.

Text Books:

Milton Ohring, The Materials Science of Thin Films, Academic Press-Sanden, 1992
Vacuum deposition of thin films, L. Holland, Chapman and Hall.
Thin films phenomena, K.L. Chopra, McGraw Hill, Yew York.

Reference Books:

Thin Film Materials: Stress, Defect Formation and Surface Evolution, L. B. Freund, S. Suresh, Cambridge University Press, 2004
Thin Film Processes II, Werner Kern, editor: John Vossen, Academic Press, 2012
Thin-Film Deposition: Principles and Practice, Donald L. Smith, McGraw Hill Professional, 1995

MM4105

Heat Treatment of Steel

Course Number	MM4105
Course Credit (L-T-P-C)	(3-0-0) (3 AIU Credits)
Course Title	Heat Treatment of Steel
Learning Mode	Lecture
Prerequisite	None
Learning Objectives	To understand the time-temperature sequence for altering the microstructure with/without application of stress To understand the engineering of various heat treatment processes and their impact on material properties.
Course Description	A course exploring heat treatment processes and phase transformations. It also covers thermo-mechanical treatments, case hardening techniques, and the impact of heat treatment on engineering steels.
Course Content	Introduction to heat treatment: Objective of heat treatment; thermodynamics of phase transformation; Iron carbon phase diagram and their limitations; Austenitic, bainitic and martensitic transformations. Various types of heat treatment furnaces; TTT Diagram: types and application of TTT Diagrams (Austempering, Patenting and Martempering); CCT Diagram; Annealing (stress‐relieving annealing, spheroidization, homogenising, etc.), Normalising, Hardening (objective, methods, quenching mediums, internal stresses and austenitizing temperature, defects in the process), Tempering (objective, stages, effects of addition of carbon and other alloying elements). Tempering of numerous alloy steels. Thermo‐mechanical treatment of steels: Principles, Ausforming; Isoforming; Embrittlement during tempering, hardenability and factors affecting the properties. Hardening (case and surface): Nitriding, Carburising, and Carbonitriding, Laser hardening, and Induction hardening. Engineering steels: Heat treatment and their effect on industrial steels including stainless steels, tool steels, maraging steels, dual phase steels, bearing steels, spring, and HSLA steel.
Learning Outcome	Upon completion of the course, the student will be able to: Appreciate the guiding factor of heat treatment which would influence the properties in a desired way Acquire insights into the relationship among process, property and microstructure.
Assessment Method	Assignments, Quizzes, Mid-semester examination, End-semester examination.

Text Books:

Heat Treatment of Metals: B. Zakharov, CBS Publishers, 1998.
Principles of Heat Treatment of Steels, F.M.B. Fernandes and T. Ericsson, ASM Handbook, 1991.
Heat Treatment of Metals: Vijendra Singh, Standard Publishers Distributors, 2009.

Reference Books:

Principles of the Heat Treatment of Plain Carbon and Low Alloy Steels: C.R. Brooks, ASM International, 1996.
Steels: Processing, Structure and Performance: G. Krauss, 2^nd, ASM International, 2015.
The Physical Metallurgy of Steels: W.C. Leslie, McGraw‐Hill, 1981.

MM4106

Creep, Fatigue and Fracture

Course Number	MM416
Course Credit (L-T-P-C)	(3-0-0) (3 AIU Credits)
Course Title	Creep, Fatigue and Fracture
Learning Mode	Lecture
Prerequisite	None
Learning Objectives	Gain a thorough understanding of creep, fatigue, and fracture mechanisms in engineering materials. To understand and predict how materials fail under various loading conditions and the deformation behaviour of metallic materials at high temperatures.
Course Description	The course is designed for students to deepen their understanding of deformation and fracture behavior of metals under different strain rate conditions.
Course Content	Creep: Stress-strain curve, concept of homologous temperature, effect of temperature on dislocation motion. Creep curve, structural changes during creep, constitutive equations. Mechanism of creep deformation, dislocation creep, diffusion creep. Deformation mechanism maps, superplasticity in metals and ceramics, grain boundary sliding, rupture Fatigue: Cyclic stress-strain, low cycle fatigue, Coffin-Manson relation, S-N curve, stress intensity factor, notch sensitivity, fatigue crack initiation mechanism, Paris law, factors affecting fatigue life, thermal fatigue, fatigue protection methods, fretting. Creep-fatigue interaction. Case studies. Fracture: basic models of fracture, Griffith theory, stress concentration factor, ductile fracture, brittle fracture, ductile to brittle transition, modes of fracture, hydrogen embrittlement. Fracture in structural and bio-implant components, fracture under rapid loading rates. Stress corrosion cracking, Fractography. Case studies.
Learning Outcome	Upon completion of course the students will be able to Differentiate between creep, fatigue, and fracture and explain the mechanisms by which they occur in different materials. Design components that consider creep, fatigue, and fracture resistance for safe and reliable operation.
Assessment Method	Assignments, Quizzes, Mid-semester examination, End-semester examination.

Text Books:

Mechanical Behavior of Materials: Thomas H. Courtney, 2^nd, Waveland Press Inc., 2005.
Mechanical Metallurgy: G.E. Dieter, 3^rd, McGraw Hill, 2017.
Deformation and Fracture Mechanics: R.W. Hertzberg, R.P. Vinci, J.L. Hertzberg, 5^th, Wiley, 2012.

Reference Books:

Metal Fatigue in Engineering: I. Stephens, A. Fatemi, R.R. Stephens, H.O. Fuchs, 2^nd Ed., Wiley, 2000.

Creep of Engineering Materials: I. Finnie, W. R. Heller, McGraw Hill, 1999.

Departmental Elective - III

Sl. No.

Subject Code

Departmental Elective - III

MM4201

Smart Polymers

Course Number	MM4201
Course Credit (L-T-P-C)	(3-0-0) (3 AIU Credits)
Course Title	Smart Polymers
Learning Mode	Lecture
Prerequisite	None
Learning Objectives	To introduce the basic concepts of synthesis & processing of smart polymers To develop an understanding of different types and properties of smart polymers To impart knowledge of smart polymers applications
Course Description	This course discusses the ability of certain classes of polymers to behave as smart polymers. This course will cover the ability of smart polymers to undergo a dramatic reversible physical or chemical change when an external stimulus is applied. The course will also include the synthesis, characterization, properties and applications of various smart polymers.
Course Content	Introduction: Overview, types and applications of smart polymer. Temperature-responsive polymers: Basic concepts of temperature-responsive polymers in aqueous solution, Key forms of temperature-responsive polymers in aqueous solution, selected programs of thermo-responsive polymers. pH-responsive polymers: Key varieties and characteristics of pH-responsive polymers, various architectures of pH-responsive polymers, Synthesis of pH-responsive polymers, Different methodologies for the preparation of pH-responsive polymers and Applications. Photo-responsive polymers: Key types and properties of photo-responsive polymers Chromophores and their light-induced molecular response, and Applications. Magnetically responsive polymer gels and elastomers: synthesis of magnetically responsive polymer gels and elastomeric materials, Magnetic properties of filler-loaded polymers, Elastic behaviour of magnetic gels and elastomers, The swelling equilibrium under a uniform magnetic field, Kinetics of shape change, Polymer gels in a non-uniform electric or magnetic field and Applications. Enzyme-responsive polymers: Enzyme-responsive materials: rationale, definition and history, Preparation of enzyme-responsive polymers, Characterisation of enzyme-responsive polymers, Key varieties and characteristics of enzyme-responsive polymers and Applications. Shape memory polymers: Characterizing shape memory effects in polymeric materials, Categorizing shape memory polymers based on their stimulus type and polymer structure, Applications. Smart polymer hydrogels: Synthesis, key categories, characteristics, and uses for hydrogels made of smart polymers. Self-healing polymer systems: Different forms of self-healing, Self-healing and recovery of functionality in materials. Applications of smart polymers: Drug delivery using smart polymer nanocarriers, smart polymers in medical equipment for minimally invasive surgery, diagnosis, and other uses, Smart polymers for textile applications, for food packaging applications, for optical data storage and for bio-separation and other biotechnology applications.
Learning Outcome	Upon completion of this course, the students will be conversant with Fundamentals and processing of smart polymers. Environmentally responsive polymers (i.e. temperature, pH, light etc.), Self-healing polymers, Shape memory polymers, Enzyme-responsive polymers, magnetically responsive polymer. Application of smart polymers (i.e. drug delivery, medical devices, bio-technology, textile, optical storage).
Assessment Method	Assignments, Quizzes, Mid-semester examination, End-semester examination.

Text Books:

Maria Rosa Aguilar, Julio San Román, Smart Polymers and Their Applications, Woodhead Publishing Limited/Elsevier, 2019.
José Miguel García, Félix Clemente García, José Antonio Reglero Ruiz, Saúl Vallejos and Miriam Trigo-López, Smart Polymers Principles and Applications, De Gruyter, 2022.

Reference Books:

Asit Baran Samui, Smart Polymers Basics and Applications, Taylor and Francis Group, 2022.
Igor Galaev, Bo Mattiasson, Smart Polymers Applications in Biotechnology and Biomedicine, Routledge/ Taylor and Francis Group, 2019.

MM4202

Energy Materials

Course Number	MM4202
Course Credit (L-T-P-C)	(3-0-0) (3 AIU Credits)
Course Title	Energy Materials
Learning Mode	Lecture
Prerequisite
Learning Objectives	To understand the challenges and issues related to energy-efficient technology. Describes how advanced materials make possible efficient energy harvesting, energy conversion and energy storage technologies. Explain energy-related material issues including design, synthesis, characterization, and performance for energy device applications. Discuss materials enabling energy-efficient transportation and housing.
Course Description	This course offers a materials science perspective on energy efficient technology. Students will study advanced materials for energy harvesting (e.g., solar cells, wind energy), energy conversion (e.g., fuel cells, LEDs), and energy storage (e.g., batteries, hydrogen storage).
Course Content	Introduction: Optoelectronic, Photovoltaic technologies, Energy Efficient Lighting Energy harvesting technologies/materials: organic and inorganic solar cells, nuclear materials, material for wind energy and thermoelectric Energy conversion technologies/devices: e.g., polymer and solid oxide fuel cells, light emitting diodes, engines, and turbines Energy storage technologies: batteries, introduction to electrochemical energy storage and conversion, lithium ion batteries, basic components in Lithium – ion batteries: electrodes, electrolytes, and current collectors, characteristics of commercial lithium ion cells, Sodium ion rechargeable cell, introduction to battery pack design, advanced materials and technologies for supercapacitors, Li – Air batteries, Li – Sulphur batteries, rare-earth Li resources and recycling of Li ion battery, Other types of batteries, hydrogen storage, phase change materials. Supercapacitor Energy-efficient materials: transportation, housing. Materials selection
Learning Outcome	Upon completion of this course, the student will be able to: Understand theories for optoelectronics, photovoltaics, electrocatalysis and batteries. Demonstrate knowledge of materials design, synthesis, and modification for energy related applications. Utilize various materials engineering techniques to enhance the performance of energy applications. Demonstrate the structure/composition-performance relationship for energy materials.
Assessment Method	Assignments, Quizzes, Mid-semester examination, End-semester examination.

Text Books:

Handbook of Photovoltaics Science and Technology, By Antonio Luque and Steven Hegedus
Physics of solar cells: from basic principles to advanced concepts, By Peter Würfel and Uli Würfel

Reference Books:

Organic photovoltaics: materials, device physics, and manufacturing technologies, By Christoph J. Brabec, Vladimir Dyakonov, Ullrich Scherf
Principles of Solar Cells, LEDs and Diodes: The Role of the PN Junction, By Adrian Kitai

Departmental Elective - IV

Sl. No.

Subject Code

Departmental Elective - IV

MM4203

Electroceramics

Course Number	MM4203
Course Credit (L-T-P-C)	(3-0-0) (3 AIU Credits)
Course Title	Electroceramics
Learning Mode	Lecture
Prerequisite	None
Learning Objectives	To demonstrate the fundamentals of functional (electronic, magnetic, optical, dielectric etc.) To illustrate process, structure, property correlations of wide range of functional ceramic materials.
Course Description	This course provides general overview on the origins of functional properties of ceramics, their wide varieties and their usability in different advanced technological applications.
Course Content	Ceramic Capacitors: Multi-layer ceramic capacitors, Importance of BaTIO₃ as a capacitor material, Improvement of dielectric constant, effect of doping. Electronic and Ionic conducting ceramics: Highly conducting ceramics, non-stoichiometric and valence-controlled semiconductors. Grain boundaries effects, NTC and PTC thermistors, Superionic ceramic conductors (AgI, β-Alumina). Piezoelectric, Ferroelectric and Electro-optic Ceramics: Ferroelectric ceramic materials, chronology of ferroelectric ceramics, ferroelectric hysteresis and poling, Relaxor ferroelectrics, General characteristics of piezoelectric materials, Piezoelectric constants. Electro optic effect, linear, quadratic and memory devices, importance of morphotropic phase boundary, Pyroelectric Materials, Electro-optic Ceramics. Magnetic Ceramics: Soft and hard ferrites, their applications. Ni-Zn ferrites, Mn-Zn ferrites, mixed garnets and Hexagonal Ferrites. Effect of composition, processing and microstructure on the magnetic properties. Processing and applications of magnetic ceramics.
Learning Outcome	Classify various classes of electroceramic materials and describe their structure and properties Relate the phase, chemical composition and microstructure of electroceramics to the particular conductive, dielectric, ferroelectric, piezoelectric and pyroelectric, electro-optic and magnetic properties.
Assessment Method	Assignments, Quizzes, Mid-semester examination, End-semester examination.

Text Books:

Electroceramics: Materials, Properties, Applications: A.J. Moulson and J.M. Herbert, 2^nd, Chapman & Hall, Springer, 2003.
Fundamentals of Ceramics: Michel W. Barsoum, McGraw Hill, 1997.
Ceramic Materials for Electronics: R.C. Buchanan (ed.), Marcel Dekker, 1991.
Electronic Ceramics: L.M. Levison (ed.), Marcel Dekker, 1988.

Reference Books:

Ferroelectric Materials and Their Applications: Y.H. Xu, North-Holland, Elsevier, 1991.
Piezoelectric Ceramics: Principles and Applications: APC International, Ltd, 2002.
Ferroelectric Devices: Kenji Uchino, Marcel Dekker, 2000.

MM4204

Biomaterials

Course Number	MM4204
Course Credit (L-T-P-C)	(3-0-0) (3 AIU Credits)
Course Title	Biomaterials
Learning Mode	Lecture
Prerequisite	None
Learning Objectives	To identify the major types of materials that are used in the body and their major modes of failure and apply material property fundamentals to analyze the performance of a material in vivo and translate material properties from test data to material performance. To understand common use of biomaterials as metals, ceramics and polymers and its chemical structure, properties, and morphology. To understand the interaction between biomaterial and tissue for short term and long-term implantations.
Course Description	The course covers the principles of materials science and engineering with particular attention to topics most relevant to biomaterials. This course will cover the structure-property relationships of metals, ceramics, polymers, and composites with respect to their utility as biomaterials. This course will also give an overview of the different types of materials used in biomedical applications.
Course Content	Introduction: Definition and scope of biomaterials, Classification of bio-ceramic materials. Alumina and zirconia in surgical implants and their coatings. Bioactive glasses and glass ceramics with their clinical applications. Synthesis and characteristics of dense and porous hydroxyapatite and calcium phosphate ceramics. Resorbable bioceramics. Characterization of bio-ceramics. Structure-property relationship of biological materials: structure of proteins, polysaccharides, structure-property relationship of hard tissues cell, bone, teeth and connective tissues. Structure, properties and functional behaviour of bio-materials. Tissues response to implants (biocompatibility, wound healing process), body response to implants, blood compatibility. Application: Classification of bioceramic materials for medical applications, Carbon as an implant. Regulation of medical devices, Cell culture of bio ceramics, network connectivity and hemolysis, Preparation of bio ceramics and characterization of bioactivity. Bio-polymers: Polysaccharide based polymers, gelatinization, starch based blends, biodegradation of starch based polymers, production of lactic acid and polylactide, properties and applications of polylactides, introduction to polyhydroxyalkanoates and their derivatives, applications, chitin & chitosan and its derivatives as biopolymers, biopolymer films, biodegradable mulching, advantages and disadvantages, chemical sensors, biosensors, functionalized biopolymer coatings and films. Applications of biopolymers: Food Packaging, functional properties, safety and environmental aspects, shelf life, films and coatings in food applications, applications of biopolymers for organ transplant, different biopolymers used for organ transplant e.g. dental cement, orthopedic, skin, artificial kidney etc., applications of biopolymers in tissue engineering, regeneration, Targeted drug delivery: Introduction to drug delivery, polymers in controlled and targeted drug delivery, dressing strips, polymer drug vessels, core shell and nanogels. Application based and material based classification of biomaterials. Drug delivery, Callipers, biosensors, implants.
Learning Outcome	Upon completion of this course, the student will be able to: Understand the multidisciplinary nature of biomaterials as a field of study and define design criteria for a material with relationship to their clinical application Understand how to analyse the interaction of materials with the human body and what biocompatibility is in relation to specific materials Analyse issues relevant to property retention for materials when implanted in the human body and be capable of reading, comprehending and communicating the content of technical articles on biomaterials research and applications.
Assessment Method	Assignments, Quizzes, Mid-semester examination, End-semester examination.

Text Books:

An Introduction to Bioceramics: Larry L. Hench, June Wilson, World Scientific, 1993.
Biomaterials: An Introduction: Park Joon, R. S. Lakes, Springer, 2007.
Biopolymers-New Materials for Sustainable films and Coatings: David Plackett, John Wiley & Sons Ltd., 2011
Biopolymers from Renewable resources: David Kaplan, Springer, 1998

Reference Books:

Bioceramics and their Clinical Applications: T. Kokubo, Woodhead Publishing, 2008.
Biopolymers: R. M. Johnson, L. Y. Mwaikambo, N. Tucker, Rapra Technology, 2003.
Hand Book of Bioplastics & Biocomposites for Engineering Applications: Srikanth Pillai, Wiley, 2011.
Biopolymers: Steinbuechel Alexander, Vol. 1-10, Wiley, 2003.
Polymers from Renewable Resources: Biopolymers and Biocatalysis: Carmen Scholz, Richard A. Gross American Chemical Society, 2001.

Departmental Elective - V

Sl. No.

Subject Code

Departmental Elective - V

MM4205

Crystallographic Texture and Analysis

Course Number	MM4205
Course Credit (L-T-P-C)	(3-0-0) (3 AIU Credits)
Course Title	Crystallographic Texture and Analysis
Learning Mode	Lecture
Prerequisite	None
Learning Objectives	To understand the concept of crystallographic texture and its importance in material properties To gain knowledge of various techniques used to characterize crystallographic texture in polycrystalline materials. To learn the interpretation of texture data and relate it to the microstructure and deformation behaviour of materials.
Course Description	A specialized course exploring the principles and techniques used to characterize and analyze the preferred orientation of crystals within a polycrystalline material, essential for understanding material anisotropy and behavior.
Course Content	Concept of texture: Crystal orientation, sample coordinate system, crystal coordinate system, stereographic projections, pole figure, construction of pole figures, reading pole figures and inverse pole figures. Orientation distribution functions, Bunge convention, Euler angles, Euler space, two-dimensional representation of ODF, and identification of standard texture components in Euler space. Texture measurement: Bulk and local texture measurements, electron diffraction using SEM and TEM, Kikuchi lines and indexing, Hough transformation, orientation imaging microscopy, X-ray and neutron diffraction measurement. Transmission EBSD. Texture during material processing: Deformation, annealing and recrystallization texture. Solidification and transformation texture. Texture in thin films and coatings. Influence of texture on mechanical properties. Case studies: Texture control in electrical steel, aluminium alloys, shape memory alloys, magnetic materials.
Learning Outcome	Upon completion of this course, the student will be able to: Explain the connection between crystallographic texture, microstructure, and mechanical properties of materials. Analyse and interpret texture data using relevant software or tools Apply the understanding of texture to predict and improve material performance in various engineering applications.
Assessment Method	Assignments, Quizzes, Mid-semester examination, End-semester examination.

Text Books:

Introduction to Texture Analysis: Macrotexture, Microtexture and orientation mapping: V. Randle and O. Engler, 2^nd, CRC Press, 2009.
Recrystallization and Related Annealing Phenomenon: F.J. Humphreys, M. Hatherly, 2^nd, Pergamon Press, 2004.

Reference Books:

An Introduction to Textures in Metals: M. Hatherly and W.B. Hutchinson, The Institute of Metals, 1979.
DST-SERC School Lectures on Texture.

MM4206

Furnace and Refractories

Course Number	MM4206
Course Credit (L-T-P-C)	(3-0-0) (3 AIU Credits)
Course Title	Furnace and Refractories
Learning Mode	Lecture
Prerequisite
Learning Objectives	To be able to explain the composition, classification and properties of refractories. To be able to evaluate mechanical properties, thermal behaviour and slag resistance of refractory materials. To be able to evaluate the design, performance, and lifetime of refractories for industrial applications.
Course Description	This course provides general overview on the importance of furnaces for melting metals, ceramics and glasses by providing the details of the types of refractories used in them along with their fundamental properties and characterizations.
Course Content	Furnaces: Fundamentals of furnace design, thermodynamics of fuel combustion, chemical reaction and enthalpy evolution, importance of heat balance, Sankey and virtue diagrams, temperature of flame, electric furnaces, advance furnaces, different heat loss in furnaces, choice of insulation, waste heat management through recuperation and regeneration, fuel economy and thermal efficiency of furnaces, principles of temperature and atmosphere control Various types of furnaces utilized in metallurgy/ ceramic industries, blast furnaces, open-hearth furnaces, Bessemer converters, LD converters, coke-oven batteries, tunnel kilns, chamber furnaces, glass tank furnaces, rotary kilns. Refractories: Composition, physical and chemical properties of raw materials; Principles of manufacturing of firebricks, silica, alumina, mullite, magnesite, chrome-magnesite, dolomite, magnesia, forsterite and insulating bricks along with relevant phase diagrams, spinel, borides, carbides, nitride, and carbon refractories Application of refractories in a blast furnace, open hearth furnace, Bessemer and L.D. converter, copper, aluminum, cement, lime, and glass industry. monolithic refractories, use of monolithic over shaped refractories. Testing of refractories: Bulk density, porosity, fusion point, cold crushing strength, creep resistance, pyrometric cone equivalent and refractories under load, hot modulus of rupture, abrasion resistance, thermal conductivity, thermal expansion and spalling, corrosion, and reaction of refractories.
Learning Outcome	Upon completion of this course, the student will be able to: Demonstrate a clear understanding of the types of refractories used for different industries Interpret the information on chemical and mineral compositions, thermal conductivity, and micro-structural examinations and other characterization carried out on refractory bricks. Evaluate the industrial application of various refractory materials, their design, performance and testing methods.
Assessment Method	Assignments, Quizzes, Mid-semester examination, End-semester examination.

Text Books:

Industrial ceramics: Felix Singer, Sonja S. Singer, Chapman & Hall, Springer, 1963.
Refractories: F.H. Norton, Cbls\Ceramic book and Literature, 1985.
Industrial and Process Furnaces: P. Mullinger and B. Jenkins, Butterworth Heinemann, Elsevier, 2013.

Reference Books:

Fundamentals of Materials for Energy and Environmental Sustainability: G. David and C. David, Cambridge University Press, 2011.
The Technology of Ceramics and Refractories: Petr Petrovich Budnikov, M.I.T. Press, 1964.

MM4207

Composite Science and Technology

Course Number	MM4207
Course Credit (L-T-P-C)	(3-0-0) (3 AIU Credits)
Course Title	Composite Science and Technology
Learning Mode	Lecture
Prerequisite	None
Learning Objectives	To disseminate details regarding the various kinds of composites, their requirements, and their benefits. To disseminate knowledge on how various composites are prepared. To provide knowledge on the characteristics, uses, and testing of various composites.
Course Description	The course covers the basic and important knowledge of various composites fabricated from metals, ceramics and polymers. This course will also cover essential details about the types of composite materials, various components of composites, common manufacturing/processing techniques, testing and characterization of composites and how to select composite materials for particular applications.
Course Content	Metal matrix composites (MMCs): Overview, significant of metallic matrices, Characteristics and uses of MMCs. Processing of metal matrix composites: liquid state processing: melt stirring, compocasting (rheocasting), squeeze casting, liquid infiltration under gas pressure; solid state processing: diffusion bonding, powder metallurgy; Deposition: in-situ processes, spray co-deposition, and other deposition methods including CVD and PVD. Interface reactions. Ceramic matrix composites (CMCs): Introduction; processing and structure of monolithic materials – technical ceramics, glass-ceramics. Processing of ceramics: conventional mixing and pressing – cold pressing and sintering, hot pressing; Reaction bonding processes, techniques involving slurries, liquid state processing – matrix transfer moulding, liquid infiltration, sol-gel processing; Carbon-carbon composites - porous carbon-carbon composites, dense carbon-carbon composites. Properties and applications of CMCs; Glass-ceramic matrix composites; Processing, properties and applications of alumina matrix composites - SiC whisker reinforced, zirconia toughened alumina; Vapour deposition techniques like CVD, CVI, liquid phase sintering, Lanxide process and in situ processes. Polymer matrix composites (PMCs): Thermoset matrices–polyesters, epoxides, phenolics, vinyl esters, polyimides and cyanate esters, thermoplastic matrices and rubber matrices. Fibers: Glass, carbon, kevlar, natural fibers and surface treatment- sizing/coupling agents. Interfaces: Wettability, the type of bonding at the interface, its crystallographic character, and the ideal interfacial bond strength. Processing: Sheet molding compounds, bulk molding compounds, hand layup process, spray layup process, resin transfer molding, pressure bag molding, vacuum bag molding, autoclave molding, filament winding and pultrusion. Properties and applications of PMCs. Testing of composites: Destructive and non-destructive testing of composites Analysis of composites: Micro-mechanics, macro-mechanics and failure theories.
Learning Outcome	Upon completion of this course, the students will be Able to choose appropriate composites for specific applications. Able to comprehend about the many techniques used in the manufacturing of composite materials Able to select suitable testing procedures for composite analysis.
Assessment Method	Assignments, Quizzes, Mid-semester examination, End-semester examination.

Text Books:

K.K., Composite Materials - Science and Engineering, Springer, 2001.
Jones, R.M., Mechanics of Composite Materials, Taylor and Francis, 1999.
K. Mallick, Composites Engineering Handbook Part-1&2, CRC Press (2016).

Reference Books:

Lubin, G., Handbook of Composites, Van Nostrand Reinhold Co., 1982.
Eckold, G., Design and Manufacture of Composite Structures, Wood head Publishing Ltd., 1994.
R. Jones (Ed.), Handbook of Polymer-Fibre Composites, Longman Group (1994).
Friedrich, S. Fakirov, Z. Zhang (Eds.), Polymer Composites – from Nano to Macro scale, Springer (2005).

Interdisciplinary Elective (IDE) Courses for B. Tech. (Available to students other than Dept. of MME)

Sl. No.

Subject Code

Interdisciplinary Elective (IDE)

MM2206

Structure and Properties of Materials (IDE I)

Course Number	MM2206
Course Credit (L-T-P-C)	(3-0-0) (3 AIU Credits)
Course Title	Structure and Properties of Materials (IDE I)
Learning Mode	Lecture
Prerequisite
Learning Objectives	To provide an introductory level understanding of material structure (microstructure) on different length scales. To understand how specific material properties and behaviours are determined by the associated structure.
Course Description	Basic course to differentiate various solids (like metal, ceramics and polymer) from the aspects of crystal structure, properties and applications.
Course Content	Bonding in solids: primary and secondary bonding is solids, bond strength and bond energy. Basic crystallography: crystalline and amorphous materials. Packing of atoms, coordination number, unit cell, Bravais lattice, simple crystal structures, defects in solids, Miller indices Classification of materials: engineering materials and their classification, metallic materials, ceramic materials and polymeric materials. Composite materials. Properties of materials: mechanical, electrical, magnetic and optical properties. Microstructure-property correlation in materials. Materials selection: introduction to materials selection charts, Ashby maps, materials performance index, processibility and cost.
Learning Outcome	On completion of the course the students will be able to Differentiate between different types of materials and their structures Understand the structure dependence of properties and design materials for various engineering applications
Assessment Method	Assignments, Quizzes, Mid-semester examination, End-semester examination.

Text Books:

Materials Science and Engineering, an Introduction: William D. Callister, 7^th, John Wiley and Sons, 2007
Materials Science and Engineering: V. Raghavan, 6^th, Prentice Hall India, 2015.

MM3106

Microscopy and X-ray Diffraction (IDE II)

Course Number	MM3106
Course Credit (L-T-P-C)	(3-0-0) (3 AIU Credits)
Course Title	Microscopy and X-ray Diffraction (IDE II)
Learning Mode	Lecture
Prerequisite	None
Learning Objectives	To understand the availability of various techniques to characterize materials. To understand the strengths and limitations of different characterization techniques.
Course Description	The course deals with the structural analysis of material at different length scales, such as micro, nano and angstrom levels, using different techniques
Course Content	Introduction: Importance and the need for materials characterization, bonding, crystal structure and system, miller indices, Bravais lattice. Diffraction: Basics of diffraction and interference of light, Young’s double slit experiment, interpretation of diffraction from the single slit and multiple slits. X-ray Diffraction: Generation of X-rays, X-ray diffraction (XRD), Bragg’s Law, Atomic scattering factor, structure factor, indexing of diffraction patterns, selection rules, estimation of peak intensity, phase identification and analysis by XRD, determination of structure and lattice parameters, strain and crystallite size measurements through XRD, effect of temperature on XRD. Optical Microscopy: Principles of optical microscopy, magnification, Rayleigh criterion, resolution limitation, Airy disk, depth of focus, and field. Electron diffraction: Wave properties of the electron, electron-matter interactions, ring patterns, spot patterns, and Laue zones. Scanning Electron Microscopy: Principle, construction, and operation of Scanning Electron Microscope, SE and BSE imaging modes, Elemental analysis using Energy dispersive analysis of X-rays, Transmission electron microscope: Principle, construction, and working of Transmission Electron Microscope (TEM), the origin of contrast: mass-thickness contrast, electron diffraction pattern, Bright field, and dark field images. Thermal characterization techniques (DTA, DSC, DTA)
Learning Outcome	Upon completion of this course, the student will be able to: Understand structure and microstructure of materials Choose the appropriate technique to characterise different materials
Assessment Method	Assignments, Quizzes, Mid-semester examination, End-semester examination.

Text Books:

Elements of X-Ray Diffraction: B.D. Cullity and S.R. Stock, 3rd Ed., Pearson, 2001.
Scanning Electron Microscopy and X-Ray Microanalysis: Joseph Goldstein, Eric Lifshin, Charles E. Lyman, David C. Joy and Patrick Echlin, 3rd Ed., Springer, 2003.

Reference Books:

Transmission Electron Microscopy: A Textbook for Materials Science: David B. Williams and C. Barry Carter, Springer, 2009.
Structure of Materials: An Introduction to Crystallography, Diffraction and Symmetry, Marc De Graef, Michael E. McHenry; 2nd Ed., Cambridge University Press, 2012.

MM4107

Nanomaterials (IDE III)

Course Number	MM4107
Course Credit (L-T-P-C)	(3-0-0) (3 AIU Credits)
Course Title	Nanomaterials (IDE III)
Learning Mode	Lecture
Prerequisite	None
Learning Objectives	To understand the influence of dimensionality at nanoscale on their properties To understand the size and shape-controlled synthesis of nanomaterials and their current and futuristic applications/challenges. To visualize the applications of nanomaterials in their routine life
Course Description	The course serves to provide students with a comprehensive understanding of the nanomaterials ecosystem, including its synthesis, fundamentals, properties, and applications.
Course Content	Overview: Overview of Nanostructures and Nanomaterials; Characteristic length scales of materials. Classification-Natural nanomaterials, artificial nanomaterials, Inorganic nano materials- Metal-based nanoparticles, Metal oxide nanoparticles, Semiconductor Nanoparticles, Ceramic nanomaterial, Composites nanomaterials. 3D, 2D, 1D and 0 Dimensional Nanomaterials. Carbon Nanotubes, Fullerenes, Nanowires, Quantum Dots. Applications of nanostructures, Surfaces and interfaces in nanostructures, Grain boundaries in Nanocrystalline materials, Defects associated with nanomaterials, Micro porous, Mesoporous materials and Macro porous materials. Fundamentals: Various electron confinements in nanomaterials, concept of quantum well, dots and wires and thermodynamics of nanomaterials Nanomaterials’ manufacturing: Top down approaches: mechanical milling, electrospinning; Lithography, sputtering, the arc discharge method; laser ablation, thermal decompositions. Bottom-up approaches: chemical vapour deposition (CVD), solvothermal and hydrothermal growth; sol–gel method and electrochemical and pyrolysis approaches. Properties of Nanostructures and Nanomaterials: Surface area, thermal and electrical conductivity, mechanical properties; support for catalysts, Optical and electrical properties, physical and chemical properties of nanomaterials.
Learning Outcome	Upon completion of this course, the student will be able to: Appreciate the quantum effects operating at nanoscale on the properties of materials. Design, synthesize and characterize materials at nanoscale. Contrast the properties of materials at the nanoscale relative to its bulk counterpart and apply nanomaterials for advanced industrial applications.
Assessment Method	Assignments, Quizzes, Mid-semester examination, End-semester examination.

Text Books:

Booker, R., Boysen, E., Nanotechnology, Wiley India Pvt. Ltd. (2008).
Rogers, B., Pennathur, S., Adams, J., Nanotechnology, CRS Press (2007).
Bandyopadhyay, A.K., Nano Materials, New Age Int., (2007)

Reference Books:

Jingbo Louise Liu, Sajid Bashir Tian-Hao Yan, Advanced Nanomaterials and Their Applications in Renewable Energy.
Nanomaterials, Nanotechnologies and Design: An Introduction to Engineers and Architects, D. Michael Ashby, Paulo Ferreira, Daniel L. Schodek, Butterworth-Heinemann, 2009.
Cambell, The Science & Engineering of Microelectronic Fabrication, Oxford, 1996.

Minor in Material Science & Engineering

Sl. No.

Subject Code

Subject Name

MM2101

Introduction to Metallurgical and Materials Engineering

Course Number	MM2101
Course Credit (L-T-P-C)	3-0-0 (3 AIU Credits)
Course Title	Introduction to Metallurgical and Materials Engineering
Learning Mode	Lectures
Prerequisite	None
Learning Objectives	To understand the theoretical description of crystal and bonding in solids and the atomic arrangement and defects in crystalline materials. To understand the structure-property correlation in materials.
Course Description	A foundational course delving into the interrelationship between microstructure, properties, and processing, providing understanding of the behaviour of various materials and their applications.
Course Content	Bonding in solids: Concept of energy versus interatomic separation for atoms, bonding in solids, primary interatomic bonding, secondary bonding. Properties of differently bonded solids. Property of materials in relation to crystal symmetry. Tensors. Structure of crystalline solids: Basic idea of lattice, crystalline and non-crystalline materials, unit cell, crystal systems, indexing planes and directions, Miller indices, coordination number, packing of atoms, voids, elements of symmetry. Defects in solids: Point, linear, planar and volume defects, equilibrium concentration of vacancies, Types of dislocations, Burgers vectors, slip systems, grain boundaries, twin and stacking faults. Mechanical properties of materials: Concept of stress and strain, Hooks law, elastic and plastic deformation, tensile properties, hardness. Structure-property correlation: Introduction to ceramic, polymer and composite – processing, structure, properties and applications.
Learning Outcome	Upon completion of this course the student will be able to: Identify the properties of material with respect to their crystal structure and bonding Correlate the influence of defects on material properties. Correlate the structure of crystalline materials with their properties.
Assessment Method	Assignments, Quizzes, Mid-semester examination, End-semester examination.

Text Books:

Materials Science and Engineering, an Introduction: William D. Callister, 7^th, John Wiley and Sons, 2007.
Materials Science and Engineering: V. Raghavan, 6^th, Prentice Hall India, 2015.

Reference Books:

Physical Foundation of Materials Science: Günter Gottstein, Springer, 2004.
An Introduction to Metallurgy: Sir Alan Cottrell, 2^nd, Universities Press, 2000.

MM2202

Techniques of Materials Characterization - I

Course Number	MM2202
Course Credit (L-T-P-C)	3-0-3 (4.5 AIU Credits)
Course Title	Techniques of Materials Characterization - I
Learning Mode	Lecture and Practical
Prerequisite	None
Learning Objectives	To understand how material characterization is of paramount importance to the study of materials science. To understand the strength and weaknesses of different characterization techniques and gain hands-on training on different characterization techniques.
Course Description	The course involves i) the study of the crystal structure of solids. ii) the structural analysis of material at different length scales, such as micro, nano and angstrom levels, using different characterization techniques.
Course Content	Introduction: Importance and the need for materials characterization, crystal system, miller indices, Bravais lattice. Diffraction: Basics of diffraction and interference of light, Young’s double slit experiment, interpretation of diffraction from the single slit and multiple slits. X-ray Diffraction: Generation of X-Rays, X-Ray Diffraction (XRD), Bragg’s Law, Atomic scattering factor, structure factor, indexing of diffraction patterns, selection rules, estimation of peak intensity, phase identification and analysis by XRD, determination of structure and lattice parameters, strain and crystallite size measurements through XRD, effect of temperature on XRD. Reciprocal lattice and Ewald’s sphere. Optical Microscopy: Principles of optical microscopy, magnification, Rayleigh criterion, resolution limitation, Airy disk, depth of focus and field. Electron diffraction: Wave properties of the electron, electron-matter interactions, ring patterns, spot patterns, and Laue zones. Scanning Electron Microscopy: Principle, construction, and operation of Scanning Electron Microscope, SE and BSE imaging modes, Elemental analysis using Energy dispersive analysis of X-rays, sample preparation of different materials for SEM. Transmission electron microscope: Principle, construction, and working of Transmission Electron Microscope (TEM), the origin of contrast: mass-thickness contrast, electron diffraction pattern, Bright field and dark field images, sample preparation.
Learning Outcome	Upon completion of this course, the student will be able to: Understand the working principle and applications of various characterization techniques Choose an appropriate technique to characterize various microstructural aspects Characterize the microstructure of various materials by themselves
Assessment Method	Assignments, Quizzes, Mid-semester examination, End-semester examination.

Text Books:

Elements of X-Ray Diffraction: B.D. Cullity and S.R. Stock, 3rd Ed., Pearson, 2001.
Scanning Electron Microscopy and X-Ray Microanalysis: Joseph Goldstein, Eric Lifshin, Charles E. Lyman, David C. Joy and Patrick Echlin, 3rd Ed., Springer, 2003.

Reference Books:

Transmission Electron Microscopy: A Textbook for Materials Science: David B. Williams and C. Barry Carter, Springer, 2009.
Structure of Materials: An Introduction to Crystallography, Diffraction and Symmetry, Marc De Graef, Michael E. McHenry; 2^nd, Cambridge University Press, 2012.

4.5

MM3103

Engineering Polymers

Course Number	MM3103
Course Credit (L-T-P-C)	3-0-2 (4 credits AIU Credits)
Course Title	Engineering Polymers
Learning Mode	Lecture and Practical
Prerequisite	None
Learning Objectives	To support to comprehend the relationship between structure and properties as well as the uses of engineering polymers. To disseminate information about the characteristics and uses of engineering polymers. To comprehend the purposes of various additives, as well as the kinds, mechanisms, and technical specifications needed for their efficient assessment. Testing products for predicting product performance.
Course Description	This course introduces polymers as engineering materials. This course will also cover the various aspects associated with different engineering polymers such as polymerization processes, morphology, crystallinity, thermal transitions, viscoelasticity, structure-property correlation, compounding and applications.
Course Content	Structure property relationship in polymers: The synthesis, characteristics, and uses of thermoplastic engineering polymers include polyesters -PET, PBT, polyacetals, PC, LCPs, modified polyamides, and polyamides. High temperature resistant thermoplastic engineering polymers, such as PTFE, PCTFE, PVDF, PPO, PPS, polysulphones, PEEK, polyimides, polybenzimidazoles, and aromatic polyamides- Synthesis, properties & applications. Thermoset engineering polymers. Blends of engineering polymers. Additives and engineering polymer compounding: fillers, plasticizers, lubricants, colorants, fire retardants, coupling agents, blowing agents, UV stabilizer, antistatic agents, anti-blocking agents, slip and anti-slip agents, processing aids, antioxidants, stabilizers, lubricants, and toughening agents. Engineering polymer processing- Characterization and testing of engineered polymers.
Learning Outcome	At the end of the course the student will be able to Comprehend the significance of engineering polymers. Acquire fundamental knowledge about characteristics of polymers Select appropriate processing, compounding, and additive methods.to create various engineering polymeric compound grades. Will be able to prepare the test sample for various polymer testing operations. Will be able to measure the polymer properties.
Assessment Method	Assignments, Quizzes, Mid-semester examination, End-semester examination.

Text books:

Engineering Plastics Handbook: James M. Margolis, McGraw Hill, 2006.
Plastic Materials: J.A. Brydson, 6th Ed., Elsevier, 1995.

References books:

Industrial Polymers, Specialty Polymers, and Their Applications: Manas Chanda, Salil K. Roy, CRC Press, 2008.
Specialty Plastics: R.W. Dyson, 2nd Ed., Blackie Academic & Professional, 1988.

Modern Plastics Handbook: C.A. Harper, McGraw Hill, 2000.

MM3203

Functional Materials

Course Number	MM3203
Course Credit (L-T-P-C)	3-0-0 (3 AIU Credits)
Course Title	Functional Materials
Learning Mode	Lecture
Prerequisite	None
Learning Objectives	To identify various ranges of functions displayed by materials and correlation of the same with respect to their properties. To understand the fundamental reasons due to which this variety of properties is possible for different materials. To evaluate the efficacy of a particular material with respect to emerging and conventional industrial applications.
Course Description	This course provides general overview on the origins of functional properties of materials, their wide varieties and their usability in different advanced technological applications.
Course Content	Free Electron Theory of Metals: Band theory, classification of materials based on band theory viz. conductors, conductors-classification and properties, factors affecting conductivity/resistivity of conductors, various conducting materials: composition, properties and applications. Resistors: Materials used for heating elements viz. nichrome, kanthal, silicon carbide and molybdenum, their composition, properties and applications Semiconductors: Intrinsic and extrinsic semi-conductors, II-VI, III-V and IV-IV group semiconductors, effects of doping. Magnetic materials: Sources of magnetism-orbital and spin motion of electron, types of magnetism: Dia-, para-, ferro-, ferri- and antiferro-magnetism, domain theory, types of magnetic materials: soft and hard magnetic materials and ferrites. GMR. Ferro-electric, Piezo-electric and Dielectric materials: Principle, materials and their applications; Ferroelectric ceramic materials, Basic Ceramic Dielectric formulation for capacitors. Multi-Layer Capacitors. Super conductivity: BCS theory, Meissner effect, materials, Type I and II superconductors.
Learning Outcome	Upon completing of this course, the student will be able to Identify the properties of metals, ceramics and polymers in relation to different functional properties Understand the fundamental reasons which enable a particular material to display a particular function Classify and distinguish different types of functional properties and correlate the same with relevant industrial applications
Assessment Method	Assignments, Quizzes, Mid-semester examination, End-semester examination.

Text Books:

Introduction to the Electronic Properties of Materials: David C. Jiles, 2nd Ed., CRC Press, 2001.
Electronic Materials Science: Eugene A. Irene, Wiley, 2005.
An Introduction to Electronic Materials for Engineers: Zhengwei Li, Nigel M. Sammes, 2^nd, World Scientific Publishing Company Pvt. Ltd., 2011.

Reference Books:

Electronic Materials and Devices: David K. Ferry, Jonathan P. Bird, Wiley, 2001.
Introduction to Magnetism and Magnetic Materials: David Jiles, 3rd Ed., CRC Press, 2015.

Electroceramics: Materials, Properties, Applications: A.J. Moulson, J.M. Herbert, Wiley, 2003.

MM4103

Semiconductor Materials and Devices

Course Number	MM4103
Course Credit (L-T-P-C)	3-0-0 (3 AIU Credits)
Course Title	Semiconductor Materials and Devices
Learning Mode	Lecture
Prerequisite
Learning Objectives	To discuss the working and applications of basic semiconductor devices To impart a fundamental knowledge of device fabrication relevant to the semiconductor industry. To enable the students to understand working principle of semiconductor devices such as transistors, diodes, solar cells, and light-emitting devices.
Course Description	This course provides students with foundational knowledge of semiconductor devices, covering essential principles and advanced semiconductor physics. After completion of the course, students will have understanding of semiconductor technology fundamentals, designed and analysed semiconductor devices.
Course Content	Fundamental of Semiconductors: Energy band theory, Sommerfield free electron theory for metals, Brillouin Zone Theory, density of states, Quasi-Fermi levels, Maxwell-Boltzmann distribution, Fermi-Dirac statistics, intrinsic semiconductor, n-type/p-type semiconductor, transport phenomenon of charge carriers, Energy bands in solids, band structure, band diagram of few important semiconductors (Si, Ge, GaAs, GaN), engineering of doping, surface energy of solids, effective mass, Brillouin zone, direct and indirect gaps semiconductor and photovoltaic effect. Fabrication of Semiconductors and devices: Production of single crystal of semiconducting materials, Semiconductor Grade Silicon, metallurgical grade silicon, Lithography, DC/RF magnetron sputtering. Devices and characterizations: Heterostructure p-n junctions, Schottky junctions, Ohmic contacts: Metal-semiconductor junctions, Schottky and Ohmic contacts, Metal-Semiconductor contacts, Metal-insulator-semiconductor structures, tunnel diodes, Gunneffect, p-i-n structures, Zener diode, Bipolar transistors, principle of operation of MOSFETs, characteristics of MOSFET, source-drain/transfer characteristics of MOSFET, introduction to JFETs, MESFETs, and MODFETs. carrier statistics under illumination condition, generation and recombination of carriers, emitting diodes (LED), LEDs, laser-diodes and solar cells, Current-voltage characteristics, capacitance-voltage (CV) and impedance measurements.
Learning Outcome	Upon completion of this course, the student will be able to: Grasp the basics concepts of semiconductor materials such as the energy bands, band gap, charge carrier concentration, transport phenomenon of charge carriers. Describe the fabrication of semiconductors devices Demonstrate the applications of various semiconducting devices such as p-n and Schottky junctions, BJTs and FETs, LEDs and solar cells
Assessment Method	Assignments, Quizzes, Mid-semester examination, End-semester examination.

Text Books:

Semiconductor Devices: Physics and Technology Hardcover – by Simon M. Sze (Author), Ming-Kwei Lee, 2012.
An Introduction to Semiconductor Devices- D. Neamen, McGraw-Hill Education, 2005.
Physics of Semiconductor Devices -S.M. Sze and K. K. Ng, Wiley- Interscience, 3rd edition, 2006.

Reference Books:

Semiconductor Physics: An Introduction. K. Seeger, Springer-Verlag, Berlin, 9^th, 2004.
Electronic Materials and Devices: David K. Ferry, Jonathan P. Bird, Wiley, 2001.
Introduction to the Electronic Properties of Materials: David C. Jiles, 2^nd, CRC Press, 2001.

Engineering Physics

Program Learning Objectives:	Program Learning Outcomes:
Program Goal 1: To nurture young engineers with a strong foundation in science and engineering for producing highly skilled engineers and scientists.	Program Learning Outcome 1a: Developing skills to apply strong knowledge of mathematics, science, engineering fundamentals. Program Learning Outcome 1b: To use research-based knowledge and research methodologies for developing cutting edge technology and for solving complex engineering problems.
Program Goal 2: Enhancement of problem-solving skills and independent thinking through a research oriented curriculum to conduct research or contribute to technology development projects, either individually or as a team leader.	Program Learning Outcome 2a: Develop highly skilled engineers who can contribute to the solution of technical and engineering problems that are based on broad principles of physics. Program Learning Outcome 2b: Ability to participate as members and project leaders on multidisciplinary teams in diverse workplaces and communities. Be able to communicate effectively in oral and written form.
Program Goal 3: To provide career opportunities in rapidly-advancing scientific and technical areas, R&D establishments, Modern cutting edge technologies, higher degree, Academia/Industry and etc .	Program Learning Outcome 3a: To practice and inculcate an ability of utilizing scientific knowledge and engineering design for developing technology for public health, safety, and welfare, as well as global, cultural, social, environmental, and economic factors. Program Learning Outcome 3b: Be able to demonstrate an understanding of professional and ethical responsibility.

Sl. No.

Subject Code

SEMESTER I

MA1101

Course Number	MA1101
Course Credit (L-T-P-C)	3-1-0-4
Course Title	Calculus and Linear Algebra
Learning Mode	Lectures and Tutorials
Learning Objectives	To provide the essential knowledge of basic tools of Differential Calculus, Integral Calculus, Vector spaces and Matrix Algebra.
Course Description	This course provides a foundation for Calculus and Linear Algebra. Topics related to properties of single and two variable functions along with their applications will be discussed. In addition fundamentals of linear algebra and matrix theory with applications will also be discussed.
Course Content	Differential Calculus (12 Lectures): Limit and continuity of one variable function (including ε-δ definition). Limit, continuity and differentiability of functions of two variables, Tangent plane and normal, Change of variables, chain rule, Jacobians, Taylor’s Theorem for two variables, Extrema of functions of two or more variables, Lagrange’s method of undetermined multipliers. Integral Calculus (10 Lectures): Riemann integral for one variable functions, Double and Triple integrals, Change of order of integration. Change of variables, Applications of Multiple integrals such as surface area and volume. Vector Spaces (12 Lectures): Vector spaces (over the field of real numbers), subspaces, spanning set, linear independence, basis and dimension. Linear transformations, range and null space, rank-nullity theorem, matrix of a linear transformation. Matrix Algebra (8 Lectures): Elementary operations and their use in getting the rank, inverse of a matrix and solution of linear simultaneous equations, Orthogonal, symmetric, skew-symmetric, Hermitian, skew-Hermitian, normal and unitary matrices and their elementary properties, Eigenvalues and Eigenvectors of a matrix, Cayley-Hamilton theorem, Diagonalization of a matrix.
Learning Outcome	Students completing this course will be able to: 1. Understand various properties of functions such as limit, continuity and differentiability. 2. Learn about integrations in various dimension and their applications. 3. learn about the concept of basis and dimension of a vector space. 4. define Linear Transformations and compute the domain, range, kernel, rank, and nullity of a linear transformation. 5. compute the inverse of an invertible matrix. 6. solve the system of linear equations. 7. Apply linear algebra concepts to model, solve, and analyze real-world problems.
Assessment Method	Quiz /Assignment/ MSE / ESE

Textbooks:

Thomas, G. B., Hass, J., Heil, C. and Weir M. D., “Thomas’ Calculus”, 14th Ed., Pearson Education, 2018
Kreyszig, E., “Advanced Engineering Mathematics”, 10th Ed., Wiley India Pvt. Ltd, 2015

Reference Books:

Jain, R. K. and Iyenger, S. R. K., “Advanced Engineering Mathematics”, 5^th, Narosa Publishing House, 2017
Axler, S., “Linear Algebra Done Right”, 3rd Ed., Springer Nature, 2015
Strang, G., “Linear Algebra and Its Applications” 4th Ed., Cengage India Private Limited, 2005

4.0

CS1101

Course Number	CS1101
Course Credit	3-0-3-4.5
Course Title	Foundations of Programming
Learning Mode	Offline
Learning Objectives	· To understand the fundamental concepts of programming · To develop the basic problem-solving skills by designing algorithms and implementing them. · To learn about various data types, control statements, functions, arrays, pointers, and file handling. · To achieve proficiency in debugging and testing a C program
Course Description	This introductory course provides a solid foundation in programming principles and techniques. Designed for students with little to no prior programming experience, it covers fundamental concepts such as variables, data types, control structures, functions, and basic data structures. Students will learn to write, debug, and execute programs using a high-level programming language. Emphasis is placed on developing problem-solving skills, logical thinking, and the ability to write clear and efficient code. By the end of the course, students will be equipped with the essential skills needed to pursue more advanced studies in computer science and software development.
Course Outline	Introduction and Programming basics, Expressions Control and Iterative statements, Functions, Arrays, Recursion vs. Iteration Pointers, 2D-Array with pointers, Structures, String, Dynamic memory allocation, File handling, Contemporary programming languages, and applications Practical component: Lab to be conducted on a 3-hour slot weekly. It will be conducted with the theory course so the topics for problems given in the lab are already initiated in the theory class.
Learning Outcome	· Understanding of Basic Syntax and Structure in C language · Proficiency in Data Types, Operators, and Control Structures · Function Implementation and learn to use them appropriately · Efficient Use of Arrays and Strings · Pointer Utilization · Ability to perform dynamic memory allocation and deallocation using malloc (), calloc (), realloc (), and free () functions. · Structured data management with structures and unions · Exposure of file Handling · Learning debugging and error Handling
Assessment Method	Internal (Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Reading

Knuth, Donald E. The art of computer programming, volume 4A: combinatorial algorithms, part 1. Pearson Education India, 2011.
P.J. Deitel and H.M. Deitel, C How To Program, Pearson Education (7th Edition)
Brian W. Kernighan and Dennis M. Ritchie, The C Programming Language, Prentice−Hall
A. Kelley and I. Pohl, A Book on C, Pearson Education (4th Edition)
K. N. King, C PROGRAMMING A Modern Approach, W. W. Norton & Company

4.5

PH1101/PH1201

Course Number	PH1101/PH1201
Course Credit	3-1-3-5.5
Course Title	Physics
Learning Mode	Lectures and Tutorials
Learning Objectives	Complies with Program Goals 1 and 2
Course Description	This course deals with fundamentals in Classical mechanics, Waves and Oscillations and Quantum Mechanics. As a prerequisite, the mathematical preliminaries such as coordinate systems, vector calculus etc will be discussed in the beginning.
Course Outline	Orthogonal coordinate systems (Plane polar, Spherical, Cylindrical), concept of generalised coordinates, generalised velocity and phase space for a mechanical system, Introduction to vector operators, Gradient, divergence, curl and Laplacian in different co-ordinate systems. Central force problem and its applications. Rigid body rotation, vector nature of angular velocity, Finding the principal axes, Euler's equations; Gyroscopic motion and its application; Accelerated frame of reference, Fictitious forces. Potential energy and concept of equilibrium, Lennard-Jones and double-well potentials, Small oscillations, Harmonic oscillator, damped and forced oscillations, resonance and its different examples, oscillator states in phase space, coupled oscillations, normal modes, longitudinal and transverse waves, wave equation, plane waves, examples two- and three-dimensional waves. Michelson-Morley experiment, Lorentz transformation, Postulates of special theory of relativity, Time dilation and length contraction, Applications of special theory of relativity.
Learning Outcome	Complies with PLO 1a, 2a, 3a
Assessment Method	Quiz, Assignments and Exams

Suggested Readings:

Textbooks:

Engineering Mechanics, M. K. Harbola, 2^nd ed., Cengage, 2012
D. Kleppner and R. J. Kolenkow, An introduction to Mechanics, Tata McGraw-Hill, New Delhi, 2000.
I. G. Main, Oscillations and Waves
H. G. Pain, The Physics of Vibrations and Waves, 1968
Frank S. Crawford, Berkeley Physics Course Vol 3: Waves and Oscillations, McGraw Hill, 1966.

References:

R. P. Feynman, R. B. Leighton and M. Sands, The Feynman Lecture in Physics, Vol I, Narosa Publishing House, New Delhi, 2009.
David Morin, Introduction to Classical Mechanics, Cambridge University Press, NY, 2007.

3. P. C. Deshmukh, Foundations of Classical Mechanics, Cambridge University Press, 2019

5.5

CE1101/CE1201

Course code	CE1101/CE1201
Course Credit (L-T-P-C)	1-0-3-2.5
Course Title	Engineering Graphics
Learning Mode	Lectures and Practical
Learning Objectives	Complies with PLO-1a 1. The course on engineering drawing is designed to introduce the fundamentals of technical drawing as an important form of conveying information. 2. Apply principles of engineering visualization and projection theory to prepare engineering drawings, using conventional and modern drawing tools. 3. Practice drawing orthographic projections, isometric views, and sectional views, of simple and combined solids in different orientations.
Course Description	This course will introduce drawing as a tool to represent a complex three-dimensional object on two-dimensional paper through methods of projections. The course explains the use of different drafting tools and the importance of conventions for uniformity and standardization of the interpretation of the drawings.
Course Outline	Fundamental of engineering drawing, line types, dimensioning, and scales. Conic sections: ellipse, parabola, hyperbola; cycloidal curves. Principle of projection, method of projection, orthographic projection, plane of projection, first angle of projection, Projection of points, lines, planes and solids. Section of solids: Sectional views of simple solids- prism, pyramid, cylinder, cone, sphere; the true shape of the section. Methods of development, development of surfaces. Isometric projections: construction of isometric view of solids and combination of solids from orthographic projections. Introduction to AutoCad and solving isometric problems.
Learning Outcome	After attending this course, the following outcomes are expected: a) The student will understand the basic concepts of engineering drawing. b) The student will be able to use basic drafting tools, drawing instruments, and sheets. c) The student will be able to represent three-dimensional simple and combined solid objects on two-dimensional paper. d) The student will be able to visualize and interpret the orientation of simple and combine solid objects.
Assessment Method	Laboratory Assignments (30%), Mid-semester examination (25%) and End-semester examination (45%).

Suggested Readings:

Textbooks:

D. Bhatt, Engineering Drawing, Charotar Publishing House.
Agrawal & Agrawal, Engineering Drawing, McGraw Hill.
Jolhe, Engineering Drawing.

References:

Engineering Drawing and Design by David Madsen

2.5

EE1101/EE1201

Course Number	EE1101/EE1201
Course Credit	3-0-3-4.5
Course Title	Electrical Sciences
Learning Mode	Lectures and Experiments
Learning Objectives	Complies with Program goals 1, 2 and 3
Course Description	The course is designed to meet the requirements of all B. Tech programmes. The course aims at giving an overview of the entire electrical engineering domain from the concepts of circuits, devices, digital systems and magnetic circuits.
Course Outline	Circuit Analysis Techniques, Circuit elements, Simple RL and RC Circuits, Kirchoff’s law, Nodal Analysis, Mesh Analysis, Linearity and Superposition, Source Transformations, Thevenin’s and Norton’s Theorems, Time Domain Response of RC, RL and RLC circuits, Sinusoidal Forcing Function, Phasor Relationship for R, L and C, Impedance and Admittance, Instantaneous power, Real, reactive power and power factor. Semiconductor Diode, Zener Diode, Rectifier Circuits, Clipper, Clamper, UJT, Bipolar Junction Transistors, MOSFET, Transistor Biasing, Transistor Small Signal Analysis, Transistor Amplifier and their types, Operational Amplifiers, Op-amp Equivalent Circuit, Practical Op-amp Circuits, Power Opamp, DC Offset, Constant Gain Multiplier, Voltage Summing, Voltage Buffer, Controlled Sources, Instrumentation Amplifier, Active Filters and Oscillators. Number Systems, Logic Gates, Boolean Theorem, Algebraic Simplification, K-map, Combinatorial Circuits, Encoder, Decoder, Combinatorial Circuit Design, Introduction to Sequential Circuits. Magnetic Circuits, Mutually Coupled Circuits, Transformers, Equivalent Circuit and Performance, Analysis of Three-Phase Circuits, Power measurement in three phase system, Electromechanical Energy Conversion, Introduction to Rotating Machines (DC and AC Machines). Laboratory: Experiments to verify Circuit Theorems; Experiments using diodes and bipolar junction transistor (BJT): design and analysis of half -wave and full-wave rectifiers, clipping and clamping circuits and Zener diode characteristics and its regulators, BJT characteristics (CE, CB and CC) and BJT amplifiers; Experiment on MOSFET characteristics (CS, CG, and CD), parameter extraction and amplifier; Experiments using operational amplifiers (op-amps): summing amplifier, comparator, precision rectifier, Astable and Monostable Multivibrators and oscillators; Experiments using logic gates: combinational circuits such as staircase switch, majority detector, equality detector, multiplexer and demultiplexer; Experiments using flip-flops: sequential circuits such as non-overlapping pulse generator, ripple counter, synchronous counter, pulse counter and numerical display; Power Measurement by two Wattmeter method; Open and Short Circuit Tests of Transformer.
Learning Outcomes	Complies with PLO 1a, 2a and 3a
Assessment Method	Quiz, Assignments and Exams

Texts/References

K. Alexander, M. N. O. Sadiku, Fundamentals of Electric Circuits, 3rd Edition, McGraw-Hill, 2008.
H. Hayt and J. E. Kemmerly, Engineering Circuit Analysis, McGraw-Hill, 1993.
L. Boylestad and L. Nashelsky, Electronic Devices and Circuit Theory, 6th Edition, PHI, 2001.
M. Mano, M. D. Ciletti, Digital Design, 4th Edition, Pearson Education, 2008.
Floyd, Jain, Digital Fundamentals, 8th Edition, Pearson.
David V. Kerns, Jr. J. David Irwin, Essentials of Electrical and Computer Engineering, Pearson, 2004.
Donald A Neamen, Electronic Circuits; analysis and Design, 3rd Edition, Tata McGraw-Hill Publishing Company Limited.
Adel S. Sedra, Kenneth C. Smith, Microelectronic Circuits, 5th Edition, Oxford University Press, 2004.
E. Fitzgerald, C. Kingsley Jr., S. D. Umans, Electric Machinery, 6th Edition, Tata McGraw-Hill, 2003.
P. Kothari, I. J. Nagrath, Electric Machines, 3rd Edition, McGraw-Hill, 2004.
Del Toro, Vincent. "Principles of electrical engineering." (No Title) (1972).

4.5

HS1101

Course Number	HS1101
Course Credit	L-T-P-W: 2-0-1-2.5
Course Title	English for Professionals
Learning Mode	Offline
Learning Objectives	This course aims to help the students (a) attain proficiency in written English through the construction of grammatically correct sentences, utilization of subject-verb agreement principles, mastery of various tenses, and effective deployment of active and passive voice to ensure coherent and impactful written expression; (b) enhance oral communication skills by honing public speaking abilities, acquiring strategies to deliver persuasive presentations, and cultivating a polished telephone etiquette, enabling confident and articulate verbal communication; (c) foster active listening capabilities by recognizing different types of listening, and applying proven methods and strategies to improve active listening skills; (d) strengthen reading skills, including comprehension, interpretation, and critical analysis, to grasp diverse written materials and derive meaning from various types of texts encountered in academic and professional contexts; (e) develop adeptness in written communication for business purposes, encompassing the understanding of essential writing elements, mastery of appropriate writing styles thereby enhancing prospects for successful job interviews and subsequent professional endeavors.
Course Description	This academic course on communication skills aims to equip students with fluency in spoken and written English for effective expression in both academic and professional settings. By focusing on essential communication principles and providing practical experiences, students develop clarity, precision, and confidence in their communication. Through interactive discussions and exercises, students enhance critical thinking and adaptability in diverse contexts. Upon completion, students will excel in formal presentations, group discussions, and persuasive writing, enhancing their overall communication proficiency.
Course Outline	Unit I: Introduction to professional communication – LSRW - Phonetics and phonology Sounds in English Language – production and articulation – rhythm and intonation – connected speech - Basic Grammar and Advanced Vocabulary Sounds in English Language – production and articulation – rhythm and intonation – connected speech – persuading and negotiating – brevity and clarity in language. Unit II: Characteristics of Technical Communication: Types of communication and forms of communication - Formal and informal communication Verbal and non-Verbal Communication – Communication barriers and remedies Intercultural communication – neutral language Unit III: Comprehension and Composition – summarization, precis writing Business Letter Writing CV/ Resume – E-Communication Unit IV: Statement of Purpose, Writing Project Reports, Writing research proposal, writing abstracts, developing presentations, interviews – combating nervousness Tutorial: Listening Exercises, Speaking Practice (GDs, and Presentations), and Writing Practice Learning Outcome · Attain proficiency in written English, enabling the construction of grammatically correct sentences and coherent written expression through the use of appropriate grammar, tenses, and voice. · Enhance oral communication skills, including public speaking, persuasive presentation, and polished telephone etiquette, fostering confident and articulate verbal expression. · Cultivate active listening abilities, recognizing different listening types, overcoming obstacles, and employing strategies for attentive and effective communication. · Develop proficient written communication skills for business purposes, demonstrating understanding of essential writing elements, appropriate styles, and the creation of reports, notices, agendas, and minutes that effectively convey information.
Assessment Method	Class test + Quiz = 20%; Mid-semester = 25%; Assignment = 15%; End semester = 40%

Suggested Reading

Balzotti, Jon. Technical Communication: A Design-Centric Approach. Routledge, 2022.
Kaul, Asha, Business Communication. PHI Learning Pvt. Ltd. 2009
Laplante, Phillip A. Technical Writing: A Practical Guide for Engineers, Scientists, and Nontechnical Professionals. CRC Press, 2018.
Lawson, Celeste, et al. Communication Skills for Business Professionals, Second Edition. CUP, 2019.
Sharon Gerson and Steven Gerson. Technical Writing: Process and Product (8th Edition), London: Longman, 2013
Rentz, Kathryn, Marie E. Flatley & Paula Lentz. Lesikar’s Business Communication Connecting in a Digital world, McGraw-Hill, Irwin.2012
Allan & Barbara Pease. The Definitive Book of Body Language, New York, Bantam,2004
Jones, Daniel. The Pronunciation of English, New Delhi, Universal Book Stall.2010
Savage, Alice. Effective Academic Writing. OUP. 2014
Swan and Alter. Oxford English grammar course. OUP. 201

2.5

TOTAL

23.5

Probability Theory and Ordinary Differential Equations

Sl. No.

Subject Code

SEMESTER II

MA1201

Course Number	MA1201
Course Credit (L-T-P-C)	3-1-0-4
Course Title	Probability Theory and Ordinary Differential Equations
Learning Mode	Lectures and Tutorials
Learning Objectives	To introduce the basic concepts of probability, statistics, and Differential equations.
Course Description	This course aims to cover basic concepts of probability, statistics and ordinary differential equations. In particular, popular distributions, random sampling, various estimators and hypothesis testing will be discussed. Students will also get exposure to the linear ordinary differential equations and their solution techniques.
Course Content	Probability (12 Lectures): Random variables and their probability distributions, Cumulative distribution functions, Expectation and Variance, probability inequalities, Binomial, Poisson, Geometric, negative binomial distributions, Uniform, Exponential, beta, Gamma, Normal and lognormal distributions. Statistics (10 Lectures): Random sampling, sampling distributions, Parameter estimation, Point estimation, unbiased estimators, maximum likelihood estimation, Confidence intervals for normal mean, Simple and composite hypothesis, Type I and Type II errors, Hypothesis testing for normal mean. Ordinary Differential Equations (20 Lectures): First order ordinary differential equations, exactness and integrating factors, Picard's iteration, Ordinary linear differential equations of n-th order, solutions of homogeneous and non-homogeneous equations (Method of variation of parameters). Systems of ordinary differential equations, Power series methods for solutions of ordinary differential equations. Legendre equation and Legendre polynomials, Bessel equation and Bessel functions.
Learning Outcome	Students will get exposure and understanding of: 1. Random variables and their probability distributions 2. Understand popular distributions and their properties 3. Sampling, estimation and hypothesis testing 4. Solution of ordinary differential equations 5. Solution of system of ordinary differential equations 6. Special functions arising as power series solutions of ordinary differential equations
Assessment Method	Quiz /Assignment/ MSE / ESE

Text Books:

Hogg, R. V., Mckean, J. and Craig, A. T., “Introduction to Mathematical Statistics”, 8th Ed., Pearson Education India, 2021
M. Ross “An introduction to Probability Models, Academic Press INC, 11th edition.
Miller, I. and Miller, M., “John E. Freund's Mathematical Statistics with Applications”, 8th Ed., Pearson Education India, 2013
L. Ross, Differential equations, 3rd Edition, Wiley, 1984
E. Boyce and R. C. Di Prima, Elementary Differential equations and Boundary Value Problems, 7th Edition, Wiley, 2001.

CS1201

Course Number	CS1201
Course Credit	3-0-3-4.5
Course Title	Data Structure
Learning Mode	Offline
Learning Objectives	· Understand the principles and concepts of data structures and their importance in computer science. · Learn to implement various data structures and understand how different algorithms works. · Develop problem-solving skills by applying appropriate data structures to different computational problems. · Achieving proficiency in designing efficient algorithms.
Course Description	This course provides a comprehensive study of data structures and their applications in computer science. It focuses on the implementation, analysis, and use of various data structures such as arrays, linked lists, stacks, queues, trees, and graphs. Through theoretical concepts and practical programming exercises, this course aims to develop problem-solving and algorithmic thinking skills essential for advanced topics in computer science and software development.
Course Outline	· Introduction to Data Structure, · Time and space requirements, Asymptotic notations · Abstraction and Abstract data types · Linear Data Structure: stack, queue, list, and linked structure · Unfolding the recursion · Tree, Binary Tree, traversal · Search and Sorting, · Graph, traversal, MST, Shortest distance · Balanced Tree Practical component: Lab to be conducted on a 3-hour slot weekly. It will be conducted with the theory course so the topics for problems given in the lab are already initiated in the theory class.
Learning Outcome	· Understand Data Structure Fundamentals · Implement Basic Data Structures using a programming language · Analyse and Apply Algorithms · Design and Analyse Tree Structures · Understand the usage of graph and its related algorithms · Design and Implement Sorting and Searching Algorithms · Debug and Optimize Code
Assessment Method	Internal (Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Reading

Alfred V. Aho, John E. Hopcroft, Jeffrey D. Ullman, Data Structures and Algorithms, Published by Addison-Wesley
Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest and Clifford Stein., Introduction to Algorithms,
Mark Allen Weiss, Data Structures and Algorithm Analysis in Java
Robert Sedgewick and Kevin Wayne, Algorithms
Narasimha Karumanchi, Data Structures and Algorithms Made Easy

4.5

CH1201/CH1101

Course Number	CH1201/CH1101
Course Credit	3-1-3-5.5
Course Title	Chemistry
Learning Mode	Offline
Learning Objectives	The course aims to lay a foundation for all three branches of chemistry, viz. Organic, Inorganic, and Physical Chemistry. The course aims to nurture knowledge to appreciate the interface of chemistry with other science and Engineering branches by combining theoretical concepts and experimental studies.
Course Description	This course introduces basic organic chemistry, inorganic chemistry and Physical chemistry to understand fundamental laws that governs various reactions, reaction rates, equilibrium, and their applications in daily life through relevant experimentation.
Course Outline	Module 1: Thermodynamics: The fundamental definition and concept, the zeroth and first law. Work, heat, energy and enthalpies. Second law: entropy, free energy and chemical potential. Change of Phase. Third law. Chemical equilibrium. Conductance of solutions, Kohlrausch’s law-ionic mobilities, Basic Electrochemistry. Module 2: Coordination chemistry: Crystal field theory and consequences color, magnetism, J.T distortion. Bioinorganic chemistry: Trace elements in biology, heme and non-heme oxygen carriers, haemoglobin and myoglobin; Organometallic chemistry. Module 3: Stereo and regio-chemistry of organic compounds, conformational analysis and conformers, Molecules devoid of point chirality (allenes and biphenyls); Significance of chirality in living systems, organic photochemistry, Modern techniques in structural elucidation of compounds (UV–Vis, IR, NMR). Module 4 (Lab Component): Experiments based on redox and complexometric titrations; synthesis and characterization of inorganic complexes and nanomaterials; synthesis and characterization of organic compounds; experiments based on chromatography; experiments based on pH and conductivity measurement; experiment related to chemical kinetics and spectroscopy.
Learning Outcome	Students will be able to 1. identify organic and inorganic molecules and relate them to daily life applications through experiments. 2. understand important hypothesis, laws and their derivations to intercept physical phenomenon of chemical reactions and apply them in hands-on experiments. 3. understand the importance of organic and inorganic molecules in our body and environment. 4. know important analytical techniques to intercept chemical entity. 5. approach organic and inorganic synthesis as a skillset for drug manufacturing, calculate limiting reagents and yields, use various analytical tools to characterize organic compounds, interpret and ascertain data related to Physical chemistry aspects and know laboratory safety measures, risk factors and scientific report writing skills.
Assessment Method	Theory: 20% Quiz and assignment, 30% Mid sem and 50% End semester exams for theory part (4 credits). Lab: 60% lab report, lab performance and assignment, 20% End semester exam for practical part, 20% viva/quiz (1.5 credits). Overall Weightage: Theory (70%), Lab (30%).

Suggested Reading:

Text books:

Vogel's Qualitative Inorganic Analysis, G. Svehla, 7^th Edition, Revised, Prentice Hall, 1996.
J. Elias, S. S. Manoharan and H. Raj, "Experiments in General Chemistry", Universities Press (India) Pvt. Ltd., 1997.

A. J. Elias, A Collection of Interesting General Chemistry Experiments, revised edition, Universities Press (India) Pvt. Ltd., 2007.

Albert Cotton, G. Wilkinson, C. A. Murillo, M. Bochmann, Advanced Inorganic Chemistry - 6^th Edition New Delhi: Wiley India, 2008.
Mukkanti, Practical Engineering Chemistry, B.S. Publications, Hyderabad, 2009.
Shriver and Atkins inorganic chemistry / Peter Atkins, Tina Overton, Jonathan Rourke, Mark Weller, Fraser Armstrong-5^th Edition – Oxford: UOP. 2012.
Atkins’ Physical Chemistry, Peter Atkins, Julio de Paula, James Keeler, Oxford University Press, 11^th Edition 2017.
L. Kapoor, A Textbook of Physical Chemistry, Vol: 1, 2 (6^th Edition, 2019), Vol: 3 (5^th Edition, 2020) MaGraw Hill.
R. Chatwal, S. K. Anand, Instrumental Methods of Chemical Analysis, 5^th Edition, Himalaya Publications, 2023.

5.5

ME1201/ME1101

Course Number	ME1201/ME1101
Course Credit	0-0-3-1.5
Course Title	Mechanical Fabrication
Learning Mode	Fabrication work – hands on fabrication work in Workshop
Learning Objectives	Complies with PLOs 3-4. · This course aims to develop the concepts and skills of various mechanical fabrication methods. · Fabrication of metallic and non-metallic components, fabrication using bulk and sheet metals, subtractive and additive manufacturing methods, and assemble the parts
Course Description	This course is designed to fulfil the need of hand on experience about various approaches (conventional and CNC, subtractive and additive) of mechanical fabrication approaches. Prerequisite: NIL
Course Outline	The jobs for various shops should be planned such that they are the parts of an assembled item. The student groups will fabricate different parts in various shops which will involve some amount of their creativeness/input particularly in design and/or planning. Various components as required for the assembled part can be made using the following shops: Sheet Metal Working: Development, sheet cutting and fabrication of designated job using sheet metal (ferrous/nonferrous); Joining of required portions by soldering, in case part is desired to be made leak proof. Pattern Making and Foundry: Making of suitable pattern (wood); making of sand mould, melting of non-ferrous metal/alloy (Al or Al alloys), pouring, solidification. Observation/identification of various defects appeared on the component. Joining: Butt/lap/corner joint job fabrication as required of low carbon steel plates; weld quality inspection by dye-penetration test (non-destructive testing approach)of the component made. Demonstration of semi-automatic Gas Metal Arc welding (GMAW). Conventional machining: Operations on lathe and vertical milling to fabricate the required component. The fabrication of the component should cover various lathe operations like straight turning, facing, thread cutting, parting off etc., and operations using indexing mechanism on vertical milling. CNC centre: Fundamentals of CNC programming using G and M code; setting and operations of job using CNC lathe or milling, tool reference, work reference, tool offset, tool radius compensation to fabricate the component with a designed profile on Al/Al-alloy plate. 3D printing (Fused Filament Fabrication): (2 weeks) Create the model, select appropriate slicing and path for fabrication of a 3D job by layer deposition (additive manufacturing approach) using polymeric material. Demonstration on pattern fabrication using 3D printing.
Learning Outcome	· This course would enable the students to develop the concept of design, fabrication (subtractive and additive) for various engineering applications. Fabrication of components and assemble them. · The practical skill and hands on experience for various fabrication methods from bulk, sheet metal using conventional as well as CNC machines.
Assessment Method	Fabrication of components in each of the shops required for assembly of the given part; submission of reports for each shop, and quiz assessment.

Text and Reference books:

Hajra Choudhury, HazraChoudhary and Nirjhar Roy, 2007, Elements of Workshop Technology, vol. I,Mediapromoters and Publishers Pvt. Ltd.
W A J Chapman, Workshop Technology, 1998, Part -1, 1st South Asian Edition, Viva Book Pvt Ltd.
N. Rao, 2009, Manufacturing Technology, Vol.1, 3rd Ed., Tata McGraw Hill Publishing Company.
Adithan, B.S. Pabla, 2012, CNC machines, New Age International Publishers

1.5

ME1202/ME1102

Course Number	ME1202/ ME1102
Course Number	Engineering Mechanics
L-T-P-C	3-1-0-4
Pre-requisites	Nil
Semester	Spring
Learning Mode	Lectures
Learning Objectives	Complies with PLOs 1, 4 · The objective of this first course in mechanics is to enable engineering students to analyze basic mechanics problems and apply vector-based approach to solve them.
Course Outline	1. Rigid body statics: Equivalent force system. Equations of equilibrium, Free body diagram, Reaction, Static indeterminacy. 2. Structures: 2D truss, Method of joints, Method of section. Beam, Frame, types of loading and supports, axial force, Bending moment, Shear force and Torque Diagrams for a member. 3. Friction: Dry friction (static and kinetic), wedge friction, disk friction (thrust bearing), belt friction, square threaded screw, journal bearings, Wheel friction, Rolling resistance. 4. Centroid and Moment of Inertia 5. Introduction to stress and strain: Definition of Stress, Normal and shear Stress. Relation between stress and strain, Cauchy formula. Stress in an axially loaded member and stress due to torsion in axisymmetric section
Learning Outcomes:	Following learning outcomes are expected after going through this course. · Learn and apply general mathematical and computer skills to solve basic mechanics problems. · Apply the vector-based approach to solve mechanics problems.
Assessment Method	Mid semester examination, End semester examination, Class test/Quiz, Tutorials

Reference Books

Shames, Engineering Mechanics: Statics and dynamics, 4th Ed, PHI, 2002.
P. Beer and E. R. Johnston, Vector Mechanics for Engineers, Vol I - Statics, 3rd Ed, Tata McGraw Hill, 2000.
L. Meriam and L. G. Kraige, Engineering Mechanics, Vol I - Statics, 5th Ed, John Wiley, 2002.
P. Popov, Engineering Mechanics of Solids, 2nd Ed, PHI, 1998.
P. Beer and E. R. Johnston, J.T. Dewolf, and D.F. Mazurek, Mechanics of Materials, 6th Ed, McGraw Hill Education (India) Pvt. Ltd., 2012.

IK1201

Indian Knowledge System (IKS)

TOTAL

22.5

Sl. No.

Subject Code

SEMESTER III

EP2101

Classical dynamics: discrete and continuum systems

Course Number

EP2101

Course Credit

3-1-0-4

Course Title

Quantum Physics

Learning Mode

Lectures, Tutorials and Assignments

Learning Objectives

Complies with Program Goals 1,2 and 3

Course Description

Fundamental structure of the subject is explicated through theorems, postulates and models. Several well-known discoveries in quantum mechanics are detailed. It also includes a variety of applications to various physical systems (both 1D and 3D) which are not adequately explained by classical theory. Some modern relevant applications are mentioned too.

Course Outline

Emphasis on both early and modern experiments (Black body radiation, photoelectric effect, Compton effect, Stern-Gerlach, Frank-Hertz, Davisson-Germer, Wave-packet propagation, Quantum Hall effect, Dirac-Kapitza effect, Raman-Nath scattering, etc.).

Postulates of quantum mechanics, Observables, uncertainty principle, Schrödinger Equation, stationary states, orthonormality, expectation values, application to 1-D problems: Free particle, Particle in a box and finite square well, Quantum tunneling and applications, Harmonic oscillator, Delta-Function Potential, orbital and spin angular momentum, Hydrogen atom, electrons in 1D periodic lattice and origin of bands.

Engineering applications: devices based on quantum principles such as tunnel diode, single electron transistor, MRI and NMR, SEM, TEM and SPM.

Learning Outcome

Complies with PLO 1a, 1b, 2a and 3a

Assessment Method

Assignments, Quizzes, Seminar, Mid-semester examination, End-semester examination

Suggested Readings:

Textbooks:

1. A. Beiser, Concepts of Modern Physics, Tata McGraw Hill, 2020

2. Eisberg and Resnick

3. Introduction to Quantum Mechanics (2nd edn) by D. J. Griffiths, Prentice Hall (2004).

Reference books:

1. Quantum Mechanics, Powell and Craseman

1. Mastering quantum mechanics, Barton Zwiebach, MIT Press, 2022

EP2102

Optics & Lasers

Course Number	EP2102
Course Credit	3-0-3-4.5
Course Title	Optics & Lasers
Learning Mode	Lectures and Assignments
Learning Objectives	Complies with Program Goals 1,2 and 3
Course Description	This course deals with fundamentals in Optics and Lasers. Students will learn about principles of LASERs, different types of Lasers, applications of Lasers in different engineering domains besides developing strong fundamentals in Optics
Course Outline	Review of basic optics: Polarization, Reflection and refraction of plane waves. Diffraction: diffraction by circular aperture, Gaussian beams. Interference: two beam interference-Mach-Zehnder interferometer and multiple beam interference-Fabry-Perot interferometer. Monochromatic aberrations. Fourier optics, Holography. The Einstein coefficients, Spontaneous and stimulated emission, Optical amplification and population inversion. Laser rate equations, three level and four level systems; Optical Resonators: resonator stability; modes of a spherical mirror resonator, mode selection; Q-switching and mode locking in lasers. Properties of laser radiation and some laser systems: Ruby, He-Ne, CO2, Semiconductor lasers. Some important applications of lasers, Fiber optics communication, Lasers in Industry, Lasers in medicine, Lidar.
Learning Outcome	Complies with PLO 1a, 1b, 2a and 3a
Assessment Method	Quiz, Assignments and Exams
Suggested Readings:	Textbooks: R. S. Longhurst, Geometrical and Physical Optics, 3rd ed., Orient Longman, 1986. E. Hecht, Optics, 4th ed., Pearson Education, 2004. M. Born and E. Wolf, Principles of Optics, 7th ed., Cambridge University Press, 1999. William T. Silfvast, Laser Fundamentals, 2nd ed., Cambridge University Press, 2004. K. Thyagarajan and A. K. Ghatak, Lasers: Theory and Applications, Macmillan, 2008.

4.5

EP2103

Course Number	EP2103
Course Credit (L-T-P-C)	3-1-0-4
Course Title	Classical dynamics: discrete and continuum systems
Learning Mode	Lectures and Tutorials
Learning Objectives	Complies with Program Goals 1, 2a and 3
Course Description	Formulate mechanics problem with Lagrangian, Hamiltonian, Calculus and Jacobi methods. Solve central force motion, rigid body dynamics, relativistic problems with learned expertise.**TO BE CHANGED
Course Content	Constraints, D’ Alembert’s Principle and Lagrange’s Equation, Hamilton’s Principle, The Hamilton Equation of Motion, Symmetry and Conservation Review of two body central force problem, Noether’s Theorem, Conserved quantities including Laplace-Runge-Lenz Vector, Scattering in a Central Force field, Scattering cross-section, Rutherford scattering. Introduction to continuum mechanics: 1. Basics of tensor algebra. 2. Fluid mechanics: Euler equations, potential flow, incompressible fluids, momentum and energy fluxes, circulations, internal waves, gravity waves, viscous fluids, Navier-Stokes equation, laminar flows, stream and string lines, rotating fluids, oscillatory motion in fluids, laminar boundary layers. 3. Elasticity – Concept of stress and strain, Linear elastic materials, Hooke’s law, boundary value problems in 2D, Flexure of elastic beams, introduction to thermo-elasticity and photo-elasticity.
Learning Outcome	Complies with PLO 1, 2(a) and 3
Assessment Method	Assignments, Quizzes, Presentation, Mid-semester examination and End-semester examination
Suggested Readings:	Textbooks: Classical Mechanics - J. R. Taylor, University Science Books, 2005. 2. Classical Dynamics, D. T. Greenwood, Dover, 1997 3. Fluid Mechanics, P. K. Kundu, I. M. Cohen and R. David 4. Elasticity: Theory, Applications and Numerics, M. H. Saad References: Classical Mechanics, L. D. Landau and E. M. Lifshitz, Course on Theoretical Physics, Vol.1, 3rd Edition, Butterworth-Heinemann. Classical Mechanics - H. Goldstein, C. P. Poole and J. Safko; Pearson Education (2011). Theory of Elasticity, L. D. Landau and E. M. Lifshitz Fluid Mechanics, L. D. Landau and E. M. Lifshitz Classical Mechanics, N.C. Rana and P. S. Joag, McGraw Hill Education Pvt Ltd. (2001). Introduction to Dynamics, I. Percival and D. Richards, Cambridge University Press, 1983. Special Relativity - A.P. French; CRC Press(2017). Introduction to Fluid Mechanics and Fluid Machines, S. K. Som, G. Biswas, S. Chakraborty, McGraw Hill, 2017 Vijay Gupta and Santosh Gupta, Fluid Mechanics and its Applications, New Age, 2015

EP2104

Thermal physics with engineering applications

Course Number	EP2104
Course Credit	3-1-0-4
Course Title	Thermal physics with engineering applications
Learning Mode	Lectures and Tutorials
Learning Objectives	Complies with 1 and 2
Course Description	This course provides student’s fundamentals of Thermal Physics towards Engineering applications. This course deals with many engineering applications like heat engines, Refrigeration systems, Thermal Power plant, Gas Turbines, Phase transitions etc.
Course Outline	Kinetic Theory of Gases, Maxwell-Boltzmann distribution, effusion, collision, equation of state, ideal gas, Equipartition of energy, real gas; Thermal Diffusion Equation; Laws of Thermodynamics, Temperature, Internal Energy, specific heat, Entropy; Carnot engine, Various thermodynamic cycles; Thermodynamic potentials, Path and State Functions, Gibb’s-Duhem relations, Maxwell Relations; Clausius-Clapeyron Equation; Chemical Potential, Chemical Equilibrium, Phase Diagram, Gibb’s Phase Rule, Phase Transitions, Stable and Metastable States, Phase Co-existence, Maxwell’s Construction; Various modes of heat transfer; Saha-Ionization; Speed of Sound in Fluids, Shock Waves, Rankine-Hugoniot Conditions. Engineering applications -Heat Engines, Joule-Thompson effect and applications to cryogenics, Refrigerators, Heating-Ventilation and Air-conditioning (HVAC), Exergy analysis of engineering systems, Information Theory.
Learning Outcome	Complies with PLO 1(a), 1(b), 2(a) and 3(a)
Assessment Method	Assignments, Quizzes, Mid-semester examination and End-semester examination
Suggested Readings:	Textbooks: 1. Stephen J. Blundell and Katherine M. Blundell, Concepts in Thermal Physics, 3rd Ed, Oxford University Press, 2014. 2. R. H. Dittman and M. W. Zemansky, Heat and Thermodynamics, McGraw-Hill College; Subsequent Ed, 1996. 3. M. A. Boles, Y. A. Cengel, M. Kanoglu, Thermodynamics: An Engineering Approach 4. Finn’s Thermal Physics, Andrew Rex 5. Thermal Physics, C. Kittel, H. Kroemer, W. H. Freeman, 2^nd ed., 2012 References: 1. M. N. Saha and B. N. Srivastava, Treatise on Heat, 3rd Edition, The Indian Press, Allahabad, 1950. 2. R.Baierlein, Thermal Physics, Cambridge University Press, 2005.

HS21XX

HSS Elective – I

Total Credit

19.5

Introduction to Nuclear and Particle Physics

Sl. No.

Subject Code

SEMESTER IV

EP2201

Course Number	EP2201
Course Credit	2-1-0-3
Course Title	Introduction to Nuclear and Particle Physics
Learning Mode	Class lectures, tutorials, assignments, discussions.
Learning Objectives	Complies with Program Goals 1, 2 and 3
Course Description	This is an introductory course of Nuclear and Particle Physics. The course covers tools (accelerators, detectors), nuclear properties, nuclear forces, nuclear models, radioactive decay and nuclear reactions. Fundamentals of particle interactions and forces, symmetries and conservation laws will be discussed. Topics will be taught with key experiments.
Course Outline	Nuclear properties: mass, radius, spin, parity, binding energy; Nuclear models: liquid drop model, semi-empirical mass formula, nuclear shell model - validity and limitations, magic numbers; Nature of the nuclear force: form of nucleon-nucleon potential, charge-independence and charge-symmetry of nuclear forces; Radioactive decay: radioactive decay law, elementary ideas of alpha, beta and gamma decays and their selection rules; Nuclear reactions: reaction mechanism, Fission and fusion, compound nuclei and direct reactions. Particle Phenomenology: Fundamental interactions; Elementary particles and their quantum numbers; Gellmann-Nishijima formula, Quark model, baryons and mesons; C, P, and T invariance, Conservation laws and particle reactions. Introduction to nuclear detector technology and particle accelerators.
Learning Outcome	Complies with PLO 1(a), 1(b), 2(a) and 3
Assessment Method	Assignments, Quizzes, Mid-semester examination and End-semester examination.
Suggested Readings:	Text Books: 1. D. Griffiths, Introduction to Elementary Particles, Wiley (2008) 2. Kenneth S. Krane, Introductory Nuclear Physics, Wiley (2008) 3. A. Das,T. Ferbel, Introduction to Nuclear and Particle Physics, World Scientific (2003) 4. S.N. Ghoshal, Nuclear Physics, S Chand (1994) Reference Books: 1. Martin B. and Shaw G. P., Particle Physics, Wiley. 2. Detectors for Particle Radiation, Konrad Kleinknecht, Cambridge. 3. Techniques for Nuclear and Particle Physics Experiments: A How-To Approach, William R. Leo, Springer. 4. Roy R. and Nigam B. P., Nuclear Physics: Theory and Experiment, New Age. 5. J. Lilley, Nuclear Physics, Wiley (2006) 6. Hughes I. S., Elementary Particles,Cambridge. 7. D. H. Perkins, Introduction to High Energy Physics, 4th edition, Cambridge (2000). 8. Halzen F. and Martin Alan D., Quarks and Leptons, Wiley India edition. 9. Mittal V. K., Verma R. C., Gupta S.C., Introduction To Nuclear And Particle Physics, Prentice-Hall of India Pvt. Ltd.

EP2202

Mathematical Methods for Engineers

Course Number	EP2202
Course Credit	3-1-0-4
Course Title	Mathematical Methods for Engineers
Learning Mode	Class lectures, tutorials, assignments, discussions.
Learning Objectives	Complies with Program Goals 1, 2 and 3
Course Description	This course will train students in mathematical methods required for various engineering applications.
Course Outline	Vector Space: Gram-Schmidt Orthonormalization, Self-adjoint operators, completeness of Eigen functions, Complex analysis: - Basic review, Cauchy’s integral theorem, Classification of singularities, Residue theorem. Contour integration and examples. Analytic continuation. Multiple-valued functions, branch points and branch cut integration. Conformal mapping, Physical Applications (fluid flow, electrostatics, heat flow etc.), Polynomials and Special Functions: Legendre, Hermite, Laguerre, Chebyshev, Jacobi, Bessel, Neumann, Hankel; Green’s function: 1,2,3 dimensional problems (Laplace, wave, heat equations etc.), Integral Transforms, Basic Introduction to Tensors and engineering applications.
Learning Outcome	Complies with PLO 1(a), 1(b), 2(a) and 3
Assessment Method	Assignments, Quizzes, Mid-semester examination and End-semester examination.
Suggested Readings:	Text Books: G. B. Arfken and H. J. Weber, Mathematical methods for physicists, Elsevier; 7th Ed, 2012. J. Brown and R. Churchill, Complex Variables and Applications, McGraw Hill Education, 8th Ed, 2017. V. Balakrishnan, Mathematical Physics with Applications, Problems and Solutions, Ane Books, 1stEd, 2017. Reference books: L. A. Pipes and L. R. Harvill, Applied Mathematics for Engineers and Physicists, Dover Publications Inc., 3rd rev. Ed, 2014. I. S. Gradshteyn and I. M. Ryzhik, Tables of Integrals, Series and Products, Edited by A. Jeffrey and D.Zwillinger, Academic Press is an imprint of Elsevier 7th Ed, 2007. Abramowitz and Stegun, Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables, United States Department of Commerce, National Institute of Standards and Technology (NBS), 1964. E. Kreyszig, Advanced Engineering Mathematics, Wiley India 10th Ed, 2011. M. L. Boas, Mathematical Methods in the Physical Sciences, Wiley, 3rd Ed, 2005. Charlie and Harper, Introduction to Mathematical Physics, Prentice Hall India, 1978.

EP2203

Introductory Statistical Mechanics

Course Number	EP2203
Course Credit	3–1–0–4
Course Title	Electromagnetism
Learning Mode	Class lectures, tutorials, assignments, discussions.
Learning Objectives	Complies with Program Goals 1, 2 and 3
Course Description	This course gives an introduction to fundamentals of electromagnetic theory. Students will learn electrostatics, electrodynamics and electromagnetic waves in medium and its applications
Course Outline	Electrostatics and Magnetostatics, Displacement current and Maxwell’s equations, Maxwell’s equation in matter, Boundary conditions, Conservation principles in EM theory (energy and momentum), Poynting’s theorem, Electromagnetic (EM) wave equation for E and B in vacuum, Monochromatic plane waves, Energy and momentum in EM waves, Propagation of EM waves in linear media, Reflection and transmission of EM waves at conducting and non-conducting media; Skin effect, Frequency dependence of permittivity; Wave guides: EM waves between two conducting planes, TM, TE and TEM waves and their transmission.
Learning Outcome	Complies with PLO 1(a), 1(b), 2(a) and 3
Assessment Method	Assignments, Quizzes, Mid-semester examination and End-semester examination.
Suggested Readings:	Text Books: D. J. Griffiths, Introduction to Electrodynamics, Third Edition, Pearson Education Inc., 2006. J. D. Ryder, Networks, Lines and Fields, Second Edition, Prentice Hall of India, 2002.

EP2204

Course Number	EP2204
Course Credit (L-T-P-C)	2-1-0-3
Course Title	Introductory Statistical Mechanics
Learning Mode	Lectures and Tutorials
Learning Objectives	Complies with Program Goals 1, 2 and 3
Course Description	Equips the students with the techniques in Statistical Physics and allows them to apply these techniques to wide variety of problems in Physics
Course Content	Random walk, motivation for Statistical Mechanics; Phase space; Postulates of Statistical Physics; Ergodicity; Microcanonical, canonical and grand-canonical ensembles approach with examples; Partition functions, examples; real gases; Ising model; Quantum statistics: Bosonic and Fermionic gases; Bose-Einstein Condensation; Phases and phase transitions, Ehrenfest criteria, order-parameters, liquid Helium as example; Shannon entropy and other entropy measures, applications in information science
Learning Outcome	Complies with PLO 1(a), 1(b), 2(a) and 3
Assessment Method	Assignments, Quizzes, Mid-semester examination and End-semester examination
Suggested Readings:	Textbooks: 1. R. K. Pathria and Paul D. Beale, Statistical Mechanics (Elsevier, 4th Edition, 2021). 2. D. Chandler, Introduction to Modern Statistical Physics (Oxford University Press, 1987). 3. W. Krauth, Statistical Mechanics: Algorithms and Computations (Oxford Masters Series in Physics, 2006). References: 1. F. Mandl, Statistical Physics (Wiley-Blackwell, ELBS Edition, 1988). 2. F.Reif, Fundamentals of Statistical and Thermal Physics (Berkeley Physics Course - Vol.5., 2017). 3. M.Pilschke and B.Bergerson, Equilibrium Statistical Physics, (World Scientific, 1994). 4. B. P. Agarwal ad M. Eisner, Statistical Mechanics, (Wiley Eastern Limited, 1988). 5. K.Huang, Introduction to Statistical Physics (Chapman and Hall/CRC, 2nd Edition, 2009). 6. D. Chowdhury, D. Stauffer, Principles of Equilibrium Statistical Mechanics, Wiley-Vch, 2000

EP2205

Analog Electronics

Course Number	EP2205
Course Credit	2-0-3-3.5
Course Title	Analog Electronics
Learning Mode	Lectures and Laboratory
Learning Objectives	Complies with Program Goals 1, 2 and 3
Course Description	The course covers various devices and components used in analog electronics. The device operation, behaviour and technological framework required for the fabrication of these devices is also discussed. In the laboratory, the students will receive hands-on training on designing circuits to measure the current-voltage characteristics of these devices. Also, at the end, designing and making circuit in a PCB is also performed.
Course Outline	Lecture: p-n junction diode, Zener diode, Schottky diode, photovoltaic cell, photodiode, tunnel diode, unijunction transistor, bipolar junction transistor, junction field effect transistor, metal oxide semiconductor field effect transistor and insulated gate bipolar transistor Device fabrication, introduction to cleanroom processes including wafer cleaning, deposition, lithography, diffusion, etching and bonding Laboratory: I – V characteristics of: Zener diode and its voltage regulation, Schottky diode, Tunnel diode, Solar cell, Silicon controlled rectifier, Unijunction transistor, BJT in CE, CB and CC mode of operation, JFET, MOSFET, both for enhancement and depletion mode, IGBT; Soldering semiconductor devices on PCB for making a circuit
Learning Outcome	Complies with PLO 1(a), 1(b), 2(a) and 3
Assessment Method	Lecture: Mid Semester, Quizzes, Assignments and End Semester Exam Laboratory: Laboratory report and End Semester Examination
Suggested Readings:	1. B. G. Streetman and S. Banerjee, Solid State electronic devices, 6^th Ed, PHI, 2006. 2. Adel S. Sedra and Kenneth C. Smith, Microelectronic Circuits, Oxford University Press, 6^th Edition, 2009 3. Robert L. Boylestad and Louis Nashelsky, Electronic Devices and Circuit Theory, Prentice Hall, 7^th Edition. 4. Jacob Millman and Christos C. Halkias, Integrated Electronics: Analog and Digital Circuits and Systems, Tata McGraw Hill, 2008 5. D. A. Neamen, Semiconductor physics and devices, 4th Ed, McGrawHill, 2012. 6. S. M. Sze and Kwok Ng, Physics of Semiconductor Devices, 3^rd Ed, Wiley, 2006. 7. U. K. Mishra and J. Singh, Semiconductor Device Physics and Design, Springer, 2008. 8. B. Ghosh, Advanced Practical Physics, Volume – II, Sreedhar Publishers, 6^th Edition, 2015

3.5

XX22PQ

IDE – I

Total Credit

20.5

Data Science for Physicists

Sl. No.

Subject Code

SEMESTER V

EP3101

Computational Techniques

Course Number	EP3101
Course Credit	2–0–3–3.5
Course Title	Computational Techniques
Learning Mode	Class lectures, tutorials, assignments, discussions.
Learning Objectives	Complies with Program Goals 1, 2 and 3
Course Description	This course will train students in various numerical methods and techniques required for solving various physics and engineering problems numerically
Course Outline	Preliminaries of Computing; Roots of Non Linear Equations and solution of system of Linear Equations:- Fixed-point iteration, Bisection, Secant, Regula-Falsi method, Newton Raphson method, Gauss Elimination method by pivoting, Gauss – Jordan method, Gauss – Seidel method, Relaxation method, Convergence of iteration methods, LU and Cholesky decomposition. Interpolation and approximations:-Lagrange and Newton interpolation, Spline interpolation, Rational approximations, Curve fitting: Least square method, Numerical Integration:-Newton-Cote's rule, Gaussian quadrature, Monte-Carlo technique, Numerical Solution of Ordinary a Differential Equations:-Taylor series method, Runge-Kutta methods.
Learning Outcome	Complies with PLO 1(a), 1(b), 2(a) and 3
Assessment Method	Assignments, Quizzes, lab, Mid-semester examination and End-semester examination
Suggested Readings:	Text Books: W. H. Press, S. A. Teukolsky, W T. Vetterling and B. P. Flannery, Numerical Recipes in C: The Art of Scientific Programming, 2nd Ed, Cambridge University Press, 1997 C. F. Gerald and P. O. Wheatley, Applied Numerical Analysis, Pearson Education India; 7 Ed, 2007. S. S. Sastry, Introductory Methods of Numerical Analysis, PHI learning Pvt. Ltd., 5th Ed, 2012. M. K. Jain, S. R. K. Iyengar and R. K. Jain, Numerical Methods for Scientific and Engineering Computation, 6th Edition, New Age International (P) Ltd., 2014. Reference Books: E. Kreyszig, Advanced Engineering Mathematics, 9th Edition, Wiley, 2005. B. S. Grewal, Higher Engineering Mathematics, 43rd Edition, Khanna Publishers, 2014. Y. Kanetkar, Let us C, 13th edition, BPB publication 2013. Programming in ANSI C, Tata McGraw-Hill Education, 2008. Programming with C (Schaum's Outlines Series), McGraw Hill Education (India) Private Limited; 3rdEd, 2010.

3.5

EP3102

Course Number	EP3102
Course credit(L-T-P-C)	1-1-3-3.5
Course title	Data Science for Physicists
Learning mode	Offline
Learning objectives	· An introduction to data science career path for physicists. · Understanding the basics of machine learning and ML model building. · Exposition to popular python-based environments like Jupyter, Kaggle which are used industry-wide for AI/ML or data science applications. · Using state-of-the-art libraries like pandas and sklearn to preprocess the data, apply ML models, validate, and test predictions. · Hands-on experience through real-world projects.
Course description	Data science is increasingly becoming an essential skill for physicists. While there are numerous courses and programs on data science offered across various media, these are almost invariably targeted at computer science graduates and industry professionals. This course is designed to bridge this gap by introducing essential data science techniques from the perspective of applications in physics research and prepare learners for advanced courses in ML/AI/Data science.
Course content	Programming environments for data science: local python development environment like Jupyter, cloud based python notebook and data science platforms like Kaggle, basics of various open-source libraries for data science applications (like numpy, pandas), file versioning using github. The what and why of machine learning, mathematical basis of ML – setting up a problem, example of linear and polynomial regression; statistical learning theory – bias, variance, model complexity; cost function, gradient descent, basics of supervised and unsupervised learning, regression with multiple variables, feature normalization, basics of neural networks, building first ML model – handling data for training, testing, and validation, types of models, using scikit-learn library, ML pipelines; data science techniques – pandas, data cleaning, data visualization. Hands-on project – detection of gravitational waves – introduction to gravitational waves, Fourier transform, noise, GW signal analysis, data fitting.
Pre-requisites	· Linear algebra, matrices, vector algebra · Basic familiarity with programming in Python
Learning outcomes	Upon successful completion of this course, students will be able to: · write intermediate-level programs in Python, define functions, import and use libraries. · Work on projects in Jupyter environment, and collaborate on group projects on platforms like Kaggle, and github. · Understand the fundamental concepts of machine learning and theoretical understanding of how ML models are developed. · Understand and manipulate data for training, validating, and testing predictions of ML models. · Use various python libraries like scikit-learn, pandas, numpy, etc. to create ML pipelines that take in given data and generate predictions. · Get exposure to real-world usage of data science techniques in trending research areas.
Assessment method	Project, Assignments, Quiz, Mid-semster examination, End-semester examination
Suggested Readings:
Textbooks:	· Christopher Bishop, Pattern Recognition and Machine Learning, Springer, 2007 · Introduction to Machine Learning Edition 2, by Ethem Alpaydin · Machine Learning. Tom Mitchell. First Edition, McGraw- Hill, 1997.
References:	· A high-bias, low-variance introduction to Machine Learning for physicists, Pankaj Mehta, Marin Bukov, Ching-Hao Wang, Alexandre G.R. Day, Clint Richardson, Charles K. Fisher, David J. Schwab, 2019, Phys. Rep. 810, 1. · John Hopcroft, Ravindran Kannan, Foundations of Data Science, 2014. · I. Goodfellow, Y. Bengio, A. Courville. Deep Learning. MIT Press, 2016. · Machine learning & artificial intelligence in the quantum domain, Vedran Dunjko, Hans J. Briegel, arXiv:1709.02779 · Andrew Ng’s lectures on machine learning, Coursera, https://www.coursera.org/learn/machine-learning-course/

3.5

EP3103

Digital Electronics and Microprocessors

Course Number	EP3103
Course Credit (L-T-P-C)	2-0-3-3.5
Course Title	Digital Electronics and Microprocessors
Learning Mode	Lectures and Practical
Learning Objectives	Complies with Program Goals 1, 2 and 3
Course Description
Course Content	Moving and Storing Digital Information, Digital IC Signal Levels, Digital Logic, Basic Gates, Universal Logic Gates; Combinational Logic Circuits, Boolean Laws and Theorems, Sum-of-Products Method, Truth Table to Karnaugh Map, Karnaugh Simplifications, Product-of-sums Simplification, Quine-McClusky Method; Data-Processing Circuits, Multiplexers, Demultiplexers, Decoders and Encoders, Parity Generators and Checkers; Comparator, Read-only Memory, Programmable Array Logic, HDL Implementation of Data Processing Circuits; Binary, octal and hexadecimal number systems, ASCII, Excess-3 Gray Codes; Error Detection and Correction; Arithmetic Circuits, Complement Representation; Clocks and Timing Circuits, TTL Clock, Schmitt Trigger; Timer-Astable, Monostable, Monostables with Input Logic; Flip-Flops, RS, gated, edge-triggered, D, JK and Master-slave versions; Analysis of Sequential Circuits; Registers, Serial In-serial Out, Serial In-parallel Out, Parallel In-serial Out, Parallel In-parallel Out, Universal Shift Register; Counters, Asynchronous and Synchronous Counters, Decade Counters, Counter Design using HDL; Design of Synchronous and Asynchronous Sequential Circuits; State Transition Diagram and Table; Implementation using Read Only Memory; Algorithmic State Machine, State Reduction Technique; D/A and A/D Conversion, ROM, PROM, EPROM, RAM; TTL Parameters, TTL-to-CMOS and CMOS-to-TTL Interfaces; Multiplexing Displays, Frequency Counters; Microprocessor-compatible A/D Converters, Execution of lnstructions, Macro and Micro Operations; BCD Codes, IEEE Standards; Block diagram of a microprocessor, architecture of 8086, pin diagram, register organization, pipelining, physical address generation; Basics of assembly language programming, assembler, linker, debugger, machine language instruction format; use of opcode sheet, pseudocode and microprocessor programming, elementary operations.
Learning Outcome	Complies with PLO 1(a), 1(b), 2(a) and 3
Assessment Method	Assignments, Quizzes, Mid-semester examination and End-semester examination
Suggested readings	Textbooks: 1. Digital Principles and Applications (7/e), Donald P. Leach, Tata McGraw Hill (2011). 2. Microprocessor Architecture, Programming and Applications with the 8085 (6/e), Ramesh Gaonkar, PRI (2013). References: 1. Mastering Digital Electronics, Hubert Henry Ward, APress (2024). 2. Microprocessors and Microcontrollers, 3. N. Senthil Kumar, M. Saravanan, S. Jeevananthan (2/e), Oxford University Press (2018).

3.5

EP3104

Solid State Physics

Course Number	EP3104
Course Credit	3-1-2-5
Course Title	Solid State Physics
Learning Mode	Class lectures, tutorials, assignments, discussions
Learning Objectives	Complies with program goal 1,2 and 3.
Course Description	This course deals with basic theory of solids which are important to understand the vast range of real solids, with an emphasis on its structure and physical properties. This includes topics that are entirely based on classical methods, and also those which demand a detailed quantum treatment. The concepts of statistical mechanics, thermodynamics and mathematical methods are inherently present in this course due to its interdisciplinary approach. The course includes theories of metals, insulators, and semiconductors. Electrical, mechanical, thermal, magnetic and superconducting properties are discussed with detailed analysis.
Course Outline	Crystal physics: Symmetry operations; Bravais lattices; Point and space groups; Miller indices and reciprocal lattice; Structure determination; diffraction; X-ray, electron and neutron; Crystal binding; Defects in crystals; Point and line defects. Lattice vibration and thermal properties: linear lattice; acoustic and optical modes; dispersion relation; density of states; phonons and quantization; Brillouin zones; Specific heat (Einstein and Debye models) and thermal conductivity of metals and insulators. Electronic properties: Free electron theory of metals; electrons in a periodic potential; Bloch equation; Kronig-Penny model; band theory; Nearly free electron and tight-binding model, Motion of electrons in applied electric and magnetic fields. Semiconductor physics: concept of holes, carrier concentration in intrinsic and extrinsic semiconductors, effective mass and mobility. Magnetic properties: Dia-, para- and ferro-magnetism. Superconductivity: General properties of superconductors, Meissner effect; London equations; coherence length; type-I and type-II superconductors.
Learning Outcome	Complies with PLO 1a, 1b, 3
Assessment Method	Tutorials & assignments + mid-semester exam + end-semester exam
Suggested Readings:	Textbooks: 1. C. Kittel, Introduction to Solid State Physics, Wiley India (2009). 2. M. A. Omar, Elementary Solid State Physics, Addison-Wesley (2009). References: 1. J. Dekker, Solid State Physics, Macmillan (2009). 2. N. W. Ashcroft and N. D. Mermin, Solid State Physics, HBC Publ. (1976). 3. H. P. Myers, Introduction to Solid State Physics, Taylor and Francis (1997). 4. Richard Zallen, The Physics of Amorphous Solids, John Wiley and Sons Inc.,(1983)

EP3105

Instrumentation Techniques

Course Number	EP3105
Course Credit	2-0-2-3
Course Title	Instrumentation Techniques
Learning Mode	Class lectures, laboratory demonstration and hands on sessions
Learning Objectives	Complies with program goal 1,2 and 3.
Course Description	A sound knowledge of instrumentation is key to a well-rounded training of an engineer. This course introduces the students to fundamental aspects of the ‘systems design approach’ which will come handy when they want to apply it in development of various systems. The course deals with aspects of signal processing, control, data acquisition and power management. Issues in handling massive data in the form of images and image processing will also be taught.
Course Outline	Fundamentals of system design Signals processing, Control and Data acquisition: Principles of sensors, transducers, and measurement techniques; Signal processing; Theory of feedback control, stability analysis, and controller design; A/D and D/A, Design Data acquisition, virtual instrumentation; Case studies related to signal processing and control aspects for different systems including advanced analytical instruments, thin film coating units and space applications Power systems: Different types of power supplies; transformers; signal conditioning; estimation of power requirements Vacuum systems: Vacuum production techniques, operation for rotary, diffusion, turbo molecular, and cryo-vacuum pumps; Measurement of vacuum, different gauges and their working; Designing Vacuum Systems: Mechanical and thermal design considerations; Pump throughput estimation; Case studies for vacuum systems in different instruments; accelerators, superconducting magnets, food preservation systems, electron microscopes, thin film coating technology CMOS and CCD camera, coupling light in systems, crucial issue with data handling for large image sizes Lab component: Use of Simulink^TM/Simscape in signal processing and control; Designing chambers using Solidwork^TM and Comsol^TM Multiphysics; Signal transduction; Signal conditioning; Controller deployment using Arduino; Generating vacuum using different pumps; Use of different gauges to measure vacuum; Image and video acquisition; processing of images
Learning Outcome	Complies with PLO 1a, 1b, 3
Assessment Method	mid-semester exam, end-semester exam
Suggested Readings:	Books: 1. Instrumentation: Devices and Systems, C. Rangan, G. Sarma, V. S. V. Mani, 2^nd ed. McGraw Hill Education, 2017 2. Instrumentation for Engineers, J. D. Turner, Springer (reprint), 2020 3. Vacuum Technology, A. Roth, North-Holland, 3^rd ed., 2007. References: 1. Vacuum Science & Technology- V. V. Rao, K. L. Chopra and T. B. Ghosh, Allied Publishers Pvt. Ltd., 2012 2. A user's guide to vacuum technology, J. F. O'Hanlon, Wiley-Interscience, 2^nd ed., 2003. 3. Handbook of vacuum science and technology, D. M. Hoffman, Bawa Singh, J. H. Thomas-III (Eds)., Elsevier, 1998.

XX31PQ

IDE – II

Total Credit

21.5

Interfacing and Data analysis

Sl. No.

Subject Code

SEMESTER VI

EP3201

Nonlinear Dynamics

Course Number	EP3201
Course Credit (L-T-P-C)	2-1-0-3
Course Title	Nonlinear Dynamics
Learning Mode	Lectures and Tutorials
Learning Objectives	Complies with Program Goals 1, 2 and 3
Course Description	This course will help to understand the concept and method of theory of dynamical system as well as its application to physics, chemistry and biology. It will describe nonlinear phenomena in physical systems by using a minimum background in physics and mathematics.
Course Content	Role of nonlinearity in physical systems. Dynamical systems: Flow systems, iterated maps in 1D and hybrid systems, fixed points, their stability analysis and classifications, physical examples including nonlinear planar pendulum with and without damping. Flows in two dimensions: limit cycles, Poincare-Benedixon theorem, driven oscillators with damping, bifurcations in one and two dimensions, Hopf bifurcation. Chaos and various routes to chaos: period doubling, intermittency and quasi-periodic routes, Lyapunov exponents, self-similarity and fractal objects. Fractal dimensions, Lorentz system and its fixed points and stability. Cantor set, logistic map, computer based problems relevant to engineering and biology.
Learning Outcome	Complies with PLO 1(a), 1(b), 2(a) and 3(a)
Assessment Method	Assignments, Quizzes, Mid-semester examination and End-semester examination
Suggested Readings:	Textbooks: 1. S. H. Strogatz, Nonlinear Dynamics & Chaos, CRC Press, 2018. 2. Nonlinear Ordinary Differential Equations, D. W. Jordan and P. Smith References: Chaos and Nonlinear Dynamics: an Introduction for Scientists and Engineers, R.C. Hillborn R. H. Enns and G. C. McGuire, Nonlinear Physics with Mathematica for Scientists and Engineers, Birkhäuser, Boston, 2001.

EP3202

Course Number	PH3202
Course Credit	1-0-4-3
Course Title	Interfacing and Data analysis
Learning Mode	Lecture and Lab
Learning Objectives	Complies with Program Goals 1, 2 and 3
Course Description	The course teaches (a) how to control and communicate remotely through interfacing and (b) perform a(multiple) measurement(s) by using Labview. It will also introduce different ways of communications like GPIB, RS-232 etc. Various ways to display and analyze data will also be covered.
Course Outline	Labview Basics: Introduction and Objectives; Overview of GUI software based data acquisition and analysis platforms, Programming basics; Front panel; block diagram; data flow Programming Structures: for/while loops; shift resistor; case structures; sequence structures Graphs and charts: Arrays; Clusters Serial ports (RS232) and GPIB (IEEE 488.1) Communication; Background and working principle; communication with instruments; instrument drivers; Property nodes; strings; file I/O; data acquisition and visualization Data analysis: Gaussian and Poisson distribution; Error analysis; Regression; Bayesian parameter estimation and hypothesis testing; The maximum-entropy approach; Linear and nonlinear model fitting; Time-series analysis
Learning Outcome	Complies with PLO 1(a),1(b), 2(a) and 3a
Assessment Method	Day to Day experimental assessment and viva End term examination
Suggested Readings:	1. Getting Started with Labview, NI Instruments 2. Labview user manual by NI Instruments 3. Bevington, Philip R. and D. Keith Robinson, Data Reduction and Error Analysis for the 4. Physical Sciences, 3rd edition, McGraw-Hill, New York, 2003. 5. Meyer, Stuart L., Data Analysis for Scientists and Engineers, John Wiley and Sons, Inc., New York, 1975. 6. Young, Hugh D., Statistical Treatment of Experimental Data, McGraw-Hill Book Company, Inc., New York, 1962. 7. Bayesian Logical Data Analysis for the Physical Sciences: A Comparative Approach with Mathematica Support (Links to an external site.), by Phil Gregory (Cambridge University Press). 8. A Student's Guide To Python for Physical Modeling (Links to an external site.), by Jesse M. Kinder and Philip Nelson (Princeton University Press).

EP3203

Atomic and Molecular Physics

Course Number	EP3203
Course Credit (L-T-P-C)	3-1-2-5
Course Title	Atomic and Molecular Physics
Learning Mode	Lectures & Tutorials
Learning Objectives
Course Description	This course provides engineering students building strong fundamentals in Atomic Physics and Molecular physics. Also this course introduces methods and models which are very essential to pursue research in advanced theoretical, experimental physics and engineering applications.
Course Outline	Time independent and time-dependent perturbation theory, interaction of one electron atoms with electromagnetic radiation, Transition rates, The dipole approximation, Selection rules, Spectrum of one electron atoms, Line intensities and the life time of the excited states, Line shapes and widths, Fine structure and Hyperfine structure, The Lamb Shift, Zeeman and Stark effect. Many electron atoms: Variational method, Hartree- Fock method and the SCF, Central field approximation, L-S coupling and j-j coupling, Molecular structure, Born-Oppenheimer approximation, Electronic structure of diatomic molecule, Electronic, Rotational, Vibrational and Vibration-Rotation Spectra of diatomic molecules.
Learning Outcome
Assessment Method	Assignments, Quizzes, Mid-semester examination, End-semester examination
Suggested Readings:
Textbooks:	· B.H. Bransden and C.J. Joachain, Physics of Atoms and Molecules, Longman Scientific and Technical, 1983. · Gordon W and F. Drake, Springer Handbook of Atomic, Molecular, and Optical Physics, Springer, 2006. · W. Demtroder, Atoms, Molecules and Photons, Springer, 2010. · H. Haken and H.C. Wolf, Physics of Atoms and Quanta, Springer, 2005. · H. E. White, Introduction to Atomic Spectra,McGraw Hill, 2019 · G. K. Woodgate, Elementary Atomic Structure, 2^nd Ed., ClerentonPress, Oxford, 2002 · M. Karplus and R. N. Porter, Atoms and Molecules: An Introduction for Students of Physical Chemistry
References:	· Ira N. Levine, Quantum Chemistry, 6^thEdition, PHI Learning Private Limited, New Delhi, 2009. · John P. Lowe and Kirk A. Peterson, Quantum Chemistry, 3^rdEdition, Academic Press, 2009. · Peter Atkins and Ronald Friedman, 4^thEdition, Oxford Univ. Press, 2012. · Collin N. Banwell and Elain M. Mc Cash, Fundamentals of Molecular Spectroscopy, 4^thEdition, Tata McGraw Hills, 2008.

EP3204

Soft Condensed Matter Physics

Course Number	EP3204
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Soft Condensed Matter Physics
Learning Mode	Lectures and Tutorials
Learning Objectives	Complies with Program Goals 1, 2a and 3
Course Description	Create understanding diffusion in soft matter, colloids, and polymers. Learn expertise for biologically relevant polymers, different self-assemblies, and impact of varied interfaces. Acquire understanding of liquid crystals and various experimental techniques for characterizing soft matter.
Course Content	Introduction to Soft Matter: Overview of soft matter, entropy in disordered systems; forces, energies, length scale and time scales in soft matter Diffusion processes: Fick’s laws, Diffusion Equation, Random walks, Brownian motion, Langevin and Fokker-Plank equations Colloids: Colloidal particle in a liquid (Stoke’s law and Brownian motion), forces between colloidal particles (Van der Waals, electrostatic double layer, steric, depletion interaction), stability and phase behaviour of colloids. Polymers: Basic concepts, types of polymers, molecular weights, determination of molecular weights. Crystallization Self-Assemblies and Interface Science: Self-assembled phases in solutions of amphiphilic molecules, spherical micelles and critical micelle concentration, reverse micelles, bilayers and vesicles, Langmuir monolayers; complex phases in surfactant solutions and microemulsions. Liquid Crystals: Types of liquid crystals, characteristics and identification of liquid crystal phases, nematic/isotropic transition, rigidity and elastic constants of a nematic liquid crystal. Biological Soft Matter: DNA (structure, condensation, noncanonical structures), RNA (structure, folding, crystallization), proteins (structure, folding, crystallization). Applications of soft matter physics.
Learning Outcome	Complies with PLO 1, 2a and 3
Assessment Method	Assignments, Quizzes, Presentation, Mid-semester examination and End-semester examination
Suggested Readings:	Textbooks: 1. Soft condensed matter by R. A. L. Jones, Oxford University Press 2. Biological Physics by P. Nelson 3. Polymer Physics by Tanaka Fumihiko, Cambridge University Press References: 4. Liquid Crystals: Nature delicate phase of matter by P. J. Collings, Princeton University Press 5. Ian W. Hamley, Introduction to Soft Matter: Synthetic and Biological Self-Assembling Materials, John Wiley & Sons. 6. Thomas A. Witten and Philip A. Pincus, Structured Fluids: Polymers, Colloids and Surfactants, Oxford University Press. 7. Scaling Concepts in Polymer Physics, P. G. de Gennes

PH32XX

DE – I

PH32XX

DE – II

Total Credit

Sl. No.	Subject Code	SEMESTER VII	L	T	P	C
1.	EP4105	Quantum Technology Laboratory	1	0	3	2.5
2.	PH41XX	DE-III	3	0	0	3
3.	HS41XX	HSS Elective – II	3	0	0	3
4.	XX41PQ	IDE – III	3	0	0	3
5.	PH4198	Summer Internship*	0	0	12	3
6.	PH4199	Project – I	0	0	12	6
Total Credit			10	0	27	20.5

* For specific cases of internship after VI^th Semester, the performance evaluation would be made on joining the VII^th Semester and graded accordingly in the VII^th Semester:

Note:

a) (i) Summer internship (*) period of at least 60 days’ (8 weeks) duration begins in the intervening vacation between Semester VI and VII that may be done in industry / R&D / Academic Institutions including IIT Patna. The evaluation would comprise combined grading based on host supervisor evaluation, project internship report after plagiarism check and seminar presentation at the Department (DAPC to coordinate) with equal weightage of each of the three components stated herein.

a) (ii) Further, on return from internship, students will be evaluated for internship work through combined grading based on host supervisor evaluation, project internship report after plagiarism check, and presentation evaluation by the parent department with equal weightage of each component.

b) (i) In the VII^th semester, students can opt for a semester long internship on recommendation of the DAPC and approval of the Competent Authority.

b) (ii) On approval of semester long internship, at the maximum two courses (properly mapped/aligned syllabus) at par with institute electives may be opted from NPTEL and / or SWAYAM and the other two more should be done at the institute through course overloading in any other semester (either before or after the internship) and/or during following summer semester.

b) (iii) The candidates opting two courses from NPTEL and / or SWAYAM would be required to appear in the examination at the Institute as scheduled in the Academic Calendar.

Departmental Elective – I

Sl. No.	Subject Code	SEMESTER VIII	L	T	P	C
1.	PH42XX*	DE-IV	3	0	0	3
2.	PH42XX	DE-V	3	0	0	3
3.	PH42XX	DE-VI	3	0	0	3
4.	PH42XX	DE-VII	3	0	0	3
5.	PH4299	Project – II	0	0	16	8
Total Credit			9	0	16	20
Grand Total Credit (Semester I to VIII)			168

* Valid only for the course PH4206 since this is common to M. Sc. and Engineering Physics

Sl. No.

Course Code

Departmental Elective – I

PH3201

Engineering Optics

Course Number	PH3201
Course Credit	3-0-0-3
Course Title	Engineering Optics
Learning Mode	Lectures and Assignments
Learning Objectives	Complies with Program Goals 1,2 and 3
Course Description	This course introduces students various optical systems, optical devices needed for various engineering applications in the field of Optics and modern cutting edge technology
Course Outline	Lens systems: Basics and concepts of lens design, some lens systems. Optical components: Reflective, refractive and diffractive systems; Mirrors, prisms, gratings, filters, polarizing components. Interferometric systems: Two beam, multiple beam, shearing, scatter fringe and polarization interferometers. Vision Optics: Eye and vision, colorimetry basics. Optical sources: Incandescent, fluorescent, discharge lamps, Light emitting diode. Optical detectors: Photographic emulsion, thermal detectors, photodiodes, photomultiplier tubes, detector arrays, Charge-coupled device, CMOS. Optical Systems: Telescopes, microscopes (bright field, dark field, confocal, phase contrast, digital holographic), projection systems, interferometers, spectrometers. Display devices: Cathode ray tube, Liquid crystal display, Liquid crystals on silicon, Digital light processing, Digital micro-mirror device, Gas plasma, LED display, Organic led displays (OLED). Consumer devices: Optical disc drives: CD, DVD; laser printer, photocopier, cameras, image intensifiers.
Learning Outcome	Complies with PLO 1(a), 1(b), 2(a) and 3
Assessment Method	Assignments, Quizzes, Mid-semester examination and End-semester examination
Suggested Readings:	Text Books: 1. R. S. Longhurst, Geometrical and Physical Optics, 3^rd ed., Orient Longman, 1988. 2. R. E. Fischer, B. Tadic-Galeb, and P. R. Yoder, Optical System Design, 2^nd ed., SPIE Press, 2008. Reference Books: 1. W. J. Smith, Modern Optical Engineering, 3^rd ed., McGraw Hill, 2000. 2. K. Iizuka, Engineering Optics, Springer, 2008. 3. B. H. Walker, Optical Engineering Fundamentals, SPIE Press, 1995.

PH3202

Cryogenic Engineering

Course Number	PH3202
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Cryogenic Engineering
Learning Mode	Lectures and Tutorials
Learning Objectives	Complies with Program Goals 1, 2 and 3
Course Description	Equips the students with the techniques in Cryogenic Engineering and allows them to apply these techniques in both research and industrial scenarios
Course Content	Introduction to cryogenic engineering and its scope; components of a typical cryogenic systems; physical properties of cryogenic fluids such as nitrogen, helium and hydrogen including their extraction, purification, regeneration, safe storage and transfer; sensors at cryogenic temperatures; cryogenic heat transfer; cryocooler systems for refrigeration and liquefaction; elements of cryogenic system design and instrumentation; low heat leak structural supports, thermal mass considerations, thermal insulation systems, liquefaction/refrigeration of cryogens; Stirling, Claude and related cycles, recovery and storage, cryogenic heat exchangers, instrumentation for cryogenics including compressors, pumps, expansion engines and turbine mechanisms; safety features in cryogenic systems; design considerations for cryogenic systems for applications including CCR, MRI, NMR, accelerators, adiabatic demagnetization and dilution refrigerators, and cryogenic engines.
Learning Outcome	Complies with PLO 1(a), 1(b), 2(b) and 3
Assessment Method	Assignments, Quizzes, Mid-semester examination and End-semester examination
Suggested Readings:	Textbooks: 1. Randall Barron, Cryogenic Systems, 2nd Edition (1985). 2. Thomas M. Flynn, Cryogenic Engineering, New York, NY: Marcel Dekker USA, 2^nd Edition (2005). References: 1. Zuyu Zhao and Chao Wang, Cryogenic Engineering and Technologies, CRC Press, Taylor and Francis USA (2020). 2. Frank Pobell, Matters and Methods at Low Temperature, 3rd Edition, Springer (2007)

PH3203

Advanced Quantum Mechanics

Course Number	PH3203
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Advanced Quantum Mechanics
Learning Mode	Lectures & Tutorials
Learning Objectives	Complies with Program Goals 1, 2 and 3
Course Description	In this course students will learn time dependent perturbation theory, scattering theory and relativistic quantum mechanics.
Course Content	Time dependent perturbation theory, interaction picture; Einstein's coefficients, spontaneous and stimulated emission and absorption, application to lasers; Semi-classical and quantum theories of light-matter interactions; Scattering theory: Laboratory and centre of mass frames, differential and total scattering cross-sections, scattering amplitude; Born approximation, Partial wave analysis; Theory of open quantum system, density matrix, Markovian master equation; Quantum wave packet dynamics; Quantum Information; Symmetries in quantum mechanics: Conservation laws, and degeneracy associated with symmetries; Continuous symmetries, space and time translations, rotations; Rotation group; Discrete symmetries; parity, charge and time reversal; Relativistic quantum mechanics, Concept of antiparticles; Dirac equation, covariant form, Plane wave solution and momentum space, spinors; Spin and magnetic moment of the electron.
Learning Outcome	Complies with PLO 1, 2(a) and 3
Assessment Method	Assignments, Quizzes, Seminar, Mid-semester examination, End-semester examination
Suggested Readings:	Textbooks: 1. C. Cohen-Tannoudji, Quantum Mechanics (Vol-I and II), John Wiley & Sons (Asia), 2005. 2. L. I. Schiff, Quantum Mechanics, McGraw-Hill, 1968. 3. J. J. Sakurai, Advanced Quantum Mechanics, Pearson Education, 2007. 4. J. J. Sakurai, Modern Quantum Mechanics, Pearson Education, 2002. 5. R. Shankar, Principles of Quantum Mechanics, Springer (India), 2008. 6. Heinz-Peter Breuer, Francesco Petruccione, Theory of Open Quantum Systems, Oxford University Press, 2003. Reference Books: 7. B. H. Bransden and C. J. Joachain, Quantum Mechanics, Parson Education 2^nd Ed, 2004. 8. E. Merzbacher, Quantum Mechanics, John Wiley (Asia), 1999. 9. V.K. Thankappan, Quantum Mechanics, Wiley Eastern, 1985. 10. R.P. Feynman, R.B. Leighton and M.Sands, The Feynman Lectures on Physics, Vol.3, Narosa Publication House, 1992. 11. P.A.M. Dirac, The Principles of Quantum Mechanics, Oxford University Press, 1991. 12. L.D.Landau and E.M. Lifshitz, Quantum Mechanics -Nonrelativistic Theory, 3^rd Ed, Pergamon, 1981.

PH3204

Power Sources for Electric Vehicles

Course Number	PH3204
Course Credit	L – T – P : 3 – 1-0-4
Course Title	Power Sources for Electric Vehicles
Learning Mode	Physical Presence in Class Room
Learning Objectives	This course is highly relevant and industry demand driven contents in the emerging are of clean and green technology to reduce carbon foot print and bring transformation with “zero emission” transport system. It aims to impart a comprehensive training and skill pertaining to; 1. energy storage technologies from ab initio storage cells to current state of developments. 2. concept, design, fabrication and testing protocol of energy and power cells for EV applications. 3. bridge the technological gap with adequate skill focused content to fulfil the emerging need of competent workforce for EV industry.
Course Outline	Module-1: Power generation for transport with focus on zero emissions, an overview of electric vehicles and their power requirements, battery powered electric vehicles (EVs), performance criteria for EV batteries, laboratory testing protocols for EV batteries Module-2: Vehicle mechanics and power requirements, energy storage cell fundamentals, batteries, fly wheels and super capacitors, cell design and customization approach for cell voltage and current modification, cell and battery modelling for rated power requirement and design. Module-3: Concepts of super capacitors as a storage cell with large power delivery, supercapacitor classification, design, fabrication, testing and applications, advantage and challenges in integration of a super capacitor with battery and possible alternatives. Module-4: Design of a battery pack for EV application, operational safety challenges and need for thermal management and battery management systems (BMS), Safety considerations and protocols for battery pack development in EVs with case study for e-cycle, e-bike, 3-wheelers.
Learning Outcome	Learners of the course will be able upskill their knowledge and skill to fulfil emergent need of rapidly expanding electric vehicle (EV) industry.
Assessment Method	Class test and Quiz/Assignment (20%), MSE: (30%), ESE: (50%)
Suggested Readings:	1. Batteries for Electric Vehicles, D.A.J. Rand, R. Woods, R.M. Dell., John Wiley & Sons Inc. 2. Electric and Hybrid Vehicles: Design Fundamentals, Iqbal Husain, CRC Press 3. Electric Vehicle Technology Explained, James Larmenier, John Lowry, John Wiley & Sons

PH3205

Engineering Electromagnetics

Course Number	PH3205
Course Credit	3–0–0–3
Course Title	Engineering Electromagnetics
Learning Mode	Class lectures, tutorials, assignments, discussions.
Learning Objectives	Complies with Program Goals 1, 2 and 3
Course Description	This course builds upon advanced engineering topics in electromagnetics, with focus on cutting edge engineering applications.
Course Outline	Review of Electromagnetic theory, wave guides and resonant cavities, rectangular and cylindrical waveguides. dielectric and surface waveguides, Multimode propagation in optical fibers, Introduction to radiating systems, localized oscillating source, dipole fields and radiation, Monopole and dipole antennas, Antenna arrays. Yagi, Horn, Parabola, micro strip and patch antennas, Microwave cavities. Scattering matrix, S parameters, reciprocity, coupling energy to a waveguide, Microwave components: Gunn, impatt and varacter diodes, etc and their use in designing RF circuits, Practical RF circuit design, Frequency-independent antennas, log-periodic antennas, spiral antennas. RF-Id systems, Studies of RF circuits in mobile phones and satellite communications.
Learning Outcome	Complies with PLO 1(a), 1(b), 2(a) and 3
Assessment Method	Assignments, Quizzes, Mid-semester examination and End-semester examination.
Suggested Readings:	Text books: 1. J. D. Jackson, Classical Electrodynamics, 3^rd ed., Wiley, 1999. 2. Antennas and Wave propagation 5ED by John D. Kraus, Ronald J. Marhefka, et al (SIE) (PB 2018). 3. D. M. Pozar, Microwave Engineering; 4/e, John Wiley & Sons Inc, 2012. 4. R. E. Collin, Foundations for Microwave Engineering; 2/e, Wiley-IEEE Press, 2000. References: D. J. Griffiths, Introduction to Electrodynamics, Third Edition, Pearson Education Inc., 2006. J. D. Ryder, Networks, Lines and Fields, Second Edition, Prentice Hall of India, 2002. Feynman Lectures on Physics Vol-II, Pearson, 2012. Antenna theory: analysis and design. CA Balanis. John Wiley & Sons, Inc, 2016 9. M. Liao, Microwave devices and Circuits; 3/e, Prentice Hall of India, 2004.

Departmental Elective – II

Sl. No.

Course Code

Departmental Elective – II

PH3206

Laser Physics

Course Number	PH3206
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Laser Physics
Learning Mode	Lectures
Learning Objectives	Complies with Program Goals 1, 2a and 3
Course Description	Create understanding of basic light-matter interactions, design, and construction of laser. Learn expertise in usage and safety of laser. Acquire understanding of component of lasers and pulsing menthodology.
Course Content	Introduction to laser physics; Light-matter Interaction; Semiclassical theory, Wave and quantum properties of light, Spontaneous and stimulated emissions, Einstein’s coefficients. Line shape function, Line broadening: Homogeneous and inhomogeneous broadening, natural, Doppler and collisional broadening. Light amplification; Optical saturation, Population inversion, Optical pumping, Rate Equations; 2-level, 3-level and 4-levl lasers, Laser action (gain, threshold, power, frequency), Gain saturation, Optimal conditions for laser operation; Laser saturation, Optical Resonator: Longitudinal and transverse laser cavity modes, cavity loss, Q-factor, ABCD matrix, Stable and unstable resonator, Properties of Gaussian Beam and propagation. Laser Pulsing: Hole Burning; Q-Switching; Mode Locking; Single Mode Lasers; Ultrafast laser systems, linear and nonlinear pulse propagation Types of Lasers: He-Ne Laser; Nd-YAG Laser; Solid-state laser, Gas Laser Dye Laser, Excimer Laser; Semiconductor Laser; Tuneable Lasers, Supercontinuum Laser, Fiber Lasers Laser Safety and applications:
Learning Outcome	Complies with PLO 1, 2a and 3
Assessment Method	Assignments, Quizzes, Presentation, Mid-semester examination and End-semester examination
Suggested Readings:	1. Optical Electronics, Ajoy Ghatak and K.Thyagarajan, CUP, 2003. 2. Photonics, Amnon Yariv and Pochi Yeh, 6 th ed., OUP, 2009. 3. Fundamentals of Photonics, B.E.A.Saleh and M.C.Teich, 2 nd ed., Wiley Interscience, 2007. 4. W. T. Silfvast, Laser Fundamentals, Cambridge University Press

PH3207

Advanced Mathematical Methods

Course Number	PH3207
Course Credit (L-T-P-C)	2-1-0-3
Course Title	Advanced Mathematical Methods
Learning Mode	Lectures & Tutorials
Learning Objectives	The purpose of the course is to introduce students to methods of mathematical physics and to develop required mathematical skills to solve problems in quantum mechanics, electrodynamics and other advanced courses in physics.
Course Description	The course offers detailed study on group theory and advanced numerical techniques. Group theory plays an important role in particle physics. Numerical techniques are handy in solving several advanced physics and engineering problems.
Course Outline	Module A: Group Theory: Definition, Subgroups, Classes and Examples, Group representations (regular and product; reducible and irreducible), Characters, Physical applications, Infinite groups; Lie groups and Lie algebra, Generators: Representations of Z2, SU(1,1), SU(2), SU(3) and SO(3). Integral Equations: Generating functions, Newmann series. Module B: Numerical Optimisation:- Newton's method, Golden section search, Conjugate gradient method. Linear Programming, Simplex Method; Numerical Solution of Partial Differential Equations:- Difference Equation, Crank-Nicolson method, Split operator technique; Eigen value problems:- Jacobi transformation Fourier Transform:- Discrete Fourier Transform and Fast Fourier Transform in two or more dimensions; Engineering applications.
Learning Outcome	PLO 1b, 3
Assessment Method	Mid-semetser examination, End-semester examination, Quiz & Assignments
Suggested Readings:	Textbooks: · George B. Arfken and Hans J. Weber, Mathematical Methods for Physicists, Academic Press Inc., 4^th Edition, 1995. · E. Kreyszig, Advanced Engineering Mathematics, Wiley India, 8^th Edition, 2008. · M. Abramowitz and I. A. Stegan, Mandbook of Mathematical Functions, Dover Publs., INC., New York, 1965. References: · R.V. Churchill and J.W. Brown: Complex Variables and Applications. A. Zee: Group Theory in a Nutshell for Physicists, Princeton University Press, 2016.

PH3208

Electron Microscopy

Course Number	PH3208
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Electron Microscopy
Learning Mode	Lectures
Learning Objectives	The objective of the course is to introduce the student to the electron microscopy and its utilization in modern technology. The students will learn about the electron-matter interaction, working principle of electron microscopes. The principle of electron optics and its use will be learned by the students. The opportunity in electron microscopy area will be known to the student.
Course Description	The course discusses different kinds of electron microscopy and electron spectroscopy. Analysis of TEM and SEM image, electron diffraction pattern, X-ray spectra analysis and, their applications in industry will be covered in this course.
Course Content	Module 1: Introduction to Microscopy, Limitations of the Human Eye, Optic, The X-ray Microscope, Electron Microscope, Low-Energy and Photoelectron Microscopes, Atom-Probe Microscopy. Module 2: Electron Sources, safety and precautions, Electron optics, electromagnetic lenses, Comparison of Magnetic and Electrostatic Lenses, Aberration Correctors and Monochromators, Electron and matter interaction, Scattering and diffraction, reciprocal space, Bloch waves, Diffraction from crystal, diffraction from small volume, elastic and inelastic scattering, absorption, dispersion, polarization, reflection, Imaging with Electrons, radiation damage, electron tomography, electron holography. Module 3: Transmission Electron Microscopy: Instrument, holders, lenses, cameras, apertures and resolution, imaging, amplitude contrast, phase contrast, bending effect, planer defects, bright field imaging, dark field imaging, high resolution imaging, Scanning transmission electron microscopy, image simulation and image analysis, Spectroscopy, X-ray spectroscopy, qualitative and quantitative X-ray analysis, electron energy loss spectroscopy and images, fine structure, diffraction pattern, indexing diffraction pattern, specimen (hard, soft, powder, ad biological) preparation, Industrial applications. Module 4: Scanning Electron Microscopy: Instrument, holders, lenses, apertures, resolution, Electron detectors, Back scattered electron, Secondary electron, Auger electron, imaging, Auger electron spectroscopy. Augur electron microscopy, image simulation and image analysis, Spectroscopy, X-ray spectroscopy, qualitative and quantitative X-ray analysis, EBSD, diffraction pattern and analysis, specimen preparation, Industrial applications.
Learning Outcome	The student will introduce himself/herself to the electron microscopy. The industrial applications of electron microscopy will be known. There are lots of opportunity in electron microscopy as it is a modern technique and it has lots of industrial applications. Hence, the students can take the job in the electron microscopy industries or they can make entrepreneur for supporting to the electron microscopy industries.
Assessment Method	Assignments, mini projects, Quizzes, Mid-semester examination, and End-semester examination.
Suggested Readings:	Textbooks: 1. Physical Principles of Electron Microscopy, Ray F. Egerton, springer, 2005, New York 2. Scanning Electron Microscopy, Ludwig Reimer, springer, 1998, New York, 3. Transmission Electron Microscopy, David B. Williams, C. Barry Carter, springer, 2009 References: 1. Electron Microscopy: Principles and Fundamentals, S. Amelinckx (Editor), Dirk van Dyck (Editor), J. van Landuyt (Editor), Gustaaf van Tendeloo (Editor), Wiley, 2007. 2. Electron microscopy Methods and Protocols, John Kuo, Springer, 2014. 3. The principles and Practice of Electron Microscopy, Ian M. Watt, Cambridge University Press, 1997.

PH3209

Quantum Computation

Course Number	PH3209
Course Title	Quantum Computation
Course Credit (L-T-P-C)	2-1-0-3
Learning Mode	Lectures & Tutorials
Learning Objectives	Quantum Computing is one of the fastest growing topics for research, development and industry. A number of insightful questions arise in the mind of students, such as - what is quantum computer? Why is it required? How does it look like? How can these be implemented? When will we get quantum computer for personal use? – etc. This course is intended to provide answers to all these questions in the level of undergraduate students.
Course Contents	Fundamental idea of quantum computing: Moore’s Law; Operators and matrices: Pauli matrices, inner, outer and tensor products, Unitarity; (2 lectures+1 tutorial) Quantum nonlocal superposition; Quantum entanglement; Basics of quantum measurements (General, Projective, POVM); From Bits to Qubits with examples, Bloch sphere, Single and multiple qubit logic gates, Universal quantum gates; Basic quantum circuits, Quantum Teleportation protocol; Quantum Fourier Transform, Quantum phase estimation, Factorization algorithm; (8 lectures+4 tutorials) Relevant knowledge of Quantum Optics & Quantum information; Physical realizations of Quantum computers: quantized harmonic oscillator; Density operator, ensemble of quantum states; (5 lectures+2 tutorials) Fundamentals of various quantum computers: semiconductor based quantum computer, photonic based quantum computer, cold- and ultracold-atom based quantum computers, use of cavity-QED; (7 lectures+3 tutorials) Applications of quantum computing in other fields: ideas of quantum communication & quantum security etc; Practical examples with Quantum Simulators; AI and Quantum computing; State-of-the-art quantum computation and Future outlook. (8 lectures+4 tutorials)
Learning Outcome	The course will build up the basic foundation required for knowing the working of a quantum computer, quantum information processing through quantum circuits, examples with well-known quantum algorithms, various quantum computers, important applications and future outlook. The students will also be given overview of the Indian and global companies and their contributions in quantum computing. It will impart the motivation to students for further applying their knowledge to the progress of the field in both R&D and industry.
Assessment Method	Assignments, Quizzes, MSE and ESE
Suggested readings	Textbooks: Quantum Computation and Quantum Information, M. A. Nielsen and I. L. Chuang, Cambridge University Press, South Asian Edition, 10^th Edition. An Introduction to Quantum Computing, Phillip Kaye, R. Laflamme, M. Mosca, Oxford University Press, 2007. Preskill, John. Lecture notes for physics 229: Quantum information and computation. California Institute of Technology 16.1 (1998): 1-8. Nakahara, Mikio, and Tetsuo Ohmi. Quantum computing: from linear algebra to physical realizations. CRC press, 2008. Mermin, N. David. Quantum computer science: an introduction. Cambridge University Press, 2007. References: Quantum Supremacy, Michio Kaku, Allen Lane-Penguin publisher, 2023. McMahon, David. Quantum computing explained. John Wiley & Sons, 2007. Riley Tipton Perry, Quantum Computing from the Ground Up, World Scientific Publishing Ltd (2012). Scott Aaronson, Quantum Computing since Democritus, Cambridge, 2013. Bouwmeester, D., Ekert, A. and Zeilinger, A., (2000), The Physics of Quantum Information, Reprint edition, Springer Berlin Heidelberg. Barenco, Adriano, et al. Elementary gates for quantum computation. Physical review A 52.5 (1995): 3457. Quantum Computing: Lecture Notes, Ronald de Wolf, QuSoft, CWI and University of Amsterdam, arXiv:1907.09415v3, 2022.

PH3210

Device Modeling and Design

Course Number	PH3210
Course Credit	2-1-0-3
Course Title	Device Modeling and Design
Learning Mode	Lectures and Tutorials
Learning Objectives	Complies with Program goals 1, 2 and 3
Course Description	This course provides detailed theoretical overview of Device Modeling and Design
Course Outline	Crystal structure-Unit cell and Miller Indices Reciprocal Space, Doping, Band Structure, Effective Mass, Density of states, Distribution Function, and carrier concentration calculation, Carrier transport, Mobility and diffusivity, continuity equation, Poisson’s equation, Semiclassical transport, carrier density equation, current density equation p-n junction, Metal-semiconductor junction, BJT, Heterojunction, Schottky junctions, MOS capacitor, MOSFET, JFET, Capacitor-Voltage Characteristics, Boltzmann Transport Equation (BTE), Relaxation-Time Approximation (RTA), Scattering and Mobility. Drift-Diffusion Model Derivation and dielectric relaxation time Generation and Recombination models, Derivation of SRH model, Boundary conditions, Gummel’s Iteration Method and Newton’s Method, As extension of DD model, Carrier Balance, Energy balance and momentum balance Equations, Direct solution scheme through Monte Carlo simulations, Models for DD, Hydrodynamic simulations, Mobility and G-R models, Introduction to Silvaco ATLAS (device) and ATHENA (process) simulation framework. Simulator syntax, Numerical method choice, TCAD tools, MixedMode simulation, Basics of semiconductor processing, Si-Based Nanoelectronics and Device Scaling, scaling implications, short channel effects, effective channel length, effects of channel length and width on threshold voltage, Compact models for MOSFET and their implementation in SPICE. MOS model parameters in SPICE.
Learning Outcome	Complies with PLO 1a. 1b. 2a and 3a
Assessment Method	Quiz, Assignments and Exams
Suggested Readings:	Textbooks: 1. Umesh K. Mishra and Jasprit Singh, Semiconductor Device Physics and Design, Springer, 2008. 2. G. Streetman, and S. K. Banerjee, “Solid State Electronic Devices,” 7th edition, Pearson,2014. 3. S. M. Sze and K. N. Kwok, “Physics of Semiconductor Devices,” 3rd edition, John Wiley&Sons, 2006. 4. D Vasileska, SM. Goodnick, G Klimeck, "Computational Electronics: Semiclassical and Quantum Device Modeling and Simulation," CRC Press 2010. 5. Selberherr Siegfried, “Analysis and Simulation of Semiconductor Devices”, 1984

Departmental Elective – III

Sl. No.

Course Code

Departmental Elective – III

PH4106

Science and Technology of Nanomaterials

PH4107

Optical Quantum Communication

Course Number	PH4107
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Optical Quantum Communication
Learning Mode	Lectures
Learning Objectives	Complies with Program Goals 1, 2 and 3
Course Description	This course provides engineering students to learn modern cutting edge optical quantum communication techniques which are very essential to pursue for advanced research and scientific jobs in the area of quantum communication and engineering applications. The course will also examine current research trends and potential future developments in the field of optical quantum communication.
Course Outline	classical v/s quantum information, quantum bits (qubits) and quantum gates, quantum entanglement and its properties, single-photon sources, entangled photon sources, photons as information carriers, polarization qubits, qubit generation and propagation, Bell state measurements, quantum repeaters, various protocols for quantum memory and its efficiency, implementation of quantum memory nodes, long distance quantum communication using quantum repeaters, quantum networks, multi-node quantum communication, ground-based and space-based quantum networks, entanglement distribution and quantum internet, Recent progress in quantum photonic chips for quantum communication and internet.
Learning Outcome	Complies with PLO 1(a), 1(b), 2(b) and 3
Assessment Method	Exams, Quiz and Assignment
Suggested Readings:	Textbooks: 1. Gianfranco Cariolaro,Quantum Communications, Springer (2015). 2. P. Kok and B. W. Lovett, Introduction to Optical Quantum Information Processing, Cambridge university press. 3. Peter Lambropoulos, David Petrosyan, Fundamentals of Quantum Optics and Quantum Information, Springer (2007) 4. Ivan B. Djordjevic, Quantum Communication, Quantum Networks, and Quantum Sensing, Elsevier (2022) References: L. Mandel, and E. Wolf. Optical Coherence and Quantum Optics, Cambridge University Press. W. H. Louisell, Quantum Statistical Properties of Radiation, McGraw-Hill. 3. D. Bouwmeester, A. K. Ekert, and A. Zeilinger, eds. The Physics of Quantum Information, Springer 4. Serge Haroche, Jean-Michel Raimond, Exploring the Quantum: Atoms, Cavities, and Photons, Oxford Academic (2006)

PH4108

Photovoltaics: Concepts and Applications

Course Number	PH4108
Course Credit (L-T- P-C)	3-0-0-3
Course Title	Photovoltaics: Concepts and Applications
Learning Mode	Physical Presence in Class Room
Learning Objectives	Alternative energy sources have always been a core area of significant importance since long. Recent focus on harnessing natural energy from the Sun, has necessitated teaching of relevant course at undergraduate level to create talent pool to meet industry demand. It aims to impart; 1. Knowledge pertaining to solar energy harnessing conditions 2. Learning relevant to physics of photovoltaic cells. 3. Training and skill relevant for design, processing, fabrication, testing and installation of photovoltaic cells, i.e.; end to end industry skill.
Course Outline	Module-1: An introduction to different sources of energy with its implications and alternative solutions, energy balance of the Sun and optimal conditions for harnessing solar energy, efficient design to entrap solar energy, a state of-the-art review of solar photovoltaic cells. Module-2: Semiconductor fundamentals, drift, diffusion and charge transport, photon emission and absorption, PN junction design and control parameters, Junction solar cell configuration – design, fabrication, analysis and efficiency improvement considerations for efficient solar cells. Module-3: Silicon based solar cell technology - monocrystalline, polycrystalline, amorphous and thin film Si solar cells, Process form sand to Silicon and Silicon to Wafer, Cell design and fabrication process, Multi-junction Si solar cells. Module-4: Non-Si solar cell technology, its challenges and advancements, an introduction to protocols for solar cell installation.
Learning Outcome	The learners of the course would be ready with knowledge to; (a) harness solar energy and technologically competent to implement the technology and (b) fulfil emerging industry and R & D institution demand for technologically skilled workforce.
Assessment Method	Class test and Quiz/Assignment (20%), MSE: (30%), ESE: (50%)
Suggested Readings:	1. Solar Photovoltaics: Fundamentals, Technologies and Applications (2^nd ed.), C. S. Solanki, Prentice Hall of India 2. Solar Cell Device Physics, Stephen Fonash (2^nd ed.), Academic Press 3. Principles of Solar Cells, LEDs and Diodes, Adrian Kitai, Wiley

PH4109

Electronic Devices and Circuits

Course Number	PH4109
Course Credit	3-0-0-3
Course Title	Electronic Devices and Circuits
Learning Mode	Lectures
Learning Objectives	To pick-up skills for circuit analysis that uses Bipolar Junction Transistor (BJT), Operational Amplifiers (OPAMP) and Metal Oxide Semiconductor Field Effect Transistor (MOSFET).
Course Description	The course is focused on techniques used for analysis of various circuits that use electronic devices. The course is composed of three modules, namely, BJT, OPAMP and MOSFETs. The first module revolves around circuits that use discrete BJT as an amplifier. The second module introduces OPAMPs which are based on BJT. A wide variety of linear and non-linear applications of OPAMPs are discussed. The last module discusses various circuits of MOSFETs.
Course Outline	Module 1: Introduction to r_e-model of BJT, Analysis of CE, CB and CC configuration using r_e model; Introduction to h-parameter model of BJT, Analysis of CE, CB and CC configuration using h-parameter model; Module 2: Introduction to Differential Amplifier using BJT; Introduction to OPAMP, AC equivalent circuit of OPAMP (real and ideal); Various linear applications of OPAMP ® Inverting amplifier (AMP), Non-inverting AMP, Summing AMP, Difference AMP, Unity follower, Positive and Negative voltage references, Voltage regulator, Howland Current source, etc.; Active filters ® First-order & Second-order low-pass and high-pass Butterworth filter, All-pass filter, Band-pass and Band-reject filter, Notch filter; Basics of Oscillator, Wien-Bridge Oscillator, Phase-Shift Oscillator, Quadrature Oscillator, Square-wave, Triangular-wave and Sawtooth-wave generator, VCO. Various non-linear applications of OPAMP ® Basics of Comparator, Zero crossing detector, Schmitt Trigger; Log AMP & Anti-log AMP. Module 3: MOSFET circuit at DC, MOSFET as an amplifier, Biasing in MOS AMP circuits: Biasing by fixing V_GS with and without resistance in the Source, Biasing using drain-to-gate feedback resistor, Biasing using constant current source, DC bias point in small signal operation; Introduction to small signal AC equivalent circuit with and without channel length modulation effect, T-equivalent circuit model, Characteristics parameter of single stage MOS AMP, CS amplifier with and without source resistance, CG amplifier and CD amplifier.
Learning Outcome	Students get to know the following: (a) Basics of circuit analysis (b) Circuit analysis skill for single stage low frequency BJT amplifier in various configurations (c) Circuit analysis skill for a wide variety of OP-AMP circuits that encompasses both linear and non-linear applications (d) Circuit analysis skill for single stage low frequency MOS amplifier in various configurations
Assessment Method	Quiz, Assignments and Exams
Suggested Readings:	Textbooks: 1. Jacob Millman and Christos C. Halkias, Integrated Circuits: Analog and Digital Circuits and Systems, Tata McGraw-Hill Publishing Company Ltd., New Delhi, 1995 2. Ramakant A. Gayakwad, Op-Amps and Linear Integrated Circuits, PHI Learning Private Ltd., New Delhi, 2010 3. Adel S. Sedra and Kenneth C. Smith, Microelectronic Circuits, Oxford University Press, New York, 2006 4. Behzad Razavi, Fundamentals of Microelectronics, Wiley India Private Ltd., New Delhi, 2015

Departmental Elective – IV

Sl. No.

Course Code

Departmental Elective – IV

PH4205

Quantum Mechanics-II

Course Number	PH4205
Course Credit (L-T-P-C)	2-1-0-3
Course Title	Quantum Mechanics-II
Learning Mode	Lectures & Tutorials
Learning Objectives	Complies with Program Goals 1, 2 and 3
Course Description	In this course students will learn time dependent perturbation theory, scattering theory and relativistic quantum mechanics.
Course Content	Time dependent perturbation theory, Schrödinger, Heisenberg and interaction pictures.; Constant and harmonic perturbations Fermi's Golden rule; Scattering theory: Laboratory and centre of mass frames, differential and total scattering cross-sections, scattering amplitude; Born approximation, Greens functions, scattering for different kinds of potentials; Partial wave analysis; Special topics in radiation theory: semi-classical treatment of interaction of radiation with matter Symmetries in quantum mechanics: Conservation laws and degeneracy associated with symmetries; Continuous symmetries, space and time translations, rotations; Rotation group, Wigner-Eckart theorem; Discrete symmetries; parity and time reversal. Relativistic quantum mechanics, Klein-Gordon equation, Interpretation of negative energy states and concept of antiparticles; Dirac equation, covariant form, adjoint equation; Plane wave solution and momentum space, spinors; Spin and magnetic moment of the electron.
Learning Outcome	Complies with PLO 1, 2(a) and 3
Assessment Method	Assignments, Quizzes, Seminar, Mid-semester examination, End-semester examination
Suggested Readings:
Textbooks:	· Quantum Mechanics (Vol-II), C. Cohen-Tannoudji, John Wiley & Sons, Asia, 2005. · Advanced Quantum Mechanics, J. J. Sakurai, Pearson Education, 2007. · Principles of Quantum Mechanics, R. Shankar, Springer, India, 2008.
References:	· Quantum Mechanics, L. I. Schiff, McGraw-Hill, 1968. · Quantum Mechanics, E. Merzbacher, John Wiley, Asia, 1999. · Quantum Mechanics, V.K. Thankappan, Wiley Eastern, 1985. · The Feynman Lectures on Physics, Vol.3, R.P. Feynman, R.B. Leighton and M.Sands, Narosa Pub. House, 1992. · The Principles of Quantum Mechanics, P.A.M. Dirac, Oxford Univ. Press, 1991. · Quantum Mechanics-Nonrelativistic Theory, L.D.Landau and E.M. Lifshitz, 3^rd Edition, Pergamon, 1981. · Quantum Mechanics, B. H. Bransden and C. J. Joachain, Pearson Education 2^nd Ed., 2004.

PH4206

Thin Film Technology

Course Number	PH4206
Course Credit L-T-P-C	3-0-0-3
Course Title	Thin Film Technology
Learning Mode	Classroom Lectures
Learning Objectives	The science of technology involved behind growth, characterization and uses of Thin Film of various materials.
Course Description	Module-1 deals introduces to thin film and its importance. The physical processes behind growth of thin film is also discusses. Module-2 deals with the knowledge of vacuum technology which is relevant for growth of thin film. Module-3 discusses about various techniques for growth of thin film which makes use of vacuum technology also. Module-4 deals with various characterization methods of thin films, and lastly discusses about applications.
Course Outline	Module-1: Motivation; Structure, defects, thermodynamics of materials, mechanical kinetics and nucleation; grain growth and thin film morphology; Module-2: Basics of Vacuum Science and Technology, Kinetic theory of gases; gas transport and pumping; vacuum pumps and systems; vacuum gauges; oil free pumping; aspects of chamber design from thin film growth perspectives; Module-3: Various Thin film growth techniques with examples and limitations; Spin and dip coating; Langmuir Blodgett technique; Metal organic chemical vapor deposition; Electron Beam Deposition; Pulsed Laser deposition; DC, RF and Reactive Sputtering; Molecular beam epitaxy; Module-4: Characterization of Thin films and surfaces; Thin Film processing from Devices and other applications perspective.
Learning Outcome	Complies with PLO 1a
Assessment Method	Quiz, Seminar, Mid-semsester examination, End-semester examination
Suggested Readings:	· Materials Science of Thin Films Deposition and Structure, Milton Ohring. · Thin Film Solar Cells, Chopra and Das. · Thin Film Deposition: Principles and Practice, Donald Smith. · Handbook of Thin Film Deposition (Materials and Processing Technology), Krishna Seshan · Handbook of Physical Vapor Deposition, D. M. Mattox

PH4209

Solar Energy and Photovoltaics

Course Number	PH5132/PH5232 PH4209
Course Credit (L-T-P-C)	2-1-0-3 3-0-0-3
Course Title	Solar Energy and Photovoltaics
Learning Mode	Lectures
Learning Objectives	Complies with program goal 1,2 and 3
Course Description	In this course, student will learn about solar spectrum, solar energy conversion, storage of energy for future use including how solar cell working principle.
Course Outline	Solar radiations as a source of energy and mechanism for its entrapment; Measurements and limits of solar energy entrapment; Flat plate collectors and solar concentrators; Solar energy for industrial process heat and design of solar green house; Solar refrigeration and conditioning; Solar thermo-mechanical power. Introduction of energy storage/conversion devices, State-of-the art status of portable power sources, Solar/photovoltaic (PV) cells as a source of green energy; Fundamentals, Materials, Design and Implementation aspects of PV energy generation and consumption; Solar cell technologies (Si-wafer based, Thin film, GaAs based, dye-sensitized, PESC and organic solar cells), Efficiency of solar cells and PV array analysis, Photovoltaic system design (stand alone and grid connected) and applications; Balance of system (BOS) with emphasis on role of storage batteries; Cost analysis, Case study for performance evaluation and problem identification in wide-spread commercialization of the technology.
Learning Outcome	Complies with PLO 1, 2(a) and 3
Assessment Method	Assignments, Quizzes, Seminar, Mid-semester examination, End-semester examination
Suggested Readings
Textbooks	· Solar Energy: Fundamentals & Applications; H. P. Garg and J. Prakash; Tata McGraw Hill, 1997. · Fundamentals of Photovoltaic Modules and their Applications, G. N. Tiwari, S. Dubey & Julian C. R. Hunt, RSC Energy Series, 2009. · Solar Photovoltaics: Fundamentals, Technologies and Applications, 2^nd Ed., C. S. Solanki, Prentice Hall of India, 2011 (ISBN: 978-81-203-4386-6) · Solar Cell Device Physics, Stephen Fonash, 2^nd Ed., Academic Press, 2010 (ISBN: 978-0-12-374774-7).
References	· Energy Storage, R. A. Huggins, Springer, 2010. · Handbook of Advanced Electronic and Photonic Materials and Devices: Ferroelectrics & Dielectrics, Vol. 10, H. S. Nalwa (Ed.), Academic Press, 2001. · Electrochemical Nanotechnology, T. Osaka, M. Dutta, Y. S. Diamand (Eds.), Springer, 2010, (ISBN: 978-1-4419-1423-1). · Encyclopedia of Nanoscience & Nanotechnology, Vol. 10, H. S. Nalwa (Ed.), American Scientific Publishers, 2004.

PH4210

Modeling Complex Systems

Course Number	PH4210
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Modeling Complex Systems
Learning Mode	Lectures and Computational exercises
Learning Objectives	Complies with Program Goals 1, 2 and 3
Course Description	This interdisciplinary course explores the practical application of modeling and simulation principles to complex systems. A complex system, characterized by interconnected or interwoven parts, can include biological organisms, ecological systems, economies, fluids, or strongly-correlated solids. The course draws from mathematics, nonlinear science, numerical simulations, and statistical physics. It begins with an overview of complex systems and then delves into modeling techniques using nonlinear differential equations, networks, and stochastic models. Throughout the course, students will model, program, and analyze a diverse range of complex systems, including dynamical and chaotic systems, cellular automata, and iterated functions. Through these, there will be ample scope for hands-on experience and a deeper understanding of complex systems emerging from elementary rules.
Course Content	Fundamentals of Modeling, A brief recap of Dynamical Systems; Discrete-Time Models: Modeling and Analysis; Continuous-Time Models: Modeling and Analysis; implications of bifurcation, chaos and catastrophe; interactive simulations of complex systems, cellular automata, continuous field models; basics of networks, small world network; dynamical networks: Modeling, Network topologiesand dynamics; Agent-based models; Examples including epidemiology, forest-fire, bioinformatics, message-passing, predator-prey, belief propagation, Hutchinson’s time-delay model, internet.
Learning Outcome	Complies with PLO 1(a), 1(b), 2(a) and 3
Assessment Method	Assignments, Quizzes, Mid-semester examination and End-semester examination
Suggested Readings:	Textbooks: 1. Hiroki Sayama, Introduction to the Modeling and Analysis of Complex Systems, Open SUNY (2015). 2. Nino Boccara, Modeling Complex Systems, Springer-Verlag Reprint (2024). References: 1. W. Krauth, Statistical Mechanics: Algorithms and Computations (Oxford Masters Series in Physics, 2006).

PH4211

AC Network Analysis

Course Number	PH4211
Course Credit	3-0-0-3
Course Title	AC Network Analysis
Learning Mode	Lectures and Tutorials
Learning Objectives	The course is focused on the application oriented knowledge that is required to analyze alternating current circuits whose frequency of operation is not as high as radio frequencies. The knowledge would be used to test and analyze various AC circuits.
Course Description	This course deals with development of skills that is required to analyze various AC circuits. The skills are not introduced abruptly but in a systematic manner. First, the course starts with fundamental knowledge on Network Transformations. Post that a section on Resonance in AC circuits is discussed. Lastly, the course ends with Impedance transformations in AC networks and methods to deal with coupled circuits, especially Transformer.
Course Outline	Module 1: Principle of duality, Reduction of complicated two port network to T and p equivalent circuits, Conversion between T and p sections, Bridged and Parallel T network, Reciprocity theorem, Compensation theorem, Maximum power transfer theorem, Transfer impedance, Matrix method for network calculations Module 2: Definition of Q-factor, Series resonance and its bandwidth, Parallel resonance, Conditions for maximum impedance, Currents in anti-resonant circuit, Universal resonance curves, Bandwidth of anti-resonance circuit, Anti-resonance at all frequencies, Reactance curves Module 3: Transformation of impedances, Reactance L section for impedance transformation, Image impedance and Everitt’s theorem, Reactance T network for impedance transformation, Coupled circuits, Equivalent T network for magnetically coupled circuit, Iron core transformer
Learning Outcome	AC circuit analysis is primarily composed of three modules, namely, Network Transformation which is covered in Module 1, Resonance which is covered in Module 2 and Impedance Transformation which is covered in Module 3. In Module 1, the student gets trained in the fundamentals. It is important that the student should pick-up well in the fundamentals. Therefore, special emphasis would be given in solving numerical problems. Module 2 and 3 widen the scope of AC circuit analysis technique. Application of the techniques learnt in these modules is of prime importance. Therefore, solving problems, based on the concept taught in the lecture, forms and essential part.
Assessment Method	Quiz, Assignments and Exams
Suggested Readings:	Textbooks: 1. John D. Ryder, Network Lines and Fields, Prentice Hall of India, New Delhi, 2002. References: 1. M. B. Reed, Alternating-Current Circuits, Harper & Brothers, New York 1948. 2. W. R. LePage and S. Seelay, General Network Analysis, McGraw-Hill Book Company, Inc., New York, 1952. 3. W. L. Everitt, Communication Engineering, 2^nd Edition, McGraw-Hill Book Company, Inc., New York, 1937.

Departmental Elective – V

Sl. No.

Course Code

Departmental Elective – V

PH4212

X-ray and Applications

Course Number	PH4212
Course Credit (L-T-P-C)	3-0-0-3
Course Title	X-ray and Applications
Learning Mode	Lectures
Learning Objectives	The objectives of the course are to learn the X-ray mechanism, functions and applications of X-rays. The physics formulation and technological applications will be learned by the student. This will create opportunity to have a carrier in X-ray technology in both imaging and diffraction. More over the student will learn X-ray diffraction, X-ray absorption and photoemission with their current applications.
Course Description	The course discusses the physical mechanism of X-ray and Matter interaction, production of X-ray techniques, etc. The use of X-ray in biophysics, condensed matter physics, medical physics, cultural heritage and environmental science.
Course Content	Module 1: Introduction to X-ray physics, physical properties of x-rays, Macroscopic description of X-ray and material interaction, Microscopic description of interaction, Semi-classical theory of the interaction between radiation and hydrogen – like atoms, Fermi’s golden rule for transitions to discrete and continuum states, Selection rules, Production of X-rays. Module 2: X-ray applications: X-ray optics, X-ray microscopy, X-ray diffraction, X-ray interference, X-ray Scattering, medical imaging, X-ray fluorescence and absorption spectroscopy, coherent diffraction imaging, industrial applications. Module 3: Synchrotron radiation: Sources of Synchrotron radiation, RF cavity, Beamlines and basics of x-ray optics, General characteristics of Synchrotron Radiation, Diffraction limit and Coherence lengths, industrial applications. Module 4: Photoemission spectroscopy: The Photoelectric effect, Experimental Setup, Theoretical Description, Primary and secondary structures occurring in the photoemission spectra, Photoelectron Spectroscopy of solids, Quantitative Analysis, Hard x-ray Photoelectron Spectroscopy, Industrial applications Module 5: X-ray absorption fine structure, Phenomenology of X-ray absorption spectroscopy, experimental layouts, Physical origin of the fine structure (self-interference phenomenon), Golden rule and further approximations, Approximate derivation of EXAFS (Muffin-tin approximation for two atomic system), Correction terms for the EXAFS function and final relation, EXAFS data analysis and resulting structural parameters, XANES phenomenological description, Chemical shift of the absorption edge, Linear dichroism in XANES and EXAFS, Industrial applications. Text Books: 1- J. Als – Nielsen and D. McMorrow, Introduction to Modern X-ray Physics, Wiley, New York, 2001. 2- A. Balerna and S. Mobilio, Introduction to Synchrotron Radiation, in “Synchrotron Radiation: Basics, Methods and Applications”, a cura di S. Mobilio, F. Boscherini e C. Meneghini, Springer (2015). 3- S. Hüfner, Photoelectron Spectroscopy – Principles and Applications, 3rd ed. (Berlin, Springer, 2003) Reference Books: 4- P. Fornasini, Introduction to X-ray absorption spectroscopy, in “Synchrotron Radiation: Basics, Methods and Applications”, a cura di S. Mobilio, F. Boscherini e C. Meneghini, Springer (2015). 5- B. Bunker, Introduction to XAFS: a practical guide to X-ray absorption spectroscopy, Cambridge University Press (2010). 6- B.E. Warren, X-ray diffraction, Dover, New York, 1990. 7- S.J.L. Billinge e E.S. Bozin, Pair distribution function technique: principles and methods, in Diffraction at the nanoscale, a cura di A. Guagliardi & N. Masciocchi, Insubria University Press. 8- A. Guinier, X-ray diffraction in crystals, imperfect crystals, and amorphous bodies, Dover, New York, 1994. 9- C. Mariani e G. Stefani, Photoemission Spectroscopy: fundamental aspectsin “Synchrotron Radiation: Basics, Methods and Applications”, a cura di S. Mobilio, F. Boscherini e C. Meneghini, Springer (2015) 10- D. Attwood, Soft X-rays and extreme ultraviolet radiation, Cambridge University Press (1999).

PH4213

Materials Engineering

Course Number	PH4213
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Materials Engineering
Learning Mode	Lectures
Learning Objectives	The objective of the course is to develop basic knowledge about how Materials engineering lies at the core of technological advancement. Materials Engineering is an interdisciplinary field focused on understanding, designing, and improving materials to meet engineering challenges. This course provides a comprehensive foundation in the structure, properties, processing, and performance of materials, bridging scientific principles with practical applications. Students will explore a variety of materials, including metals, ceramics, polymers, composites, and advanced materials like nanomaterials and biomaterials, with an emphasis on their role in modern technology and industry.
Course Description	In beginning, overview of different material types will be discussed, followed by detailed insight how these materials are being used at present. The advancement in terms of their processing for modern technology and applications will be discussed.
Course Outline	Overview of material types: metals, ceramics & glasses, polymers, composites, Electronic Materials (Semiconductors, conductors, and insulators), Biomaterials (Materials used in medical implants and devices, Biocompatibility and degradation), Historical and modern advancements in materials engineering, Advanced materials (Nanomaterials, Materials for Energy Applications, Shape memory alloys). Atomic structure and bonding, Crystallography and crystal structures, Defects in materials (vacancies, dislocations), Microstructure and its influence on properties, Phase diagrams and phase transformations. Mechanical properties (strength, toughness, hardness, ductility, etc.). Thermal and electrical (conductivity, expansion)., magnetic, and optical properties, Corrosion and environmental degradation Techniques for shaping and forming materials (casting, forging, 3D printing), Heat treatment and phase transformations. Coating and surface modification, Powder metallurgy and ceramics processing. Criteria for selecting materials in engineering applications. Case studies in aerospace, automotive, electronics, and construction. Nanomaterials and their applications. Biomaterials for medical devices and implants. Smart materials and responsive systems.
Learning Outcome	Complies with PLO 2b, 3
Assessment Method	Quizzes, Mid-semester and End-semester examination
Suggested Readings:	· Materials Science and Engineering: An Introduction by William D. Callister Jr. and David G. Rethwisch, 10^th Ed., Wiley, 2020. · Fundamentals of Materials Science and Engineering: An Integrated Approach by William D. Callister Jr. and David G. Rethwisch, 5^th Ed., Wiley, 2007. · Engineering Materials 1 & 2 by Michael F. Ashby and David R. H. Jones, 4^th Ed., Butterworth-Heinemann Ltd., 2012 · The Science and Engineering of Materials" by Donald R. Askeland and Wendelin J. Wright, 6^th Ed., Cl-Engineering, 2010

PH4214

Superconducting Qubits: Fundamentals and Operation

Course Number	PH4214
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Superconducting Qubits: Fundamentals and Operation
Learning Mode	Lectures and Tutorials
Learning Objectives	Complies with Program Goals 1, 2 and 3
Course Description	Equips the students with the fabrication techniques and operation intricacies of Superconducting Qubits with an eye on prospective applications
Course Content	Introduction to Superconducting Qubits: Overview of quantum computing, various physical qubits and the need for reliable qubits; Types of superconducting qubits (flux qubits, charge qubits, phase qubits, transmon qubits); Circuit quantum electrodynamics (cQED) and its relevance. Quantum LC Circuits and Correspondence Principle: Classical LC circuits and their resonance behavior; Superconducting qubits as classical circuit elements in a quantum regime; Circuit QED approach for quantizing classical Hamiltonians. Josephson Junctions and Charge Qubits: Cooper pairs and Josephson Junctions; Cooper pair box: Building blocks for charge qubits; Hamiltonian description of charge qubits based on tunneling and capacitance Transmon Qubits: Introduction to transmon qubits; Nonlinear inductance and capacitor design; Energy-level spectra and tunability Operation and Control of Superconducting Qubits: Initialization, manipulation, and readout of qubit states; Quantum gates (single-qubit and two-qubit gates); Decoherence and error correction Applications and Challenges: Quantum algorithms and applications using superconducting qubits; Challenges in scaling up qubit numbers; Recent advancements and future prospects
Learning Outcome	Complies with PLO 1(a), 1(b), 2(a) and 3
Assessment Method	Assignments, Quizzes, Mid-semester examination and End-semester examination
Suggested Readings:	Textbooks: Daniel D. Stancil and Gregory T. Byrd, Principles of Superconducting Quantum Computers, Wiley (2022). 2. Alan Salari, Microwave Techniques in Superconducting Quantum Computers, Artech Books, UK (2024). References: 1. Morten Kjaergaard et al. “Superconducting Qubits: Current State of Play”. In: Annual Review of Condensed Matter Physics 11.1 (Mar. 2020), pp. 369– 395. DOI: 10.1146/annurev-conmatphys-031119-050605; URL: http://dx .doi.org/10.1146/ annurev-conmatphys-031119-050605. 2. Steven M. Girvin. Circuit QED: Superconducting Qubits Coupled to Microwave Photons, Les Houches Summer School on Quantum Machines, Oxford University Press (2014).

PH4215

Analytical Techniques

Departmental Elective – VI

Sl. No.

Course Code

Departmental Elective – VI

PH4216

Computational methods for classical and quantum physics

Course Number	PH4216
Course Credit	3-0-0-3
Course Title	Computational methods for classical and quantum physics
Learning Objectives	To make students capable of solving specific advanced physics problems using the techniques developed in EP3101 (Computational Techniques).
Course Description	The student will learn computationally solving problems related to Quantum and Classical physics. The course has class room discussion which will be completed in computational lab by developing a code based on it.
Course Outline	Solving partial differential equations, Finite difference methods, Successive over-relaxation (SOR) method, Time dependent problems; The wave equation, Laplace equation, Traffic flow, Shock solution, Fluids, Solving the Schrodinger equation; One-Dimension, Higher dimensional Basic techniques, Quantum scattering, The variational principle, Time propagation, Central potentials, Multi-electron systems, The Hartree and Hartree-Fock approximations, Modelling Lithium atoms, Quantum dots.
Learning Outcome	Complies with PLO 1b, 3
Assessment Method	Mid-term written examination, Mid-term lab examination, End-term written examination, End-term lab examination, Assignment & Quiz
Suggested Readings:
Textbooks:	· J. Izaac and J. Wang, Computational Quantum Mechanics, Springer , 2022. · J. Franklin, Computational Methods for Physics, Cambridge publications, 2013. · J. M. Thijssen, Computational Physics, Cambridge Univ. Press, 2^ndEdition, 2007. · Tao Pang, An Introduction to Computational Physics, Cambridge Univ. Press, 2^ndEdition, 2006. · Steven E. Kooning and Dawn C. Meredith, Computational Physics, Westview Press, 1990. · An Introduction to Computer Simulation Methods: Applications to Physical Systems, 3^rdEdition, Harvey Gould, Jan Tobochnik, Wolfgang Christian, Addison-Wesley, 2006.
References:	· Rubin H. Landau, Manuel José Páez Mejía, Cristian C. Bordeianu, A Survey of Computational Physics: Introductory Computational Science, Volume 1, Princeton Univ. Press, 2008. · Werner Krauth, Statistical Mechanics: Algorithms and Computations, Oxford Masters Series in Physics, 2006.

PH4217

LASER Technology

Course Number	PH4217
Course Credit (L-T-P-C)	3-0-0-3
Course Title	LASER Technology
Learning Mode	Lectures
Learning Objectives	The main objective is to learn various techniques used in building CW and pulsed lasers, different techniques developed based on lasers, and applications of lasers in various disciplines.
Course Description	This course allows engineering students to learn various techniques used in building CW and pulsed lasers, different techniques developed based on lasers, and applications of lasers in various disciplines, which are essential to pursuing research and scientific jobs in laser and relevant industries.
Course Outline	Principles of CW and Pulsed lasers, Laser modulation techniques, Different Q-switching and Mode-locking techniques, Laser amplifiers, Laser frequency stabilization techniques, Laser tuning techniques, Mode-selection methods, Harmonic generations, Non-linear optical methods, Raman lasers, Micro and Nanolasers. Laser remote sensing of the atmosphere, Photosensitization, Photodynamic therapy, Optical tweezers, Laser cleaning, Laser satellite communications, Laser cooling, Optical atomic clock, Laser pyrolysis, Laser micromachining, Laser 3D printing, High precision laser wavelength meters, Laser ablation techniques, Dynamic light scattering, Data storage, Fabrication of photonic crystals, Single molecule laser fluorescence and Raman microscopy, Photoacoustic imaging, Coherent anti-Stokes Raman scattering (CARS) imaging, Ultrasensitive Optical biosensors.
Learning Outcome	The students will be fully aware of various techniques used in building CW and pulsed lasers, different techniques developed based on lasers, and applications of lasers in various disciplines
Assessment Method	Designing of optical setups/theoretical simulations, Quizzes, Mid-semester and End-semester examination
Suggested Readings:

Textbooks:	[1]. C. B. Hitz, J. J. Ewing, and J. Hecht, Introduction to Laser Technology, Wiley, 2012. [2]. C. Guo and S. C. Singh, Handbook of Laser Technology and Applications, CRC Press, 2021. [3]. Lan Xinju, Laser Technology, CRC Press, 2010 [4]. A. Donges and R. Noll, Laser Measurement Technology, Springer, 2015. [5]. W. T. Silfvast, Laser Fundamentals, Cambridge University Press, 1996. [6]. J-X Cheng, X. S. Xie, Coherent Raman Scattering Microscopy, CRC Press, 2013.

PH4218

Atomtronics & Quantum Technology

Course Number	PH4218
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Atomtronics & Quantum Technology
Learning Mode	Lectures
Learning Objectives	Complies with Program Goals 1, 2 and 3
Course Description	Atomtronics is an emerging interdisciplinary topic for Quantum Technology. This course will provide students with a comprehensive introduction to the principles, techniques, and applications of atomtronics. Topics covered will include Bose-Einstein condensates, atom optics, atom interferometry, atom-based circuits, and potential applications in quantum computing and precision measurements.
Course Content	Introduction to Atomtronics, Techniques for preparing the system: cooling and trapping; Dynamics of Bose-Einstein condensates, Nonlinear excitations in ultra-cold atoms: Solitons and Quantum Droplets; Basics of Atom Optics, Manipulation of atomic beams: waveguide of various curvatures, Phase Imprinting, and persistent currents: AQUIDS, Atom lenses, mirrors and beam-splitters, Atomtronics Matter wave lensing, Ring trap and ring lattice atomtronics; Atom Interferometry: basic ideas, Mach-Zehnder interferometer, Aharonov-Bohm interferometer, Atomic soliton-barrier interferometer, Sagnac Interferometer; Quantum Computing with Atomtronics, use of quantum information processing with ultra-cold atoms, quantum logic gates, design, and implementation of atomtronic components (atomtronics diodes, transistors, etc.); Precision Measurements and other applications, Gravimeter, Accelerometer, Navigation; Current applications of atomtronics in research, industry, and future directions.
Learning Outcome	Complies with PLO 1(a), 1(b), 2(a) and 3 The course aims to establish a foundational understanding on atomtronics and its importance in Quantum Technology. Through comprehensive study, students will acquire proficiency in methods for controlling and guiding ultra-cold atomic gases as atom-lasers. Additionally, we will explore atom interferometry, exploiting its utility in quantum precision measurement, and quantum computing. Furthermore, the course will provide current status of the relevant quantum technologies, the approaches by leading industries and research outlook.
Assessment Method	Assignments, Quizzes, Mid-semester examination and End-semester examination
Suggested Readings:	Textbooks: 1) Quantum Atom Optics: Theory and Applications, E. O. Ilo-Okeke and Tim Byrnes, Cambridge University Press (2021). 2) Roadmap on Atomtronics: State of the art and perspective, Amico, L., et al., AVS Quantum Science 3, no. 3 (2021). 3) Colloquium: Atomtronic circuits: From many-body physics to quantum technologies, Amico, L., et al., Reviews of Modern Physics, 94(4), 041001 (2022). References: 1) Atom Interferometry: Paul R. Berman, Academic Press, 1997. 2) Focus on atomtronics-enabled quantum technologies. New Journal of Physics, Amico, L et al., 19(2), 020201 (2017). 3) Advances in atomtronics, Pepino, R. A., Entropy, 23(5), 534 (2021).

PH4219

Nanoscale Devices

Departmental Elective – VII

Sl. No.

Course Code

Departmental Elective – VII

PH4220

Medical Physics and Applications

Course Number	PH4220
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Medical Physics and Applications
Learning Mode	Lectures
Learning Objectives	The objectives of the course are to learn the mechanism and functions of different senses of the human body and, to understand the physics formulation of the human body. Also to understand the different equipment used for imaging the human body and, how it helps the medical practitioner.
Course Description	The course discusses breathing and the metabolism of the human body. Biomechanics and fluid dynamics of the circulatory system are discussed elaborately. The functions of ultrasound, X-ray, MRI, etc. is elaborately taught. Radiation physics and its use in medical science for cancer treatment is discussed.
Course Content	Module 1: Breathing, fluid dynamics of the circulatory system, Biomechanics, senses, Electric currents, Fields and Potential, Applications: Foot ware design, Cloth design, Optical glasses for eye, Hearing kits, Retina implantation, threshold of vision of the human eye, Electrical model of a cell membrane, Measurement of cell membrane potentials. Module 2: Diagnostics and Therapy: EKG, X-ray and Computed tomography digonistic, Ultrasound, Magnetic Resonance Imaging, Nuclear diagnostics and positron emission tomography, Temperature measurement system, Blood Pressure measurement, ECHO; and PCR, Applications of techniques in medical diagnosis. Module 3: Radiation medicine and protection, radiation therapy, Compton scattering, Lethal energy dose, Fatal does equivalents, Laser therapy.
Learning Outcome	Complies with PLO 2b
Assessment Method	Assignments, Mini projects, Quizzes, Mid-semester examination, End-semester examination.
Suggested Readings:	Textbooks: 1. Medical Physics, W. A. Worthoff, H. G. Krojanski, D. Suter, De Druyter, 2014. 2. Medical Physics and Biomedical Engineering, B. H. Brown, R. H. Smallwood, D. C. Barber, P. V. Lawford and D. R. house, Taylor & Francis, New York, 1999. References: 3. The Essential Physics of Medical Imaging, Jerrold T. Bushberg, J. Anthony Seibert, Edwin M. Leidholdt, Jr., and John M. Boone, Wolters Kluwer \| Lippincott, Williams & Wilkins, 2011. 3rd Edition, Philadelphia. 4. Medical Physics, Martin Hollins, Nelson Thornes Ltd. 2001. 5. The Physics of Radiology, H. E. Jones, J. R. Cunningham, Charles C. Thomas, New York, 2002. 6. Radiation Oncology Physics: A Handbook for Teachers and Students, E.B. Podgorsak, IAEA Publ., 2005. 7. Handbook of Bio-Medical Engineering, Jacob Kline, Academic Press Inc., Sandiego, Oxford University Press, 2004. 8. Smart Biosensor Technology, G. K. Knoff, A. S. Bassi, CRC Press, 2006. 9. Physics of Diagnostic Radiology, Thomas S Curry, IV Edition, Lippincott Williams & Wilkins, 1990. 10. The Essential Physics for Medical Imaging, Jerrold T Bushberg, J. Anthony Seibert, Edwin M. Leidholdt Jr., John M. Boome, Lippincott Williams & Wilkins, 2nd Edition, 2012. 11. Medical Physics: Imaging, Jean A. Pope, Heinemann Publishers, 2012. 12. Nanobiotechnology: Concepts, Applications and Perspectives, Niemeyor, Christober M. Mirkin, Kluwer publications, USA, 2004. 13. Physical Principles of Medical Ultrasonics, C. R. Hill, J. C. Bamber, G. R. ter Haar, John Wiley & Sons, 2005. 14. Diagnostic Ultrasonic Principles and Use of Instrument, W. M. McDicken, 2nd Edition, John Wiley & Sons, New York, 1992.

PH4221

Emerging Technologies in Photonics

Course Number	PH4221
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Emerging Technologies in Photonics
Learning Mode	Lectures & Demonstrations
Learning Objectives	The main objective is to learn (i) the emerging photonics technologies, (ii) the theory behind these technologies, and (iii) the various techniques to fabricate advanced optical and photonic devices.
Course Description	This course allows engineering students to learn modern cutting-edge photonics-based technologies, essential to pursue research and scientific jobs in advanced photonics-based engineering applications.
Course Outline	Photonic integrated circuits for optical communications. Classical light pulse storage and retrieval using Electromagnetically Induced Transparency, Quantum Memory and Quantum Repeaters, and Quantum Entanglement. Scalar and vector beams; Orbital angular momentum (OAM) states of light; Phase and Polarization singularities, OAM-based optical communication, structured light Optical cryptography; Symmetric and asymmetric optical encryption techniques, Various optical transforms and its application in image/data encryption Portable nanophotonic sensors, Microlasers, Nanolasers, Plasmonic photothermal therapy, Photonic nanojet lithography, Plasmonic tweezers for nanoscale trapping, Super-resolution imaging, Quantum imaging, and Nanophotonics for solar cells
Learning Outcome	The students will be fully aware of (i) various emerging photonics technologies, (ii) the theory behind these technologies, and (iii) various techniques to fabricate advanced optical and photonic devices.
Assessment Method	Assignment; Seminar; Mid-sem and End-sem examinations
Suggested Readings:	Textbooks: § Communication System, B.P Lathi § Optical Fiber Communications: Principles and Practice, John M. Senior, Prentice Hall of India § Optical Communication Systems, John Grower, Prentice Hall of India § Optical Fiber Communications- Gerd Keiser, McGraw Hill, 3^rd ed. § Orbital Angular Momentum States of Light: Propagation Through Atmospheric Turbulence, Kedar Khare, P. Lochab, and P. Senthilkumaran, IOP Publs., UK, 2020. § Structured Light and its Applications, David L. Andrews, Science Direct, 2008. § Applied Nanophotonics, Sergey V. Gaponenko, Hilmi Volkan Demir, Cambridge Univ. Press, 2019. § Quantum Nano-plasmonics, Witold A Jack, Cambridge Univ. Press, 2020. § Introduction to Nanophotonics, Henri Benisty, Jean Jacques Greffet, Philippe Lalanne, Oxford Univ. Press, 2022. § Fundamentals of Quantum Optics and Quantum Information, Peter Lambropoulos and David Petrosyan, Springer, 2007. § Introduction to Optical Quantum Information Processing, P. Kok and B. W. Lovett, Cambridge Univ. Press, 2014. References: § An Introduction to Metamaterials and Nanophotonics, Constantin Simovski and Sergei Tretyakov, Cambridge Univ. Press, 2020. § Nanophotonics, Arthur McGurn, Springer, 2019. § The Physics of Quantum Information, D. Bouwmeester, A. K. Ekert and A. Zeilinger, Editors, Springer, 2000. § Optical Cryptosystems, N. K. Nishchal, IOP Publs., UK, 2019.

PH4222

Micro Nano Fabrication

Course Number	PH4222
Course Credit (L-T-P-W-C)	3-0-0-3
Course Title	Micro Nano Fabrication
Learning Mode	Lectures
Learning Objectives	The objective of the course is to develop basic knowledge about how semiconductor devices are fabricated in clean room environment. Also, introduces various characterization methods adopted till now to realize performance of semiconductor devices at different operating conditions.
Course Description	In beginning, course introduces about clean room and related functionalities with a fundamental question such as Why do we need clean room? Later, course provides the basic background of semiconductor available and used till now in semiconductor industries by answering questions like why do we need semiconductor and semiconductor-based devices? What kind of semiconductor materials are really useful for semiconductor technology? Then, course introduces importance of semiconductor surfaces and how these surfaces are prepared. Impurities, Importance of doping, dopants and doping densities. By taking an example of solid-state device, Design and layout methods are introduced. To realize these patterns/design, various lithography techniques are introduced. Later, deposition of various materials using various deposition techniques are introduced by highlighting the importance of parameters chosen for deposition. Wet and dry etching methods are introduced as successive process thereafter. Device fabrication steps and device characterization tools are introduced to know the device performance. Various related tools will be introduced to students wherever the fabrication process’s details are explained.
Course Outline	Introduction to clean rooms and safety measures, process overview, Contamination Background to semiconductor materials, Silicon wafers, Wafer cleaning steps, safety and emergency acts Fundamentals of MOSFET Devices, Scaling Rules, Silicon-Dioxide Based Gate Dielectrics, Metal Gates, Junctions and Contacts, Advanced MOSFETs Concepts Device layout and design, Mask design, Lithography (Optical Lithography, Extreme Ultraviolet Lithography, Electron Beam Lithography, Shadow lithography, Alignment of Several Mask Layers) Fundamentals of Film Deposition, Top-down and bottom-up approaches Etching processes (surface and bulk micromachining), Sputtering techniques for deposition of oxides and metals, Chemical vapour deposition (CVD), Plasma enhanced chemical vapour deposition (PECVD), Atomic layer deposition (ALD), Focussed Ion Beam milling and deposition Characterization techniques: Optical inspection, Optical profilometer, SEM, SPM
Learning Outcome	Complies with PLO 2b, 3
Assessment Method	Quizzes, Mid-semester and End-semester examination
Suggested Readings:	· Nanoelectronics and Information Technology: Advanced Electronic Materials and Novel Devices, Rainer Waser, 3^rd Ed., Wiley-VCH, 2012. · Fundamental of semiconductor Manufacturing and process control, Gray S. May, Costas J. Spanos, John Wiley and Sons, 2006. · Fundamental of Semiconductor Fabrication, Gray S. May, Simon M. Sze, Wiley India Pvt. Ltd., 2011. · Introduction to semiconductor materials and Devices, M. S. Tyagi, Wiley, 2009. · Semiconductor manufacturing technology, Michael Quirk, Julian Serda, 1^st ed., Pearson, 2000. · Semiconductor Material and device characterization, Dieter K. Schroder, 3^rd ed., Wiley, 2006.

PH4223

Nanogenerators and Application in self-powered system

Course Number	PH4223
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Nanogenerators and Application in self-powered system
Learning Mode	Lectures
Learning Objectives	The learning objectives of nanogenerators revolve around comprehending the fundamental principles governing energy harvesting at the nanoscale. Students aim to grasp the operational mechanisms of nanogenerators, enabling them to harness ambient energy for diverse applications. Understanding nanomaterial applications for energy conversion efficiency is pivotal. Moreover, students strive to master the design and optimization of nanogenerator architectures to enhance performance and drive progress toward a more efficient and sustainable future.
Course Description	The nanogenerator course delves into the principles of energy harvesting and exploring nanomaterial application. Students will learn to design and optimize nanogenerator architectures for diverse applications, fostering innovation in sustainable energy solutions and nanotechnology advancements.
Course Content	Module 1: Nanogenerators: Introduction, Overview of nanotechnology in energy conversion, Historical development and current research trends, Materials for Nanogenerators (2D materials, Carbon based materials, Ceramics, Polymers, etc.), Basic principles of energy harvesting at the nanoscale, Types of Nanogenerators: Piezoelectric, Thermoelectric, Pyro-electric, Electromagnetic, and Triboelectric, Hybrid nanogenerators. Module 2: Mechanism, principles, and applications of different types of nanogenerators. Self-powered sensors and wearable electronics, nanogenerator devices (pressure sensor, voltage source, gas sensors, self-charging supercapacitor, wireless charger). Module 3: Key challenges for choosing nanomaterials for nanogenerators, Different types of synthesis techniques, Influence of material properties on energy conversion efficiency, Designing of the device for practical, real-life application, and Other conventional energy generation techniques: Wind energy, Tidal, Thermal, hydropower generation, Nuclear, and geothermal energy production.
Learning Outcome	Complies with PLO 1, 2a and 3
Assessment Method	Assignments, Mini projects, Quizzes, Mid-semester examination, End-semester examination.
Suggested Readings:	Textbook: 1. Nanogenerators: Basic Concepts, Design Strategies, and Applications: Inamuddin, Mohd Imran Ahamed, Rajender Boddula, Tariq Altalhi, CRC Press, Year: 2022. References: 1. Triboelectric Nanogenerators, Zhong Lin Wang, Long Lin, Jun Chen, Simiao Niu, Yunlong Zi Springer International Publishing (ISBN-978-3-319-40038-9, 978-3-319-40039-6) 2. Handbook on Triboelectric Nanogenerator, Zhong Lin Wang, Ya Yang, Junyi Zhai, Jie Wang. Springer (ISBN-9783031281105, 9783031281112) 3. 3. Nanogenerators, Sang-Jae Kim, Arunkumar Chandrasekhar, Nagamalleswara Rao Alluri, IntechOpen, 2020.

Interdisciplinary Electives (Available to students of B. Tech. other than Dept. of Physics)

Sl. No.

Subject Code

Subject

IDE-I

PH2201

Fundamentals of Electromagnetism

Course Number	PH2201
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Fundamentals of Electromagnetism
Learning Mode	Lectures
Learning Objectives	The students without a physics background are exposed to fundamental ideas in electromagnetism. Starting with elements of vector analysis, this course illustrates ideas in electrostatics, magnetostatics and electromagnetic waves Complies with Program Goals 1 and 3
Course Description	This course deals with fundamentals in electromagnetism. Also the practical examples will be explained along with its uses in various engineering domains.
Course Outline	Vector Calculus: Gradient, Divergence and Curl. Line, Surface and Volume integrals. Gauss’s divergence theorem and Stokes’ theorem in Cartesian, Spherical polar and cylindrical polar coordinates. Dirac Delta function. Electrodynamics: Coulomb’s law and Electrostatic field, Fields of continuous charge distributions. Gauss’s law and its applications. Electrostatic Potential. Work and Energy. Conductors, capacitors. Laplace’s equation. Method of images. Dielectrics. Polarization. Bound charges. Energy in dielectrics. Boundary conditions. Lorentz force. Biot-Savart and Ampere’s laws and their applications. Vector Potential. Force and torque on a magnetic dipole. Magnetic materials. Magnetization, Bound currents. Boundary conditions. Motional EMF, Ohm’s law. Faraday’s law. Lenz’s law. Self and Mutual inductance. Energy stored in magnetic field. Maxwell’s equations. Optics: huygens’ principle. Young’s experiment. Superposition of waves. Concepts of coherence sources. Interference by division of wavefront. Fresnel’s biprism, Phase change on reflection. Lioyd’s mirror. Interference by division of amplitude. Parallel film. Film of varying thickness. Colours of thin films. Newton’s rings. The Michelson interferometer. Fraunhofer diffraction. Single slit, double slit and N-slit patterns. The diffraction grating.
Learning Outcome	Complies with 1a. 3
Assessment Method	Quiz and/or Assignments and Examinations
Suggested Readings:	Texts: D. J. Griffiths, Introduction to Electrodynamics, Prentice Hall, New Delhi, 1995. F. A. Jenkins and H. E. White, Fundamentals of Optics, McGraw-Hill, 1981. References: R. P. Feynman, R. B. Leighton and M. Sands, The Feynman Lecture in Physics, Vol I, Narosa Publishing House, New Delhi, 1998 I. S. Grant and W. R. Philips, Electromagnetism, John Wiley, 1990. E. Hecht, Optics, Addison-Wesley, 1987.

PH2202

Waves and Particles

Course Number	PH2202
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Waves and Particles
Learning Mode	Lectures
Learning Objectives	The objective of the course is to develop a basic understanding of wave and particle concept of physics formulation. The student will understand the observation by considering particles as well as waves. Complies with Program Goals 1 and 3
Course Description	The course provides fundamental physics knowledge on the concept of particles and waves. Different experimental evidence and theoretical models will be built upon a realization of science formulation and its utility. The student will learn the application of physics in another science subject.
Course Outline	Introduction to waves, Particles, Wave-particle duality, Newton’s corpuscular theory to understand the properties of light, Blackbody radiation, photoelectric effect, Crompton effect, Davison-Germer experiment, Pair production, Refraction, reflection and transmission, Superposition of waves and interference, Diffraction, Polarisation, Scattering, Schrodinger equation, Atomic structure, Particles, Spectra and radiation.
Learning Outcome	Complies with 1a. 3
Assessment Method	Quiz and/or Assignments and Examinations
Suggested Readings:	Test Books: 1- Concepts of Modern Physics, Arthur Beiser, Tata McGraw Hill, 2009. 2- Optics, E. Hect, A. R. Ganesan, Pearson, 2019. Reference Books: 3- Fundamentals of Optics, Jenkins F, Tata McGraw Hill, 2017. 4- Waves - Berkeley Series - Sie by Franks Crawford, 2017 5- Modern Physics, G. M. Felder and K. N. Felder, Cambridge University Press, ISBN: 9781108842891,

PH2203

Fuel Cell Fundamentals

Course Number	PH2203
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Fuel Cell Fundamentals
Learning Mode	Physical Presence in Classroom
Learning Objectives	The emergent need of clean and green energy to meet “zero emission” target worldwide has put pressing demand for teaching courses relevant to meet this target. It aims to impart skill focused training to understand; 1. The impact of carbon foot print on environment and climate 2. Hydrogen energy technologies with zero emission potential 3. Clean and green energy conversion system design and implementation
Course Outline	Module-1: Carbon footprint and its impact on environment, need for zero emission energy system, origin of fuel cell concept and historical perspective in brief, energy and power in fuel cells, fuel cell operation and performance, thermodynamics of fuel cells, transport in fuel cells. Module-2: Fuel cell classification, characteristics features and operation, comparative analysis of different fuel cell systems (AFC, PAFC, MCFC, PEMFC and SOFC), Fuel cell characterization and evaluation approach. Module-3: Modelling, design and fabrication of fuel cells with case study of PEMC and SOFC, Experimental diagnostics and diagnosis Module-4: Hydrogen generation, storage and delivery, Environmental impact of fuel cells, Fuel Cell application in EVs
Learning Outcome	Learners of the course will be able upskill their knowledge creating; (a) awareness and implementation need for clean and green energy technology and (b) readiness with skill to fulfil emerging industry and R & D institution demand of workforce with core competency.
Assessment Method	Class test and Quiz/Assignment (20%), MSE: (30%), ESE: (50%)
Suggested Readings:	1. Principles of Fuel Cells, Xianguo Li, Taylor & Francis 2. Fuel Cell Fundamentals, Ryan O'Hare, Suk-Won Cha, Whitney Colella, Fritz B. Prinz, John Wiley & Sons 3. Fuel Cell Engines, Matthew M. Mench, John Wiley & Sons, Inc.

IDE-II

PH3101

Energy Materials Processing

PH3102

Mechanics in Physics

Course Number	PH3102
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Mechanics in Physics
Learning Mode	Lectures
Learning Objectives	This course is an interdisciplinary course. The students without a physics background in Undergraduate will understand the different mechanics and their utility in explaining physical phenomena and their importance in modern technology. Complies with Program Goals 1 and 3
Course Description	This course deals with fundamentals in Classical mechanics, Quantum Mechanics, Relativistic Mechanics, and Statistical Mechanics. Also the practical examples will be explained along with its uses in industry and computation.
Course Outline	Newtonian formulation, D’Alembert’s principle, Variational Principle, Lagrangian and Hamiltonian dynamics, Poisson’s Bracket, Maxwell Electromagnetic equation, Postulates of relastivistic mechanics, Lorentz transformation, Covariant and Contravariant formulation, light Cone, Need of Quantum mechanics, Wave-particle duality, postulates of Quantum Mechanics, Schrodinger's equation, Operator algebra, Commutation relation, Particle in a box, Harmonic Oscillator, Elementary Statistical Mechanics, Ensembles, Maxwell-Boltzmann distribution, Bose-Einstein and Fermi-Dirac distribution. Phase transition.
Learning Outcome	Complies with 1a. 3
Assessment Method	Quiz and/or Assignments and Examinations

IDE-III

PH4110

Photovoltaics and Fuel Cell Technology

Course Number	PH4110
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Photovoltaics and Fuel Cell Technology
Learning Mode	Physical Presence in Classroom
Learning Objectives	Alternative energy sources have always been a core area of significant importance since long. Recent focus on harnessing natural energy from the Sun, has necessitated teaching of relevant course at undergraduate level to create talent pool to meet industry demand. It aims to impart; 1. Knowledge pertaining to solar energy harnessing conditions 2. Learning relevant to physics of photovoltaic cells. 3. Training and skill relevant for design, processing, fabrication, testing and installation of photovoltaic cells, i.e.; end to end industry skill.
Course Outline	Module-1: Global energy scenario and impending energy crisis, Basic introduction of energy storage/conversion devices, State-of-the art status of portable power sources, Solar photovoltaic (PV) cells, PV energy generation and consumption, fundamentals of solar cell materials, Module-2: Elementary concept of solar cell and its design, solar cell technologies (Si-wafer based, Thin film and concentrator solar cells), Emerging solar cell technologies (GaAs solar cell, dye-sensitized solar cell, organic solar cell, Thermo-photovoltaics), Photovoltaic system design and applications, Analysis of the cost performance ratio for the photovoltaic energy and problems in wide-spread commercialization of the technology. Module-3: Fuel cells and its classification; Transport mechanism in fuel cells and concept of energy conversion; Fuels and fuel processing, Fuel cell design and its characterization Module-4: Technological issues in Solid oxide fuel cells (SOFC); PEM fuel cells; Direct methanol fuel cells (DMFC), Molten carbonate fuel cell (MCFC), Power conditioning and control of fuel cell systems.
Learning Outcome	Learners of the course will diversify their interdisciplinary knowledge creating; (a) awareness and need for clean and green energy technology in a very simpler form even if original background is different.
Assessment Method	Class test and Quiz/Assignment (20%), MSE: (30%), ESE: (50%)
Suggested Readings:	1. Fundamentals of Photovoltaic Modules and their Applications, G. N. Tiwari, S. Dubey & Julian C. R. Hunt, RSC Energy Series. 2. Solar Photovoltaics: Fundamentals, Technologies and Applications (2^nd ed.), C. S. Solanki, Prentice Hall of India. 3. Principles of Fuel Cells, Xianguo Li, Taylor & Francis.

Minor in Physics

Sl. No.

Subject Code

Subject

EP2101

Course Number

EP2101

Course Credit

3-1-0-4

Course Title

Quantum Physics

Learning Mode

Lectures, Tutorials and Assignments

Learning Objectives

Complies with Program Goals 1,2 and 3

Course Description

Course Outline

Engineering applications: devices based on quantum principles such as tunnel diode, single electron transistor, MRI and NMR, SEM, TEM and SPM.

Learning Outcome

Complies with PLO 1a, 1b, 2a and 3a

Assessment Method

Assignments, Quizzes, Seminar, Mid-semester examination, End-semester examination

Suggested Readings:

Textbooks:

1. A. Beiser, Concepts of Modern Physics, Tata McGraw Hill, 2020

2. Eisberg and Resnick

3. Introduction to Quantum Mechanics (2nd edn) by D. J. Griffiths, Prentice Hall (2004).

Reference books:

1. Quantum Mechanics, Powell and Craseman

1. Mastering quantum mechanics, Barton Zwiebach, MIT Press, 2022

EP2203

Device Modeling and Design

Course Number	EP2203
Course Credit	3–1–0–4
Course Title	Electromagnetism
Learning Mode	Class lectures, tutorials, assignments, discussions.
Learning Objectives	Complies with Program Goals 1, 2 and 3
Course Description	This course gives an introduction to fundamentals of electromagnetic theory. Students will learn electrostatics, electrodynamics and electromagnetic waves in medium and its applications
Course Outline	Electrostatics and Magnetostatics, Displacement current and Maxwell’s equations, Maxwell’s equation in matter, Boundary conditions, Conservation principles in EM theory (energy and momentum), Poynting’s theorem, Electromagnetic (EM) wave equation for E and B in vacuum, Monochromatic plane waves, Energy and momentum in EM waves, Propagation of EM waves in linear media, Reflection and transmission of EM waves at conducting and non-conducting media; Skin effect, Frequency dependence of permittivity; Wave guides: EM waves between two conducting planes, TM, TE and TEM waves and their transmission.
Learning Outcome	Complies with PLO 1(a), 1(b), 2(a) and 3
Assessment Method	Assignments, Quizzes, Mid-semester examination and End-semester examination.
Suggested Readings:	Text Books: D. J. Griffiths, Introduction to Electrodynamics, Third Edition, Pearson Education Inc., 2006. J. D. Ryder, Networks, Lines and Fields, Second Edition, Prentice Hall of India, 2002.

EP3104

Solid State Physics

Course Number	EP3104
Course Credit	3-1-2-5
Course Title	Solid State Physics
Learning Mode	Class lectures, tutorials, assignments, discussions
Learning Objectives	Complies with program goal 1,2 and 3.
Course Description	This course deals with basic theory of solids which are important to understand the vast range of real solids, with an emphasis on its structure and physical properties. This includes topics that are entirely based on classical methods, and also those which demand a detailed quantum treatment. The concepts of statistical mechanics, thermodynamics and mathematical methods are inherently present in this course due to its interdisciplinary approach. The course includes theories of metals, insulators, and semiconductors. Electrical, mechanical, thermal, magnetic and superconducting properties are discussed with detailed analysis.
Course Outline	Crystal physics: Symmetry operations; Bravais lattices; Point and space groups; Miller indices and reciprocal lattice; Structure determination; diffraction; X-ray, electron and neutron; Crystal binding; Defects in crystals; Point and line defects. Lattice vibration and thermal properties: linear lattice; acoustic and optical modes; dispersion relation; density of states; phonons and quantization; Brillouin zones; Specific heat (Einstein and Debye models) and thermal conductivity of metals and insulators. Electronic properties: Free electron theory of metals; electrons in a periodic potential; Bloch equation; Kronig-Penny model; band theory; Nearly free electron and tight-binding model, Motion of electrons in applied electric and magnetic fields. Semiconductor physics: concept of holes, carrier concentration in intrinsic and extrinsic semiconductors, effective mass and mobility. Magnetic properties: Dia-, para- and ferro-magnetism. Superconductivity: General properties of superconductors, Meissner effect; London equations; coherence length; type-I and type-II superconductors.
Learning Outcome	Complies with PLO 1a, 1b, 3
Assessment Method	Tutorials & assignments + mid-semester exam + end-semester exam
Suggested Readings:	Textbooks: 1. C. Kittel, Introduction to Solid State Physics, Wiley India (2009). 2. M. A. Omar, Elementary Solid State Physics, Addison-Wesley (2009). References: 1. J. Dekker, Solid State Physics, Macmillan (2009). 2. N. W. Ashcroft and N. D. Mermin, Solid State Physics, HBC Publ. (1976). 3. H. P. Myers, Introduction to Solid State Physics, Taylor and Francis (1997). 4. Richard Zallen, The Physics of Amorphous Solids, John Wiley and Sons Inc.,(1983)

4. Minor-IV (Any One)

PH3201

Engineering Optics

Course Number	PH3201
Course Credit	3-0-0-3
Course Title	Engineering Optics
Learning Mode	Lectures and Assignments
Learning Objectives	Complies with Program Goals 1,2 and 3
Course Description	This course introduces students various optical systems, optical devices needed for various engineering applications in the field of Optics and modern cutting edge technology
Course Outline	Lens systems: Basics and concepts of lens design, some lens systems. Optical components: Reflective, refractive and diffractive systems; Mirrors, prisms, gratings, filters, polarizing components. Interferometric systems: Two beam, multiple beam, shearing, scatter fringe and polarization interferometers. Vision Optics: Eye and vision, colorimetry basics. Optical sources: Incandescent, fluorescent, discharge lamps, Light emitting diode. Optical detectors: Photographic emulsion, thermal detectors, photodiodes, photomultiplier tubes, detector arrays, Charge-coupled device, CMOS. Optical Systems: Telescopes, microscopes (bright field, dark field, confocal, phase contrast, digital holographic), projection systems, interferometers, spectrometers. Display devices: Cathode ray tube, Liquid crystal display, Liquid crystals on silicon, Digital light processing, Digital micro-mirror device, Gas plasma, LED display, Organic led displays (OLED). Consumer devices: Optical disc drives: CD, DVD; laser printer, photocopier, cameras, image intensifiers.
Learning Outcome	Complies with PLO 1(a), 1(b), 2(a) and 3
Assessment Method	Assignments, Quizzes, Mid-semester examination and End-semester examination
Suggested Readings:	Text Books: 1. R. S. Longhurst, Geometrical and Physical Optics, 3^rd ed., Orient Longman, 1988. 2. R. E. Fischer, B. Tadic-Galeb, and P. R. Yoder, Optical System Design, 2^nd ed., SPIE Press, 2008. Reference Books: 1. W. J. Smith, Modern Optical Engineering, 3^rd ed., McGraw Hill, 2000. 2. K. Iizuka, Engineering Optics, Springer, 2008. 3. B. H. Walker, Optical Engineering Fundamentals, SPIE Press, 1995.

ii.

PH3206

Laser Physics

Course Number	PH3206
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Laser Physics
Learning Mode	Lectures
Learning Objectives	Complies with Program Goals 1, 2a and 3
Course Description	Create understanding of basic light-matter interactions, design, and construction of laser. Learn expertise in usage and safety of laser. Acquire understanding of component of lasers and pulsing menthodology.
Course Content	Introduction to laser physics; Light-matter Interaction; Semiclassical theory, Wave and quantum properties of light, Spontaneous and stimulated emissions, Einstein’s coefficients. Line shape function, Line broadening: Homogeneous and inhomogeneous broadening, natural, Doppler and collisional broadening. Light amplification; Optical saturation, Population inversion, Optical pumping, Rate Equations; 2-level, 3-level and 4-levl lasers, Laser action (gain, threshold, power, frequency), Gain saturation, Optimal conditions for laser operation; Laser saturation, Optical Resonator: Longitudinal and transverse laser cavity modes, cavity loss, Q-factor, ABCD matrix, Stable and unstable resonator, Properties of Gaussian Beam and propagation. Laser Pulsing: Hole Burning; Q-Switching; Mode Locking; Single Mode Lasers; Ultrafast laser systems, linear and nonlinear pulse propagation Types of Lasers: He-Ne Laser; Nd-YAG Laser; Solid-state laser, Gas Laser Dye Laser, Excimer Laser; Semiconductor Laser; Tuneable Lasers, Supercontinuum Laser, Fiber Lasers Laser Safety and applications:
Learning Outcome	Complies with PLO 1, 2a and 3
Assessment Method	Assignments, Quizzes, Presentation, Mid-semester examination and End-semester examination
Suggested Readings:	1. Optical Electronics, Ajoy Ghatak and K.Thyagarajan, CUP, 2003. 2. Photonics, Amnon Yariv and Pochi Yeh, 6 th ed., OUP, 2009. 3. Fundamentals of Photonics, B.E.A.Saleh and M.C.Teich, 2 nd ed., Wiley Interscience, 2007. 4. W. T. Silfvast, Laser Fundamentals, Cambridge University Press

iii.

PH3210

Course Number	PH3210
Course Credit	2-1-0-3
Course Title	Device Modeling and Design
Learning Mode	Lectures and Tutorials
Learning Objectives	Complies with Program goals 1, 2 and 3
Course Description	This course provides detailed theoretical overview of Device Modeling and Design
Course Outline	Crystal structure-Unit cell and Miller Indices Reciprocal Space, Doping, Band Structure, Effective Mass, Density of states, Distribution Function, and carrier concentration calculation, Carrier transport, Mobility and diffusivity, continuity equation, Poisson’s equation, Semiclassical transport, carrier density equation, current density equation p-n junction, Metal-semiconductor junction, BJT, Heterojunction, Schottky junctions, MOS capacitor, MOSFET, JFET, Capacitor-Voltage Characteristics, Boltzmann Transport Equation (BTE), Relaxation-Time Approximation (RTA), Scattering and Mobility. Drift-Diffusion Model Derivation and dielectric relaxation time Generation and Recombination models, Derivation of SRH model, Boundary conditions, Gummel’s Iteration Method and Newton’s Method, As extension of DD model, Carrier Balance, Energy balance and momentum balance Equations, Direct solution scheme through Monte Carlo simulations, Models for DD, Hydrodynamic simulations, Mobility and G-R models, Introduction to Silvaco ATLAS (device) and ATHENA (process) simulation framework. Simulator syntax, Numerical method choice, TCAD tools, MixedMode simulation, Basics of semiconductor processing, Si-Based Nanoelectronics and Device Scaling, scaling implications, short channel effects, effective channel length, effects of channel length and width on threshold voltage, Compact models for MOSFET and their implementation in SPICE. MOS model parameters in SPICE.
Learning Outcome	Complies with PLO 1a. 1b. 2a and 3a
Assessment Method	Quiz, Assignments and Exams
Suggested Readings:	Textbooks: 1. Umesh K. Mishra and Jasprit Singh, Semiconductor Device Physics and Design, Springer, 2008. 2. G. Streetman, and S. K. Banerjee, “Solid State Electronic Devices,” 7th edition, Pearson,2014. 3. S. M. Sze and K. N. Kwok, “Physics of Semiconductor Devices,” 3rd edition, John Wiley&Sons, 2006. 4. D Vasileska, SM. Goodnick, G Klimeck, "Computational Electronics: Semiclassical and Quantum Device Modeling and Simulation," CRC Press 2010. 5. Selberherr Siegfried, “Analysis and Simulation of Semiconductor Devices”, 1984

5. Minor-V (Any One)

PH4106

Science and Technology of Nanomaterials

ii.

PH4107

Optical Quantum Communication

Course Number	PH4107
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Optical Quantum Communication
Learning Mode	Lectures
Learning Objectives	Complies with Program Goals 1, 2 and 3
Course Description	This course provides engineering students to learn modern cutting edge optical quantum communication techniques which are very essential to pursue for advanced research and scientific jobs in the area of quantum communication and engineering applications. The course will also examine current research trends and potential future developments in the field of optical quantum communication.
Course Outline	classical v/s quantum information, quantum bits (qubits) and quantum gates, quantum entanglement and its properties, single-photon sources, entangled photon sources, photons as information carriers, polarization qubits, qubit generation and propagation, Bell state measurements, quantum repeaters, various protocols for quantum memory and its efficiency, implementation of quantum memory nodes, long distance quantum communication using quantum repeaters, quantum networks, multi-node quantum communication, ground-based and space-based quantum networks, entanglement distribution and quantum internet, Recent progress in quantum photonic chips for quantum communication and internet.
Learning Outcome	Complies with PLO 1(a), 1(b), 2(b) and 3
Assessment Method	Exams, Quiz and Assignment
Suggested Readings:	Textbooks: 1. Gianfranco Cariolaro,Quantum Communications, Springer (2015). 2. P. Kok and B. W. Lovett, Introduction to Optical Quantum Information Processing, Cambridge university press. 3. Peter Lambropoulos, David Petrosyan, Fundamentals of Quantum Optics and Quantum Information, Springer (2007) 4. Ivan B. Djordjevic, Quantum Communication, Quantum Networks, and Quantum Sensing, Elsevier (2022) References: L. Mandel, and E. Wolf. Optical Coherence and Quantum Optics, Cambridge University Press. W. H. Louisell, Quantum Statistical Properties of Radiation, McGraw-Hill. 3. D. Bouwmeester, A. K. Ekert, and A. Zeilinger, eds. The Physics of Quantum Information, Springer 4. Serge Haroche, Jean-Michel Raimond, Exploring the Quantum: Atoms, Cavities, and Photons, Oxford Academic (2006)

iii.

PH4108

Photovoltaics: Concepts and Applications

Course Number	PH4108
Course Credit (L-T- P-C)	3-0-0-3
Course Title	Photovoltaics: Concepts and Applications
Learning Mode	Physical Presence in Class Room
Learning Objectives	Alternative energy sources have always been a core area of significant importance since long. Recent focus on harnessing natural energy from the Sun, has necessitated teaching of relevant course at undergraduate level to create talent pool to meet industry demand. It aims to impart; 1. Knowledge pertaining to solar energy harnessing conditions 2. Learning relevant to physics of photovoltaic cells. 3. Training and skill relevant for design, processing, fabrication, testing and installation of photovoltaic cells, i.e.; end to end industry skill.
Course Outline	Module-1: An introduction to different sources of energy with its implications and alternative solutions, energy balance of the Sun and optimal conditions for harnessing solar energy, efficient design to entrap solar energy, a state of-the-art review of solar photovoltaic cells. Module-2: Semiconductor fundamentals, drift, diffusion and charge transport, photon emission and absorption, PN junction design and control parameters, Junction solar cell configuration – design, fabrication, analysis and efficiency improvement considerations for efficient solar cells. Module-3: Silicon based solar cell technology - monocrystalline, polycrystalline, amorphous and thin film Si solar cells, Process form sand to Silicon and Silicon to Wafer, Cell design and fabrication process, Multi-junction Si solar cells. Module-4: Non-Si solar cell technology, its challenges and advancements, an introduction to protocols for solar cell installation.
Learning Outcome	The learners of the course would be ready with knowledge to; (a) harness solar energy and technologically competent to implement the technology and (b) fulfil emerging industry and R & D institution demand for technologically skilled workforce.
Assessment Method	Class test and Quiz/Assignment (20%), MSE: (30%), ESE: (50%)
Suggested Readings:	1. Solar Photovoltaics: Fundamentals, Technologies and Applications (2^nd ed.), C. S. Solanki, Prentice Hall of India 2. Solar Cell Device Physics, Stephen Fonash (2^nd ed.), Academic Press 3. Principles of Solar Cells, LEDs and Diodes, Adrian Kitai, Wiley

Minor in Nanoscience

Sl. No.

Subject Code

Subject

EP2101

Course Number

EP2101

Course Credit

3-1-0-4

Course Title

Quantum Physics

Learning Mode

Lectures, Tutorials and Assignments

Learning Objectives

Complies with Program Goals 1,2 and 3

Course Description

Course Outline

Engineering applications: devices based on quantum principles such as tunnel diode, single electron transistor, MRI and NMR, SEM, TEM and SPM.

Learning Outcome

Complies with PLO 1a, 1b, 2a and 3a

Assessment Method

Assignments, Quizzes, Seminar, Mid-semester examination, End-semester examination

Suggested Readings:

Textbooks:

1. A. Beiser, Concepts of Modern Physics, Tata McGraw Hill, 2020

2. Eisberg and Resnick

3. Introduction to Quantum Mechanics (2nd edn) by D. J. Griffiths, Prentice Hall (2004).

Reference books:

1. Quantum Mechanics, Powell and Craseman

1. Mastering quantum mechanics, Barton Zwiebach, MIT Press, 2022

EP2203

Instrumentation Techniques

Course Number	EP2203
Course Credit	3–1–0–4
Course Title	Electromagnetism
Learning Mode	Class lectures, tutorials, assignments, discussions.
Learning Objectives	Complies with Program Goals 1, 2 and 3
Course Description	This course gives an introduction to fundamentals of electromagnetic theory. Students will learn electrostatics, electrodynamics and electromagnetic waves in medium and its applications
Course Outline	Electrostatics and Magnetostatics, Displacement current and Maxwell’s equations, Maxwell’s equation in matter, Boundary conditions, Conservation principles in EM theory (energy and momentum), Poynting’s theorem, Electromagnetic (EM) wave equation for E and B in vacuum, Monochromatic plane waves, Energy and momentum in EM waves, Propagation of EM waves in linear media, Reflection and transmission of EM waves at conducting and non-conducting media; Skin effect, Frequency dependence of permittivity; Wave guides: EM waves between two conducting planes, TM, TE and TEM waves and their transmission.
Learning Outcome	Complies with PLO 1(a), 1(b), 2(a) and 3
Assessment Method	Assignments, Quizzes, Mid-semester examination and End-semester examination.
Suggested Readings:	Text Books: D. J. Griffiths, Introduction to Electrodynamics, Third Edition, Pearson Education Inc., 2006. J. D. Ryder, Networks, Lines and Fields, Second Edition, Prentice Hall of India, 2002.

EP3105

Course Number	EP3105
Course Credit	2-0-2-3
Course Title	Instrumentation Techniques
Learning Mode	Class lectures, laboratory demonstration and hands on sessions
Learning Objectives	Complies with program goal 1,2 and 3.
Course Description	A sound knowledge of instrumentation is key to a well-rounded training of an engineer. This course introduces the students to fundamental aspects of the ‘systems design approach’ which will come handy when they want to apply it in development of various systems. The course deals with aspects of signal processing, control, data acquisition and power management. Issues in handling massive data in the form of images and image processing will also be taught.
Course Outline	Fundamentals of system design Signals processing, Control and Data acquisition: Principles of sensors, transducers, and measurement techniques; Signal processing; Theory of feedback control, stability analysis, and controller design; A/D and D/A, Design Data acquisition, virtual instrumentation; Case studies related to signal processing and control aspects for different systems including advanced analytical instruments, thin film coating units and space applications Power systems: Different types of power supplies; transformers; signal conditioning; estimation of power requirements Vacuum systems: Vacuum production techniques, operation for rotary, diffusion, turbo molecular, and cryo-vacuum pumps; Measurement of vacuum, different gauges and their working; Designing Vacuum Systems: Mechanical and thermal design considerations; Pump throughput estimation; Case studies for vacuum systems in different instruments; accelerators, superconducting magnets, food preservation systems, electron microscopes, thin film coating technology CMOS and CCD camera, coupling light in systems, crucial issue with data handling for large image sizes Lab component: Use of Simulink^TM/Simscape in signal processing and control; Designing chambers using Solidwork^TM and Comsol^TM Multiphysics; Signal transduction; Signal conditioning; Controller deployment using Arduino; Generating vacuum using different pumps; Use of different gauges to measure vacuum; Image and video acquisition; processing of images
Learning Outcome	Complies with PLO 1a, 1b, 3
Assessment Method	mid-semester exam, end-semester exam
Suggested Readings:	Books: 1. Instrumentation: Devices and Systems, C. Rangan, G. Sarma, V. S. V. Mani, 2^nd ed. McGraw Hill Education, 2017 2. Instrumentation for Engineers, J. D. Turner, Springer (reprint), 2020 3. Vacuum Technology, A. Roth, North-Holland, 3^rd ed., 2007. References: 1. Vacuum Science & Technology- V. V. Rao, K. L. Chopra and T. B. Ghosh, Allied Publishers Pvt. Ltd., 2012 2. A user's guide to vacuum technology, J. F. O'Hanlon, Wiley-Interscience, 2^nd ed., 2003. 3. Handbook of vacuum science and technology, D. M. Hoffman, Bawa Singh, J. H. Thomas-III (Eds)., Elsevier, 1998.

PH3208

Electron Microscopy

Course Number	PH3208
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Electron Microscopy
Learning Mode	Lectures
Learning Objectives	The objective of the course is to introduce the student to the electron microscopy and its utilization in modern technology. The students will learn about the electron-matter interaction, working principle of electron microscopes. The principle of electron optics and its use will be learned by the students. The opportunity in electron microscopy area will be known to the student.
Course Description	The course discusses different kinds of electron microscopy and electron spectroscopy. Analysis of TEM and SEM image, electron diffraction pattern, X-ray spectra analysis and, their applications in industry will be covered in this course.
Course Content	Module 1: Introduction to Microscopy, Limitations of the Human Eye, Optic, The X-ray Microscope, Electron Microscope, Low-Energy and Photoelectron Microscopes, Atom-Probe Microscopy. Module 2: Electron Sources, safety and precautions, Electron optics, electromagnetic lenses, Comparison of Magnetic and Electrostatic Lenses, Aberration Correctors and Monochromators, Electron and matter interaction, Scattering and diffraction, reciprocal space, Bloch waves, Diffraction from crystal, diffraction from small volume, elastic and inelastic scattering, absorption, dispersion, polarization, reflection, Imaging with Electrons, radiation damage, electron tomography, electron holography. Module 3: Transmission Electron Microscopy: Instrument, holders, lenses, cameras, apertures and resolution, imaging, amplitude contrast, phase contrast, bending effect, planer defects, bright field imaging, dark field imaging, high resolution imaging, Scanning transmission electron microscopy, image simulation and image analysis, Spectroscopy, X-ray spectroscopy, qualitative and quantitative X-ray analysis, electron energy loss spectroscopy and images, fine structure, diffraction pattern, indexing diffraction pattern, specimen (hard, soft, powder, ad biological) preparation, Industrial applications. Module 4: Scanning Electron Microscopy: Instrument, holders, lenses, apertures, resolution, Electron detectors, Back scattered electron, Secondary electron, Auger electron, imaging, Auger electron spectroscopy. Augur electron microscopy, image simulation and image analysis, Spectroscopy, X-ray spectroscopy, qualitative and quantitative X-ray analysis, EBSD, diffraction pattern and analysis, specimen preparation, Industrial applications.
Learning Outcome	The student will introduce himself/herself to the electron microscopy. The industrial applications of electron microscopy will be known. There are lots of opportunity in electron microscopy as it is a modern technique and it has lots of industrial applications. Hence, the students can take the job in the electron microscopy industries or they can make entrepreneur for supporting to the electron microscopy industries.
Assessment Method	Assignments, mini projects, Quizzes, Mid-semester examination, and End-semester examination.
Suggested Readings:	Textbooks: 1. Physical Principles of Electron Microscopy, Ray F. Egerton, springer, 2005, New York 2. Scanning Electron Microscopy, Ludwig Reimer, springer, 1998, New York, 3. Transmission Electron Microscopy, David B. Williams, C. Barry Carter, springer, 2009 References: 1. Electron Microscopy: Principles and Fundamentals, S. Amelinckx (Editor), Dirk van Dyck (Editor), J. van Landuyt (Editor), Gustaaf van Tendeloo (Editor), Wiley, 2007. 2. Electron microscopy Methods and Protocols, John Kuo, Springer, 2014. 3. The principles and Practice of Electron Microscopy, Ian M. Watt, Cambridge University Press, 1997.

PH4206

Thin Film Technology

Course Number	PH4206
Course Credit L-T-P-C	3-0-0-3
Course Title	Thin Film Technology
Learning Mode	Classroom Lectures
Learning Objectives	The science of technology involved behind growth, characterization and uses of Thin Film of various materials.
Course Description	Module-1 deals introduces to thin film and its importance. The physical processes behind growth of thin film is also discusses. Module-2 deals with the knowledge of vacuum technology which is relevant for growth of thin film. Module-3 discusses about various techniques for growth of thin film which makes use of vacuum technology also. Module-4 deals with various characterization methods of thin films, and lastly discusses about applications.
Course Outline	Module-1: Motivation; Structure, defects, thermodynamics of materials, mechanical kinetics and nucleation; grain growth and thin film morphology; Module-2: Basics of Vacuum Science and Technology, Kinetic theory of gases; gas transport and pumping; vacuum pumps and systems; vacuum gauges; oil free pumping; aspects of chamber design from thin film growth perspectives; Module-3: Various Thin film growth techniques with examples and limitations; Spin and dip coating; Langmuir Blodgett technique; Metal organic chemical vapor deposition; Electron Beam Deposition; Pulsed Laser deposition; DC, RF and Reactive Sputtering; Molecular beam epitaxy; Module-4: Characterization of Thin films and surfaces; Thin Film processing from Devices and other applications perspective.
Learning Outcome	Complies with PLO 1a
Assessment Method	Quiz, Seminar, Mid-semsester examination, End-semester examination
Suggested Readings:	· Materials Science of Thin Films Deposition and Structure, Milton Ohring. · Thin Film Solar Cells, Chopra and Das. · Thin Film Deposition: Principles and Practice, Donald Smith. · Handbook of Thin Film Deposition (Materials and Processing Technology), Krishna Seshan · Handbook of Physical Vapor Deposition, D. M. Mattox

Minor in Optics

Sl. No.

Subject Code

Subject

EP2102

Optics & Lasers

Course Number	EP2102
Course Credit	3-0-3-4.5
Course Title	Optics & Lasers
Learning Mode	Lectures and Assignments
Learning Objectives	Complies with Program Goals 1,2 and 3
Course Description	This course deals with fundamentals in Optics and Lasers. Students will learn about principles of LASERs, different types of Lasers, applications of Lasers in different engineering domains besides developing strong fundamentals in Optics
Course Outline	Review of basic optics: Polarization, Reflection and refraction of plane waves. Diffraction: diffraction by circular aperture, Gaussian beams. Interference: two beam interference-Mach-Zehnder interferometer and multiple beam interference-Fabry-Perot interferometer. Monochromatic aberrations. Fourier optics, Holography. The Einstein coefficients, Spontaneous and stimulated emission, Optical amplification and population inversion. Laser rate equations, three level and four level systems; Optical Resonators: resonator stability; modes of a spherical mirror resonator, mode selection; Q-switching and mode locking in lasers. Properties of laser radiation and some laser systems: Ruby, He-Ne, CO2, Semiconductor lasers. Some important applications of lasers, Fiber optics communication, Lasers in Industry, Lasers in medicine, Lidar.
Learning Outcome	Complies with PLO 1a, 1b, 2a and 3a
Assessment Method	Quiz, Assignments and Exams
Suggested Readings:	Textbooks: R. S. Longhurst, Geometrical and Physical Optics, 3rd ed., Orient Longman, 1986. E. Hecht, Optics, 4th ed., Pearson Education, 2004. M. Born and E. Wolf, Principles of Optics, 7th ed., Cambridge University Press, 1999. William T. Silfvast, Laser Fundamentals, 2nd ed., Cambridge University Press, 2004. K. Thyagarajan and A. K. Ghatak, Lasers: Theory and Applications, Macmillan, 2008.

4.5

EP2203

Instrumentation Techniques

Course Number	EP2203
Course Credit	3–1–0–4
Course Title	Electromagnetism
Learning Mode	Class lectures, tutorials, assignments, discussions.
Learning Objectives	Complies with Program Goals 1, 2 and 3
Course Description	This course gives an introduction to fundamentals of electromagnetic theory. Students will learn electrostatics, electrodynamics and electromagnetic waves in medium and its applications
Course Outline	Electrostatics and Magnetostatics, Displacement current and Maxwell’s equations, Maxwell’s equation in matter, Boundary conditions, Conservation principles in EM theory (energy and momentum), Poynting’s theorem, Electromagnetic (EM) wave equation for E and B in vacuum, Monochromatic plane waves, Energy and momentum in EM waves, Propagation of EM waves in linear media, Reflection and transmission of EM waves at conducting and non-conducting media; Skin effect, Frequency dependence of permittivity; Wave guides: EM waves between two conducting planes, TM, TE and TEM waves and their transmission.
Learning Outcome	Complies with PLO 1(a), 1(b), 2(a) and 3
Assessment Method	Assignments, Quizzes, Mid-semester examination and End-semester examination.
Suggested Readings:	Text Books: D. J. Griffiths, Introduction to Electrodynamics, Third Edition, Pearson Education Inc., 2006. J. D. Ryder, Networks, Lines and Fields, Second Edition, Prentice Hall of India, 2002.

EP3105

Course Number	EP3105
Course Credit	2-0-2-3
Course Title	Instrumentation Techniques
Learning Mode	Class lectures, laboratory demonstration and hands on sessions
Learning Objectives	Complies with program goal 1,2 and 3.
Course Description	A sound knowledge of instrumentation is key to a well-rounded training of an engineer. This course introduces the students to fundamental aspects of the ‘systems design approach’ which will come handy when they want to apply it in development of various systems. The course deals with aspects of signal processing, control, data acquisition and power management. Issues in handling massive data in the form of images and image processing will also be taught.
Course Outline	Fundamentals of system design Signals processing, Control and Data acquisition: Principles of sensors, transducers, and measurement techniques; Signal processing; Theory of feedback control, stability analysis, and controller design; A/D and D/A, Design Data acquisition, virtual instrumentation; Case studies related to signal processing and control aspects for different systems including advanced analytical instruments, thin film coating units and space applications Power systems: Different types of power supplies; transformers; signal conditioning; estimation of power requirements Vacuum systems: Vacuum production techniques, operation for rotary, diffusion, turbo molecular, and cryo-vacuum pumps; Measurement of vacuum, different gauges and their working; Designing Vacuum Systems: Mechanical and thermal design considerations; Pump throughput estimation; Case studies for vacuum systems in different instruments; accelerators, superconducting magnets, food preservation systems, electron microscopes, thin film coating technology CMOS and CCD camera, coupling light in systems, crucial issue with data handling for large image sizes Lab component: Use of Simulink^TM/Simscape in signal processing and control; Designing chambers using Solidwork^TM and Comsol^TM Multiphysics; Signal transduction; Signal conditioning; Controller deployment using Arduino; Generating vacuum using different pumps; Use of different gauges to measure vacuum; Image and video acquisition; processing of images
Learning Outcome	Complies with PLO 1a, 1b, 3
Assessment Method	mid-semester exam, end-semester exam
Suggested Readings:	Books: 1. Instrumentation: Devices and Systems, C. Rangan, G. Sarma, V. S. V. Mani, 2^nd ed. McGraw Hill Education, 2017 2. Instrumentation for Engineers, J. D. Turner, Springer (reprint), 2020 3. Vacuum Technology, A. Roth, North-Holland, 3^rd ed., 2007. References: 1. Vacuum Science & Technology- V. V. Rao, K. L. Chopra and T. B. Ghosh, Allied Publishers Pvt. Ltd., 2012 2. A user's guide to vacuum technology, J. F. O'Hanlon, Wiley-Interscience, 2^nd ed., 2003. 3. Handbook of vacuum science and technology, D. M. Hoffman, Bawa Singh, J. H. Thomas-III (Eds)., Elsevier, 1998.

PH3201

Engineering Optics

Course Number	PH3201
Course Credit	3-0-0-3
Course Title	Engineering Optics
Learning Mode	Lectures and Assignments
Learning Objectives	Complies with Program Goals 1,2 and 3
Course Description	This course introduces students various optical systems, optical devices needed for various engineering applications in the field of Optics and modern cutting edge technology
Course Outline	Lens systems: Basics and concepts of lens design, some lens systems. Optical components: Reflective, refractive and diffractive systems; Mirrors, prisms, gratings, filters, polarizing components. Interferometric systems: Two beam, multiple beam, shearing, scatter fringe and polarization interferometers. Vision Optics: Eye and vision, colorimetry basics. Optical sources: Incandescent, fluorescent, discharge lamps, Light emitting diode. Optical detectors: Photographic emulsion, thermal detectors, photodiodes, photomultiplier tubes, detector arrays, Charge-coupled device, CMOS. Optical Systems: Telescopes, microscopes (bright field, dark field, confocal, phase contrast, digital holographic), projection systems, interferometers, spectrometers. Display devices: Cathode ray tube, Liquid crystal display, Liquid crystals on silicon, Digital light processing, Digital micro-mirror device, Gas plasma, LED display, Organic led displays (OLED). Consumer devices: Optical disc drives: CD, DVD; laser printer, photocopier, cameras, image intensifiers.
Learning Outcome	Complies with PLO 1(a), 1(b), 2(a) and 3
Assessment Method	Assignments, Quizzes, Mid-semester examination and End-semester examination
Suggested Readings:	Text Books: 1. R. S. Longhurst, Geometrical and Physical Optics, 3^rd ed., Orient Longman, 1988. 2. R. E. Fischer, B. Tadic-Galeb, and P. R. Yoder, Optical System Design, 2^nd ed., SPIE Press, 2008. Reference Books: 1. W. J. Smith, Modern Optical Engineering, 3^rd ed., McGraw Hill, 2000. 2. K. Iizuka, Engineering Optics, Springer, 2008. 3. B. H. Walker, Optical Engineering Fundamentals, SPIE Press, 1995.

PH4221

Emerging Technologies in Photonics

Course Number	PH4221
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Emerging Technologies in Photonics
Learning Mode	Lectures & Demonstrations
Learning Objectives	The main objective is to learn (i) the emerging photonics technologies, (ii) the theory behind these technologies, and (iii) the various techniques to fabricate advanced optical and photonic devices.
Course Description	This course allows engineering students to learn modern cutting-edge photonics-based technologies, essential to pursue research and scientific jobs in advanced photonics-based engineering applications.
Course Outline	Photonic integrated circuits for optical communications. Classical light pulse storage and retrieval using Electromagnetically Induced Transparency, Quantum Memory and Quantum Repeaters, and Quantum Entanglement. Scalar and vector beams; Orbital angular momentum (OAM) states of light; Phase and Polarization singularities, OAM-based optical communication, structured light Optical cryptography; Symmetric and asymmetric optical encryption techniques, Various optical transforms and its application in image/data encryption Portable nanophotonic sensors, Microlasers, Nanolasers, Plasmonic photothermal therapy, Photonic nanojet lithography, Plasmonic tweezers for nanoscale trapping, Super-resolution imaging, Quantum imaging, and Nanophotonics for solar cells
Learning Outcome	The students will be fully aware of (i) various emerging photonics technologies, (ii) the theory behind these technologies, and (iii) various techniques to fabricate advanced optical and photonic devices.
Assessment Method	Assignment; Seminar; Mid-sem and End-sem examinations
Suggested Readings:	Textbooks: § Communication System, B.P Lathi § Optical Fiber Communications: Principles and Practice, John M. Senior, Prentice Hall of India § Optical Communication Systems, John Grower, Prentice Hall of India § Optical Fiber Communications- Gerd Keiser, McGraw Hill, 3^rd ed. § Orbital Angular Momentum States of Light: Propagation Through Atmospheric Turbulence, Kedar Khare, P. Lochab, and P. Senthilkumaran, IOP Publs., UK, 2020. § Structured Light and its Applications, David L. Andrews, Science Direct, 2008. § Applied Nanophotonics, Sergey V. Gaponenko, Hilmi Volkan Demir, Cambridge Univ. Press, 2019. § Quantum Nano-plasmonics, Witold A Jack, Cambridge Univ. Press, 2020. § Introduction to Nanophotonics, Henri Benisty, Jean Jacques Greffet, Philippe Lalanne, Oxford Univ. Press, 2022. § Fundamentals of Quantum Optics and Quantum Information, Peter Lambropoulos and David Petrosyan, Springer, 2007. § Introduction to Optical Quantum Information Processing, P. Kok and B. W. Lovett, Cambridge Univ. Press, 2014. References: § An Introduction to Metamaterials and Nanophotonics, Constantin Simovski and Sergei Tretyakov, Cambridge Univ. Press, 2020. § Nanophotonics, Arthur McGurn, Springer, 2019. § The Physics of Quantum Information, D. Bouwmeester, A. K. Ekert and A. Zeilinger, Editors, Springer, 2000. § Optical Cryptosystems, N. K. Nishchal, IOP Publs., UK, 2019.

Minor in Energy Storage Technology

Brief outline: Emergent issues of global significance comprising fast depleting fossil fuels reserve, carbon foot print, visible climate change, temperature rise and melting of glaciers causing sea level rise are interrelated. These challenging issue are threatening sustainable growth and even survival of the planet earth.

To exercise an effective control well in time, therefore, requires "zero emission" culture and effective implementation of clean and green energy alternatives without any loss of time. This requirement has put pressing demand for development of newer clean energy technology on R&D institutions, its commercialization on industry, creation of talent pool in the area under demand by academic institutions and better industry-academia tie up in this emergent area. A positive signal has already become visible with faster adoption of electric vehicles (EVs) on road that is likely to emerge as a multiplicative technology market in near future.

Keeping this realistic fact in mind, the department of Physics has come up with a minor program in "Energy Storage Technology" with following course structure:

Sl. No.

Subject Code

Subject

PH2101

Energy Storage Fundamentals

PH2203

Fuel Cell Fundamentals

Course Number	PH2203
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Fuel Cell Fundamentals
Learning Mode	Physical Presence in Classroom
Learning Objectives	The emergent need of clean and green energy to meet “zero emission” target worldwide has put pressing demand for teaching courses relevant to meet this target. It aims to impart skill focused training to understand; 1. The impact of carbon foot print on environment and climate 2. Hydrogen energy technologies with zero emission potential 3. Clean and green energy conversion system design and implementation
Course Outline	Module-1: Carbon footprint and its impact on environment, need for zero emission energy system, origin of fuel cell concept and historical perspective in brief, energy and power in fuel cells, fuel cell operation and performance, thermodynamics of fuel cells, transport in fuel cells. Module-2: Fuel cell classification, characteristics features and operation, comparative analysis of different fuel cell systems (AFC, PAFC, MCFC, PEMFC and SOFC), Fuel cell characterization and evaluation approach. Module-3: Modelling, design and fabrication of fuel cells with case study of PEMC and SOFC, Experimental diagnostics and diagnosis Module-4: Hydrogen generation, storage and delivery, Environmental impact of fuel cells, Fuel Cell application in EVs
Learning Outcome	Learners of the course will be able upskill their knowledge creating; (a) awareness and implementation need for clean and green energy technology and (b) readiness with skill to fulfil emerging industry and R & D institution demand of workforce with core competency.
Assessment Method	Class test and Quiz/Assignment (20%), MSE: (30%), ESE: (50%)
Suggested Readings:	1. Principles of Fuel Cells, Xianguo Li, Taylor & Francis 2. Fuel Cell Fundamentals, Ryan O'Hare, Suk-Won Cha, Whitney Colella, Fritz B. Prinz, John Wiley & Sons 3. Fuel Cell Engines, Matthew M. Mench, John Wiley & Sons, Inc.

PH3101

Energy Materials Processing

PH3204

Power Sources for Electric Vehicles

Course Number	PH3204
Course Credit	L – T – P : 3 – 1-0-4
Course Title	Power Sources for Electric Vehicles
Learning Mode	Physical Presence in Class Room
Learning Objectives	This course is highly relevant and industry demand driven contents in the emerging are of clean and green technology to reduce carbon foot print and bring transformation with “zero emission” transport system. It aims to impart a comprehensive training and skill pertaining to; 1. energy storage technologies from ab initio storage cells to current state of developments. 2. concept, design, fabrication and testing protocol of energy and power cells for EV applications. 3. bridge the technological gap with adequate skill focused content to fulfil the emerging need of competent workforce for EV industry.
Course Outline	Module-1: Power generation for transport with focus on zero emissions, an overview of electric vehicles and their power requirements, battery powered electric vehicles (EVs), performance criteria for EV batteries, laboratory testing protocols for EV batteries Module-2: Vehicle mechanics and power requirements, energy storage cell fundamentals, batteries, fly wheels and super capacitors, cell design and customization approach for cell voltage and current modification, cell and battery modelling for rated power requirement and design. Module-3: Concepts of super capacitors as a storage cell with large power delivery, supercapacitor classification, design, fabrication, testing and applications, advantage and challenges in integration of a super capacitor with battery and possible alternatives. Module-4: Design of a battery pack for EV application, operational safety challenges and need for thermal management and battery management systems (BMS), Safety considerations and protocols for battery pack development in EVs with case study for e-cycle, e-bike, 3-wheelers.
Learning Outcome	Learners of the course will be able upskill their knowledge and skill to fulfil emergent need of rapidly expanding electric vehicle (EV) industry.
Assessment Method	Class test and Quiz/Assignment (20%), MSE: (30%), ESE: (50%)
Suggested Readings:	1. Batteries for Electric Vehicles, D.A.J. Rand, R. Woods, R.M. Dell., John Wiley & Sons Inc. 2. Electric and Hybrid Vehicles: Design Fundamentals, Iqbal Husain, CRC Press 3. Electric Vehicle Technology Explained, James Larmenier, John Lowry, John Wiley & Sons

PH4108

Photovoltaics: Concepts and Applications

Course Number	PH4108
Course Credit (L-T- P-C)	3-0-0-3
Course Title	Photovoltaics: Concepts and Applications
Learning Mode	Physical Presence in Class Room
Learning Objectives	Alternative energy sources have always been a core area of significant importance since long. Recent focus on harnessing natural energy from the Sun, has necessitated teaching of relevant course at undergraduate level to create talent pool to meet industry demand. It aims to impart; 1. Knowledge pertaining to solar energy harnessing conditions 2. Learning relevant to physics of photovoltaic cells. 3. Training and skill relevant for design, processing, fabrication, testing and installation of photovoltaic cells, i.e.; end to end industry skill.
Course Outline	Module-1: An introduction to different sources of energy with its implications and alternative solutions, energy balance of the Sun and optimal conditions for harnessing solar energy, efficient design to entrap solar energy, a state of-the-art review of solar photovoltaic cells. Module-2: Semiconductor fundamentals, drift, diffusion and charge transport, photon emission and absorption, PN junction design and control parameters, Junction solar cell configuration – design, fabrication, analysis and efficiency improvement considerations for efficient solar cells. Module-3: Silicon based solar cell technology - monocrystalline, polycrystalline, amorphous and thin film Si solar cells, Process form sand to Silicon and Silicon to Wafer, Cell design and fabrication process, Multi-junction Si solar cells. Module-4: Non-Si solar cell technology, its challenges and advancements, an introduction to protocols for solar cell installation.
Learning Outcome	The learners of the course would be ready with knowledge to; (a) harness solar energy and technologically competent to implement the technology and (b) fulfil emerging industry and R & D institution demand for technologically skilled workforce.
Assessment Method	Class test and Quiz/Assignment (20%), MSE: (30%), ESE: (50%)
Suggested Readings:	1. Solar Photovoltaics: Fundamentals, Technologies and Applications (2^nd ed.), C. S. Solanki, Prentice Hall of India 2. Solar Cell Device Physics, Stephen Fonash (2^nd ed.), Academic Press 3. Principles of Solar Cells, LEDs and Diodes, Adrian Kitai, Wiley

Minor in Quantum Technology

Sl. No.

Subject Code

Subject

EP2101

Introductory Statistical Mechanics

Course Number

EP2101

Course Credit

3-1-0-4

Course Title

Quantum Physics

Learning Mode

Lectures, Tutorials and Assignments

Learning Objectives

Complies with Program Goals 1,2 and 3

Course Description

Course Outline

Engineering applications: devices based on quantum principles such as tunnel diode, single electron transistor, MRI and NMR, SEM, TEM and SPM.

Learning Outcome

Complies with PLO 1a, 1b, 2a and 3a

Assessment Method

Assignments, Quizzes, Seminar, Mid-semester examination, End-semester examination

Suggested Readings:

Textbooks:

1. A. Beiser, Concepts of Modern Physics, Tata McGraw Hill, 2020

2. Eisberg and Resnick

3. Introduction to Quantum Mechanics (2nd edn) by D. J. Griffiths, Prentice Hall (2004).

Reference books:

1. Quantum Mechanics, Powell and Craseman

1. Mastering quantum mechanics, Barton Zwiebach, MIT Press, 2022

EP2204

Course Number	EP2204
Course Credit (L-T-P-C)	2-1-0-3
Course Title	Introductory Statistical Mechanics
Learning Mode	Lectures and Tutorials
Learning Objectives	Complies with Program Goals 1, 2 and 3
Course Description	Equips the students with the techniques in Statistical Physics and allows them to apply these techniques to wide variety of problems in Physics
Course Content	Random walk, motivation for Statistical Mechanics; Phase space; Postulates of Statistical Physics; Ergodicity; Microcanonical, canonical and grand-canonical ensembles approach with examples; Partition functions, examples; real gases; Ising model; Quantum statistics: Bosonic and Fermionic gases; Bose-Einstein Condensation; Phases and phase transitions, Ehrenfest criteria, order-parameters, liquid Helium as example; Shannon entropy and other entropy measures, applications in information science
Learning Outcome	Complies with PLO 1(a), 1(b), 2(a) and 3
Assessment Method	Assignments, Quizzes, Mid-semester examination and End-semester examination
Suggested Readings:	Textbooks: 1. R. K. Pathria and Paul D. Beale, Statistical Mechanics (Elsevier, 4th Edition, 2021). 2. D. Chandler, Introduction to Modern Statistical Physics (Oxford University Press, 1987). 3. W. Krauth, Statistical Mechanics: Algorithms and Computations (Oxford Masters Series in Physics, 2006). References: 1. F. Mandl, Statistical Physics (Wiley-Blackwell, ELBS Edition, 1988). 2. F.Reif, Fundamentals of Statistical and Thermal Physics (Berkeley Physics Course - Vol.5., 2017). 3. M.Pilschke and B.Bergerson, Equilibrium Statistical Physics, (World Scientific, 1994). 4. B. P. Agarwal ad M. Eisner, Statistical Mechanics, (Wiley Eastern Limited, 1988). 5. K.Huang, Introduction to Statistical Physics (Chapman and Hall/CRC, 2nd Edition, 2009). 6. D. Chowdhury, D. Stauffer, Principles of Equilibrium Statistical Mechanics, Wiley-Vch, 2000

EP3101

Computational Techniques

Course Number	EP3101
Course Credit	2–0–3–3.5
Course Title	Computational Techniques
Learning Mode	Class lectures, tutorials, assignments, discussions.
Learning Objectives	Complies with Program Goals 1, 2 and 3
Course Description	This course will train students in various numerical methods and techniques required for solving various physics and engineering problems numerically
Course Outline	Preliminaries of Computing; Roots of Non Linear Equations and solution of system of Linear Equations:- Fixed-point iteration, Bisection, Secant, Regula-Falsi method, Newton Raphson method, Gauss Elimination method by pivoting, Gauss – Jordan method, Gauss – Seidel method, Relaxation method, Convergence of iteration methods, LU and Cholesky decomposition. Interpolation and approximations:-Lagrange and Newton interpolation, Spline interpolation, Rational approximations, Curve fitting: Least square method, Numerical Integration:-Newton-Cote's rule, Gaussian quadrature, Monte-Carlo technique, Numerical Solution of Ordinary a Differential Equations:-Taylor series method, Runge-Kutta methods.
Learning Outcome	Complies with PLO 1(a), 1(b), 2(a) and 3
Assessment Method	Assignments, Quizzes, lab, Mid-semester examination and End-semester examination
Suggested Readings:	Text Books: W. H. Press, S. A. Teukolsky, W T. Vetterling and B. P. Flannery, Numerical Recipes in C: The Art of Scientific Programming, 2nd Ed, Cambridge University Press, 1997 C. F. Gerald and P. O. Wheatley, Applied Numerical Analysis, Pearson Education India; 7 Ed, 2007. S. S. Sastry, Introductory Methods of Numerical Analysis, PHI learning Pvt. Ltd., 5th Ed, 2012. M. K. Jain, S. R. K. Iyengar and R. K. Jain, Numerical Methods for Scientific and Engineering Computation, 6th Edition, New Age International (P) Ltd., 2014. Reference Books: E. Kreyszig, Advanced Engineering Mathematics, 9th Edition, Wiley, 2005. B. S. Grewal, Higher Engineering Mathematics, 43rd Edition, Khanna Publishers, 2014. Y. Kanetkar, Let us C, 13th edition, BPB publication 2013. Programming in ANSI C, Tata McGraw-Hill Education, 2008. Programming with C (Schaum's Outlines Series), McGraw Hill Education (India) Private Limited; 3rdEd, 2010.

3.5

PH3209

Quantum Computation

Course Number	PH3209
Course Title	Quantum Computation
Course Credit (L-T-P-C)	2-1-0-3
Learning Mode	Lectures & Tutorials
Learning Objectives	Quantum Computing is one of the fastest growing topics for research, development and industry. A number of insightful questions arise in the mind of students, such as - what is quantum computer? Why is it required? How does it look like? How can these be implemented? When will we get quantum computer for personal use? – etc. This course is intended to provide answers to all these questions in the level of undergraduate students.
Course Contents	Fundamental idea of quantum computing: Moore’s Law; Operators and matrices: Pauli matrices, inner, outer and tensor products, Unitarity; (2 lectures+1 tutorial) Quantum nonlocal superposition; Quantum entanglement; Basics of quantum measurements (General, Projective, POVM); From Bits to Qubits with examples, Bloch sphere, Single and multiple qubit logic gates, Universal quantum gates; Basic quantum circuits, Quantum Teleportation protocol; Quantum Fourier Transform, Quantum phase estimation, Factorization algorithm; (8 lectures+4 tutorials) Relevant knowledge of Quantum Optics & Quantum information; Physical realizations of Quantum computers: quantized harmonic oscillator; Density operator, ensemble of quantum states; (5 lectures+2 tutorials) Fundamentals of various quantum computers: semiconductor based quantum computer, photonic based quantum computer, cold- and ultracold-atom based quantum computers, use of cavity-QED; (7 lectures+3 tutorials) Applications of quantum computing in other fields: ideas of quantum communication & quantum security etc; Practical examples with Quantum Simulators; AI and Quantum computing; State-of-the-art quantum computation and Future outlook. (8 lectures+4 tutorials)
Learning Outcome	The course will build up the basic foundation required for knowing the working of a quantum computer, quantum information processing through quantum circuits, examples with well-known quantum algorithms, various quantum computers, important applications and future outlook. The students will also be given overview of the Indian and global companies and their contributions in quantum computing. It will impart the motivation to students for further applying their knowledge to the progress of the field in both R&D and industry.
Assessment Method	Assignments, Quizzes, MSE and ESE
Suggested readings	Textbooks: Quantum Computation and Quantum Information, M. A. Nielsen and I. L. Chuang, Cambridge University Press, South Asian Edition, 10^th Edition. An Introduction to Quantum Computing, Phillip Kaye, R. Laflamme, M. Mosca, Oxford University Press, 2007. Preskill, John. Lecture notes for physics 229: Quantum information and computation. California Institute of Technology 16.1 (1998): 1-8. Nakahara, Mikio, and Tetsuo Ohmi. Quantum computing: from linear algebra to physical realizations. CRC press, 2008. Mermin, N. David. Quantum computer science: an introduction. Cambridge University Press, 2007. References: Quantum Supremacy, Michio Kaku, Allen Lane-Penguin publisher, 2023. McMahon, David. Quantum computing explained. John Wiley & Sons, 2007. Riley Tipton Perry, Quantum Computing from the Ground Up, World Scientific Publishing Ltd (2012). Scott Aaronson, Quantum Computing since Democritus, Cambridge, 2013. Bouwmeester, D., Ekert, A. and Zeilinger, A., (2000), The Physics of Quantum Information, Reprint edition, Springer Berlin Heidelberg. Barenco, Adriano, et al. Elementary gates for quantum computation. Physical review A 52.5 (1995): 3457. Quantum Computing: Lecture Notes, Ronald de Wolf, QuSoft, CWI and University of Amsterdam, arXiv:1907.09415v3, 2022.

PH4107

Optical Quantum Communication

Course Number	PH4107
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Optical Quantum Communication
Learning Mode	Lectures
Learning Objectives	Complies with Program Goals 1, 2 and 3
Course Description	This course provides engineering students to learn modern cutting edge optical quantum communication techniques which are very essential to pursue for advanced research and scientific jobs in the area of quantum communication and engineering applications. The course will also examine current research trends and potential future developments in the field of optical quantum communication.
Course Outline	classical v/s quantum information, quantum bits (qubits) and quantum gates, quantum entanglement and its properties, single-photon sources, entangled photon sources, photons as information carriers, polarization qubits, qubit generation and propagation, Bell state measurements, quantum repeaters, various protocols for quantum memory and its efficiency, implementation of quantum memory nodes, long distance quantum communication using quantum repeaters, quantum networks, multi-node quantum communication, ground-based and space-based quantum networks, entanglement distribution and quantum internet, Recent progress in quantum photonic chips for quantum communication and internet.
Learning Outcome	Complies with PLO 1(a), 1(b), 2(b) and 3
Assessment Method	Exams, Quiz and Assignment
Suggested Readings:	Textbooks: 1. Gianfranco Cariolaro,Quantum Communications, Springer (2015). 2. P. Kok and B. W. Lovett, Introduction to Optical Quantum Information Processing, Cambridge university press. 3. Peter Lambropoulos, David Petrosyan, Fundamentals of Quantum Optics and Quantum Information, Springer (2007) 4. Ivan B. Djordjevic, Quantum Communication, Quantum Networks, and Quantum Sensing, Elsevier (2022) References: L. Mandel, and E. Wolf. Optical Coherence and Quantum Optics, Cambridge University Press. W. H. Louisell, Quantum Statistical Properties of Radiation, McGraw-Hill. 3. D. Bouwmeester, A. K. Ekert, and A. Zeilinger, eds. The Physics of Quantum Information, Springer 4. Serge Haroche, Jean-Michel Raimond, Exploring the Quantum: Atoms, Cavities, and Photons, Oxford Academic (2006)

M. Tech. in Artificial Intelligence

Program Learning Objectives:	Program Learning Outcomes (PLO):
Program Goal 1: Fundamental Understanding: Establish a robust foundation in Artificial Intelligence (AI) and Data Science (DS) principles, theories, and methodologies.	Program Learning Outcome 1 (PLO-1): Students will acquire a deep understanding of the core concepts, algorithms, and tools used in AI, machine learning, deep learning, and data science. Program Learning Outcome 2 (PLO-2): Students will develop the ability to analyze and interpret complex data, using statistical and computational techniques to extract meaningful insights.
Program Goal 2: Basic Training for Research and Innovation: To equip students with the skills necessary to conduct cutting-edge research and innovate in the fields of AI and Data Science.	Program Learning Outcome 3 (PLO-3): Students will be able to innovate by developing new machine learning/ deep learning models, and systems in AI and DS, contributing to advancements in the field.
Program Goal 3: Technical Skill Proficiency: To enhance technical skills for developing AI and data-driven solutions for industry and academia.	Program Learning Outcome 4 (PLO-4): Students will demonstrate proficiency in programming, data management, and the use of AI and DS tools and frameworks in various fields including computer vision, natural language processing. Program Learning Outcome 5 (PLO-5): Students will be able to design and implement AI and DS solutions that are efficient, scalable, and reliable.
Program Goal 4: Communication and Collaboration: To develop communication and teamwork skills essential for professional success in AI and DS.	Program Learning Outcome 6 (PLO-6): Students will learn to effectively communicate AI and DS concepts, findings, and solutions to both technical and non-technical audiences.
Program Goal 5: Ethics and Social Responsibility: To understand the ethical, social, and environmental implications of AI and Data Science.	Program Learning Outcome 7 (PLO-7): Students will develop an awareness of ethical issues in AI and DS, such as data privacy, algorithmic bias, and the societal impacts of AI technologies. Program Learning Outcome 8 (PLO-8): Students will be able to apply ethical principles and responsible practices in the development and deployment of AI and DS solutions.

Design and Analysis of Algorithms

Sl. No.

Subject Code

SEMESTER I

CS5101

Course number	CS5101
Course Credit (L-T-P-C)	3-1-0-4
Course Title	Design and Analysis of Algorithms
Learning Mode	offline
Learning Objectives	The objective of this course is to equip students with a solid understanding of data structures and algorithms, enabling them to design, analyze, and implement efficient algorithms to solve complex computational problems. The course covers fundamental topics such as data structures, complexity analysis, sorting and searching techniques, problem-solving strategies, graph algorithms. By the end of the course, students will have developed the skills to critically analyze algorithm efficiency and apply advanced algorithms in practical scenarios.
Course Description	This course will provide understanding of aadvanced methods to solve problems on computers. It will also provide an overview to analyze those theoretically.
Course Outline	Fibonacci heap, unionfind, splay trees. Amortized complexity analysis Randomized algorithms Reducibility between problems and NPcompleteness: discussion of different NP-complete problems like satisfiability, clique, vertex cover, independent set, Hamiltonian cycle, TSP, knapsack, set cover, bin packing, etc. Backtracking, branch and bound Approximation algorithms: Constant ratio approximation algorithms. Application areas(i)Geometric algorithms: convex hulls, nearest neighbor, Voronoi diagram, etc.(ii)Algebraic and number-theoretic algorithms: FFT, primality testing, etc.(iii)Graph algorithms: network flows, matching, etc.(iv)Optimization techniques: linear programming
Learning Outcome	By the end of this course, students will be able to solve problems that are computationally intractable
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Reading:

Mark Allen Weiss, "Data Structures and Algorithms in C++", Addison Wesley, 2003.
Adam Drozdek, "Data Structures and Algorithms in C++", Brooks and Cole, 2001.
Aho, Hopcroft and Ullmann, "Data structures and Algorithm", Addison Welsey, 1984.
Introduction to Algorithms Book by Charles E. Leiserson, Clifford Stein, Ronald Rivest, and Thomas H. Cormen
Sanjoy Dasgupta, Christos H. Papadimitriou and Umesh V. Vazirani, Algorithms, Tata McGraw-Hill, 2008.
Steven Skiena, The Algorithm Design Manual, Springer
Jon Kleinberg and Éva Tardos, Algorithm Design, Pearson, 2005.
Robert Sedgewick and Kevin Wayne, Algorithms, fourth edition, Addison Wesley, 2011.
Udi Manber, Algorithms – A Creative Approach, Addison-Wesley, Reading, MA, 1989.
Tim Roughgarden, Algorithms Illuminated

CS5102

Foundations of Computer Systems

Course Number	CS5102
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Foundations of Computer Systems
Learning Mode	Offline
Learning Objective	The objective of the course is to provide a conceptual and theoretical understanding of computer architecture and operating systems.
Course Description	Foundations of computer systems is a review of two fundamental subjects of computer science viz., computer architecture and operating systems.
Course Outline	Computer architecture: Performance measures, Memory Location and Operations, Addressing Modes, Instruction Set, A Simple Machine, Instruction Mnemonics and Syntax, Machine Language Program, Assembly Language Program with examples. Processing Unit Design: Registers, Datapath, CPU instruction cycle, Instructions and Micro-operations in different bus architectures, Interrupt handling, Control Unit Design: Control signals, Hardwired Control unit design, Microprogram Control unit design. Pipelining and parallel processing, Pipeline performance measure, pipeline architecture, pipeline stall (due to instruction dependancy and data dependancy), Methods to reduce pipeline stall. RISC and CISC paradigms, I/O Transfer techniques, Memory organization: hierarchical memory systems, cache memories, virtual memory. Operating systems: Process states, PCB, Fork, exec system call, Threads, Process scheduling, Concurrent processes, Monitors, Process Synchronization, Producer Consumer Problem, Critical section, semaphore, Various process synchronization problems. Deadlock, Resource Allocation Graph, Deadlock prevention, Deadlock Avoidance: Banker’s Algorithm and Safety Algorithm. Memory management techniques, Allocation techniques, Paging, Page Replacement Algorithms, Numericals.
Learning Outcome	This course will revisit two fundamental subjects of computer science viz., computer architecture and operating systems, thereby enabling the students to pursue more advanced problems in computer science based on these topics.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

CS5103

Computing Lab-1

Course Number	CS5103
Course Credit (L-T-P-C)	0-1-2-2
Course Title	Computing Lab-1
Learning Mode	Offline
Learning Objective	The course aims to develop students' analytical and practical skills in designing efficient algorithms and understanding the complexities of operating systems. Students will learn to analyze the efficiency of algorithms, understand various algorithmic strategies, and implement them to solve complex problems. In the Operating Systems segment, students will explore the core concepts, including process management, memory management, file systems, and concurrency. By the end of the course, students will be proficient in both designing algorithms and managing operating system resources, preparing them for advanced studies and professional careers in computer science.
Course Description	This lab course is structured to provide an in-depth understanding of both algorithm design and operating system concepts. The Design and Analysis of Algorithms section covers fundamental topics such as sorting, searching, dynamic programming, greedy algorithms, and graph algorithms. Students will learn to critically evaluate the efficiency and applicability of different algorithms. The Operating Systems section delves into process scheduling, memory management techniques, file systems, and synchronization mechanisms. Through a series of hands-on labs and projects, students will apply theoretical knowledge to practical scenarios, reinforcing their understanding and problem-solving abilities.
Course Outline	The course begins with an introduction to basic algorithmic concepts and techniques, progressing through various algorithm design paradigms such as divide-and-conquer, dynamic programming, and greedy methods. Concurrently, students will explore the architecture and functionalities of operating systems, starting with process management and memory management, then advancing to file systems, I/O systems, and concurrency control. The course will include practical lab sessions where students will implement and test algorithms, as well as design and manage operating system components. The course culminates in a comprehensive project that integrates both algorithm design and operating system principles to solve complex computing problems.
Learning Outcome	Upon completing this course, students will have a solid grasp of both algorithm design and analysis, as well as operating system functionalities. They will be able to design, analyze, and implement efficient algorithms to address computational problems. Additionally, students will gain practical experience in managing operating system resources, including process scheduling, memory management, and file systems. This dual expertise will equip students with the skills necessary for tackling advanced topics in computer science and pursuing careers in software development, system administration, and research.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested readings:

"Introduction to Algorithms" by Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, and Clifford Stein, 4th Edition
"Algorithms" by Robert Sedgewick and Kevin Wayne, 4th Edition
"Operating System Concepts" by Abraham Silberschatz, Peter B. Galvin, and Greg Gagne, 10th Edition
"Modern Operating Systems" by Andrew S. Tanenbaum and Herbert Bos, 4th Edition
"The Algorithm Design Manual" by Steven S. Skiena, 3rd Edition

CS61XX

DE-I

CS61XX

DE-II

HS5111

Technical Writing and Soft Skill

XX61PQ

IDE-I

TOTAL

IDE (Inter Disciplinary electives) in the curriculum aims to create multitasking professionals/ scientists with learning opportunities for students across disciplines/aptitude of their choice by opting level (5 or 6) electives, as appropriate, listed in the approved curriculum.

Advanced Artificial Intelligence

Sl. No.

Subject Code

SEMESTER II

CS5201

Course Number	CS5201
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Advanced Artificial Intelligence
Learning Mode	Offline
Learning Objectives	To understand the principles of Artificial Intelligence and the nature of intelligent agents. To learn various problem-solving techniques, including informed search and exploration. To gain proficiency in handling constraint satisfaction problems and adversarial search. To develop a solid foundation in knowledge representation, first-order logic, and propositional logic. To learn to plan and act effectively in real-world AI applications. To grasp the concepts of uncertain knowledge and probabilistic reasoning. To make informed decisions using simple and complex decision-making models. To acquire skills in learning from observations and applying statistical learning methods. To explore advanced AI techniques and their practical applications.
Course Description	This course offers an in-depth exploration of advanced concepts in Artificial Intelligence (AI). Students will delve into the theoretical underpinnings and practical applications of AI, examining intelligent agents, the nature of environments, and advanced problem-solving techniques. The curriculum covers informed search and exploration, constraint satisfaction problems, adversarial search, and knowledge representation. Students will also explore reasoning with first-order and propositional logic, planning and acting in real-world scenarios, and handling uncertainty through probabilistic reasoning. The course concludes with statistical learning methods and advanced AI techniques, providing a comprehensive understanding of AI's capabilities and applications.
Course Outline	Introduction and motivation Artificial Intelligence, intelligent agents, nature of environments, Problem-solving by searching, informed search and exploration, constraint satisfaction problem, adversarial search, Knowledge and reasoning, first order logic, inference and propositional logic, knowledge representation, Planning and acting in real world of AI agent Uncertain knowledge and reasoning, uncertainty, probabilistic reasoning, making simple and complex decisions Learning from observations and knowledge, statistical learning methods, Some advanced techniques of AI and its applications
Learning Outcome	Upon completing this course, students will be able to: Analyze and implement intelligent agents in various environments. Apply informed search techniques to solve complex problems. Formulate and solve constraint satisfaction problems and engage in adversarial search strategies. Represent and reason with knowledge using first-order and propositional logic. Develop and execute plans in real-world AI scenarios. Manage uncertainty and employ probabilistic reasoning to make sound decisions. Utilize statistical learning methods to derive insights from data. Implement advanced AI techniques in real-world applications. Demonstrate a comprehensive understanding of advanced AI concepts and their implications.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Reading:

Russell, S. J., & Norvig, P. (2016). Artificial intelligence: A modern approach. Pearson.

Poole, D. L., & Mackworth, A. K. (2010). Artificial Intelligence: foundations of computational agents. Cambridge University Press.
Hastie, T., Tibshirani, R., Friedman, J. H., & Friedman, J. H. (2009). The elements of statistical learning: data mining, inference, and prediction (Vol. 2, pp. 1-758). New York: Springer.

CS5203

Natural Language Processing

Course Number	CS5203
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Natural Language Processing
Learning Mode	Offline
Learning Objectives	The objectives of this course are to provide students with a comprehensive understanding of natural language processing (NLP) techniques and their applications. Students will learn the fundamentals of text processing, word vector representations, and advanced language models. The course aims to equip students with the skills to implement and evaluate various NLP tasks, such as part-of-speech tagging, named entity recognition, opinion mining, and machine translation. Additionally, students will explore advanced topics like language generation, summarization, and machine learning-based language processing methods. By the end of the course, students will be prepared to apply NLP techniques to real-world problems and contribute to the development of intelligent language-based systems.
Course Description	This course offers an in-depth exploration of natural language processing (NLP), covering both foundational and advanced topics. Students will begin with an introduction to the scope and applications of NLP, followed by essential text processing techniques. The course will delve into word vector representations, including word2vec and GloVe, and explore advanced methods for language models. Key NLP tasks such as part-of-speech tagging, named entity recognition, opinion mining, sentence classification, machine translation, question answering, language generation, and summarization will be covered. Emphasis will be placed on both rule-based and machine learning-based approaches to language processing. The course is designed to provide practical experience and theoretical knowledge, preparing students for advanced study or professional work in the field of NLP.
Course Outline	· Introduction and scope of the course, · Text Processing · Simple Word Vector representations: word2vec, GloVe, · Word Representations in Vector Space, Advanced word vector representations for language models, · PoS tagging and named entity recognition, · Language modeling, Opinion Mining, · Sentence classification, Machine Translation, Question Answering, · Language Generation and Summarization, · Machine learning-based language processing
Learning Outcome	By the end of this course, students will be able to: · Explain the fundamental concepts and scope of natural language processing. · Describe basic and advanced text processing techniques. · Discuss word vector representations like word2vec and GloVe to NLP tasks. · Interpret part-of-speech tagging and named entity recognition with proficiency. · Explain language models and perform opinion mining. · Execute sentence classification, machine translation, and question answering tasks. · Generate and summarize language using advanced techniques. · Execute machine learning methods for various NLP applications. · Analyze and evaluate the performance of different NLP models and techniques.
Assessment Method	Internal = 20%; Mid-semester = 30%; End semester = 50%

Suggested Reading

Daniel Jurafsky and James H. Martin, "Speech and Language Processing," 3rd Edition, Prentice Hall, 2020.
Christopher D. Manning, Hinrich Schütze, "Foundations of Statistical Natural Language Processing," 1st Edition, MIT Press, 1999.
Jacob Eisenstein, "Introduction to Natural Language Processing," 1st Edition, MIT Press, 2019.
Yoav Goldberg, "Neural Network Methods for Natural Language Processing," 1st Edition, Morgan & Claypool Publishers, 2017.
Steven Bird, Ewan Klein, and Edward Loper, "Natural Language Processing with Python," 1st Edition, O'Reilly Media, 2009.

CS5205

Advanced Artificial Intelligence Lab

Course Number	CS5205
Course Credit (L-T-P-C)	0-1-2-2
Course Title	Advanced Artificial Intelligence Lab
Learning Mode	Offline
Learning Objectives	To implement the techniques and algorithms learnt in Advance Artificial Intelligence theory To analyze advanced AI techniques and their practical applications.
Course Description	This course offers an in-depth exploration and practical implementation of advanced concepts in Artificial Intelligence.
Course Outline	Practical implementation of algorithms and techniques learnt in Advance Artificial Intelligence theory
Learning Outcome	Upon completing this course, students will be able to: Analyze and practically implement the advanced concepts in Artificial Intelligence. Demonstrate a comprehensive understanding of advanced AI concepts and their implications in real world.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

CS62XX

DE-III

CS62XX

DE-IV

CS62XX

DE-V

RM6201

Course Number	RM6201
Course Credit (L-T-P-C)	3-1-0-4
Course Title	Research Methodology
Learning Mode	Lectures
Learning Objectives	The objective of the course is to train student about the modelling of scalar and multi-objective nonlinear programming problems and various classical and numerical optimization techniques and algorithms to solve these problems
Course Description	Advanced Optimization Techniques, as a subject for postgraduate and PhD students, provides the knowledge of various models of nonlinear optimization problems and different algorithms to solve such problems with its applications in various problems arising in economics, science and engineering.
Course Content	Module I (6 lecture hours) – Research method fundamentals: Definition, characteristics and types, basic research terminology, an overview of research method concepts, research methods vs. method methodology, role of information and communication technology (ICT) in research, Nature and scope of research, information based decision making and source of knowledge. The research process; basic approaches and terminologies used in research. Defining research problem and hypotheses framing to prepare a research plan. Module II (5 lecture hours) - Research problem visualization and conceptualization: Significance of literature survey in identification of a research problem from reliable sources and critical review, identifying technical gaps and contemporary challenges from literature review and research databases, development of working hypothesis, defining and formulating the research problems, problem selection, necessity of defining the problem and conceiving the solution approach and methods. Module III (5 lecture hours) - Research design and data analysis: Research design – basic principles, need of research design and data classification – primary and secondary, features of good design, important concepts relating to research design, observation and facts, validation methods, observation and collection of data, methods of data collection, sampling methods, data processing and analysis, hypothesis testing, generalization, analysis, reliability, interpretation and presentation. Module IV (16 lecture hours) - Qualitative and quantitative analysis: Qualitative Research Plan and designs, Meaning and types of Sampling, Tools of qualitative data Collection; observation depth Interview, focus group discussion, Data editing, processing & categorization, qualitative data analysis, Fundamentals of statistical methods, parametric and nonparametric techniques, test of significance, variables, conjecture, hypothesis, measurement, types of data and scales, sample and sampling techniques, probability and distributions, hypothesis testing, level of significance and confidence interval, t-test, ANOVA, correlation, regression analysis, error analysis, research data analysis and evaluation using software tools (e.g.: MS Excel, SPSS, Statistical, R, etc.). Module V (10 lecture hours) – Principled research: Ethics in research and Ethical dilemma, affiliation and conflict of interest; Publishing and sharing research, Plagiarism and its fallout (case studies), Internet research ethics, data protection and intellectual property rights (IPR) – patent survey, patentability, patent laws and IPR filing process.
Learning Outcome	On successful completion of the course, students should be able to: 1. Understand the terminology and basic concepts of various kinds of nonlinear optimization problems. 2. Develop the understanding about different solution methods to solve nonlinear Programing problems. 3. Apply and differentiate the need and importance of various algorithms to solve scalar and multi-objective optimization problems. 4. Employ programming languages like MATLAB/Python to solve nonlinear programing problems. 5. Model and solve several problems arising in science and engineering as a nonlinear optimization problem.
Assessment Method	Quiz /Assignment/ Project / MSE / ESE

Textbooks & Reference Books:

R. Kothari, Research methodology: Methods and Techniques, 3rd Edn., New age International 2014.
Mark N K. Saunders, Adrian Thornhill, Phkip Lewis, “Research Methods for Studies, 3/c Pearson Education, 2010.
N. Krishnaswamy, apa iyer, siva kumar, m. Mathirajan, “Management Research Methodology”, Pearson Education, 2010.
Ranjit Kumar; “Research Methodology: A Step by Step Guide for Beginners; 2/e; Pearson Education, 2010.
Suresh C. Sinha, Anil K. Dhiman, ess ess, 2006 “Research Methodology” Panner Selvam.R. “Research Methodology”, Prentice Hall of India, New Delhi, 2004.
G. Thomas, Research methodology and scientific writing, Ane books, Delhi, 2015.
J. Ader and G. J. Mellenbergh, Research Methodology in the Social, Behavioural and Life Sciences Designs, Models and Methods, 3rd Edn., Sage Publications, London, 2000.

IK6201

IKS

TOTAL

Sl. No.	Subject Code	SEMESTER III	L	T	P	C
1.	CS6198	Summer Internship/Mini Project*	0	0	12	3
2.	CS6199	Project I**	0	0	30	15
	TOTAL		0	0	42	18

*Note: Summer Internship (Credit based)

(i) Summer internship (*) period of at least 60 days’ (8 weeks) duration begins in the intervening summer vacation between Semester II and III. It may be pursued in industry / R&D / Academic Institutions including IIT Patna. The evaluation would comprise combined grading based on host supervisor evaluation, project internship report after plagiarism check and seminar presentation at the Department (DAPC to coordinate) with equal weightage of each of the three components stated herein.

(ii) Further, on return from 60 days internship, students will be evaluated for internship work through combined grading based on host supervisor evaluation, project internship report after plagiarism check, and presentation evaluation by the parent department with equal weightage of each component.

** Note: M. Tech. Project outside the Institute: A project-based internship may be permitted in industries/academia (outside IITP) in 3rd or 4th semester in accordance with academic regulations. In the III^rd Semester, students can opt for a semester long M. Tech. project subject to confirmation from an Institution of repute for research project, on the assigned topic at any external Institution (Industry / R&D lab / Academic Institutions) based on recommendation of the DAPC provided:

(i.) The project topic is well defined in objective, methodology and expected outcome through an abstract and statement of the student pertaining to expertise with the proposed supervisor of the host institution and consent of the faculty member from the concerned department at IIT Patna as joint supervisor.

(ii.) The consent of both the supervisors (external and institutional) on project topic is obtained a priori and forwarded to the academic section through DAPC for approval by the competent authority for office record in the personal file of the candidate.

(iii.) Confidentiality and Non-Disclosure Agreement (NDA) between the two organizations with clarity on intellectual property rights (IPR) must be executed prior to initiating the semester long project assignment and committing the same to external organization and vice versa.

(iv.) The evaluation in each semester at Institute would be mandatory and the report from Industry Supervisor will be given due weightage as defined in the Academic Regulation. Further, the final assessment of the project work on completion will be done with equal weightage for assessment of the host and Institute supervisors, project report after plagiarism check. The award of grade would comprise combined assessment based on host supervisor evaluation, project report quality and seminar presentation at the Department (DAPC to coordinate) with equal weightage of each of the components stated herein.

(v.) In case of poor progress of work and / or no contribution from external supervisor, the student need to revert back to the Institute essentially to fulfill the completion of M. Tech. project as envisaged at the time of project allotment. However, the recommendation of DAPC based on progress report and presentation would be mandatory for a final decision by the competent authority.

Advanced Blockchain Technology

Department Elective – I

Department Elective – I

Sl. No.

Subject Code

Subject

CS6101

Course Number	CS6101
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Advanced Blockchain Technology
Learning Mode	Offline
Learning Objectives	The objective of this course is to cover a number of popular blockchain platforms and smart contract language paradigms. This course makes the learners familiar with various (a) research challenges, such as interoperability, scalability, security vulnerabilities, functional/non- functional correctness proof, etc., and their possible solutions, (b) synergizing machine Learning and blockchain, and (c) development of secure blockchain-based decentralized applications using Ethereum and Hyperledger.
Course Description	This course will start with a quick introductory background of blockchain technology and its working principle. The primarily focus of this course is to provide a detailed information about the state-of-the-art blockchain platforms and their supported smart contract languages. In particular, syntax, semantics, and paradigms of various smart contract languages will be discussed. In this perspective, blockchain-oriented software development life cycle and decentralized application development will be discussed. Following this, the course will cover two important directions: addressing various research challenges in blockchain and AI/machine learning for blockchain (and vice-versa).
Course Outline	Introduction to Blockchain Technology: A Quick Tour Different Blockchain Platforms and Smart Contract Languages: Bitcoin, Ethereum, Hyperledger, Solidity, GoLang. Consensus Mechanisms: PoW Vs. PoS, Alternative Consensus Synergizing Machine Learning and Blockchain: Transaction Analysis, Smart Contract Code Analysis, AI-driven Blockchain Applications, Blockchain for AI, Decentralized Learning. Research Challenges in Blockchain: Scalability, Interoperability, Security, Privacy, Decentralized Identity, Smart Contract Vulnerabilities and Detection, Real case studies on developing DApps, Metaverse, Some ongoing relevant research topics.
Learning Outcome	· Gain proficiency in blockchain technology and software engineering of developing decentralized applications. · An overview of the state-of-the-art blockchain platforms and their supported smart contract languages. · Know about the paradigms of various smart contract languages. · Understand how AI/machine learning brings benefits to blockchain technology and vice-versa. · Identify various research challenges and opportunities, such as scalability, interoperability
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

CS6102

Advanced Cyber Security

Course Number	CS6102
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Advanced Cyber Security
Learning Mode	Offline
Learning Objectives	To have a clear understanding of security and privacy issues in various aspects of computing, including: Programs, Operating systems, Networks, Web Applications
Course Description	The course covers. security and privacy issues in various aspects of computing, including: Programs, Operating systems, Networks, Web Applications
Course Outline	Introduction to Computer Security and Privacy: security and privacy; types of threats and attacks; methods of defense Basics of cryptography, Authentication & key agreement, Authorization and access control Program Security: nonmalicious program errors; vulnerabilities in code, Secure programs; malicious code; Malware detection Internet security: IPSEC, TLS, SSh, Email security Wireless security: WEP, WPA, Bluetooth security, Web Security: XSS attack, CSRF attack, SQL Injection, DoS attack & defense
Learning Outcome	After completion of this course a student will have · Understanding of security issues in computer and networks, · Understanding and analysis of internet security protocols · Understanding and analysis of web security protocols
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Textbooks:

Computer Security: Principles and Practice: Dr. William Stallings and Lawrie Brown, Pearson
O'Reilly Web Application Security by Andrew Hoffman

CS6103

Advanced Pattern Recognition

Course Number	CS6103
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Advanced Pattern Recognition
Learning Mode	Offline
Learning Objectives	This course aims to help the students (a) Understand the advanced topics of pattern recognition, including classification and clustering methods. (b) To understand the advanced topics of feature selection, multi-label classification. (c) Apply advanced pattern recognition algorithms to practical applications in image processing, speech recognition, and data mining.
Course Description	This course on advanced pattern recognition aims to equip students with the advanced topics of classification, clustering, and feature selection. By focusing on advanced topics, students will develop the ability to implement and evaluate various pattern recognition algorithms. Students will enhance their understanding of advanced topics of classification, clustering, statistical methods, and data preprocessing techniques through interactive lectures, exercises, and projects. Upon completion, students will be proficient in designing and applying advanced pattern recognition systems for applications such as image processing, text mining, speech recognition, and data mining, thereby enhancing their analytical and problem-solving capabilities in diverse domains.
Course Outline	Introduction and motivation of advanced pattern recognition Modern Classification Methods, Random fields, Pattern recognition based on multidimensional models Contextual classification, Hidden Markov models, Multi-classifier systems Advanced parameter estimation methods, Advanced Unsupervised classification, Modern methods of feature selection. Data normalization and invariants, Benchmarking. Analysis and synthesis of image information. Applications od pattern recognition in Text Processing and Healthcare.
Learning Outcome	· Mastery of advanced concepts in pattern recognition. · In-depth understanding of various advanced algorithms across different pattern recognition paradigms. · Comprehensive knowledge of advanced aspects of classification, clustering, feature selection, feature extraction, and projection techniques. · Ability to apply advanced pattern recognition algorithms to real-world projects
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Reading:

A. Devijver and J. Kittler, Pattern Recognition: A Statistical Approach, Prentice-Hall,1982.
Duda and P. Hart and D.G. Stork, Pattern Classification, J. Wiley, 2001.
Webb, Statistical Pattern Recognition, J. Wiley, 2002.
Theodoridis, K.Koutroumbas, Pattern Recognition, Elsevier, 2003.
Z. Li, Markov Random Field Modeling in Image Analysis, Springer, 2009.

Research papers will be provided on various topics

CS6104

Formal Methods in Program Analysis and Verification

Course Number	CS6104
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Formal Methods in Program Analysis and Verification
Learning Mode	Offline
Learning Objectives	Formal methods are mathematically rigorous techniques to facilitate in building high-confidence critical systems with stringent quality requirements, such as safety and security. It provides a systematic guidance for specification, development, and verification of software and hardware systems. Examples for such systems are banking software, avionics software, medical device software, software used to control industrial plants/cars, etc. This course will provide necessary background on formal methods and their role in software engineering practice. A range of formal methods will be introduced along with practical case studies of their use. Students will learn how these methods can be used to build reliable software, hardware, and security protocols.
Course Description	This course will start with the fundamentals of set theory, relation and function, lattice theory, propositional and predicate logic, and proof techniques. In order to demonstrate how to analyze and verify a software system, this course will discuss the following three formal approaches using suitable examples: (a) Abstract Interpretation Theory, (b) Temporal Logic and Model Checking, and (c) Deductive Reasoning. In this context, formalism of syntax and semantics of programming languages will be explained considering a simple imperative language WHILE. All these approaches will be illustrated using real-life examples, such as microwave oven, mutual exclusion problem, etc.
Course Outline	Introduction: Introduction to critical systems, Introduction to formal methods and its role, Dependability, Testing Vs. Verification Formal Syntax and Semantics: the WHILE Language, Syntax Vs. Semantics, Formal Program Semantics - Operational, Denotational, Axiomatic Formal Program Analysis: Program Slicing, Dataflow Analysis, Fixpoint Algorithm, Abstract Interpretation Framework Formal Program Verification: Deductive Reasoning; Predicate Abstraction and CEGAR, Temporal Logic and Model Checking, Role of some other formal methods in software engineering New Research Directions: Recent trends on the application of formal methods in Machine Learning and Blockchain Tools: Introduction to various state-of-the-art Analyzers and Verifiers (e.g., NuSMV, UPPAAL, SPIN, ASTREE, CBMC, etc.)
Learning Outcome	· Gain proficiency in formal methods and their role in critical systems. · Understanding formal tools and techniques for analysis and verification of software source codes. · Learning how to define semantics of a software formally, and how to abstract its semantics at different levels of precision in order to capture its run-time behavioral properties of interests. · Learning temporal logic to express system’s time-varying behaviors. · Applying automatic software verification tools based on model checking and deductive reasoning. · Hands-on experience with NuSMV, Uppaal, Z3 SMT solver, etc. · Application of formal methods in cutting edge research domains including Robotics, IoT, Blockchain Smart Contracts.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Readings:

Flemming Nielson, Hanne Nielson, Chris Hankin. Principles of Program Analysis, Springer, 1999.
Edmund M. Clarke, Orna Grumberg, Doron A. Peled. Model Checking, The MIT Press,
Glynn Winskel. The formal semantics of programming languages: an introduction, The MIT Press, 1993.
José Bacelar Almeida, Maria João Frade, Jorge Sousa Pinto, Simão Melo de Sousa. Rigorous Software Development: An Introduction to Program Verification. Springer- Verlag London, 2011
Recent Research Papers relevant to the

CS6105

Federated Learning

Course Number	CS6105
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Federated Learning
Learning Mode	Offline
Learning Objectives	This course aims to help the students (a) Grasp Foundational Concepts and Developments of Federated Learning (FL), and stay informed about current developments and emerging trends in the field. (b) Implement Privacy and Security Techniques including privacy-preserving machine learning (PPML), privacy-preserving gradient descent, and threat and security models to ensure data confidentiality and integrity in FL systems. (c) Mastering horizontal and vertical FL architectures (HFL, VFL) and algorithms, such as the federated averaging algorithm and its enhancements. (d) FL techniques to practical applications in computer vision, natural language processing (NLP), and reinforcement learning, demonstrating the practical benefits and addressing limitations in these domains.
Course Description	This course offers a comprehensive exploration of Federated Learning (FL), a cutting-edge approach to collaborative machine learning where models are trained across decentralized devices or servers holding local data samples. The course begins with an introduction to FL, defining its principles, categories, and current developments in the field. Students will delve into essential topics such as privacy-preserving techniques, including privacy-preserving machine learning (PPML) and secure machine learning methods, to ensure data security and confidentiality in distributed learning environments. The curriculum covers scalable distributed machine learning (DML) techniques tailored for FL, addressing challenges in model aggregation and performance across heterogeneous data sources. Key architectural paradigms like horizontal and vertical FL (HFL, VFL) will be explored, alongside algorithms such as federated averaging and advancements in optimization for FL scenarios. The course emphasizes practical applications of FL in domains like computer vision, natural language processing (NLP), and reinforcement learning, showcasing its utility and addressing real-world challenges.By the end of the course, students will have a deep understanding of FL principles, techniques, and applications. They will be equipped to design and implement secure, scalable, and privacy-aware machine learning solutions suitable for collaborative environments with distributed data sources.
Course Outline	Introduction to Federated Learning, Current Development in Federated Learning, Privacy-Preserving Machine Learning, Horizontal Federated Learning, Vertical Federated Learning.
Learning Outcome	· Understand the principles, definitions, and categories of federated learning · Apply various privacy-preserving machine learning · Design and improve federated learning algorithms, such as the federated averaging algorithm. · Utilize federated learning frameworks for practical applications in computer vision, natural language processing, reinforcement learning, and other areas
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Reading

Deppeler, A., 2020. Automated Machine Learning and Federated Learning. The AI Book: The Artificial Intelligence Handbook for Investors, Entrepreneurs and FinTech Visionaries, pp.248-250.
Relevant research articles.

Department Elective – II

Department Elective - II

Sl. No.

Subject Code

Subject

CS6106

Advanced Cloud Computing

Course Number	CS6106
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Advanced Cloud Computing
Learning Mode	Offline
Learning Objectives	This course aims to help the students understand (a) how and why cloud systems work and the cloud technologies that manifest these concepts, such as those from Amazon AWS and Microsoft Azure; (b) distributed systems concepts like virtualisation, data parallelism, CAP theorem, and performance analysis at scale; (c) Big Data programming patterns such as Map-Reduce (Hadoop), Vertex-centric graphs (Giraph), Continuous Dataflows (Storm), and NoSQL storage systems to build Cloud applications; (d) Cloud native computing and micro-services.
Course Description	This course provides an in-depth understanding of cloud computing, virtualisation, and distributed systems. It covers foundational concepts, advanced techniques, and real-world applications. Students will explore various aspects of cloud infrastructure, virtualisation technologies, distributed algorithms, and cloud-native computing. By the end of the course, students will be equipped with the knowledge and skills to design, implement, and manage cloud-based solutions and distributed systems effectively.
Course Outline	Cloud computing features and categories. Virtualization: Virtualization Models, Types of Virtualization: Processor virtualization, Memory virtualization, Full virtualization, Para virtualization, Device virtualization. Virtual Machine: Live VM Migration Stages, Virtual Machine Migration for Enterprise Data Centers, Data Center Workloads, Provisioning methods, Resource provisioning. Geo-distributed Clouds: Server Virtualization, Network Virtualization, Approaches for Networking of VMs: Hardware approach: Single-root I/O virtualization (SR-IOV), Software approach: Open vSwitch, Mininet and its applications. Software Defined Network for Multi-tenant Data Centers: Network virtualization, Case Study: VL2, NVP Geo-distributed Cloud Data Centers: Inter-Data Center Networking, Data center interconnection techniques: MPLS, Google’s B4 and Microsoft’s Swan. Leader Election algorithms in Cloud. Google’s Chubby and Apache Zookeeper. Time and Clock Synchronization in Cloud Data Centers, Datacenter time protocol (DTP. Consensus, Paxos and Recovery in Clouds. Cloud Storage: Key-value stores/NoSQL,Design of Apache Cassandra, HBase. Peer to Peer Systems in Cloud Computing. Cloud application: MapReduce Examples. Advances in Cloud Computing with decentralization and Edge Computing.
Learning Outcome	Cloud Computing as a Distributed Systems: Explain and contrast the role of Cloud computing within this space. Cloud Virtualization, Abstractions and Enabling Technologies: Explain virtualisation and their role in elastic computing. Characterise the distinctions between Infrastructure, Platform and Software as a Service (IaaS, PaaS, SaaS) abstractions, and Public and Private Clouds, and analyse their advantages and disadvantages. Programming Patterns for "Big Data" Applications on Cloud: Demonstrate using Map-Reduce, Vertex-Centric and Continuous Dataflow programming models. Application Execution Models on Clouds: Compare synchronous and asynchronous execution patterns. Design and implement Cloud applications that can scale up on a VM and out across multiple VMs. Illustrate the use of NoSQL Cloud storage for information storage. Performance, scalability and consistency on Clouds: Explain the distinctions between Consistency, Availability and Partitioning (CAP theorem), and discuss the types of Cloud applications that exhibit these features.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Reading:

Distributed and Cloud Computing From Parallel Processing to the Internet of Things; Kai Hwang, Jack Dongarra, Geoffrey Fox Publisher: Morgan Kaufmann, Elsevier, 2013.
Cloud Computing: Principles and Paradigms; Rajkumar Buyya, James Broberg, and Andrzej M. Goscinski Publisher: Wiley, 2011.
Distributed Algorithms Nancy Lynch Publisher: Morgan Kaufmann, Elsevier, 1996.

Cloud Computing Bible Barrie Sosinsky Publisher: Wiley, 2011.
Cloud Computing: Principles, Systems and Applications, Nikos Antonopoulos, Lee Gillam Publisher: Springer, 2012.

CS6107

Advanced Edge Computing

Course Number	CS6107
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Advanced Edge Computing
Learning Mode	Offline
Learning Objectives	Upon successful completion of this course, students will be able to: (a) understand the fundamental concepts and limitations of cloud computing and identify the advantages of edge computing; (b) describe various edge computing architectures and differentiate them from traditional cloud models; (c) comprehend the principles of distributed systems as they apply to edge computing environments; (d) explore the functionalities of edge data centers and lightweight edge clouds; (e) deploy and manage containerized applications using Docker and Kubernetes in edge computing contexts; and (f) implement and evaluate edge storage systems and end-to-end edge pipelines utilising MQTT and Kafka, as well as investigate advanced edge computing technologies for real-world applications.
Course Description	This course delves into the emerging field of edge computing, providing a comprehensive understanding of its architectures, systems, and technologies. Students will explore the limitations of traditional cloud computing and learn about the advantages and applications of edge computing. The course covers key concepts in distributed systems, edge data centers, and lightweight edge clouds and includes hands-on experience with Docker, Kubernetes, and edge storage systems. Additionally, students will gain insights into end-to-end edge pipelines using MQTT and Kafka and examine advanced edge computing technologies. By the end of the course, students will be equipped with the knowledge and skills to design, implement, and manage edge computing solutions.
Course Outline	Cloud Computing Basics.Edge Computing basics. Edge Computing Use-Cases, Benefits. Different Types of Edge. Edge Deployment Modes. Edge Computing in 5G, Multi-access Edge Computing (MEC) and Mobile Edge Computing.
Learning Outcome	Critically evaluate advanced edge computing architectures, such as hierarchical, mesh, and hybrid models, considering their suitability for specific use cases and environments. Analyses emerging technologies and trends in advanced edge computing, such as edge AI, blockchain, and serverless computing, and assess their potential impact. Design and implement innovative edge computing solutions that leverage advanced techniques, such as federated learning, edge caching, and dynamic resource allocation. Evaluate the performance and scalability of advanced edge computing systems using benchmarking, simulation, and experimentation. Investigate advanced techniques for ensuring security, privacy, and data integrity in edge computing ecosystems, such as secure enclaves, encryption, and access control mechanisms. Explore specialised applications of advanced edge computing in domains such as healthcare, smart cities, and autonomous systems, analysing their requirements and challenges.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Reading:

Fog and Edge Computing: Principles and Paradigms, Rajkumar Buyya (Editor), Satish Narayana Srirama (Editor), Wiley, 2019
Cloud Computing: Principles and Paradigms, Editors: Rajkumar Buyya, James Broberg, Andrzej M. Goscinski, Wiley, 2011
Cloud and Distributed Computing: Algorithms and Systems, Rajiv Misra, Yashwant Patel, Wiley 2020.
Besides these books, we will provide Journal papers as references.

CS6108

Advanced Computational Data Analysis

Course Number	CS6108
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Advanced Computational Data Analysis
Learning Mode	Offline
Learning Objective	In this subject, the students will be trained with the knowledge of various advanced data analytics techniques encountered in real life.
Course Description	Current Physical systems/devices are highly complex and fast and operate with high data acquisition and generation capabilities. Data generated from such systems require advanced level of analytics for apprehension and further usage. This course aims to give a broad understanding what is advanced level data analytics techniques and how they play a critical role in analysing modern day physical systems acquired data.
Course Outline	Introduction, Operation of physical systems and data generation, Complexity, Drawbacks and Challenges in data generation from physical devices. Requirement of advanced data analytics. Foundations of advanced data analytics principles, mathematical models, probabilistic models, optimization models, deep learning and machine learning models. Role of advanced data analytics in data apprehension and compression, curve-based approximation techniques, interpolation techniques, machine learning models for data interpretation. Statistical models to advanced data analytics, data analytics for 2D and 3D data processing and data manipulation, application of advanced data analytics to real life cases, problem solving.
Learning Outcome	1 · Gain understanding on data generation systems and the role of advanced data analytics. · Apply the Mathematical models of advanced data analytics to real time · Understand the utilities of statistical models and ML models for advanced data analytics.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Readings:

Signal Processing: A Mathematical Approach, Charles L. Byrne, Second Edition, Chapman & Hall, 2014.
Digital Functions and Data Reconstruction: Digital-Discrete Methods, Li M Chen, Springer, 2013.
Machine Learning with Neural Networks: An Introduction for Scientists and Engineers, Bernhard Mehlig, Cambridge University Press, 2021
Signal Processing and Machine Learning with Applications, Michael M. Richter, Sheuli Paul, Veton Këpuska, Marius Silaghi, Springer Cham, 2022
Data Compression: The Complete Reference, David Solomon, 4^th Edition, Springer, 2007

CS6109

Reinforcement Learning

Course Number	CS6109
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Reinforcement Learning
Learning Mode	Offline
Learning Objectives	This course aims to help the students (a) Understand the foundational concepts and mathematical frameworks of reinforcement learning. (b) Gain proficiency in key reinforcement learning algorithms, including dynamic programming, Monte Carlo methods, and temporal-difference learning (c) Apply deep reinforcement learning techniques to solve complex problems using methods such as deep Q-networks and policy gradient algorithms. (d) Explore recent advancements and applications of reinforcement learning, including multi-agent systems and ethical considerations.
Course Description	This specialized course on reinforcement learning aims to give students a deep understanding of the algorithms and methodologies used to train agents to make decisions through trial and error. Students will learn to develop and implement reinforcement learning models by focusing on foundational theories and practical applications. Students will explore key concepts such as Markov decision processes, policy gradients, Q-learning, and deep reinforcement learning through a mix of theoretical lectures, coding exercises, and project-based learning. Upon completion, students will be equipped to design and apply reinforcement learning solutions to complex problems in fields such as robotics, game development, and autonomous systems, enhancing their expertise in this dynamic area of artificial intelligence.
Course Outline	Foundations: Basics of machine learning and reinforcement learning (RL) terminology. Probability Concepts: Axioms of probability, random variables, distributions, and correlation. Markov Decision Process: Introduction to MDPs, Markov property, and Bellman equations. State and Action Value Functions: Concepts of MDP, state, and action value functions. Tabular Methods and Q-networks: Dynamic programming, Monte Carlo, TD learning, and deep Q-networks. Policy Optimization: Policy-based methods, REINFORCE algorithm, and actor-critic methods. Recent Advances and Applications: Meta-learning, multi-agent RL, ethics in RL, and real-world applications.
Learning Outcome	· Mastery of fundamental principles and mathematical frameworks of reinforcement learning. · Proficiency in implementing key reinforcement learning algorithms and techniques. · Ability to apply deep reinforcement learning methods to complex, real-world problems. · Understanding of recent advancements in reinforcement learning and their ethical implications.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Reading:

Reinforcement Learning: An Introduction by Richard S. Sutton and Andrew G. Barto, The MIT Press (1 January 1998).
Deep Reinforcement Learning Hands-On by Maxim Lapan, Packt Publishing Limited (21 June 2018).
Algorithms for Reinforcement Learning by Csaba Szepesvari, Morgan and Claypool Publishers (2010)
Deep Reinforcement Learning: Fundamentals, Research and Applications by Hao Dong, Springer Verlag (2020)

CS6110

Advanced Graph Machine Learning

Course Number	CS6110
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Advanced Graph Machine Learning
Learning Mode	Offline
Learning Objectives	Several real world systems can be represented as a network of entities that are connected to each other through some relations. Often the number of entities is immensely large, thus forming a very large network. Typical examples of such large networks include network of entities in knowledge graphs, co-occurrence graph of the keywords in natural languages, interaction graph of users in social networks, protein-protein interaction graphs and the network of routers in Internet to name a few. Study of these networks is often needed for relational learning tasks, as well as for developing frameworks for representing the intrinsic structure of the data. This course will mainly deal with both the traditional as well as current state of the art machine learning techniques to be applied on Graphs for different downstream tasks.
Course Description	The course will provide knowledge on the representation and statistical descriptions of large networks, along with traditional machine learning and deep learning techniques applied on graphs. Several use cases of Graph Machine Learning across different domains including Natural Language Processing, Social Network Analysis and Computational Biology would be studied.
Course Outline	Introduction and background knowledge of graphs; Network Measures and Metrices; Spectral Analysis of Graphs and its applications; Random Networks; Properties of Random Networks; Overview of machine learning applications on graphs; Feature based learning on graphs, Shallow embedding and deep Learning techniques for generating node and graph representations – Graph Neural Networks, Graph Attention Networks, Graph Transformers; Graph Neural Networks Pretraining techniques; Generative models for graphs; Models for scale-free and small-world networks; Temporal networks, Modeling temporal networks
Learning Outcome	Course training via lectures & tutorial sessions to · Represent and analyze the structure of graphs · Discover recurring and significant patterns of interconnections in your data with network motifs and community structure. · Gain Knowledge on traditional machine learning techniques applied on graphs · Leverage graph-structured data to make better predictions using graph neural networks · Understand the problems in dealing with large graphs for machine learning tasks and learn how to improvise · Analyze temporal and dynamic graphs · Scaling neural networks with generative models for graphs.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Readings:

E.J. Newman, Networks - An introduction , Oxford Univ Press, 2010.

Yao Ma and Jilian Tang, Deep Learning on Graphs, Cambridge University Press, 2021
Goyal, Palash and Emilio Ferrara. “Graph embedding techniques, applications, and performance: A survey.” Knowl.-Based Syst. 151 (2018): 78-94.

CS6111

Advanced Time Series Analysis

Course Number	CS6111
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Advanced Time Series Analysis
Learning Mode	Offline
Learning Objectives	· This course on advance time series will teach both the fundamental concepts time series analysis, as well as recent trends in time series analysis. · Students will learn to design successful time series data applications with sequential Neural Networks. · Deploy Nonlinear Auto-regressive Network with Exogenous Inputs · Adapt Deep Neural Networks for Time Series Forecasting and classification
Course Description	This course provides advanced concepts in time series analysis including some fundamentals of time series, data pre-processing, feature selection, Variety of modeling techniques, Anomaly Detection in Time Series and forecasting financial series using statistical, econometric, machine learning, and deep learning approaches and Practical Applications and Deployment of models.
Course Outline	Introduction to classical time series methods, time series Virtualization Univariate Stationary Processes; Granger Causality; Vector Autoregressive Processes Nonstationary Processes; Cointegration; Cointegration in Single Equation Models: Representation,Estimation and Testing. Applied Predictive Modeling Techniques; Autoregressive Conditional Heteroskedasticity. Finance and Algorithmic trading: Machine Learning and Deep Learning in Stock Price Prediction Machine Learning, Deep Learned Time series Analysis, Risk and Portfolio Management Practical Applications and Deployment of models; applications of convolutional neural network (CNN) and long-and-short-term memory (LSTM) network architectures; designing predictive models for financial time series data Stock Price Prediction using Deep Learning and Natural Language Processing
Learning Outcome	At the end of the course, students will have achieved the following learning objectives. problems relating to obtaining, cleaning, simulating, and storing time series data. Variety of modeling techniques that can be used for recent time series analysis techniques of financial time series analysis and forecasting financial series using statistical, econometric, machine learning, and deep learning approaches. Apply more recently developed methods, such as machine learning and neural network, to time series data, highlighting the challenges of data processing and data layout when time series data is used for fitting models
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Textbooks:

Kirchgässner, Gebhard, Jürgen Wolters, and Uwe Hassler. Introduction to modern time series analysis. Springer Science & Business Media, 2012.
Lazzeri, F. (2020). Machine learning for time series forecasting with Python. John Wiley & Sons.
Jaydip, Sen, and Mehtab Sidra. Machine Learning in the Analysis and Forecasting of Financial Time Series. 2022.

Department Elective – III

Department Elective – III

Sl. No.

Subject Code

Subject

CS6201

Artificial Internet of Things

Course Number	CS6201
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Artificial Internet of Things
Learning Mode	Offline
Learning Objectives	· Gain a comprehensive understanding of the convergence of Artificial Intelligence (AI) and Internet of Things (IoT), including basic concepts, architectures, and applications. · Learn various AI techniques and their applications in IoT, including machine learning, deep learning, and data analytics. · Develop skills in designing and implementing IoT systems, integrating sensors, and managing data flow. · Understand the processes for collecting, storing, processing, and analyzing IoT data using AI techniques. · Identify and mitigate security risks and privacy concerns in AIoT systems. · Analyze various real-world applications of AIoT in industries such as healthcare, smart cities, agriculture, and manufacturing. · Understand the regulatory and ethical considerations related to AIoT technologies and their deployment.
Course Description	This course provides an in-depth exploration of the convergence of Artificial Intelligence (AI) and the Internet of Things (IoT), known as AIoT. It covers the fundamental principles and technologies of both AI and IoT, demonstrating how they can be integrated to create intelligent, autonomous systems. Students will learn about IoT architecture, AI algorithms, machine learning, data analytics, and the implementation of AI-driven IoT solutions. Through hands-on projects and real-world case studies, students will gain practical experience in developing smart applications for various domains such as smart cities, healthcare, industrial automation, and smart homes.
Course Outline	Introduction to AIoT, Intersection of AI and IoT, Benefits and challenges of AIoT Fundamentals of IoT, IoT Architecture and Protocols, Layers of IoT architecture, Communication protocols and standards, IoT Devices and Sensors Fundamentals of Artificial Intelligence, Machine Learning and Deep Learning, Overview of AI tools and frameworks AIoT System Architecture, Components and Designing AIoT, Edge Computing in AIoT, Edge vs. cloud computing, AI Models for IoT Data Management in AIoT, Data Processing and Analysis, Handling large-scale IoT data, Big data technologies and platforms AIoT Applications and Use Cases: Smart Homes and Buildings, Healthcare and Wearables, Industrial IoT (IIoT), Smart Cities and Transportation AIoT Platforms and Tools: AI Development Tools, Case Studies of AIoT Solutions, AIoT Project Development, Future Trends and Innovations in AIoT
Learning Outcome	At the end of course, students will learn: · Students should grasp the foundational concepts of AI and IoT, including machine learning algorithms, data analytics, sensor technologies, and network protocols. · Ability to integrate AI algorithms with IoT devices and platforms to create intelligent systems capable of data collection, analysis, and decision-making in real-time. · Proficiency in developing AI-driven IoT applications, including sensor data processing, predictive analytics, anomaly detection, and automation. · Awareness of security challenges and solutions in AIoT systems, including data privacy, authentication, encryption, and intrusion detection. · Knowledge of optimization techniques for AIoT systems to enhance performance, scalability, and energy efficiency.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Reading:

Olivier Hersent, David Boswarthick, and Omar Elloumi, The Internet of Things: Key Applications and Protocols, Wiley
Maciej Kranz, Building the Internet of Things: Implement New Business Models, Disrupt Competitors, Transform Your Industry, Wiley
John Paul Mueller and Luca Massaron, Machine Learning for the Internet of Things: Practical Guide, Packt

CS6202

Game Theory

Course Number	CS6202
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Game Theory
Learning Mode	Offline
Learning Objectives	Learn the principles of decision theory and its relevance to game theory. Understand and analyze extensive form games, including game trees and backward induction. Identify and compute pure and mixed strategy Nash equilibria. Analyze matrix games, specifically two-player zero-sum games. Understand Bayesian games and apply Bayesian equilibrium concepts to games with incomplete information. Analyze and compute subgame perfect equilibria in dynamic games. Explore coalitional games, including the core and the Shapley value. Explore auction theory and its various models and applications. Utilize game theory concepts in practical applications such as IoT, wireless networks, and cloud computing.
Course Description	This course aims to establish a solid foundation in both game theory and mechanism design, enabling participants to apply these principles rigorously to solve problems. By the end of the course, students will be equipped to model real-world scenarios using game theory, analyze these scenarios with game-theoretic concepts, and design effective and robust solutions, including mechanisms, algorithms, and protocols suitable for rational and intelligent agents.
Course Outline	Non-cooperative Game Theory: Decision theory, Extensive Form Games, Strategic Form Games, Dominant Strategy Equilibria, Pure Strategy Nash Equilibrium, Mixed Strategy Nash Equilibrium, Computation of Nash Equilibrium, Complexity of Computing Nash Equilibrium, Matrix Games (Two Player Zero-sum Games), Bayesian Games, Subgame Perfect Equilibrium. Cooperative Game: Correlated Strategies and Correlated Equilibrium, Two Person Bargaining Problem, Coalitional Games, Core, Shapley Value. Mechanism Design: Introduction to Mechanism Design, Social Choice Functions and their properties, Incentive Compatibility, Auction theory and its variants. Applications: IoT, Wireless Networks, Cloud Computing
Learning Outcome	By the end of this course, students will be able to: Describe the principles of decision theory and its importance in game theory. Formulate and solve strategic form games, identifying dominant strategy equilibria and Nash equilibria. Analyze and solve matrix games, particularly two-player zero-sum games. Formulate Bayesian games and determine Bayesian equilibria for games with incomplete information. Compute subgame perfect equilibria for dynamic games using appropriate techniques. Apply the concepts of correlated strategies and correlated equilibria in cooperative settings. Analyze and solve two-person bargaining problems. Analyze social choice functions and their properties, focusing on incentive compatibility. Utilize game theory concepts to address practical problems in IoT, wireless networks, and cloud computing.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Textbook:

M. Osborne, An Introduction to Game Theory, Oxford University Press.
Y. Narahari. Game Theory and Mechanism Design. IISc Press and the World Scientific.

Reference Book:

M. Maschler, E. Solan, and S. Zamir, Game Theory. Cambridge University Press
D. Niyato, & W. Saad. Game theory in wireless and communication networks. Cambridge University Press.

CS6203

Text Mining and Analytics

Course Number	CS6203
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Text Mining and Analytics
Learning Mode	Offline
Learning Objectives	· To understand the fundamental principles and scope of text mining and analytics. · To acquire skills in data collection, cleaning, and integration for text data. · To learn text preprocessing techniques including tokenization, stemming, stopword removal, and normalization. · To construct knowledge graphs by linking entities and extracting relationships. · To identify and mine frequent patterns and apply advanced pattern mining techniques. · To extract features from text data and apply clustering and classification methods. · To implement practical applications such as sentiment analysis and text summarization. · To utilize advanced techniques for enhanced text data analysis and mining.
Course Description	This course provides a comprehensive understanding of the principles and techniques of text mining and analytics. Students will learn about data collection, cleaning, integration, and preprocessing methods essential for handling text data. The course covers knowledge graph construction, pattern mining, feature extraction, and advanced text clustering and classification techniques. Practical applications such as sentiment analysis and text summarization are also explored. By the end of the course, students will be prepared to tackle real-world challenges in data mining and text analytics.
Course Outline	Text mining and analytics introduction: Overview, motivation, scope, Data Collection and Pre-processing: Techniques for collecting data from various sources, Text data cleaning and integration, descriptive analytics Text preprocessing: tokenization, stemming, stopword removal, and normalization Knowledge graph construction: Basics of graphs, entity linking, relationship extraction Concepts of frequent patterns, closed patterns, max-patterns, and association rules, mining frequent patterns: apriori algorithm, pattern-growth approach. Advanced: mining sequential patterns Feature extraction, Bag-of-Words, TF-IDF, word embeddings Clustering and classifying text data, Expectation-maximization (EM) algorithm for text data, Latent Dirichlet Allocation (LDA) for topic modeling, and some advanced techniques Some applications: sentiment analysis, text summarization, etc. Some advanced topics and project
Learning Outcome	· By the end of this course, students will be able to: · Understand the core principles and scope of text mining and analytics. · Collect, clean, and integrate text data from various sources. · Apply text preprocessing techniques such as tokenization, stemming, and normalization. · Construct and utilize knowledge graphs for entity linking and relationship extraction. · Identify and mine various patterns in text data, including frequent, closed, and sequential patterns. · Extract features from text data using methods like Bag-of-Words, TF-IDF, and word embeddings. · Perform text clustering and classification using algorithms such as EM and LDA. · Implement practical text analytics applications such as sentiment analysis and text summarization. · Utilize advanced techniques for enhanced text data analysis and mining.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Reading

Srivastava, A. N., & Sahami, M. (Eds.). (2009). Text mining: Classification, clustering, and applications. CRC press.
Chakraborty, G., Pagolu, M., & Garla, S. (2014). Text mining and analysis: practical methods, examples, and case studies using SAS. SAS Institute.
Sarkar, D. (2016). Text analytics with python (Vol. 2). New York, NY, USA:: Apress.
Witten, I. H., Frank, E., Hall, M. A., Pal, C. J., & Data, M. (2005, June). Practical machine learning tools and techniques. In Data mining (Vol. 2, No. 4, pp. 403-413). Amsterdam, The Netherlands: Elsevier.

Department Elective – IV

Department Elective - IV

Sl. No.

Subject Code

Subject

CS6204

Knowledge Distillation

Course Number	CS6204
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Knowledge Distillation
Learning Mode	Offline
Learning Objectives	This course aims to help the students (a) understand and apply knowledge distillation techniques; (b) master deep neural network compression methods; (c) deploy ML/DNN models on edge devices like Raspberry Pi and others; (d) analyze and optimize model performance in resource-constrained environments; (e) identify the research opportunity in the domain of knowledge distillation and DNN compression on resource-constrained devices.
Course Description	This course delves into advanced techniques for enabling machine learning on resource-constrained devices. Beginning with an introduction to on-device training, students will explore the principles and methods of knowledge distillation and deep neural network (DNN) compression. The course covers practical strategies for implementing machine learning and deep neural networks on devices with limited computational resources. Additionally, students will learn to combine knowledge distillation and compression techniques to optimise performance, making sophisticated machine-learning models viable on edge devices.
Course Outline	· Introduction to on-device training: Overview of resource-constrained edge devices and their significance, possibilities of enabling machine learning (ML) and deep neural networks (DNN) models on resource-constrained devices, applications and use cases of ML/DNN on edge devices. · Knowledge Distillation: Concept and principles of knowledge distillation, Teacher-student model framework, Applications and benefits of knowledge distillation. Advanced techniques in knowledge distillation, Implementation of knowledge distillation in various frameworks, and Practical exercises on distilling models. · Deep Neural Network Compression: Overview of DNN compression techniques, Quantization and its impact on model performance, Pruning methods for model size reduction. Low-rank factorization, Weight sharing and clustering, Hands-on implementation of compression techniques. · ML/DNN on resource-constrained devices: Introduction to edge devices: Raspberry Pi, NVIDIA Jetson, etc, Setting up an AI development environment on Raspberry Pi, Case study: Running a pre-trained model on Raspberry Pi. TensorFlow Lite, ONNX, etc, Practical exercises with TensorFlow Lite on Raspberry Pi. · Combining Knowledge Distillation and Compression: Integrating knowledge distillation and compression for optimal performance, Strategies for balancing accuracy and efficiency, Real-world examples and case studies.
Learning Outcome	· Explain and implement knowledge distillation techniques. · Apply DNN compression methods such as quantisation and pruning. · Set up and optimise ML/DNN models on Raspberry Pi using TensorFlow Lite and ONNX. · Evaluate and enhance ML/DNN model performance on edge devices. · Create real-time applications, including object detection and predictive maintenance. · Plan, develop and present comprehensive projects that may lead to publication.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Reading:

Deep Learning for Edge AI” by John Doe
Knowledge Distillation: Principles, Methods and Applications” by Jane Smith
Official documentation and tutorials for TensorFlow Lite, ONNX, and edge devices
“Knowledge Distillation: A Survey” Jianping Gou, Baosheng Yu, Stephen John Maybank, Dacheng Tao
K. Nan, S. Liu, J. Du and H. Liu, "Deep model compression for mobile platforms: A survey," in Tsinghua Science and Technology, vol. 24, no. 6, pp. 677-693, Dec. 2019, doi: 10.26599/TST.2018.9010103.
Mishra et al.. "A survey on deep neural network compression: Challenges, overview, and solutions."

CS6205

Physics of Neural Network

Course Number	CS6205
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Physics of Neural Network
Learning Mode	Offline
Learning Objectives	This course aims to help the students (a) Comprehend the basic models and structures of neural networks, including the structure and function of the central nervous system, associative memory, and information storage and recall principles. (b) Gain detailed knowledge of various neuron types, such as stochastic and cybernetic neurons, and different network architectures, including layered and perceptron networks.(c) Learn to apply neural network models to practical applications such as time series prediction, game playing (e.g., Backgammon), and protein structure prediction, as well as exploring their use in biomedicine and economics.(d) Delve into advanced topics like pattern recognition, unsupervised learning, evolutionary algorithms, combinatorial optimization, VLSI, specialized networks (e.g., Hopfield networks, Kohonen maps), and advanced learning techniques like back-propagation and solving optimization problems.
Course Description	This course offers a comprehensive exploration of neural networks, encompassing their fundamental models, the structure of the central nervous system, and a brief historical overview. Students will delve into the core principles of associative memory, information storage and recall, and learning mechanisms such as Hebb's rule. The curriculum covers a variety of neuron types, including stochastic and cybernetic neurons, and introduces layered and perceptron network architectures. Throughout the course, students will investigate practical applications of neural networks, ranging from time series prediction to strategic game playing (e.g., Backgammon) and protein structure prediction. The course also highlights the role of neural networks in biomedicine and economics, showcasing their versatility and impact. Advanced topics are thoroughly explored, including pattern recognition, unsupervised learning, and evolutionary algorithms. Students will engage with combinatorial optimization, VLSI design, and specialized network models such as Hopfield networks and Kohonen maps. The course emphasizes the significance of back-propagation, learning functions, and optimization problem-solving techniques. By the end of the course, students will have a deep understanding of neural networks' theoretical foundations and practical applications, equipping them with the skills to leverage these powerful tools in various scientific and industrial domains.
Course Outline	Models of Neural Networks, A Brief History of Neural Network Models, Prediction of the Secondary Structure of Proteins, Associative Memory for Time Sequences
Learning Outcome	· Understand the basic concept of PINN · Apply the concept of Partial Differential in PINN · Analysis of Optimization techniques for PINNs. · Demonstrate the practical utility of PINNs in handling complex, real-time applications that require efficient and accurate simulations.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Reading:

Müller, B., Reinhardt, J. and Strickland, M.T., 2012. Neural networks: an introduction.
Peretto, P., 1992. An introduction to the modeling of neural networks (Vol. 2).
Relevant research articles.

CS6206

Selected Topics in Wireless Networks

Course Number	CS6206
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Selected Topics in Wireless Networks
Learning Mode	Offline
Learning Objectives	In this subject, the students will be trained with the knowledge of 802.11 wireless networks, including protocol knowledge and the associated security vulnerabilities.
Course Description	In the consumer, industrial, and military sectors, 802.11-based wireless access networks have been widely used due to their convenience. This application, however, is reliant on the unstated assumptions of availability and anonymity. The management and media access protocols of 802.11 may be particularly vulnerable to malicious denial-of-service (DoS) and various security attacks. This course analyzes these 802.11-specific attacks, including their applicability, effectiveness, and proposed low-cost implementation improvements to mitigate the underlying vulnerabilities.
Course Outline	Introduction to Wireless Networks: Basic principles, types of wireless networks (Wi-Fi, Bluetooth, cellular), and network topologies. Wireless Communication Fundamentals: Radio frequency, signal propagation, modulation techniques, and interference management. Network Protocols and Standards: IEEE 802.11 (Wi-Fi), IEEE 802.15 (Bluetooth), and cellular standards (2G, 3G, 4G, 5G). Network Design and Architecture: System design, frequency reuse, and resource allocation. Mobility and Handoff: Techniques for managing mobility, handoff processes, and roaming. Security in Wireless Networks: Security protocols, encryption, and threat mitigation. Emerging Technologies: Overview of 6G, IoT, in-network caching
Learning Outcome	On successful completion of the course, students should be able to: · Understand the fundamentals of 802.11 wireless networks · Describe the WLAN services-association, disassociation, re-association, distribution, integration, authentication, de authentication and data delivery services · Comprehend the vulnerabilities associated with 802.11 protocol.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Text Books and References:

"Wireless Communications: Principles and Practice" by Theodore S. Rappaport (2nd Edition)
"802.11 Wireless Networks: The Definitive Guide" by Matthew S. Gast (2nd Edition)
"Wireless Communications & Networks" by William Stallings (2nd Edition)
"Wireless Communications: Principles and Practice" by Andreas F. Molisch (2nd Edition)
"Fundamentals of Wireless Communication" by David Tse and Pramod Viswanath (1st Edition)
"Next Generation Wireless LANs: 802.11n and 802.11ac" by Eldad Perahia and Robert Stacey (2nd Edition)
"Wireless Networking: Understanding Internetworking Challenges" by Anurag Kumar, D. Manjunath, and Joy Kuri (1st Edition)
"Wireless Communications: Principles and Practice" by Kaveh Pahlavan and Prashant Krishnamurthy (1st Edition)

CS6207

Advanced Big Data Analytics

Course Number	CS6207
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Advanced Big Data Analytics
Learning Mode	Offline
Learning Objectives	The primary objective of this course is to equip students with advanced knowledge and skills in big data analytics. By the end of the course, students will be able to understand and apply advanced data analysis techniques, develop and implement big data solutions, and leverage big data technologies for strategic decision-making. Additionally, students will gain proficiency in using big data tools and platforms, enhance their ability to handle and analyze large datasets, and develop critical thinking skills for solving complex data-driven problems.
Course Description	This course provides an in-depth exploration of advanced big data analytics, focusing on the theoretical foundations, practical techniques, and cutting-edge technologies in the field. Students will learn about various aspects of big data, including data acquisition, storage, processing, and analysis. The course covers advanced topics such as machine learning algorithms for big data, real-time data processing, and big data visualization. Emphasis will be placed on the use of big data tools and platforms such as Hadoop, Spark, and NoSQL databases. Through hands-on projects and case studies, students will develop the skills needed to design and implement big data solutions for a variety of applications.
Course Outline	Introduction to Big Data Analytics: Definition and characteristics (Volume, Velocity, Variety, Veracity, and Value), Importance and challenges of Big Data. Big Data Ecosystem: Components and architecture, Key players and technologies in Big Data (e.g., Hadoop, Spark). Big Data vs. Traditional Data: Differences in processing and analysis, Applications of Big Data Analytics- Industry-specific applications and Case studies Data Acquisition and Storage: Structured, semi-structured, and unstructured data and Data generation and collection methods. Distributed file systems (e.g., HDFS), NoSQL databases (e.g., MongoDB, Cassandra), and Cloud storage options, ETL (Extract, Transform, Load) processes, Data pipelines and workflow automation, Insuring data integrity and accuracy, Data privacy and security considerations Data Processing Frameworks: Hadoop MapReduce architecture and workflow, Advantages and limitations, Apache Storm, Apache Flink, and Kafka Streams, Real-time data processing and its significance, Apache Spark architecture and RDDs (Resilient Distributed Datasets), Spark SQL, Spark Streaming, and MLlib Machine Learning for Big Data: Introduction to Machine Learning, Machine Learning Algorithms, Machine Learning Tools and Libraries, Training and evaluating models on large datasets, Scalability and performance optimization Real-Time Data Processing: Importance and applications of real-time analytics, Apache Kafka, Apache Flink, and Apache Storm, Designing and implementing real-time data workflows, Industry examples and best practices Big Data Visualization: Making data comprehensible and actionable, Visualization Tools and Techniques, Building user-friendly and interactive data dashboards, Intersection of data science and big data analytics, Integrating AI techniques with big data analytics, Processing and analyzing IoT-generated data, Distributed computing at the edge of networks, and Industry-Specific Case Studies- Healthcare, finance, retail, and other industries.
Learning Outcome	Understand the key concepts and significance of big data analytics. Acquire, store, and manage large datasets using appropriate big data technologies. Apply advanced data processing techniques using Hadoop and Spark. Implement machine learning algorithms for big data applications. Perform real-time data processing and analysis. Utilize big data visualization tools to interpret and present data insights. Develop and implement comprehensive big data solutions for various industry applications. Critically evaluate and solve complex data-driven problems using advanced analytics techniques.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Reading:

Marz, N., & Warren, J. (2015). Big Data: Principles and Best Practices of Scalable Real-Time Data Systems (1st ed.). Manning Publications.
White, T. (2015). Hadoop: The Definitive Guide (4th ed.). O'Reilly Media.
Karau, H., & Warren, R. (2017). High Performance Spark: Best Practices for Scaling and Optimizing Apache Spark (1st ed.). O'Reilly Media.
Gulla, U., Gupta, S., & Kumar, V. (2020). Practical Big Data Analytics: Hands-on Techniques to Implement Enterprise Analytics and Machine Learning Using Hadoop, Spark, NoSQL and R (2nd ed.). Packt Publishing.

Zikopoulos, P. C., Eaton, C., & deRoos, D. (2012). Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data (1st ed.). McGraw-Hill Education.

Department Elective – V

Department Elective - V

Sl. No.

Subject Code

Subject

CS6208

Quantum Machine Learning

Course Number	CS6208
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Quantum Machine Learning
Learning Mode	Offline
Learning Objectives	This course aims to help the students (a) proficiency in implementing and applying classical machine learning algorithms, including classification, regression, gradient descent, and neural networks. (b) grasp the foundational principles of quantum computing, quantum states, qubits, and basic quantum operations.(c) advanced quantum algorithms and their applications in machine learning and computational tasks. (d) gain practical experience in implementing quantum algorithms and simulating quantum processes.
Course Description	This course offers a comprehensive exploration of machine learning (ML) and quantum computing (QC) principles, preparing students to navigate the intersection of classical and quantum computational paradigms. Students will master classical ML techniques including classification, regression, neural networks, and optimization methods like gradient descent. In the quantum computing segment, foundational concepts such as quantum states, qubits, and basic quantum operations (e.g., Hadamard gates) will be covered, alongside encoding classical data on quantum systems and implementing basic quantum algorithms. Advanced topics include variational quantum algorithms, quantum support vector machines, the HHL algorithm for linear systems, and quantum neural networks. Through lectures, practical exercises using quantum programming frameworks, and real-world applications, students will develop a dual proficiency in classical ML and quantum computing, equipping them for roles in research, development, or applications across industries leveraging emerging quantum technologies.
Course Outline	Overview of Machine Learning, Quantum Circuit, Variational quantum algorithm, Quantum Neural Network
Learning Outcome	· Understanding of Machine Learning and Quantum Computing Fundamentals. · Apply the concept of feature vectors, encode data in Quantum computing. · Analysis of Variational quantum algorithms to solve complex problems. · Implementation and analysis of advanced quantum machine learning algorithms.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Textbooks:

Nielsen, M.A. and Chuang, I.L., 2010. Quantum computation and quantum information.
Schuld, M. and Petruccione, F., 2021. Machine learning with quantum computers (Vol. 676). Berlin.
Relevant research articles.

Reference books:

Kasirajan, V., 2021. Fundamentals of quantum computing.

Quantum Machine Learning, Link: http://sites.iiserpune.ac.in/~santh/course/QML/qml.html

CS6209

Meta Learning

Course Number	CS6209
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Meta Learning
Learning Mode	Offline
Learning Objectives	This course aims to help the students (a) Gain a solid understanding of the foundational principles of meta-learning, including model evaluation, basic machine learning concepts, and their limitations. (b) Delve into advanced techniques such as deep learning, transfer learning, and multitask learning, and understand how these methodologies enhance meta-learning capabilities. (c) Develop proficiency in key meta-learning strategies, including model-based, metric-based, and optimization-based approaches, and familiarize yourself with advanced architectures like memory-augmented networks and conditional sequential neural networks (CSNNs). (d) Apply meta-learning techniques to practical applications in various domains, such as computer vision, natural language processing (NLP), reinforcement learning, healthcare, recommendation systems, and climate science, demonstrating the ability to solve complex real-world problems.
Course Description	This comprehensive course provides an in-depth overview of meta-learning, guiding students from foundational principles to advanced techniques. The curriculum begins with the basics of model evaluation, machine learning concepts, and their inherent limitations. Students will then explore advanced topics such as deep learning, transfer learning, and multitask learning, gaining a robust understanding of how these methodologies enhance the capabilities of meta-learning systems.Key meta-learning strategies are thoroughly examined, including model-based, metric-based, and optimization-based approaches. The course features advanced architectures like memory-augmented networks and conditional sequential neural networks (CSNNs), showcasing their roles in improving learning efficiency and effectiveness.Practical applications of meta-learning are highlighted across various fields, including computer vision, natural language processing (NLP), reinforcement learning, healthcare, recommendation systems, and climate science. These examples demonstrate the versatility and power of meta-learning in addressing complex, real-world problems.By the end of the course, students will be equipped with a robust understanding of meta-learning principles and techniques, enabling them to leverage these advanced methodologies to solve intricate problems across diverse domains.
Course Outline	Meta-Learning Basics and Background, Evaluation of Meta learning, Model-Based Meta-Learning Approaches, Metric-Based Meta-Learning Approaches, Optimization-Based Meta-Learning Approaches
Learning Outcome	· Understand and articulate the foundational principles of meta-learning · Apply probabilistic modeling and Bayesian inference to quantify uncertainty and improve model robustness in decision-making processes. · Analysis of Optimization-Based Meta-Learning Approaches. · Explore and address new challenges in emerging applications
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Reading:

Zou, L., 2022. Meta-learning: Theory, algorithms and applications.
Brazdil, P., Van Rijn, J.N., Soares, C. and Vanschoren, J., 2022. Metalearning: applications to automated machine learning and data mining (p. 346).
Relevant research articles.

CS6210

Selective Topics in Generative AI

Course Number	CS6210
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Selective Topics in Generative AI
Learning Mode	Offline
Learning Objectives	· To gain a comprehensive understanding of advanced AI architectures, particularly in the context of Generative AI. · To develop proficiency in implementing and evaluating a variety of Generative AI techniques and models. · To understand the principles and applications of Generative Pre-trained Transformers and other application-specific architectures. · To explore and address ethical considerations and biases in Generative AI, emphasizing the importance of explainability. · To engage with advanced topics and apply knowledge through hands-on projects.
Course Description	This course provides an in-depth exploration of Generative AI (GenAI), focusing on advanced AI architectures such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Generative Pre-trained Transformers (GPT). Students will learn about hybrid and emerging models, application-specific architectures, and the ethical considerations and biases in Generative AI. The course includes hands-on projects to design, implement, and evaluate sophisticated generative AI models, emphasizing innovation and practical problem-solving skills.
Course Outline	Introduction to advanced AI, overview of advanced AI architectures and Generative AI (GenAI) Generative Adversarial Network (GAN): various GAN architectures, DCGAN Advanced Variational AutoEncoder (VAE): hierarchical VAEs, Semi-supervised VAE Hybrid and emerging models: Energy-based models, diffusion models, autoregressive and flow-based models, attention mechanism in generative models Generative Pre-trained Transformers (GPT): architectural details and variations Advanced application-specific architecture: Models for Image-to-Text generation, Text-to-Image generation, Prompt engineering, Multimodality Ethical consideration and bias in Generative AI, Explainability Some advanced topics and project.
Learning Outcome	· Master various Generative AI architectures, including GANs, VAEs, and emerging models. · Demonstrate proficiency in implementing and evaluating advanced Generative AI techniques, such as hierarchical VAEs and energy-based models. · Understand the design principles and applications of Generative Pre-trained Transformers (GPT) and application-specific architectures. · Analyze and address ethical considerations and biases in Generative AI, emphasizing the importance of explainability. · Explore advanced topics in Generative AI and apply acquired knowledge through hands-on projects, fostering innovation and practical problem-solving skills.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Reading:

Foster, D. (2022). Generative deep learning: : Teaching Machines to Paint, Write, Compose, and Play. " O'Reilly Media, Inc.".
Valle, R. (2019). Hands-On Generative Adversarial Networks with Keras: Your guide to implementing next-generation generative adversarial networks. Packt Publishing Ltd.

Research Papers and Articles from Journals such as JMLR, IEEE Transactions on Neural Networks and Learning Systems, etc., and Conference Proceedings from NeurIPS, ICML, and CVPR,etc.

Interdisciplinary Elective (IDE) Course for M. Tech. (Available to non CSE Dept. students)

IDE from CSE - IDE-I

Sl. No.

Subject Code

Subject

CS6112

Drone Data Processing & Analysis

Course Number	CS6112
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Drone Data Processing & Analysis
Learning Mode	Offline
Learning Objectives	This course aims to help the students (a) understand the integration of various sensors and platforms, including optical, thermal, LiDAR, multispectral, and hyperspectral sensors; (b) identify and analyze the use of drones in civilian and remote sensing applications; (c) to learn the importance and techniques of sensor calibration and boresighting to ensure data accuracy and reliability; (d) comprehend the operational requirements for UAVs and develop a Concept of Operation (CONOP) including risk assessment for safe and effective drone missions; (e) gain proficiency in using advanced data processing software tools to generate high-quality digital products from drone data; and (f) to evaluate data quality using accuracy metrics and understand the latest mapping standards to ensure high-precision geospatial data.
Course Description	This course provides an in-depth exploration of advanced drone systems, focusing on integrating and applying various sensors, including optical, thermal, LiDAR, multispectral, and hyperspectral. Students will learn how to integrate these sensors with different drone platforms and apply them in various fields such as agriculture, construction, environmental monitoring, and urban planning. The course covers using drones in remote sensing for resource management, disaster response, and scientific research, emphasising the importance of sensor calibration, boresighting methods, and operational best practices. Additionally, students will gain hands-on experience with leading software tools for drone data processing and understand the latest standards for geospatial data accuracy. Regulatory compliance, safety, security, and privacy issues will also be addressed. Practical applications and industry case studies will be analysed to illustrate successful drone data processing projects.
Course Outline	Importance of Calibration, Methods and best practices for boresighting to align sensors accurately, Key operational requirements and best practices, Developing and implementing a Concept of Operation (CONOP) for drone missions, Risk assessment Introduction to leading software tools for drone data processing (e.g., Pix4D, Agisoft Metashape, DroneDeploy), Steps from data acquisition to final product generation Understanding the latest standards for geospatial data accuracy, Techniques for identifying and correcting errors in drone data, Creating digital elevation models (DEMs) and digital terrain models (DTMs) Current Rules and Regulations in India, Compliance and Certification, Comparison with global regulatory standards, UAV Safety Issues, Security Concerns, Privacy Issues Practical Applications and Case Studies, Analysis of successful drone data processing projects in various industries
Learning Outcome	Evaluate and apply drone technology in diverse civilian and remote sensing scenarios, identifying the benefits and challenges of each application. Execute proper calibration and boresighting techniques to ensure the accuracy and reliability of sensor data. Create and implement an effective CONOP for drone missions, including risk assessment and mitigation strategies. Efficiently use leading data processing software tools to process drone data and generate high-quality digital products. Assess data quality using established accuracy metrics and apply the latest mapping standards to ensure high-precision geospatial data. Navigate and comply with current drone regulations in India and understand international regulatory frameworks.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Reading

Barnhart, R., Michael, M., Marshall, D., and Shappee, E. ed. 2016. Introduction to Unmanned Aircraft Systems, 2nd edition. Boca Raton. CRC Press.
Fahlstrom, P. and Gleason, T. 2012. Introduction to UAV Systems. 4th edition. United Kingdom. John Wiley & Sons Ltd.
Wolf, P., DeWitt, B., and Wilkinson, B. 2014. Elements of Photogrammetry with Applications in GIS, 4th edition. McGraw-Hil
Introduction to UAV Systems, Paul G. Fahlstrom and Thomas J. Gleason
Drone Technology in Architecture, Engineering, and Construction, Daniel Tal and Jon Altschuld
UAV or Drones for Remote Sensing Applications, edited by Felipe Gonzalez Toro and Antonios Tsourdos

M. Tech. in Computer Science & Engineering

Program Learning Objectives:	Program Learning Outcomes (PLO):
Program Goal 1: Advanced Knowledge Acquisition: To deepen students’ knowledge and understanding of advanced theoretical and practical aspects in the major fields of Computer Science and Engineering (CSE).	Program Learning Outcome 1: PLO-1: Students will demonstrate a profound understanding of advanced computing principles, data structure, algorithms, high-level programming languages. Program Learning Outcome 2: PLO-2: Students will be able to understand advanced computational problem-solving techniques, design efficient algorithms, and implement software solutions.
Program Goal 2: Research Proficiency and Innovation: To equip students with the skills necessary to conduct high-quality research in Computer Science and Engineering, contributing to the advancement of the field.	Program Learning Outcome 3: PLO-3: Students will be able to identify research gaps, formulate hypotheses, design experiments, and utilize statistical methods to analyze research data, leading to significant contributions to the field. Program Learning Outcome 4: PLO-4: Students will demonstrate the ability to innovate by developing novel software, hardware solutions, and computational models that address current and emerging challenges in the field.
Program Goal 3: Specialized Skill Development: To enhance students' expertise in system development, security, and other specialized areas within CSE.	Program Learning Outcome 5: PLO-5: Students will be capable of designing, implementing, and securing complex computing systems, with a focus on various emerging areas.
Program Goal 4: Professional and Communication Skills: To cultivate effective communication skills and professional behavior necessary for successful careers in academia, research, and industry.	Program Learning Outcome 6: PLO-6: Students will effectively communicate complex technical information through scholarly writing, presentations, and collaboration, demonstrating clarity, precision with exhibit in leadership and teamwork skills.
Program Goal 5: Ethical Responsibility and Societal Impact: To instill a sense of ethical responsibility and awareness of the societal impact of technology.	Program Learning Outcome 7: PLO-7: Students will understand and apply ethical principles in research and professional practices, ensuring that their work positively impacts society and adheres to global standards. Program Learning Outcome 8: PLO-8: Students will be prepared to address societal challenges through technological solutions, contributing to sustainable development and social welfare.

Design and Analysis of Algorithms

Sl. No.

Subject Code

SEMESTER I

CS5101

Course number	CS5101
Course Credit (L-T-P-C)	3-1-0-4
Course Title	Design and Analysis of Algorithms
Learning Mode	offline
Learning Objectives	The objective of this course is to equip students with a solid understanding of data structures and algorithms, enabling them to design, analyze, and implement efficient algorithms to solve complex computational problems. The course covers fundamental topics such as data structures, complexity analysis, sorting and searching techniques, problem-solving strategies, graph algorithms. By the end of the course, students will have developed the skills to critically analyze algorithm efficiency and apply advanced algorithms in practical scenarios.
Course Description	This course will provide understanding of aadvanced methods to solve problems on computers. It will also provide an overview to analyze those theoretically.
Course Outline	Fibonacci heap, unionfind, splay trees. Amortized complexity analysis Randomized algorithms Reducibility between problems and NPcompleteness: discussion of different NP-complete problems like satisfiability, clique, vertex cover, independent set, Hamiltonian cycle, TSP, knapsack, set cover, bin packing, etc. Backtracking, branch and bound Approximation algorithms: Constant ratio approximation algorithms. Application areas(i)Geometric algorithms: convex hulls, nearest neighbor, Voronoi diagram, etc.(ii)Algebraic and number-theoretic algorithms: FFT, primality testing, etc.(iii)Graph algorithms: network flows, matching, etc.(iv)Optimization techniques: linear programming
Learning Outcome	By the end of this course, students will be able to solve problems that are computationally intractable
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Reading:

Mark Allen Weiss, "Data Structures and Algorithms in C++", Addison Wesley, 2003.
Adam Drozdek, "Data Structures and Algorithms in C++", Brooks and Cole, 2001.
Aho, Hopcroft and Ullmann, "Data structures and Algorithm", Addison Welsey, 1984.
Introduction to Algorithms Book by Charles E. Leiserson, Clifford Stein, Ronald Rivest, and Thomas H. Cormen
Sanjoy Dasgupta, Christos H. Papadimitriou and Umesh V. Vazirani, Algorithms, Tata McGraw-Hill, 2008.
Steven Skiena, The Algorithm Design Manual, Springer
Jon Kleinberg and Éva Tardos, Algorithm Design, Pearson, 2005.
Robert Sedgewick and Kevin Wayne, Algorithms, fourth edition, Addison Wesley, 2011.
Udi Manber, Algorithms – A Creative Approach, Addison-Wesley, Reading, MA, 1989.
Tim Roughgarden, Algorithms Illuminated

CS5102

Foundations of Computer Systems

Course Number	CS5102
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Foundations of Computer Systems
Learning Mode	Offline
Learning Objective	The objective of the course is to provide a conceptual and theoretical understanding of computer architecture and operating systems.
Course Description	Foundations of computer systems is a review of two fundamental subjects of computer science viz., computer architecture and operating systems.
Course Outline	Computer architecture: Performance measures, Memory Location and Operations, Addressing Modes, Instruction Set, A Simple Machine, Instruction Mnemonics and Syntax, Machine Language Program, Assembly Language Program with examples. Processing Unit Design: Registers, Datapath, CPU instruction cycle, Instructions and Micro-operations in different bus architectures, Interrupt handling, Control Unit Design: Control signals, Hardwired Control unit design, Microprogram Control unit design. Pipelining and parallel processing, Pipeline performance measure, pipeline architecture, pipeline stall (due to instruction dependancy and data dependancy), Methods to reduce pipeline stall. RISC and CISC paradigms, I/O Transfer techniques, Memory organization: hierarchical memory systems, cache memories, virtual memory. Operating systems: Process states, PCB, Fork, exec system call, Threads, Process scheduling, Concurrent processes, Monitors, Process Synchronization, Producer Consumer Problem, Critical section, semaphore, Various process synchronization problems. Deadlock, Resource Allocation Graph, Deadlock prevention, Deadlock Avoidance: Banker’s Algorithm and Safety Algorithm. Memory management techniques, Allocation techniques, Paging, Page Replacement Algorithms, Numericals.
Learning Outcome	This course will revisit two fundamental subjects of computer science viz., computer architecture and operating systems, thereby enabling the students to pursue more advanced problems in computer science based on these topics.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Readings:

Silberschatz, P. B. Galvin and G. Gagne, Operating System Concepts, 7^th Ed, John Wiley and Sons, 2004.
Singhal and N. Shivratri, Advanced Concepts in Operating Systems, McGraw Hill, 1994.
David A Patterson and John L Hennessy, Computer Organisation and Design: The Hardware/Software Interface, Morgan Kaufmann, 1994. ISBN 1-55860-281-X.

CS5103

Computing Lab-1

Course Number	CS5103
Course Credit (L-T-P-C)	0-1-2-2
Course Title	Computing Lab-1
Learning Mode	Offline
Learning Objective	The course aims to develop students' analytical and practical skills in designing efficient algorithms and understanding the complexities of operating systems. Students will learn to analyze the efficiency of algorithms, understand various algorithmic strategies, and implement them to solve complex problems. In the Operating Systems segment, students will explore the core concepts, including process management, memory management, file systems, and concurrency. By the end of the course, students will be proficient in both designing algorithms and managing operating system resources, preparing them for advanced studies and professional careers in computer science.
Course Description	This lab course is structured to provide an in-depth understanding of both algorithm design and operating system concepts. The Design and Analysis of Algorithms section covers fundamental topics such as sorting, searching, dynamic programming, greedy algorithms, and graph algorithms. Students will learn to critically evaluate the efficiency and applicability of different algorithms. The Operating Systems section delves into process scheduling, memory management techniques, file systems, and synchronization mechanisms. Through a series of hands-on labs and projects, students will apply theoretical knowledge to practical scenarios, reinforcing their understanding and problem-solving abilities.
Course Outline	The course begins with an introduction to basic algorithmic concepts and techniques, progressing through various algorithm design paradigms such as divide-and-conquer, dynamic programming, and greedy methods. Concurrently, students will explore the architecture and functionalities of operating systems, starting with process management and memory management, then advancing to file systems, I/O systems, and concurrency control. The course will include practical lab sessions where students will implement and test algorithms, as well as design and manage operating system components. The course culminates in a comprehensive project that integrates both algorithm design and operating system principles to solve complex computing problems.
Learning Outcome	Upon completing this course, students will have a solid grasp of both algorithm design and analysis, as well as operating system functionalities. They will be able to design, analyze, and implement efficient algorithms to address computational problems. Additionally, students will gain practical experience in managing operating system resources, including process scheduling, memory management, and file systems. This dual expertise will equip students with the skills necessary for tackling advanced topics in computer science and pursuing careers in software development, system administration, and research.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested readings:

"Introduction to Algorithms" by Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, and Clifford Stein, 4th Edition
"Algorithms" by Robert Sedgewick and Kevin Wayne, 4th Edition
"Operating System Concepts" by Abraham Silberschatz, Peter B. Galvin, and Greg Gagne, 10th Edition
"Modern Operating Systems" by Andrew S. Tanenbaum and Herbert Bos, 4th Edition
"The Algorithm Design Manual" by Steven S. Skiena, 3rd Edition

CS61XX

DE-I

CS61XX

DE-II

HS5111

Technical Writing and Soft Skill

XX61PQ

IDE-I

TOTAL

Advanced Artificial Intelligence

Sl. No.

Subject Code

SEMESTER II

CS5201

Course Number	CS5201
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Advanced Artificial Intelligence
Learning Mode	Offline
Learning Objectives	To understand the principles of Artificial Intelligence and the nature of intelligent agents. To learn various problem-solving techniques, including informed search and exploration. To gain proficiency in handling constraint satisfaction problems and adversarial search. To develop a solid foundation in knowledge representation, first-order logic, and propositional logic. To learn to plan and act effectively in real-world AI applications. To grasp the concepts of uncertain knowledge and probabilistic reasoning. To make informed decisions using simple and complex decision-making models. To acquire skills in learning from observations and applying statistical learning methods. To explore advanced AI techniques and their practical applications.
Course Description	This course offers an in-depth exploration of advanced concepts in Artificial Intelligence (AI). Students will delve into the theoretical underpinnings and practical applications of AI, examining intelligent agents, the nature of environments, and advanced problem-solving techniques. The curriculum covers informed search and exploration, constraint satisfaction problems, adversarial search, and knowledge representation. Students will also explore reasoning with first-order and propositional logic, planning and acting in real-world scenarios, and handling uncertainty through probabilistic reasoning. The course concludes with statistical learning methods and advanced AI techniques, providing a comprehensive understanding of AI's capabilities and applications.
Course Outline	Introduction and motivation Artificial Intelligence, intelligent agents, nature of environments, Problem-solving by searching, informed search and exploration, constraint satisfaction problem, adversarial search, Knowledge and reasoning, first order logic, inference and propositional logic, knowledge representation, Planning and acting in real world of AI agent Uncertain knowledge and reasoning, uncertainty, probabilistic reasoning, making simple and complex decisions Learning from observations and knowledge, statistical learning methods, Some advanced techniques of AI and its applications
Learning Outcome	Upon completing this course, students will be able to: Analyze and implement intelligent agents in various environments. Apply informed search techniques to solve complex problems. Formulate and solve constraint satisfaction problems and engage in adversarial search strategies. Represent and reason with knowledge using first-order and propositional logic. Develop and execute plans in real-world AI scenarios. Manage uncertainty and employ probabilistic reasoning to make sound decisions. Utilize statistical learning methods to derive insights from data. Implement advanced AI techniques in real-world applications. Demonstrate a comprehensive understanding of advanced AI concepts and their implications.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Reading:

Russell, S. J., & Norvig, P. (2016). Artificial intelligence: A modern approach. Pearson.

Poole, D. L., & Mackworth, A. K. (2010). Artificial Intelligence: foundations of computational agents. Cambridge University Press.
Hastie, T., Tibshirani, R., Friedman, J. H., & Friedman, J. H. (2009). The elements of statistical learning: data mining, inference, and prediction (Vol. 2, pp. 1-758). New York: Springer.

CS5202

Theoretical Computer Science

Course Number	CS5202
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Theoretical Computer Science
Learning Mode	Offline
Learning Objectives	· Explore advanced topics in theoretical computer science, including computational complexity, automata theory, and algorithms. · Develop rigorous mathematical reasoning and problem-solving skills applicable to theoretical computer science. · Understand foundational concepts such as Turing machines, formal languages, and computational models. · Engage in independent research and scholarly exploration of theoretical computer science topics. · Apply theoretical insights to practical problems in computer science and related fields.
Course Description	This course offers an advanced study of theoretical computer science, focusing on formal models of computation, computational complexity theory, and algorithmic analysis. Students will delve into abstract concepts and mathematical techniques essential for understanding the limits and capabilities of computing systems. Topics covered include formal languages, automata theory, computability theory, complexity classes, and advanced algorithms. Through lectures, seminars, and research projects, students will develop a deep understanding of theoretical frameworks and their implications for solving real-world computational problems.
Course Outline	Introduction to Theoretical Computer Science , Historical overview and scope of theoretical computer science , Mathematical foundations in computer science , Overview of formal models of computation Automata Theory , Finite automata and regular languages , Context-free grammars and pushdown automata , Turing machines and computability Formal Languages and Computability , Formal language definitions and properties , Decidability and undecidability , Church-Turing thesis and implications Computational Complexity , Time and space complexity classes , NP-completeness and beyond , Complexity hierarchies and reductions Advanced Topics in Algorithms , Design and analysis of algorithms , Approximation algorithms and randomized algorithms , Advanced data structures Logic in Computer Science , Propositional and first-order logic , Model theory and logical reasoning , Applications of logic in computing Theory of Computation , Formal models beyond Turing machines , Quantum computing and computational models , Complexity aspects of quantum computation Cryptography and Computational Complexity , Basics of cryptography and cryptanalysis , Complexity-theoretic foundations of cryptography , Applications of cryptography in secure computation
Learning Outcome	· Demonstrate advanced knowledge of theoretical computer science principles and methodologies. · Apply mathematical reasoning and formal methods to analyze computational problems. · Evaluate the computational complexity of algorithms and problems using theoretical frameworks. · Conduct independent research in theoretical computer science and contribute to scholarly discourse. · Translate theoretical insights into practical solutions for complex computational challenges.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Reading

"Introduction to the Theory of Computation" (3rd Edition) by Michael Sipser
"Computational Complexity: A Modern Approach" (1st Edition) by Sanjeev Arora and Boaz Barak
"Automata Theory, Languages, and Computation" (3rd Edition) by John E. Hopcroft, Rajeev Motwani, and Jeffrey D. Ullman
"Algorithm Design" (1st Edition) by Jon Kleinberg and Éva Tardos
"Introduction to Algorithms" (3rd Edition) by Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, and Clifford Stein

CS5204

Computing Lab-2

Course Number	CS5204
Course Credit (L-T-P-C)	0-1-2-2
Course Title	Computing Lab-2
Learning Mode	Offline
Learning Objectives	The primary objective of this course is to equip students with practical skills in Shell Scripting and Android Programming. Students will gain proficiency in writing and debugging shell scripts to automate tasks in Unix-based systems. They will also develop Android applications, understanding the core principles of mobile app development. By the end of the course, students will be able to apply scripting techniques to streamline workflows and create functional Android applications that meet industry standards.
Course Description	This lab course is designed to provide a dual focus on Shell Scripting and Android Programming, offering students a robust understanding of both domains. The Shell Scripting component covers the fundamentals of scripting in Unix-like environments, including file manipulation, process control, and automation of repetitive tasks. The Android Programming section introduces students to the Android development environment, covering topics such as user interface design, event handling, data storage, and network communication. Through a series of hands-on lab exercises and projects, students will develop the skills necessary to write efficient shell scripts and build user-friendly Android applications.
Course Outline	The course begins with an introduction to Unix-based systems and basic shell commands, progressing to more advanced scripting techniques such as control structures, functions, and error handling. Concurrently, students will be introduced to the Android Studio IDE, basic components of Android apps, and fundamental programming concepts. As the course advances, students will delve deeper into both subjects, working on complex shell scripts and developing feature-rich Android applications. The course will culminate in a final project where students integrate their knowledge from both areas to solve real-world problems.
Learning Outcome	By the end of this course, students will have a thorough understanding of both Shell Scripting and Android Programming. They will be able to write and debug complex shell scripts to automate system tasks, enhancing productivity and efficiency. In addition, students will be proficient in developing Android applications, from conceptualization to deployment, equipped with the knowledge to design intuitive user interfaces and implement backend functionalities. This dual skill set will prepare students for careers in system administration, software development, and mobile application development.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Reading:

"The Linux Command Line: A Complete Introduction" by William E. Shotts, 2nd Edition
"Learning the bash Shell: Unix Shell Programming" by Cameron Newham, 3rd Edition
"Android Programming: The Big Nerd Ranch Guide" by Bill Phillips, Chris Stewart, and Kristin Marsicano, 4th Edition
"Head First Android Development: A Brain-Friendly Guide" by Dawn Griffiths and David Griffiths, 2nd Edition
"Unix and Linux System Administration Handbook" by Evi Nemeth, Garth Snyder, Trent R. Hein, and Ben Whaley, 5th Edition

CS5205

Advanced Artificial Intelligence Lab

Course Number	CS5205
Course Credit (L-T-P-C)	0-1-2-2
Course Title	Advanced Artificial Intelligence Lab
Learning Mode	Offline
Learning Objectives	To implement the techniques and algorithms learnt in Advance Artificial Intelligence theory To analyze advanced AI techniques and their practical applications.
Course Description	This course offers an in-depth exploration and practical implementation of advanced concepts in Artificial Intelligence.
Course Outline	Practical implementation of algorithms and techniques learnt in Advance Artificial Intelligence theory
Learning Outcome	Upon completing this course, students will be able to: Analyze and practically implement the advanced concepts in Artificial Intelligence. Demonstrate a comprehensive understanding of advanced AI concepts and their implications in real world.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

CS62XX

DE-III

CS62XX

DE-IV

RM6201

Course Number	RM6201
Course Credit (L-T-P-C)	3-1-0-4
Course Title	Research Methodology
Learning Mode	Lectures
Learning Objectives	The objective of the course is to train student about the modelling of scalar and multi-objective nonlinear programming problems and various classical and numerical optimization techniques and algorithms to solve these problems
Course Description	Advanced Optimization Techniques, as a subject for postgraduate and PhD students, provides the knowledge of various models of nonlinear optimization problems and different algorithms to solve such problems with its applications in various problems arising in economics, science and engineering.
Course Content	Module I (6 lecture hours) – Research method fundamentals: Definition, characteristics and types, basic research terminology, an overview of research method concepts, research methods vs. method methodology, role of information and communication technology (ICT) in research, Nature and scope of research, information based decision making and source of knowledge. The research process; basic approaches and terminologies used in research. Defining research problem and hypotheses framing to prepare a research plan. Module II (5 lecture hours) - Research problem visualization and conceptualization: Significance of literature survey in identification of a research problem from reliable sources and critical review, identifying technical gaps and contemporary challenges from literature review and research databases, development of working hypothesis, defining and formulating the research problems, problem selection, necessity of defining the problem and conceiving the solution approach and methods. Module III (5 lecture hours) - Research design and data analysis: Research design – basic principles, need of research design and data classification – primary and secondary, features of good design, important concepts relating to research design, observation and facts, validation methods, observation and collection of data, methods of data collection, sampling methods, data processing and analysis, hypothesis testing, generalization, analysis, reliability, interpretation and presentation. Module IV (16 lecture hours) - Qualitative and quantitative analysis: Qualitative Research Plan and designs, Meaning and types of Sampling, Tools of qualitative data Collection; observation depth Interview, focus group discussion, Data editing, processing & categorization, qualitative data analysis, Fundamentals of statistical methods, parametric and nonparametric techniques, test of significance, variables, conjecture, hypothesis, measurement, types of data and scales, sample and sampling techniques, probability and distributions, hypothesis testing, level of significance and confidence interval, t-test, ANOVA, correlation, regression analysis, error analysis, research data analysis and evaluation using software tools (e.g.: MS Excel, SPSS, Statistical, R, etc.). Module V (10 lecture hours) – Principled research: Ethics in research and Ethical dilemma, affiliation and conflict of interest; Publishing and sharing research, Plagiarism and its fallout (case studies), Internet research ethics, data protection and intellectual property rights (IPR) – patent survey, patentability, patent laws and IPR filing process.
Learning Outcome	On successful completion of the course, students should be able to: 1. Understand the terminology and basic concepts of various kinds of nonlinear optimization problems. 2. Develop the understanding about different solution methods to solve nonlinear Programing problems. 3. Apply and differentiate the need and importance of various algorithms to solve scalar and multi-objective optimization problems. 4. Employ programming languages like MATLAB/Python to solve nonlinear programing problems. 5. Model and solve several problems arising in science and engineering as a nonlinear optimization problem.
Assessment Method	Quiz /Assignment/ Project / MSE / ESE

Textbooks & Reference Books:

R. Kothari, Research methodology: Methods and Techniques, 3rd Edn., New age International 2014.
Mark N K. Saunders, Adrian Thornhill, Phkip Lewis, “Research Methods for Studies, 3/c Pearson Education, 2010.
N. Krishnaswamy, apa iyer, siva kumar, m. Mathirajan, “Management Research Methodology”, Pearson Education, 2010.
Ranjit Kumar; “Research Methodology: A Step by Step Guide for Beginners; 2/e; Pearson Education, 2010.
Suresh C. Sinha, Anil K. Dhiman, ess ess, 2006 “Research Methodology” Panner Selvam.R. “Research Methodology”, Prentice Hall of India, New Delhi, 2004.
G. Thomas, Research methodology and scientific writing, Ane books, Delhi, 2015.
J. Ader and G. J. Mellenbergh, Research Methodology in the Social, Behavioural and Life Sciences Designs, Models and Methods, 3rd Edn., Sage Publications, London, 2000.

IK6201

IKS

TOTAL

Sl. No.	Subject Code	SEMESTER III	L	T	P	C
1.	CS6198	Summer Internship/Mini Project*	0	0	12	3
2.	CS6199	Project I**	0	0	30	15
	TOTAL		0	0	42	18

*Note: Summer Internship (Credit based)

(iii.) Confidentiality and Non Disclosure Agreement (NDA) between the two organizations with clarity on intellectual property rights (IPR) must be executed prior to initiating the semester long project assignment and committing the same to external organization and vice versa.

Advanced Blockchain Technology

Department Elective - I

Sl. No.

Subject Code

Subject

CS6101

Course Number	CS6101
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Advanced Blockchain Technology
Learning Mode	Offline
Learning Objectives	The objective of this course is to cover a number of popular blockchain platforms and smart contract language paradigms. This course makes the learners familiar with various (a) research challenges, such as interoperability, scalability, security vulnerabilities, functional/non- functional correctness proof, etc., and their possible solutions, (b) synergizing machine Learning and blockchain, and (c) development of secure blockchain-based decentralized applications using Ethereum and Hyperledger.
Course Description	This course will start with a quick introductory background of blockchain technology and its working principle. The primarily focus of this course is to provide a detailed information about the state-of-the-art blockchain platforms and their supported smart contract languages. In particular, syntax, semantics, and paradigms of various smart contract languages will be discussed. In this perspective, blockchain-oriented software development life cycle and decentralized application development will be discussed. Following this, the course will cover two important directions: addressing various research challenges in blockchain and AI/machine learning for blockchain (and vice-versa).
Course Outline	Introduction to Blockchain Technology: A Quick Tour Different Blockchain Platforms and Smart Contract Languages: Bitcoin, Ethereum, Hyperledger, Solidity, GoLang. Consensus Mechanisms: PoW Vs. PoS, Alternative Consensus Synergizing Machine Learning and Blockchain: Transaction Analysis, Smart Contract Code Analysis, AI-driven Blockchain Applications, Blockchain for AI, Decentralized Learning. Research Challenges in Blockchain: Scalability, Interoperability, Security, Privacy, Decentralized Identity, Smart Contract Vulnerabilities and Detection, Real case studies on developing DApps, Metaverse, Some ongoing relevant research topics.
Learning Outcome	· Gain proficiency in blockchain technology and software engineering of developing decentralized applications. · An overview of the state-of-the-art blockchain platforms and their supported smart contract languages. · Know about the paradigms of various smart contract languages. · Understand how AI/machine learning brings benefits to blockchain technology and vice-versa. · Identify various research challenges and opportunities, such as scalability, interoperability
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

CS6102

Advanced Cyber Security

Course Number	CS6102
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Advanced Cyber Security
Learning Mode	Offline
Learning Objectives	To have a clear understanding of security and privacy issues in various aspects of computing, including: Programs, Operating systems, Networks, Web Applications
Course Description	The course covers. security and privacy issues in various aspects of computing, including: Programs, Operating systems, Networks, Web Applications
Course Outline	Introduction to Computer Security and Privacy: security and privacy; types of threats and attacks; methods of defense Basics of cryptography, Authentication & key agreement, Authorization and access control Program Security: nonmalicious program errors; vulnerabilities in code, Secure programs; malicious code; Malware detection Internet security: IPSEC, TLS, SSh, Email security Wireless security: WEP, WPA, Bluetooth security, Web Security: XSS attack, CSRF attack, SQL Injection, DoS attack & defense
Learning Outcome	After completion of this course a student will have · Understanding of security issues in computer and networks, · Understanding and analysis of internet security protocols · Understanding and analysis of web security protocols
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Textbooks:

Computer Security: Principles and Practice: Dr. William Stallings and Lawrie Brown, Pearson
O'Reilly Web Application Security by Andrew Hoffman

CS6103

Advanced Pattern Recognition

Course Number	CS6103
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Advanced Pattern Recognition
Learning Mode	Offline
Learning Objectives	This course aims to help the students (a) Understand the advanced topics of pattern recognition, including classification and clustering methods. (b) To understand the advanced topics of feature selection, multi-label classification. (c) Apply advanced pattern recognition algorithms to practical applications in image processing, speech recognition, and data mining.
Course Description	This course on advanced pattern recognition aims to equip students with the advanced topics of classification, clustering, and feature selection. By focusing on advanced topics, students will develop the ability to implement and evaluate various pattern recognition algorithms. Students will enhance their understanding of advanced topics of classification, clustering, statistical methods, and data preprocessing techniques through interactive lectures, exercises, and projects. Upon completion, students will be proficient in designing and applying advanced pattern recognition systems for applications such as image processing, text mining, speech recognition, and data mining, thereby enhancing their analytical and problem-solving capabilities in diverse domains.
Course Outline	Introduction and motivation of advanced pattern recognition Modern Classification Methods, Random fields, Pattern recognition based on multidimensional models Contextual classification, Hidden Markov models, Multi-classifier systems Advanced parameter estimation methods, Advanced Unsupervised classification, Modern methods of feature selection. Data normalization and invariants, Benchmarking. Analysis and synthesis of image information. Applications od pattern recognition in Text Processing and Healthcare.
Learning Outcome	· Mastery of advanced concepts in pattern recognition. · In-depth understanding of various advanced algorithms across different pattern recognition paradigms. · Comprehensive knowledge of advanced aspects of classification, clustering, feature selection, feature extraction, and projection techniques. · Ability to apply advanced pattern recognition algorithms to real-world projects
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Reading:

A. Devijver and J. Kittler, Pattern Recognition: A Statistical Approach, Prentice-Hall,1982.
Duda and P. Hart and D.G. Stork, Pattern Classification, J. Wiley, 2001.
Webb, Statistical Pattern Recognition, J. Wiley, 2002.
Theodoridis, K.Koutroumbas, Pattern Recognition, Elsevier, 2003.
Z. Li, Markov Random Field Modeling in Image Analysis, Springer, 2009.

Research papers will be provided on various topics

CS6104

Formal Methods in Program Analysis and Verification

Course Number	CS6104
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Formal Methods in Program Analysis and Verification
Learning Mode	Offline
Learning Objectives	Formal methods are mathematically rigorous techniques to facilitate in building high-confidence critical systems with stringent quality requirements, such as safety and security. It provides a systematic guidance for specification, development, and verification of software and hardware systems. Examples for such systems are banking software, avionics software, medical device software, software used to control industrial plants/cars, etc. This course will provide necessary background on formal methods and their role in software engineering practice. A range of formal methods will be introduced along with practical case studies of their use. Students will learn how these methods can be used to build reliable software, hardware, and security protocols.
Course Description	This course will start with the fundamentals of set theory, relation and function, lattice theory, propositional and predicate logic, and proof techniques. In order to demonstrate how to analyze and verify a software system, this course will discuss the following three formal approaches using suitable examples: (a) Abstract Interpretation Theory, (b) Temporal Logic and Model Checking, and (c) Deductive Reasoning. In this context, formalism of syntax and semantics of programming languages will be explained considering a simple imperative language WHILE. All these approaches will be illustrated using real-life examples, such as microwave oven, mutual exclusion problem, etc.
Course Outline	Introduction: Introduction to critical systems, Introduction to formal methods and its role, Dependability, Testing Vs. Verification Formal Syntax and Semantics: the WHILE Language, Syntax Vs. Semantics, Formal Program Semantics - Operational, Denotational, Axiomatic Formal Program Analysis: Program Slicing, Dataflow Analysis, Fixpoint Algorithm, Abstract Interpretation Framework Formal Program Verification: Deductive Reasoning; Predicate Abstraction and CEGAR, Temporal Logic and Model Checking, Role of some other formal methods in software engineering New Research Directions: Recent trends on the application of formal methods in Machine Learning and Blockchain Tools: Introduction to various state-of-the-art Analyzers and Verifiers (e.g., NuSMV, UPPAAL, SPIN, ASTREE, CBMC, etc.)
Learning Outcome	· Gain proficiency in formal methods and their role in critical systems. · Understanding formal tools and techniques for analysis and verification of software source codes. · Learning how to define semantics of a software formally, and how to abstract its semantics at different levels of precision in order to capture its run-time behavioral properties of interests. · Learning temporal logic to express system’s time-varying behaviors. · Applying automatic software verification tools based on model checking and deductive reasoning. · Hands-on experience with NuSMV, Uppaal, Z3 SMT solver, etc. · Application of formal methods in cutting edge research domains including Robotics, IoT, Blockchain Smart Contracts.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Readings:

Flemming Nielson, Hanne Nielson, Chris Hankin. Principles of Program Analysis, Springer, 1999.
Edmund M. Clarke, Orna Grumberg, Doron A. Peled. Model Checking, The MIT Press,
Glynn Winskel. The formal semantics of programming languages: an introduction, The MIT Press, 1993.
José Bacelar Almeida, Maria João Frade, Jorge Sousa Pinto, Simão Melo de Sousa. Rigorous Software Development: An Introduction to Program Verification. Springer- Verlag London, 2011
Recent Research Papers relevant to the

Advanced Computational Data Analysis

Department Elective - II

Sl. No.

Subject Code

Subject

CS6106

Advanced Cloud Computing

Course Number	CS6106
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Advanced Cloud Computing
Learning Mode	Offline
Learning Objectives	This course aims to help the students understand (a) how and why cloud systems work and the cloud technologies that manifest these concepts, such as those from Amazon AWS and Microsoft Azure; (b) distributed systems concepts like virtualisation, data parallelism, CAP theorem, and performance analysis at scale; (c) Big Data programming patterns such as Map-Reduce (Hadoop), Vertex-centric graphs (Giraph), Continuous Dataflows (Storm), and NoSQL storage systems to build Cloud applications; (d) Cloud native computing and micro-services.
Course Description	This course provides an in-depth understanding of cloud computing, virtualisation, and distributed systems. It covers foundational concepts, advanced techniques, and real-world applications. Students will explore various aspects of cloud infrastructure, virtualisation technologies, distributed algorithms, and cloud-native computing. By the end of the course, students will be equipped with the knowledge and skills to design, implement, and manage cloud-based solutions and distributed systems effectively.
Course Outline	Cloud computing features and categories. Virtualization: Virtualization Models, Types of Virtualization: Processor virtualization, Memory virtualization, Full virtualization, Para virtualization, Device virtualization. Virtual Machine: Live VM Migration Stages, Virtual Machine Migration for Enterprise Data Centers, Data Center Workloads, Provisioning methods, Resource provisioning. Geo-distributed Clouds: Server Virtualization, Network Virtualization, Approaches for Networking of VMs: Hardware approach: Single-root I/O virtualization (SR-IOV), Software approach: Open vSwitch, Mininet and its applications. Software Defined Network for Multi-tenant Data Centers: Network virtualization, Case Study: VL2, NVP Geo-distributed Cloud Data Centers: Inter-Data Center Networking, Data center interconnection techniques: MPLS, Google’s B4 and Microsoft’s Swan. Leader Election algorithms in Cloud. Google’s Chubby and Apache Zookeeper. Time and Clock Synchronization in Cloud Data Centers, Datacenter time protocol (DTP. Consensus, Paxos and Recovery in Clouds. Cloud Storage: Key-value stores/NoSQL,Design of Apache Cassandra, HBase. Peer to Peer Systems in Cloud Computing. Cloud application: MapReduce Examples. Advances in Cloud Computing with decentralization and Edge Computing.
Learning Outcome	Cloud Computing as a Distributed Systems: Explain and contrast the role of Cloud computing within this space. Cloud Virtualization, Abstractions and Enabling Technologies: Explain virtualisation and their role in elastic computing. Characterise the distinctions between Infrastructure, Platform and Software as a Service (IaaS, PaaS, SaaS) abstractions, and Public and Private Clouds, and analyse their advantages and disadvantages. Programming Patterns for "Big Data" Applications on Cloud: Demonstrate using Map-Reduce, Vertex-Centric and Continuous Dataflow programming models. Application Execution Models on Clouds: Compare synchronous and asynchronous execution patterns. Design and implement Cloud applications that can scale up on a VM and out across multiple VMs. Illustrate the use of NoSQL Cloud storage for information storage. Performance, scalability and consistency on Clouds: Explain the distinctions between Consistency, Availability and Partitioning (CAP theorem), and discuss the types of Cloud applications that exhibit these features.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Reading:

Distributed and Cloud Computing From Parallel Processing to the Internet of Things; Kai Hwang, Jack Dongarra, Geoffrey Fox Publisher: Morgan Kaufmann, Elsevier, 2013.
Cloud Computing: Principles and Paradigms; Rajkumar Buyya, James Broberg, and Andrzej M. Goscinski Publisher: Wiley, 2011.
Distributed Algorithms Nancy Lynch Publisher: Morgan Kaufmann, Elsevier, 1996.

Cloud Computing Bible Barrie Sosinsky Publisher: Wiley, 2011.
Cloud Computing: Principles, Systems and Applications, Nikos Antonopoulos, Lee Gillam Publisher: Springer, 2012.

CS6107

Advanced Edge Computing

Course Number	CS6107
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Advanced Edge Computing
Learning Mode	Offline
Learning Objectives	Upon successful completion of this course, students will be able to: (a) understand the fundamental concepts and limitations of cloud computing and identify the advantages of edge computing; (b) describe various edge computing architectures and differentiate them from traditional cloud models; (c) comprehend the principles of distributed systems as they apply to edge computing environments; (d) explore the functionalities of edge data centers and lightweight edge clouds; (e) deploy and manage containerized applications using Docker and Kubernetes in edge computing contexts; and (f) implement and evaluate edge storage systems and end-to-end edge pipelines utilising MQTT and Kafka, as well as investigate advanced edge computing technologies for real-world applications.
Course Description	This course delves into the emerging field of edge computing, providing a comprehensive understanding of its architectures, systems, and technologies. Students will explore the limitations of traditional cloud computing and learn about the advantages and applications of edge computing. The course covers key concepts in distributed systems, edge data centers, and lightweight edge clouds and includes hands-on experience with Docker, Kubernetes, and edge storage systems. Additionally, students will gain insights into end-to-end edge pipelines using MQTT and Kafka and examine advanced edge computing technologies. By the end of the course, students will be equipped with the knowledge and skills to design, implement, and manage edge computing solutions.
Course Outline	Cloud Computing Basics.Edge Computing basics. Edge Computing Use-Cases, Benefits. Different Types of Edge. Edge Deployment Modes. Edge Computing in 5G, Multi-access Edge Computing (MEC) and Mobile Edge Computing.
Learning Outcome	Critically evaluate advanced edge computing architectures, such as hierarchical, mesh, and hybrid models, considering their suitability for specific use cases and environments. Analyses emerging technologies and trends in advanced edge computing, such as edge AI, blockchain, and serverless computing, and assess their potential impact. Design and implement innovative edge computing solutions that leverage advanced techniques, such as federated learning, edge caching, and dynamic resource allocation. Evaluate the performance and scalability of advanced edge computing systems using benchmarking, simulation, and experimentation. Investigate advanced techniques for ensuring security, privacy, and data integrity in edge computing ecosystems, such as secure enclaves, encryption, and access control mechanisms. Explore specialised applications of advanced edge computing in domains such as healthcare, smart cities, and autonomous systems, analysing their requirements and challenges.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Reading:

Fog and Edge Computing: Principles and Paradigms, Rajkumar Buyya (Editor), Satish Narayana Srirama (Editor), Wiley, 2019
Cloud Computing: Principles and Paradigms, Editors: Rajkumar Buyya, James Broberg, Andrzej M. Goscinski, Wiley, 2011
Cloud and Distributed Computing: Algorithms and Systems, Rajiv Misra, Yashwant Patel, Wiley 2020.
Besides these books, we will provide Journal papers as references.

CS6108

Course Number	CS6108
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Advanced Computational Data Analysis
Learning Mode	Offline
Learning Objective	In this subject, the students will be trained with the knowledge of various advanced data analytics techniques encountered in real life.
Course Description	Current Physical systems/devices are highly complex and fast and operate with high data acquisition and generation capabilities. Data generated from such systems require advanced level of analytics for apprehension and further usage. This course aims to give a broad understanding what is advanced level data analytics techniques and how they play a critical role in analysing modern day physical systems acquired data.
Course Outline	Introduction, Operation of physical systems and data generation, Complexity, Drawbacks and Challenges in data generation from physical devices. Requirement of advanced data analytics. Foundations of advanced data analytics principles, mathematical models, probabilistic models, optimization models, deep learning and machine learning models. Role of advanced data analytics in data apprehension and compression, curve-based approximation techniques, interpolation techniques, machine learning models for data interpretation. Statistical models to advanced data analytics, data analytics for 2D and 3D data processing and data manipulation, application of advanced data analytics to real life cases, problem solving.
Learning Outcome	1 · Gain understanding on data generation systems and the role of advanced data analytics. · Apply the Mathematical models of advanced data analytics to real time · Understand the utilities of statistical models and ML models for advanced data analytics.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Readings:

Signal Processing: A Mathematical Approach, Charles L. Byrne, Second Edition, Chapman & Hall, 2014.
Digital Functions and Data Reconstruction: Digital-Discrete Methods, Li M Chen, Springer, 2013.
Machine Learning with Neural Networks: An Introduction for Scientists and Engineers, Bernhard Mehlig, Cambridge University Press, 2021
Signal Processing and Machine Learning with Applications, Michael M. Richter, Sheuli Paul, Veton Këpuska, Marius Silaghi, Springer Cham, 2022
Data Compression: The Complete Reference, David Solomon, 4^th Edition, Springer, 2007

CS6109

Reinforcement Learning

Course Number	CS6109
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Reinforcement Learning
Learning Mode	Offline
Learning Objectives	This course aims to help the students (a) Understand the foundational concepts and mathematical frameworks of reinforcement learning. (b) Gain proficiency in key reinforcement learning algorithms, including dynamic programming, Monte Carlo methods, and temporal-difference learning (c) Apply deep reinforcement learning techniques to solve complex problems using methods such as deep Q-networks and policy gradient algorithms. (d) Explore recent advancements and applications of reinforcement learning, including multi-agent systems and ethical considerations.
Course Description	This specialized course on reinforcement learning aims to give students a deep understanding of the algorithms and methodologies used to train agents to make decisions through trial and error. Students will learn to develop and implement reinforcement learning models by focusing on foundational theories and practical applications. Students will explore key concepts such as Markov decision processes, policy gradients, Q-learning, and deep reinforcement learning through a mix of theoretical lectures, coding exercises, and project-based learning. Upon completion, students will be equipped to design and apply reinforcement learning solutions to complex problems in fields such as robotics, game development, and autonomous systems, enhancing their expertise in this dynamic area of artificial intelligence.
Course Outline	Foundations: Basics of machine learning and reinforcement learning (RL) terminology. Probability Concepts: Axioms of probability, random variables, distributions, and correlation. Markov Decision Process: Introduction to MDPs, Markov property, and Bellman equations. State and Action Value Functions: Concepts of MDP, state, and action value functions. Tabular Methods and Q-networks: Dynamic programming, Monte Carlo, TD learning, and deep Q-networks. Policy Optimization: Policy-based methods, REINFORCE algorithm, and actor-critic methods. Recent Advances and Applications: Meta-learning, multi-agent RL, ethics in RL, and real-world applications.
Learning Outcome	· Mastery of fundamental principles and mathematical frameworks of reinforcement learning. · Proficiency in implementing key reinforcement learning algorithms and techniques. · Ability to apply deep reinforcement learning methods to complex, real-world problems. · Understanding of recent advancements in reinforcement learning and their ethical implications.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Reading:

Reinforcement Learning: An Introduction by Richard S. Sutton and Andrew G. Barto, The MIT Press (1 January 1998).
Deep Reinforcement Learning Hands-On by Maxim Lapan, Packt Publishing Limited (21 June 2018).
Algorithms for Reinforcement Learning by Csaba Szepesvari, Morgan and Claypool Publishers (2010)
Deep Reinforcement Learning: Fundamentals, Research and Applications by Hao Dong, Springer Verlag (2020)

CS6110

Advanced Graph Machine Learning

Course Number	CS6110
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Advanced Graph Machine Learning
Learning Mode	Offline
Learning Objectives	Several real world systems can be represented as a network of entities that are connected to each other through some relations. Often the number of entities is immensely large, thus forming a very large network. Typical examples of such large networks include network of entities in knowledge graphs, co-occurrence graph of the keywords in natural languages, interaction graph of users in social networks, protein-protein interaction graphs and the network of routers in Internet to name a few. Study of these networks is often needed for relational learning tasks, as well as for developing frameworks for representing the intrinsic structure of the data. This course will mainly deal with both the traditional as well as current state of the art machine learning techniques to be applied on Graphs for different downstream tasks.
Course Description	The course will provide knowledge on the representation and statistical descriptions of large networks, along with traditional machine learning and deep learning techniques applied on graphs. Several use cases of Graph Machine Learning across different domains including Natural Language Processing, Social Network Analysis and Computational Biology would be studied.
Course Outline	Introduction and background knowledge of graphs; Network Measures and Metrices; Spectral Analysis of Graphs and its applications; Random Networks; Properties of Random Networks; Overview of machine learning applications on graphs; Feature based learning on graphs, Shallow embedding and deep Learning techniques for generating node and graph representations – Graph Neural Networks, Graph Attention Networks, Graph Transformers; Graph Neural Networks Pretraining techniques; Generative models for graphs; Models for scale-free and small-world networks; Temporal networks, Modeling temporal networks
Learning Outcome	Course training via lectures & tutorial sessions to · Represent and analyze the structure of graphs · Discover recurring and significant patterns of interconnections in your data with network motifs and community structure. · Gain Knowledge on traditional machine learning techniques applied on graphs · Leverage graph-structured data to make better predictions using graph neural networks · Understand the problems in dealing with large graphs for machine learning tasks and learn how to improvise · Analyze temporal and dynamic graphs · Scaling neural networks with generative models for graphs.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Readings:

E.J. Newman, Networks - An introduction , Oxford Univ Press, 2010.

Yao Ma and Jilian Tang, Deep Learning on Graphs, Cambridge University Press, 2021
Goyal, Palash and Emilio Ferrara. “Graph embedding techniques, applications, and performance: A survey.” Knowl.-Based Syst. 151 (2018): 78-94.

CS6111

Advanced Time Series Analysis

Course Number	CS6111
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Advanced Time Series Analysis
Learning Mode	Offline
Learning Objectives	· This course on advance time series will teach both the fundamental concepts time series analysis, as well as recent trends in time series analysis. · Students will learn to design successful time series data applications with sequential Neural Networks. · Deploy Nonlinear Auto-regressive Network with Exogenous Inputs · Adapt Deep Neural Networks for Time Series Forecasting and classification
Course Description	This course provides advanced concepts in time series analysis including some fundamentals of time series, data pre-processing, feature selection, Variety of modeling techniques, Anomaly Detection in Time Series and forecasting financial series using statistical, econometric, machine learning, and deep learning approaches and Practical Applications and Deployment of models.
Course Outline	Introduction to classical time series methods, time series Virtualization Univariate Stationary Processes; Granger Causality; Vector Autoregressive Processes Nonstationary Processes; Cointegration; Cointegration in Single Equation Models: Representation,Estimation and Testing. Applied Predictive Modeling Techniques; Autoregressive Conditional Heteroskedasticity. Finance and Algorithmic trading: Machine Learning and Deep Learning in Stock Price Prediction Machine Learning, Deep Learned Time series Analysis, Risk and Portfolio Management Practical Applications and Deployment of models; applications of convolutional neural network (CNN) and long-and-short-term memory (LSTM) network architectures; designing predictive models for financial time series data Stock Price Prediction using Deep Learning and Natural Language Processing
Learning Outcome	At the end of the course, students will have achieved the following learning objectives. problems relating to obtaining, cleaning, simulating, and storing time series data. Variety of modeling techniques that can be used for recent time series analysis techniques of financial time series analysis and forecasting financial series using statistical, econometric, machine learning, and deep learning approaches. Apply more recently developed methods, such as machine learning and neural network, to time series data, highlighting the challenges of data processing and data layout when time series data is used for fitting models
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Textbooks:

Kirchgässner, Gebhard, Jürgen Wolters, and Uwe Hassler. Introduction to modern time series analysis. Springer Science & Business Media, 2012.
Lazzeri, F. (2020). Machine learning for time series forecasting with Python. John Wiley & Sons.
Jaydip, Sen, and Mehtab Sidra. Machine Learning in the Analysis and Forecasting of Financial Time Series. 2022.

CS6113

Cyber Physical Systems

Course number	CS6113
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Cyber Physical Systems
Learning Mode	offline
Learning Objectives	To learn how to model and design the joint dynamics of software, networks, and physical processes., To develop the skills to realize embedded systems that are safe, reliable, and efficient in their use of resources., To learn to think critically about technologies that are available for achieving such joint dynamics.
Course Description	This course will provide an overview of modeling, building, analyzing methods for cyber physical systems.
Course Outline	Models of computation: finite state machines, threads, ordinary differential equations, hybrid systems, actors, discrete-events, data flow Basic analysis, control, and systems simulation: Bisimulations, reachability analysis, controller synthesis, approximating continuous-time systems. Interfacing with the physical world: sensor/actuator modeling and calibration, concurrency in dealing with multiple real-time streams, handling numerical imprecision in software Mapping to embedded platforms: real-time operating systems, execution time analysis, scheduling, concurrency Distributed embedded systems: Protocol design, predictable networking, security
Learning Outcome	· Basic understanding of cyber physical systems · To develop the skills to realize embedded systems that are safe, reliable, and efficient in their use of resources., · To learn to think critically about technologies that are available for achieving such joint dynamics.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Reading:

Introduction to Embedded Systems - A Cyber-Physical Systems Approach, Second Edition, by E. A. Lee and S. A. Seshia, 2015
Vahid, F. and T. Givargis (2010). Programming Embedded Systems - An Introduction to Time-Oriented Programming, UniWorld Publishing.
Schaumont, P. R. (2010). A Practical Introduction to Hardware/Software Codesign, Springer.
A. Lee and P. Varaiya, Structure and Interpretation of Signals and Systems, Addison-Wesley, 2003.

Artificial Internet of Things

Department Elective - III

Sl. No.

Subject Code

Subject

CS6201

Course Number	CS6201
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Artificial Internet of Things
Learning Mode	Offline
Learning Objectives	· Gain a comprehensive understanding of the convergence of Artificial Intelligence (AI) and Internet of Things (IoT), including basic concepts, architectures, and applications. · Learn various AI techniques and their applications in IoT, including machine learning, deep learning, and data analytics. · Develop skills in designing and implementing IoT systems, integrating sensors, and managing data flow. · Understand the processes for collecting, storing, processing, and analyzing IoT data using AI techniques. · Identify and mitigate security risks and privacy concerns in AIoT systems. · Analyze various real-world applications of AIoT in industries such as healthcare, smart cities, agriculture, and manufacturing. · Understand the regulatory and ethical considerations related to AIoT technologies and their deployment.
Course Description	This course provides an in-depth exploration of the convergence of Artificial Intelligence (AI) and the Internet of Things (IoT), known as AIoT. It covers the fundamental principles and technologies of both AI and IoT, demonstrating how they can be integrated to create intelligent, autonomous systems. Students will learn about IoT architecture, AI algorithms, machine learning, data analytics, and the implementation of AI-driven IoT solutions. Through hands-on projects and real-world case studies, students will gain practical experience in developing smart applications for various domains such as smart cities, healthcare, industrial automation, and smart homes.
Course Outline	Introduction to AIoT, Intersection of AI and IoT, Benefits and challenges of AIoT Fundamentals of IoT, IoT Architecture and Protocols, Layers of IoT architecture, Communication protocols and standards, IoT Devices and Sensors Fundamentals of Artificial Intelligence, Machine Learning and Deep Learning, Overview of AI tools and frameworks AIoT System Architecture, Components and Designing AIoT, Edge Computing in AIoT, Edge vs. cloud computing, AI Models for IoT Data Management in AIoT, Data Processing and Analysis, Handling large-scale IoT data, Big data technologies and platforms AIoT Applications and Use Cases: Smart Homes and Buildings, Healthcare and Wearables, Industrial IoT (IIoT), Smart Cities and Transportation AIoT Platforms and Tools: AI Development Tools, Case Studies of AIoT Solutions, AIoT Project Development, Future Trends and Innovations in AIoT
Learning Outcome	At the end of course, students will learn: · Students should grasp the foundational concepts of AI and IoT, including machine learning algorithms, data analytics, sensor technologies, and network protocols. · Ability to integrate AI algorithms with IoT devices and platforms to create intelligent systems capable of data collection, analysis, and decision-making in real-time. · Proficiency in developing AI-driven IoT applications, including sensor data processing, predictive analytics, anomaly detection, and automation. · Awareness of security challenges and solutions in AIoT systems, including data privacy, authentication, encryption, and intrusion detection. · Knowledge of optimization techniques for AIoT systems to enhance performance, scalability, and energy efficiency.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Reading:

Olivier Hersent, David Boswarthick, and Omar Elloumi, The Internet of Things: Key Applications and Protocols, Wiley
Maciej Kranz, Building the Internet of Things: Implement New Business Models, Disrupt Competitors, Transform Your Industry, Wiley
John Paul Mueller and Luca Massaron, Machine Learning for the Internet of Things: Practical Guide, Packt

CS6202

Game Theory

Course Number	CS6202
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Game Theory
Learning Mode	Offline
Learning Objectives	Learn the principles of decision theory and its relevance to game theory. Understand and analyze extensive form games, including game trees and backward induction. Identify and compute pure and mixed strategy Nash equilibria. Analyze matrix games, specifically two-player zero-sum games. Understand Bayesian games and apply Bayesian equilibrium concepts to games with incomplete information. Analyze and compute subgame perfect equilibria in dynamic games. Explore coalitional games, including the core and the Shapley value. Explore auction theory and its various models and applications. Utilize game theory concepts in practical applications such as IoT, wireless networks, and cloud computing.
Course Description	This course aims to establish a solid foundation in both game theory and mechanism design, enabling participants to apply these principles rigorously to solve problems. By the end of the course, students will be equipped to model real-world scenarios using game theory, analyze these scenarios with game-theoretic concepts, and design effective and robust solutions, including mechanisms, algorithms, and protocols suitable for rational and intelligent agents.
Course Outline	Non-cooperative Game Theory: Decision theory, Extensive Form Games, Strategic Form Games, Dominant Strategy Equilibria, Pure Strategy Nash Equilibrium, Mixed Strategy Nash Equilibrium, Computation of Nash Equilibrium, Complexity of Computing Nash Equilibrium, Matrix Games (Two Player Zero-sum Games), Bayesian Games, Subgame Perfect Equilibrium. Cooperative Game: Correlated Strategies and Correlated Equilibrium, Two Person Bargaining Problem, Coalitional Games, Core, Shapley Value. Mechanism Design: Introduction to Mechanism Design, Social Choice Functions and their properties, Incentive Compatibility, Auction theory and its variants. Applications: IoT, Wireless Networks, Cloud Computing
Learning Outcome	By the end of this course, students will be able to: Describe the principles of decision theory and its importance in game theory. Formulate and solve strategic form games, identifying dominant strategy equilibria and Nash equilibria. Analyze and solve matrix games, particularly two-player zero-sum games. Formulate Bayesian games and determine Bayesian equilibria for games with incomplete information. Compute subgame perfect equilibria for dynamic games using appropriate techniques. Apply the concepts of correlated strategies and correlated equilibria in cooperative settings. Analyze and solve two-person bargaining problems. Analyze social choice functions and their properties, focusing on incentive compatibility. Utilize game theory concepts to address practical problems in IoT, wireless networks, and cloud computing.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Textbook:

M. Osborne, An Introduction to Game Theory, Oxford University Press.
Y. Narahari. Game Theory and Mechanism Design. IISc Press and the World Scientific.

Reference Book:

M. Maschler, E. Solan, and S. Zamir, Game Theory. Cambridge University Press
D. Niyato, & W. Saad. Game theory in wireless and communication networks. Cambridge University Press.

CS6203

Text Mining and Analytics

Course Number	CS6203
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Text Mining and Analytics
Learning Mode	Offline
Learning Objectives	· To understand the fundamental principles and scope of text mining and analytics. · To acquire skills in data collection, cleaning, and integration for text data. · To learn text preprocessing techniques including tokenization, stemming, stopword removal, and normalization. · To construct knowledge graphs by linking entities and extracting relationships. · To identify and mine frequent patterns and apply advanced pattern mining techniques. · To extract features from text data and apply clustering and classification methods. · To implement practical applications such as sentiment analysis and text summarization. · To utilize advanced techniques for enhanced text data analysis and mining.
Course Description	This course provides a comprehensive understanding of the principles and techniques of text mining and analytics. Students will learn about data collection, cleaning, integration, and preprocessing methods essential for handling text data. The course covers knowledge graph construction, pattern mining, feature extraction, and advanced text clustering and classification techniques. Practical applications such as sentiment analysis and text summarization are also explored. By the end of the course, students will be prepared to tackle real-world challenges in data mining and text analytics.
Course Outline	Text mining and analytics introduction: Overview, motivation, scope, Data Collection and Pre-processing: Techniques for collecting data from various sources, Text data cleaning and integration, descriptive analytics Text preprocessing: tokenization, stemming, stopword removal, and normalization Knowledge graph construction: Basics of graphs, entity linking, relationship extraction Concepts of frequent patterns, closed patterns, max-patterns, and association rules, mining frequent patterns: apriori algorithm, pattern-growth approach. Advanced: mining sequential patterns Feature extraction, Bag-of-Words, TF-IDF, word embeddings Clustering and classifying text data, Expectation-maximization (EM) algorithm for text data, Latent Dirichlet Allocation (LDA) for topic modeling, and some advanced techniques Some applications: sentiment analysis, text summarization, etc. Some advanced topics and project
Learning Outcome	· By the end of this course, students will be able to: · Understand the core principles and scope of text mining and analytics. · Collect, clean, and integrate text data from various sources. · Apply text preprocessing techniques such as tokenization, stemming, and normalization. · Construct and utilize knowledge graphs for entity linking and relationship extraction. · Identify and mine various patterns in text data, including frequent, closed, and sequential patterns. · Extract features from text data using methods like Bag-of-Words, TF-IDF, and word embeddings. · Perform text clustering and classification using algorithms such as EM and LDA. · Implement practical text analytics applications such as sentiment analysis and text summarization. · Utilize advanced techniques for enhanced text data analysis and mining.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Reading

Srivastava, A. N., & Sahami, M. (Eds.). (2009). Text mining: Classification, clustering, and applications. CRC press.
Chakraborty, G., Pagolu, M., & Garla, S. (2014). Text mining and analysis: practical methods, examples, and case studies using SAS. SAS Institute.
Sarkar, D. (2016). Text analytics with python (Vol. 2). New York, NY, USA:: Apress.
Witten, I. H., Frank, E., Hall, M. A., Pal, C. J., & Data, M. (2005, June). Practical machine learning tools and techniques. In Data mining (Vol. 2, No. 4, pp. 403-413). Amsterdam, The Netherlands: Elsevier.

CS6204

Knowledge Distillation

Course Number	CS6204
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Knowledge Distillation
Learning Mode	Offline
Learning Objectives	This course aims to help the students (a) understand and apply knowledge distillation techniques; (b) master deep neural network compression methods; (c) deploy ML/DNN models on edge devices like Raspberry Pi and others; (d) analyze and optimize model performance in resource-constrained environments; (e) identify the research opportunity in the domain of knowledge distillation and DNN compression on resource-constrained devices.
Course Description	This course delves into advanced techniques for enabling machine learning on resource-constrained devices. Beginning with an introduction to on-device training, students will explore the principles and methods of knowledge distillation and deep neural network (DNN) compression. The course covers practical strategies for implementing machine learning and deep neural networks on devices with limited computational resources. Additionally, students will learn to combine knowledge distillation and compression techniques to optimise performance, making sophisticated machine-learning models viable on edge devices.
Course Outline	· Introduction to on-device training: Overview of resource-constrained edge devices and their significance, possibilities of enabling machine learning (ML) and deep neural networks (DNN) models on resource-constrained devices, applications and use cases of ML/DNN on edge devices. · Knowledge Distillation: Concept and principles of knowledge distillation, Teacher-student model framework, Applications and benefits of knowledge distillation. Advanced techniques in knowledge distillation, Implementation of knowledge distillation in various frameworks, and Practical exercises on distilling models. · Deep Neural Network Compression: Overview of DNN compression techniques, Quantization and its impact on model performance, Pruning methods for model size reduction. Low-rank factorization, Weight sharing and clustering, Hands-on implementation of compression techniques. · ML/DNN on resource-constrained devices: Introduction to edge devices: Raspberry Pi, NVIDIA Jetson, etc, Setting up an AI development environment on Raspberry Pi, Case study: Running a pre-trained model on Raspberry Pi. TensorFlow Lite, ONNX, etc, Practical exercises with TensorFlow Lite on Raspberry Pi. · Combining Knowledge Distillation and Compression: Integrating knowledge distillation and compression for optimal performance, Strategies for balancing accuracy and efficiency, Real-world examples and case studies.
Learning Outcome	· Explain and implement knowledge distillation techniques. · Apply DNN compression methods such as quantisation and pruning. · Set up and optimise ML/DNN models on Raspberry Pi using TensorFlow Lite and ONNX. · Evaluate and enhance ML/DNN model performance on edge devices. · Create real-time applications, including object detection and predictive maintenance. · Plan, develop and present comprehensive projects that may lead to publication.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Reading:

Deep Learning for Edge AI” by John Doe
Knowledge Distillation: Principles, Methods and Applications” by Jane Smith
Official documentation and tutorials for TensorFlow Lite, ONNX, and edge devices
“Knowledge Distillation: A Survey” Jianping Gou, Baosheng Yu, Stephen John Maybank, Dacheng Tao
K. Nan, S. Liu, J. Du and H. Liu, "Deep model compression for mobile platforms: A survey," in Tsinghua Science and Technology, vol. 24, no. 6, pp. 677-693, Dec. 2019, doi: 10.26599/TST.2018.9010103.
Mishra et al.. "A survey on deep neural network compression: Challenges, overview, and solutions."

CS6208

Quantum Machine Learning

Course Number	CS6208
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Quantum Machine Learning
Learning Mode	Offline
Learning Objectives	This course aims to help the students (a) proficiency in implementing and applying classical machine learning algorithms, including classification, regression, gradient descent, and neural networks. (b) grasp the foundational principles of quantum computing, quantum states, qubits, and basic quantum operations.(c) advanced quantum algorithms and their applications in machine learning and computational tasks. (d) gain practical experience in implementing quantum algorithms and simulating quantum processes.
Course Description	This course offers a comprehensive exploration of machine learning (ML) and quantum computing (QC) principles, preparing students to navigate the intersection of classical and quantum computational paradigms. Students will master classical ML techniques including classification, regression, neural networks, and optimization methods like gradient descent. In the quantum computing segment, foundational concepts such as quantum states, qubits, and basic quantum operations (e.g., Hadamard gates) will be covered, alongside encoding classical data on quantum systems and implementing basic quantum algorithms. Advanced topics include variational quantum algorithms, quantum support vector machines, the HHL algorithm for linear systems, and quantum neural networks. Through lectures, practical exercises using quantum programming frameworks, and real-world applications, students will develop a dual proficiency in classical ML and quantum computing, equipping them for roles in research, development, or applications across industries leveraging emerging quantum technologies.
Course Outline	Overview of Machine Learning, Quantum Circuit, Variational quantum algorithm, Quantum Neural Network
Learning Outcome	· Understanding of Machine Learning and Quantum Computing Fundamentals. · Apply the concept of feature vectors, encode data in Quantum computing. · Analysis of Variational quantum algorithms to solve complex problems. · Implementation and analysis of advanced quantum machine learning algorithms.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Textbooks:

Nielsen, M.A. and Chuang, I.L., 2010. Quantum computation and quantum information.
Schuld, M. and Petruccione, F., 2021. Machine learning with quantum computers (Vol. 676). Berlin.
Relevant research articles.

Reference books:

Kasirajan, V., 2021. Fundamentals of quantum computing.

Quantum Machine Learning, Link: http://sites.iiserpune.ac.in/~santh/course/QML/qml.html

CS6210

Selective Topics in Generative AI

Course Number	CS6210
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Selective Topics in Generative AI
Learning Mode	Offline
Learning Objectives	· To gain a comprehensive understanding of advanced AI architectures, particularly in the context of Generative AI. · To develop proficiency in implementing and evaluating a variety of Generative AI techniques and models. · To understand the principles and applications of Generative Pre-trained Transformers and other application-specific architectures. · To explore and address ethical considerations and biases in Generative AI, emphasizing the importance of explainability. · To engage with advanced topics and apply knowledge through hands-on projects.
Course Description	This course provides an in-depth exploration of Generative AI (GenAI), focusing on advanced AI architectures such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Generative Pre-trained Transformers (GPT). Students will learn about hybrid and emerging models, application-specific architectures, and the ethical considerations and biases in Generative AI. The course includes hands-on projects to design, implement, and evaluate sophisticated generative AI models, emphasizing innovation and practical problem-solving skills.
Course Outline	Introduction to advanced AI, overview of advanced AI architectures and Generative AI (GenAI) Generative Adversarial Network (GAN): various GAN architectures, DCGAN Advanced Variational AutoEncoder (VAE): hierarchical VAEs, Semi-supervised VAE Hybrid and emerging models: Energy-based models, diffusion models, autoregressive and flow-based models, attention mechanism in generative models Generative Pre-trained Transformers (GPT): architectural details and variations Advanced application-specific architecture: Models for Image-to-Text generation, Text-to-Image generation, Prompt engineering, Multimodality Ethical consideration and bias in Generative AI, Explainability Some advanced topics and project.
Learning Outcome	· Master various Generative AI architectures, including GANs, VAEs, and emerging models. · Demonstrate proficiency in implementing and evaluating advanced Generative AI techniques, such as hierarchical VAEs and energy-based models. · Understand the design principles and applications of Generative Pre-trained Transformers (GPT) and application-specific architectures. · Analyze and address ethical considerations and biases in Generative AI, emphasizing the importance of explainability. · Explore advanced topics in Generative AI and apply acquired knowledge through hands-on projects, fostering innovation and practical problem-solving skills.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Reading:

Foster, D. (2022). Generative deep learning: : Teaching Machines to Paint, Write, Compose, and Play. " O'Reilly Media, Inc.".
Valle, R. (2019). Hands-On Generative Adversarial Networks with Keras: Your guide to implementing next-generation generative adversarial networks. Packt Publishing Ltd.

Research Papers and Articles from Journals such as JMLR, IEEE Transactions on Neural Networks and Learning Systems, etc., and Conference Proceedings from NeurIPS, ICML, and CVPR,etc.

Selected Topics in Wireless Networks

Department Elective - IV

Sl. No.

Subject Code

Subject

CS6206

Course Number	CS6206
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Selected Topics in Wireless Networks
Learning Mode	Offline
Learning Objectives	In this subject, the students will be trained with the knowledge of 802.11 wireless networks, including protocol knowledge and the associated security vulnerabilities.
Course Description	In the consumer, industrial, and military sectors, 802.11-based wireless access networks have been widely used due to their convenience. This application, however, is reliant on the unstated assumptions of availability and anonymity. The management and media access protocols of 802.11 may be particularly vulnerable to malicious denial-of-service (DoS) and various security attacks. This course analyzes these 802.11-specific attacks, including their applicability, effectiveness, and proposed low-cost implementation improvements to mitigate the underlying vulnerabilities.
Course Outline	Introduction to Wireless Networks: Basic principles, types of wireless networks (Wi-Fi, Bluetooth, cellular), and network topologies. Wireless Communication Fundamentals: Radio frequency, signal propagation, modulation techniques, and interference management. Network Protocols and Standards: IEEE 802.11 (Wi-Fi), IEEE 802.15 (Bluetooth), and cellular standards (2G, 3G, 4G, 5G). Network Design and Architecture: System design, frequency reuse, and resource allocation. Mobility and Handoff: Techniques for managing mobility, handoff processes, and roaming. Security in Wireless Networks: Security protocols, encryption, and threat mitigation. Emerging Technologies: Overview of 6G, IoT, in-network caching
Learning Outcome	On successful completion of the course, students should be able to: · Understand the fundamentals of 802.11 wireless networks · Describe the WLAN services-association, disassociation, re-association, distribution, integration, authentication, de authentication and data delivery services · Comprehend the vulnerabilities associated with 802.11 protocol.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Text Books and References:

"Wireless Communications: Principles and Practice" by Theodore S. Rappaport (2nd Edition)
"802.11 Wireless Networks: The Definitive Guide" by Matthew S. Gast (2nd Edition)
"Wireless Communications & Networks" by William Stallings (2nd Edition)
"Wireless Communications: Principles and Practice" by Andreas F. Molisch (2nd Edition)
"Fundamentals of Wireless Communication" by David Tse and Pramod Viswanath (1st Edition)
"Next Generation Wireless LANs: 802.11n and 802.11ac" by Eldad Perahia and Robert Stacey (2nd Edition)
"Wireless Networking: Understanding Internetworking Challenges" by Anurag Kumar, D. Manjunath, and Joy Kuri (1st Edition)
"Wireless Communications: Principles and Practice" by Kaveh Pahlavan and Prashant Krishnamurthy (1st Edition)

CS6207

Advanced Big Data Analytics

Course Number	CS6207
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Advanced Big Data Analytics
Learning Mode	Offline
Learning Objectives	The primary objective of this course is to equip students with advanced knowledge and skills in big data analytics. By the end of the course, students will be able to understand and apply advanced data analysis techniques, develop and implement big data solutions, and leverage big data technologies for strategic decision-making. Additionally, students will gain proficiency in using big data tools and platforms, enhance their ability to handle and analyze large datasets, and develop critical thinking skills for solving complex data-driven problems.
Course Description	This course provides an in-depth exploration of advanced big data analytics, focusing on the theoretical foundations, practical techniques, and cutting-edge technologies in the field. Students will learn about various aspects of big data, including data acquisition, storage, processing, and analysis. The course covers advanced topics such as machine learning algorithms for big data, real-time data processing, and big data visualization. Emphasis will be placed on the use of big data tools and platforms such as Hadoop, Spark, and NoSQL databases. Through hands-on projects and case studies, students will develop the skills needed to design and implement big data solutions for a variety of applications.
Course Outline	Introduction to Big Data Analytics: Definition and characteristics (Volume, Velocity, Variety, Veracity, and Value), Importance and challenges of Big Data. Big Data Ecosystem: Components and architecture, Key players and technologies in Big Data (e.g., Hadoop, Spark). Big Data vs. Traditional Data: Differences in processing and analysis, Applications of Big Data Analytics- Industry-specific applications and Case studies Data Acquisition and Storage: Structured, semi-structured, and unstructured data and Data generation and collection methods. Distributed file systems (e.g., HDFS), NoSQL databases (e.g., MongoDB, Cassandra), and Cloud storage options, ETL (Extract, Transform, Load) processes, Data pipelines and workflow automation, Insuring data integrity and accuracy, Data privacy and security considerations Data Processing Frameworks: Hadoop MapReduce architecture and workflow, Advantages and limitations, Apache Storm, Apache Flink, and Kafka Streams, Real-time data processing and its significance, Apache Spark architecture and RDDs (Resilient Distributed Datasets), Spark SQL, Spark Streaming, and MLlib Machine Learning for Big Data: Introduction to Machine Learning, Machine Learning Algorithms, Machine Learning Tools and Libraries, Training and evaluating models on large datasets, Scalability and performance optimization Real-Time Data Processing: Importance and applications of real-time analytics, Apache Kafka, Apache Flink, and Apache Storm, Designing and implementing real-time data workflows, Industry examples and best practices Big Data Visualization: Making data comprehensible and actionable, Visualization Tools and Techniques, Building user-friendly and interactive data dashboards, Intersection of data science and big data analytics, Integrating AI techniques with big data analytics, Processing and analyzing IoT-generated data, Distributed computing at the edge of networks, and Industry-Specific Case Studies- Healthcare, finance, retail, and other industries.
Learning Outcome	Understand the key concepts and significance of big data analytics. Acquire, store, and manage large datasets using appropriate big data technologies. Apply advanced data processing techniques using Hadoop and Spark. Implement machine learning algorithms for big data applications. Perform real-time data processing and analysis. Utilize big data visualization tools to interpret and present data insights. Develop and implement comprehensive big data solutions for various industry applications. Critically evaluate and solve complex data-driven problems using advanced analytics techniques.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Reading:

Marz, N., & Warren, J. (2015). Big Data: Principles and Best Practices of Scalable Real-Time Data Systems (1st ed.). Manning Publications.
White, T. (2015). Hadoop: The Definitive Guide (4th ed.). O'Reilly Media.
Karau, H., & Warren, R. (2017). High Performance Spark: Best Practices for Scaling and Optimizing Apache Spark (1st ed.). O'Reilly Media.
Gulla, U., Gupta, S., & Kumar, V. (2020). Practical Big Data Analytics: Hands-on Techniques to Implement Enterprise Analytics and Machine Learning Using Hadoop, Spark, NoSQL and R (2nd ed.). Packt Publishing.
Zikopoulos, P. C., Eaton, C., & deRoos, D. (2012). Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data (1st ed.). McGraw-Hill Education.

CS6215

Quantum Cyber Security

Course Number	CS6215
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Quantum Cyber Security
Learning Mode	Physical
Learning Objectives	To have a clear understanding of Quantum technology and that brings on the security and privacy of communication and computation
Course Description	The course covers various effects that developing quantum technologies will have on cyber security.
Course Outline	Quantum information concepts: qubits, mixed states, operations, distance measures, quantum circuits, quantum algorithms (factoring, discrete logarithms, search). Classical Cryptography, encryption, authentication and key distribution protocols, Security analysis, quantum cryptography, quantum-key-distribution protocols, Security and implementation aspects. classical protocols and their security under quantum attacks, general quantum attacks (superposition attacks)
Learning Outcome	After completion of this course a student will have · Understanding of the quantum technologies. · Demonstrate their understanding of the power of quantum algorithms and be able to use the basic mathematical formalism for quantum information and quantum cryptography Test whether a classical cryptosystem is secure against a range of quantum attacks Use security notions for quantum information, such as encryption and authentication, in quantum cryptographic protocols
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Readings:

Quantum Computation and Quantum Information by Nielsen and Chuang
Cryptography: Theory and Practice by D.R. Stinson

CS6216

High Performance Computing

Course Number	CS6216
Course Credit (L-T-P-C)	3-0-0-3
Course Title	High Performance Computing
Learning Mode	Offline
Learning Objectives	The course is designed to provide basic understanding of structure, and function of various building blocks of high performance Computing System. Students will be able to design various functional units and components and to identify the elements of modern GPUs and their impact on processor/GPU/TPU and parallel architecture design including memory
Course Description	Using a set of fundamental techniques and technologies, the high performance systems theme broadly explains how computing platforms work at various levels of abstraction, including both software and hardware. The course introduces HPS architecture with focus on parallel architectures
Course Outline	Computer types, Structure with basic computer components - instruction sets of some common CPUs/GPUs; Parallel Processing Concepts: a) Levels of parallelism (instruction, transaction, task, thread, memory, function) Models (SIMD, MIMD, SIMT, SPMD, Dataflow Models, Demand-driven Computation etc) c) Architectures: N-wide superscalar architectures, multi-core, multi-threaded Parallel Programming with CUDA: a) Processor Architecture, Interconnect, Communication, Memory Organization, and Programming Models in high performance computing architectures: Fundamental Design Issues in Parallel Computing: a) Synchronization b) Scheduling, c) Job Allocation d) Job Partitioning, e) Dependency Analysis,f) Mapping Parallel Algorithms onto Parallel Architectures g) Performance Analysis of Parallel Algorithms Power-Aware Computing and Communication: a) Power-aware Processing Techniques Advanced Topics:(a) Petascale Computing,(b) Optics in Parallel Computing,(c) Quantum Computers,(d) Recent developments in Nanotechnology and its impact on HPC
Learning Outcome	The student will be able to : · Appreciate understanding of the HPC blocks, key terminology, and current industry trends in high performance computer architecture. · Understand parallel programming and evaluate and compare the architectural features of the state of the art high performance commodity hardware platforms. · Understand the processor (CPU/GPU/TPU) subsystem. · Employ concepts of the HPS memory subsystem and hierarchy
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Text Books:

"Highly Parallel Computing", by George S. Almasi and Alan Gottlieb
"Advanced Computer Architecture: Parallelism, Scalability, Programmability", by Kai Hwang, McGraw Hill 1993
"Parallel Computer Architecture: A hardware/Software Approach", by David Culler Jaswinder Pal Singh, Morgan Kaufmann, 1999.
"Scalable Parallel Computing", by Kai Hwang, McGraw Hill 1998.
"Principles and Practices on Interconnection Networks", by William James Dally and Brian Towles, Morgan Kauffman 2004.
GPU Gems 3 --- by Hubert Nguyen (Chapter 29 to Chapter 41)
Introduction to Parallel Computing, Ananth Grama, Anshul Gupta, George Karypis, and Vipin Kumar, 2nd edition, Addison-Welsey, © 2003.
Petascale Computing: Algorithms and Applications, David A. Bader (Ed.), Chapman & Hall/CRC Computational Science Series, © 2007.

CS6211

Selected topics on Cryptography

Course Number	CS6211
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Selected topics on Cryptography
Learning Mode	physical
Learning Objectives	To have a clear understanding of design and analysis of different advanced cryptographic protocols
Course Description	The course covers design and analysis of advanced cryptographic protocols
Course Outline	Mathematical Background: Modular Arithmetic, Finite Fields, Elliptic Curves over Finite Fields Crypto basics: SKE. PKE Zero-knowledge proofs, and protocols, Secret sharing, Commitment, Oblivious transfer, Secure multiparty computation, Homomorphic encryption, obfuscation, Post quantum cryptography
Learning Outcome	After completion of this course a student will have · Understanding and analysis of symmetric key cryptography · Understanding and analysis of asymmetric key cryptography · Designing and analysis of advanced cryptographic protocols · Understanding and analysis of post quantum cryptographic protocols
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Readings:

Doug Stinson, Cryptography: Theory and Practice, Chapman and Hall/CRC

Interdisciplinary Elective (IDE) Course for M. Tech. (Available to students other than CSE)

IDE from CSE - IDE-I

Sl. No.

Subject Code

Subject

CS6112

Drone Data Processing & Analysis

Course Number	CS6112
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Drone Data Processing & Analysis
Learning Mode	Offline
Learning Objectives	This course aims to help the students (a) understand the integration of various sensors and platforms, including optical, thermal, LiDAR, multispectral, and hyperspectral sensors; (b) identify and analyze the use of drones in civilian and remote sensing applications; (c) to learn the importance and techniques of sensor calibration and boresighting to ensure data accuracy and reliability; (d) comprehend the operational requirements for UAVs and develop a Concept of Operation (CONOP) including risk assessment for safe and effective drone missions; (e) gain proficiency in using advanced data processing software tools to generate high-quality digital products from drone data; and (f) to evaluate data quality using accuracy metrics and understand the latest mapping standards to ensure high-precision geospatial data.
Course Description	This course provides an in-depth exploration of advanced drone systems, focusing on integrating and applying various sensors, including optical, thermal, LiDAR, multispectral, and hyperspectral. Students will learn how to integrate these sensors with different drone platforms and apply them in various fields such as agriculture, construction, environmental monitoring, and urban planning. The course covers using drones in remote sensing for resource management, disaster response, and scientific research, emphasising the importance of sensor calibration, boresighting methods, and operational best practices. Additionally, students will gain hands-on experience with leading software tools for drone data processing and understand the latest standards for geospatial data accuracy. Regulatory compliance, safety, security, and privacy issues will also be addressed. Practical applications and industry case studies will be analysed to illustrate successful drone data processing projects.
Course Outline	Importance of Calibration, Methods and best practices for boresighting to align sensors accurately, Key operational requirements and best practices, Developing and implementing a Concept of Operation (CONOP) for drone missions, Risk assessment Introduction to leading software tools for drone data processing (e.g., Pix4D, Agisoft Metashape, DroneDeploy), Steps from data acquisition to final product generation Understanding the latest standards for geospatial data accuracy, Techniques for identifying and correcting errors in drone data, Creating digital elevation models (DEMs) and digital terrain models (DTMs) Current Rules and Regulations in India, Compliance and Certification, Comparison with global regulatory standards, UAV Safety Issues, Security Concerns, Privacy Issues Practical Applications and Case Studies, Analysis of successful drone data processing projects in various industries
Learning Outcome	Evaluate and apply drone technology in diverse civilian and remote sensing scenarios, identifying the benefits and challenges of each application. Execute proper calibration and boresighting techniques to ensure the accuracy and reliability of sensor data. Create and implement an effective CONOP for drone missions, including risk assessment and mitigation strategies. Efficiently use leading data processing software tools to process drone data and generate high-quality digital products. Assess data quality using established accuracy metrics and apply the latest mapping standards to ensure high-precision geospatial data. Navigate and comply with current drone regulations in India and understand international regulatory frameworks.
Assessment Method	Internal(Quiz/Assignment/Project), Mid-Term, End-Term

Suggested Reading

Barnhart, R., Michael, M., Marshall, D., and Shappee, E. ed. 2016. Introduction to Unmanned Aircraft Systems, 2nd edition. Boca Raton. CRC Press.
Fahlstrom, P. and Gleason, T. 2012. Introduction to UAV Systems. 4th edition. United Kingdom. John Wiley & Sons Ltd.
Wolf, P., DeWitt, B., and Wilkinson, B. 2014. Elements of Photogrammetry with Applications in GIS, 4th edition. McGraw-Hil
Introduction to UAV Systems, Paul G. Fahlstrom and Thomas J. Gleason
Drone Technology in Architecture, Engineering, and Construction, Daniel Tal and Jon Altschuld
UAV or Drones for Remote Sensing Applications, edited by Felipe Gonzalez Toro and Antonios Tsourdos

M. Tech. in Geotechnical Engineering

Program Learning Objectives:	Program Learning Outcomes:
Program Goal 1: Equip the students with strong foundation in civil and environmental engineering for both research and industrial scenarios.	Program Learning Outcome 1a: Student develops ability to design and conduct experiments. Program Learning Outcome 1b: Student is able to organize and analyze the experiment data to draw conclusions.
Program Goal 2: Provide scientific and technical knowledge in planning, design, construction, operation and maintenance of civil engineering infrastructure.	Program Learning Outcome 2: Students are able to (i) develop material and process specifications, (ii) analyze and design projects, (iii) perform estimate and costing and (iv) manage technical activities.
Program Goal 3: Prepares the students to apply knowledge in policy and decision making related to civil engineering infrastructure.	Program Learning Outcome 3a: Student develops understanding of professional and ethical responsibility. Program Learning Outcome 3b: Student is able to consider economic, environmental, and societal contexts while developing engineering solutions.
Program Goal 4: Prepare students to attain leadership careers to meet the challenges and demands in civil engineering practice.	Program Learning Outcome 4a: Students is prepared for leading roles/profiles in government sector, construction industry, consultancy services, NGOs, corporate houses and international organizations. Program Learning Outcome 4b: Student develops ability to identify, formulate, and solve engineering problems.
Program Goal 5: Nurture interdisciplinary education for finding innovative solutions.	Program Learning Outcome 5: Student is able to solve complex engineering problems by applying principles of engineering and science.

Semsester - I

Sl. No.

Subject Code

SEMESTER I

HS5111

Technical Writing and Soft Skill

CE5104

Geotechnical Exploration

Course	CE5104 Core-1: Geotechnical Exploration
Course Credit (L-T-P-C)	3-0-2-4
Course Title	Geotechnical Exploration
Learning Mode	Lectures and Practical
Learning Objectives	Complies with PLO- number 1, 2, 3, and 4 1. Equip the students with a strong foundation in civil and environmental engineering for both research and industrial scenarios. 2. Prepares the students to apply knowledge in policy and decision making related to civil engineering infrastructure. 3. Prepare students to attain leadership careers to meet the challenges and demands in civil engineering practice. 4. Prepare the student for various field exploration techniques usually applied in the industry.
Course Description	Geotechnical exploration part is not covered in the basic soil mechanics course in detail. Therefore, attendees will learn about these field and laboratory experiments and can apply effectively when they are working in the industry. The course started with the basic knowledge gained by the attendee during undergraduate level regarding the Geotechnical exploration. Later on they will learn about various field tests, laboratory tests, sample collection etc. This course intends to provide various tools in terms of field and laboratory experiment. Attendees can use these tools for effective design of civil engineering structures.
Course Outline	Introduction: Planning for Subsurface Exploration, problems in soil investigations. Methodology: Site investigation methods, Drilling techniques, Sampling techniques, In-situ field testing. Type of soil exploration methods: Geophysical exploration, SPT, CPT, DCPT, Pressuremeter tests, Flat dilatometer test, Field vane shear test. Sample collection: Types of samplers, Sample Disturbance, Preservation and transportation of sample. Laboratory Tests: CBR, Triaxial Test on soil and rocks, Splitting Tension Test on Rocks, Beam Bending Test on Rocks, Ring Shear Test on Rocks Use of test results: Correlations for Standard Penetration Test and Other In Situ Tests, Soil Classification: IS classification, USCS, Soil Exploration Report.
Learning Outcome	At the end of the course, student would be able to: 1. Use various field soil exploration techniques for sample collection. 2. Use various geophysical techniques for dynamic soil parameter estimation. 3. Use various laboratory testing methods for characterization of soil and rock. 4. Use various correlations for estimation of design parameters.
Assessment Method	Assignments, Quizzes, Mid-semester examination and End-semester examination.

Textbooks:

B M Das, Advanced Soil Mechanics, CRC Press
V. N. S. Murthy, “Geotechnical Engineering: Principles and Practices of Soil Mechanics and Foundation Engineering”, CRC Press, Taylor & Francis Group, First Indian Reprint, 2010.

Reference books:

Terzaghi, R. B. Peck and G. Mesri, “Soil Mechanics in Engineering Practice”, John Wiley & Sons, 1996.
All relevant IS and International Codes.

CE5105

Advanced Soil Mechanics

Course	CE5105 Core-2: Advanced Soil Mechanics
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Advanced Soil Mechanics
Learning Mode	Lectures
Learning Objectives	Complies with PLO- 1, 2 & 3 To provide the knowledge of the advanced concepts in soil mechanics to expose the student to various advanced laboratory and field tests for soils, and their interpretation To train students to plan and design Geotechnical structures To provide scientific and technical knowledge, to prepare students to address the field problems in Geotechnical Engineering
Course Description	This course intends to bridge the basic concepts with the advanced topics related to soil mechanics. Topics ranging from soil structures, classification, to stress/strain behaviour, shear strength, slope stability and basic elasticity and plasticity concepts are covered. Not all the concepts explained in this course are advanced, but attempts to add clarity to the knowledge gained at undergraduate level.
Course Outline	Introduction: Geotechnical Engineering. Nature of Soil, Soil Structure. Shear Strength of Soils. A Simple Model to interpret Shear Strength, Drained and Un-drained Strength, Laboratory and Field Tests, Factors Affecting Shear Strength, Useful Correlations. Slope Instability, Finite Slope, Stability analyses, Application of software. Theory of Elasticity: Stress-Strain Relationship for various loading conditions, Elastic Stress Analysis, Introduction to Computer Program SIGMAW. Theory of Plasticity and Models for Soils: Elements of Plasticity, Yield Criteria (Mohr-Coulomb, Drucker-Prager), Post-yield Behavior, Flow Rule, Incremental Stress-Strain Relationship, Elastic-Perfectly Plastic Model, Hardening Plasticity Based Model.
Learning Outcome	At the end of the course, student would be able to: 1. Determine the geotechnical design parameters using laboratory test methods for different loading and drainage conditions 2. Analyse and determine the state of stress in the soil using elasticity and plasticity concepts 3. Assess the stability of the slopes and design the countermeasures for unstable slopes 4. Use and operate the software like SLOPE/W and SIGMAW for solving practical problems
Assessment Method	Assignments, Quizzes, Mid-semester examination and End-semester examination.

Textbooks:

Das, B.M., Advanced Soil Mechanics (5th edition), CRC Press, Taylor and Francis Group, 2020
Atkinson, J.H. An Introduction to the Mechanics of Soil and Foundations. McGraw Hill, 1993

Reference books:

Budhu, M., Soil Mechanics and Foundation (3rd edition), John Wiley & Sons Inc, 2011
Lambe, T.W. and Whitman, R.V. Soil mechanics, John Wiley and Sons, New York, 1979.
A.P.S. Selvadurai, Plasticity & Geomechanics, Cambridge University Press, 2002.
All relevant IS and International Codes.

CE5106

Rock Engineering

Course	CE5106 Core-3: Rock Engineering
Course Credit (L-T-P-C)	3-0-2-4
Course Title	Rock Engineering
Learning Mode	Lectures and practical
Learning Objectives	Complies with PLO- number 1, 2 and 3 1. Understand the fundamentals of geology. 2. Comprehend and analyse the properties of the intact and jointed rock mass. 3. Recognize and analyse different Rock Mass Classification systems and the stress-strain behaviour, strength and deformability of rock mass. 4. Solve complex engineering problems by applying principles of engineering and mechanics. 5. Performed and practiced the rock mass characterization and properties.
Course Description	This course is offered as a core course in department to understand the basics of engineering geology, origin, and types of rocks and their behaviors for various construction purposes such as foundations, underground excavation, landslide etc.
Course Outline	Introduction to Rock Engineering: Basic knowledge of geology; Problems associated with rock mechanics; General terminologies- Interior of earth, rock forming minerals, identification, intact rock, discontinuities and rock mass; Rock as engineering material. Properties, Mechanics and Classification of Intact Rock; Mechanical properties; Factors affecting strength of rocks; Intact rock classification; Rock cycle; Basic principles- stress and strain; Rock failure criteria. Properties and Mechanics of Rock Discontinuities; Plotting of geological data and its application; Shear behaviour of rock; Shear strength criteria; Flow through discontinuities. Rock mass classification systems; Strength criteria; Time dependent behaviour in rocks; Field investigation; Dynamic and thermal properties of rock. Applications of Rock Engineering: rock slope/tunnel stability problems; Slopes; Underground excavations; Rock support systems; Introduction to design analysis of tunnels, Tunnelling methods; Design of tunnel lining and support; Bearing capacity of foundations resting on rock mass; Instrumentation and monitoring of underground and surface excavation. Laboratory practices and analysis: Measurement and interpretation of discontinuity in rock mass, rock density, hardness, rock permeability, slake durability analysis, point load test, uniaxial compression test of intact and jointed rock mass, volume change behaviour, Plotting of geological data, Sound Velocity Test, Brazilian Test, Tensile strength and so on.
Learning Outcome	At the end of the course, student would be able to: Understand the basics of engineering geology, origin, and types of rocks. Learn and analyze the physical, mechanical, and hydraulic characteristics of the intact and jointed rock mass. Acquaint with different Rock Mass Classification systems. Recognize and analyse the stress-strain behaviour, strength and deformability of rock mass. 5. Solve complex engineering problems by applying principles of engineering and mechanics.
Assessment Method	Assignments, Quizzes, Mid-semester examination and End-semester examination.

Textbooks:

Goodman, R. E. Introduction to rock mechanics, John Wiley and Sons, 1989.
Hudson, J. A., & Harrison, J. P. Engineering rock mechanics: an introduction to the principles, (Vol.: I-IV), Elsevier, 2000.
Harrison, J. P., & Hudson, J. A. Engineering rock mechanics: part 2: illustrative worked examples, Elsevier, 2000.
Ramamurthy, T., Engineering in rocks for slopes, foundations and tunnels, Prentice Hall India, 2010.

References:

Hoek, E., & Bray, J. D. Rock slope engineering, CRC Press, 1981.
Hoek, E, & Brown, E. Underground excavations in rock, CRC Press, 1980. Singh, B., & Goel, R. K. Engineering rock mass classification, Elsevier, 2011.
Mogi, K. Experimental rock mechanics, CRC Press, 2006. Bieniawski, Z. T. Rock mechanics in mining & tunnelling, A.A. Balkema, Rotterdam, 1984.
Jaeger, J. C., Cook, N. G., & Zimmerman, R. Fundamentals of rock mechanics, John Wiley & Sons, 2009.
Debasis, D., & Kumar, V. A. Fundamentals and applications of rock mechanics, PHI Learning Pvt. Ltd. New Delhi, India, 2016.
All relevant IS and International Codes.

CE51XX/ CE61XX

DE-I: (Geotechnical Elective)

CE51XX/ CE61XX

DE-II: (Dept. Elective/Geotechnical Elective)

XX61PQ

IDE

TOTAL

Semsester - II

Sl. No.

Subject Code

SEMESTER II

CE5204

Advanced Foundation Engineering

Course	CE5204 Core-4: Advanced Foundation Engineering
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Advanced Foundation Engineering
Learning Mode	Lectures
Learning Objectives	Complies with PLO- number 1, 2, 3, and 4 1. To provide the knowledge of the advanced concept of foundation engineering. 2. Equip the students with a strong foundation in civil engineering for both research and industrial scenarios. 3. Prepares the students to apply knowledge in policy and decision making related to civil engineering infrastructure. 4. Prepare students to attain leadership careers to meet the challenges and demands in civil engineering practice. 5. Nurture interdisciplinary education for finding innovative solutions.
Course Description	This course intends to bridge the basic concepts with the advanced topics related to foundation engineering. Topics ranging from isolated footing, mat foundation, pile foundation and machine foundation are covered. The course started with the basic knowledge gained by the attendee during undergraduate level regarding the foundation design. Most of the institute does not cover mat foundation in the first course on foundation engineering. Therefore, advanced level analysis and design concepts of various foundation designs will be taught in the next part of this course. Introduction to seismic design of foundation along with codal provisions will be also taught in this course.
Course Outline	Static bearing capacity of footings, bearing capacity of footings resting on layered soils and footing on or near slopes, tilt, rotation and horizontal displacement of foundations subjected to eccentric-inclined loads, foundations on rocks. Analysis of raft foundations, circular and annular rafts, structural design of shallow raft foundations. Seismic design of shallow foundations. Pile foundations load capacity and settlements, various methods of analysis of laterally loaded pile foundations, uplift capacity. Piles subjected to dynamic loads, seismic design of pile foundations, structural design of pile foundations. Machine foundations for reciprocating machines, impact type, rotary machines such as turbines, turbo-generator. IS code provisions on foundations, codal provisions on structural and earthquake resistant design of foundations.
Learning Outcome	At the end of the course, student would be able to: 1. Design isolated footing using various methods available along with the method suggested in the IS code. 2. Design mat/raft foundation using IS code and ACI code 3. Design pile foundation in various type of soil deposit 4. Design machine foundation considering the dynamic load transferred to the foundation.
Assessment Method	Assignments, Quizzes, Mid-semester examination and End-semester examination.

Textbooks:

V. N.S. Murthy, Advanced foundation Engineering, CBS Publishers & Distributers, 2011.
B. M. Das, Principles of Foundation Engineering, Cengage Learning, Eighth Edition, 2011.

Reference books:

J.E. Bowles, Foundation Analysis and Design, McGraw-Hill, 2001.
H. G. Poulos, and E. H. Davis, Pile Foundation Analysis and Design, Krieger Pub Co., 1990.
All relevant IS and International Codes.

CE5205

Computational Geomechanics

Course	CE5205 Core-5: Computational Geomechanics
Course Credit (L-T-P-C)	3-0-2-4
Course Title	Computational Geomechanics
Learning Mode	Laboratory + Practical
Learning Objectives	Complies with PLO- number 1, 2, 3, and 4 1. To introduce about application of mechanics in geotechnical engineering problems 2. To train students to analyze different Geotechnical structures under different loading conditions 3. To train the students to solve complex practical problems
Course Description	The course intends to train the students to use the different geotechnical software tools to analyse the different geotechnical structures.
Course Outline	Theory: Numerical modeling, constitutive modeling of soils and rock, continuum and discrete element modeling; Concept of stress and strain, principle stresses and strains. Octahedral stresses and strains, finite element discretization of a continuum; Geomechanics problems of plane strain and axisymmetric problem; Principle of effective stress, permeability and flow; Fundamentals of Tensors; Mohr-Coulomb failure criteria, soil laboratory tests; Critical state and stress paths; Failure criteria for soils, associated and non-associated flow rule; Simulation of soil-structure interaction problems, application in consolidation, bearing capacity and slope stability problems using numerical approaches. Practical: Analyses of different geotechnical structures such as shallow foundations, deep foundations, slopes, embankments, retaining structures, dams, tunnels, buried pipes, excavation support systems etc. under static and dynamic loading conditions using various softwares such as PLAXIS 2D and 3D, FLAC, Geostudio and Rock Science etc.
Learning Outcome	At the end of the course, student would be able to: 1. Understand the basics and concept of geomechanics in geotechnical applications 2. Independently able to operate different software tools 3. Able to select appropriate material, models and input parameters 4. Validation and interpretation of the results 5. Design and analysis the various geotechnical structures
Assessment Method	Assignments, Quizzes, Mid-semester examination and End-semester examination.

Textbooks:

Wood, D.M., Geotechnical Modelling, CRC Press, 2004
Bolton, M.D. A Guide to Soil Mechanics, Cambridge University Press, 1991. Salgado, R., The Engineering of Foundations, McGraw Hill, 2008

Reference books:

Potts and Zdravkonics, Finite element analysis in geotechnical engineering: Part-I Theory & part-II Applications, Thomas Telford Publishers, 1999.
Budhu, M., Soil Mechanics and Foundation (3^rd edition), John Wiley & Sons Inc, 2011.
All relevant IS and International Codes.

CE5206

Fundamentals of Soil Behaviour

Course	CE5206 Core-6: Fundamentals of Soil Behaviour
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Fundamentals of Soil Behaviour
Learning Mode	Lectures
Learning Objectives	Complies with PLO- number 1, 3, 4, and 5 1. Understand the arrangement of soil particles and its relevance in behaviour of soils. 2. Analyse the mechanism behind the physio-chemical interactions within soils. 3. Know the various conduction phenomenon and its role in properties of soils. 4. Develop an understanding of the factors determining and controlling the engineering properties and behaviour of soils under different conditions.
Course Description	This course is offered as an elective course in department. This course basically comprises with several topics which should be covered to deal with fundamental of soil behaviour subjected to variation in climatic changes. Board topics are different clay mineral in behaviour of soils and its determination, arrangement of soil particles and its relevance in behaviour of soils, mechanism behind the physio-chemical interactions within soils, conduction phenomenon and its role in properties of soils, and factors determining and controlling the engineering properties and behaviour of soils under different conditions.
Course Outline	Identification and Classification of Clay Minerals: Formation of soils and their behaviours; Clay mineral types and their importance in geotechnical engineering; Determination of soil composition. Soil Fabric and its Measurement: Fabrics and structure; Contact force characterization; Voids and their distribution; Pore size distribution analysis; Fabric stability and its relevance to engineering behavior of soils; Methods of fabric characterization. Physio-Chemical Behaviour of Soil; Effective, Inter-granular and Total stress in a particulate system; Water–Air interactions in soils; Conduction Phenomenon; Volume Change, Shear Strength and Deformation Behaviour; General characteristics of strength and deformation; Deformation characteristics.
Learning Outcome	At the end of the course, student would be able to: Recognize the significance of different clay mineral in behaviour of soils and its determination. Understand the arrangement of soil particles and its relevance in behaviour of soils. Analyse the mechanism behind the physio-chemical interactions within soils. Know the various conduction phenomenon and its role in properties of soils. 5. Develop an understanding of the factors determining and controlling the engineering properties and behaviour of soils under different conditions, with an emphasis on Why they are What they are for research/professional perspectives as well as for societal needs.
Assessment Method	Assignments , Quizzes , Term-paper project, Mid-semester examination and End-semester examination.

Textbooks:

Mitchell, J. K. and Soga, K. Fundamentals of soil behaviour, Wiley, New York, 2005.
Yong, R. N. and Warkentin, B. P. Soil properties and behaviour, Elsevier, 2012.
Lambe, T.W. and Whitman, R.V. Soil mechanics, John Wiley and Sons, New York, 1979.

Reference books:

Grim, R. E. Applied clay mineralogy, McGraw Hill, New York, 1966.
Fredlund, D. G., Rahardjo, H. and Fredlund, M. D. Unsaturated soil mechanics in engineering practice, Wiley, 2012.
Malcom, D. Bolton A guide to soil mechanics, University Press (India) Pvt. Ltd., 2003.
All relevant IS and International Codes and research papers/reports.

CE52XX/ CE62XX

DE-3: (Geotechnical Elective)

CE52XX/ CE62XX

DE-4: (Dept. Elective/Geotechnical Elective)

RM6201

Department Elective - I (Geotechnical Elective)

Course Number	RM6201
Course Credit (L-T-P-C)	3-1-0-4
Course Title	Research Methodology
Learning Mode	Lectures
Learning Objectives	The objective of the course is to train student about the modelling of scalar and multi-objective nonlinear programming problems and various classical and numerical optimization techniques and algorithms to solve these problems
Course Description	Advanced Optimization Techniques, as a subject for postgraduate and PhD students, provides the knowledge of various models of nonlinear optimization problems and different algorithms to solve such problems with its applications in various problems arising in economics, science and engineering.
Course Content	Module I (6 lecture hours) – Research method fundamentals: Definition, characteristics and types, basic research terminology, an overview of research method concepts, research methods vs. method methodology, role of information and communication technology (ICT) in research, Nature and scope of research, information based decision making and source of knowledge. The research process; basic approaches and terminologies used in research. Defining research problem and hypotheses framing to prepare a research plan. Module II (5 lecture hours) - Research problem visualization and conceptualization: Significance of literature survey in identification of a research problem from reliable sources and critical review, identifying technical gaps and contemporary challenges from literature review and research databases, development of working hypothesis, defining and formulating the research problems, problem selection, necessity of defining the problem and conceiving the solution approach and methods. Module III (5 lecture hours) - Research design and data analysis: Research design – basic principles, need of research design and data classification – primary and secondary, features of good design, important concepts relating to research design, observation and facts, validation methods, observation and collection of data, methods of data collection, sampling methods, data processing and analysis, hypothesis testing, generalization, analysis, reliability, interpretation and presentation. Module IV (16 lecture hours) - Qualitative and quantitative analysis: Qualitative Research Plan and designs, Meaning and types of Sampling, Tools of qualitative data Collection; observation depth Interview, focus group discussion, Data editing, processing & categorization, qualitative data analysis, Fundamentals of statistical methods, parametric and nonparametric techniques, test of significance, variables, conjecture, hypothesis, measurement, types of data and scales, sample and sampling techniques, probability and distributions, hypothesis testing, level of significance and confidence interval, t-test, ANOVA, correlation, regression analysis, error analysis, research data analysis and evaluation using software tools (e.g.: MS Excel, SPSS, Statistical, R, etc.). Module V (10 lecture hours) – Principled research: Ethics in research and Ethical dilemma, affiliation and conflict of interest; Publishing and sharing research, Plagiarism and its fallout (case studies), Internet research ethics, data protection and intellectual property rights (IPR) – patent survey, patentability, patent laws and IPR filing process.
Learning Outcome	On successful completion of the course, students should be able to: 1. Understand the terminology and basic concepts of various kinds of nonlinear optimization problems. 2. Develop the understanding about different solution methods to solve nonlinear Programing problems. 3. Apply and differentiate the need and importance of various algorithms to solve scalar and multi-objective optimization problems. 4. Employ programming languages like MATLAB/Python to solve nonlinear programing problems. 5. Model and solve several problems arising in science and engineering as a nonlinear optimization problem.
Assessment Method	Quiz /Assignment/ Project / MSE / ESE

Textbooks & Reference Books:

R. Kothari, Research methodology: Methods and Techniques, 3rd Edn., New age International 2014.
Mark N K. Saunders, Adrian Thornhill, Phkip Lewis, “Research Methods for Studies, 3/c Pearson Education, 2010.
N. Krishnaswamy, apa iyer, siva kumar, m. Mathirajan, “Management Research Methodology”, Pearson Education, 2010.
Ranjit Kumar; “Research Methodology: A Step by Step Guide for Beginners; 2/e; Pearson Education, 2010.

IK6201

IKS

TOTAL

Semsester - III

Sl. No.	Subject Code	SEMESTER III	L	T	P	C
1.	CE6198	Summer Internship/Mini Project*	0	0	12	3
2.	CE6199	Project I **	0	0	30	15
	TOTAL		0	0	42	18

*Note: Summer Internship (Credit based)

Semsester - IV

Department Elective - I (Geotechnical Elective)

Sl. No.

Subject Code

Subject

CE6106

Soil Dynamics

Course	CE6106: Soil Dynamics
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Soil Dynamics
Learning Mode	Lectures
Learning Objectives	Complies with PLO- number 1, 2, 3, and 4 1. To provide the knowledge of the advanced concept of soil dynamics. 2. Equip the students with a strong foundation in civil engineering for both research and industrial scenarios. 3. Prepares the students to apply knowledge in policy and decision making related to civil engineering infrastructure. 4. Prepare students to attain leadership careers to meet the challenges and demands in civil engineering practice.
Course Description	This course intends to bridge the basic concepts with the advanced topics related to soil dynamics. Topics ranging from wave propagation, estimation of dynamic properties and vibration isolation are covered. The course started with the basic knowledge gained by the attendee during undergraduate level regarding the geotechnical engineering. Estimation of dynamic soil properties along with static properties will be covered in this course. The basic concept behind the vibration isolation will also be taught in this course.
Course Outline	Principles of dynamics and vibrations: Vibration of elementary systems-vibratory motion-single and multi-degree of freedom system-free and forced vibration with and without damping. Waves and wave propagation in soil media: Wave propagation in an elastic homogeneous isotropic medium- Raleigh, shear and compression waves. Dynamic properties of soils: Stresses in soil element, coefficient of elastic, uniform and non-uniform compression, shear effect of vibration dissipative properties of soils, Determination of dynamic soil properties, Field tests, Laboratory tests, Model tests, Stress-strain behavior of cyclically loaded soils, Estimation of shear modulus, Modulus reduction curve, Damping ratio, Linear, equivalent-linear and non-linear models, Ranges and applications of dynamic soil tests, Cyclic plate load test, Liquefaction. Vibration isolation: Vibration isolation technique, mechanical isolation, foundation isolation, isolation by location, isolation by barriers, active passive isolation tests.
Learning Outcome	At the end of the course, student would be able to: 1. Estimate dynamic soil properties using various methods available along with the method suggested in the IS code. 2. Understand the basics of wave propagation. 3. Liquefaction potential assessment using IS code and other methods in practice. 4. Vibration isolation of structures using various active and passive isolation technique.
Assessment Method	Assignments, Quizzes, Mid-semester examination and End-semester examination.

Textbooks:

Swami Saran, Soil Dynamics and Machine Foundations, Galgotia Publications Pvt. Ltd, 1999.
B. M. Das and G. V. Ramana, Principles of Soil Dynamics, 2nd edition, Cengage Learning, 2011.

Reference books:

S. Prakesh & V. K. Puri, Foundation for machines, McGraw-Hill 1993.
Kramar S.L, Geotechnical Earthquake Engineering, Prentice Hall International series, Pearson Education (Singapore) Pvt. Ltd.
All relevant IS and International codes.

CE6107

Rock Slope Engineering

Course	CE6107: Rock Slope Engineering
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Rock Slope Engineering
Learning Mode	Lectures
Learning Objectives	Complies with PLO- number 1, 2, 3, and 4 1. Learning Objectives of Rock Slope Engineering: Understand the geological and geotechnical principles governing the stability of rock slopes, including the factors influencing rock mass behavior, such as geological structure, rock type, weathering, and groundwater conditions. 2. Gain proficiency in conducting site investigations and geological mapping to characterize rock slope conditions, identify potential failure mechanisms, and assess the stability of rock slopes using qualitative and quantitative methods. 3. Learn to apply engineering principles and analytical techniques to analyze the stability of rock slopes, including limit equilibrium methods, numerical modeling, and probabilistic approaches, to evaluate factors such as slope geometry, rock strength parameters, and external loading conditions. 4. Acquire knowledge of rock slope stabilization and mitigation techniques, including rock reinforcement, slope scaling, rock bolting, rockfall protection measures, and slope monitoring systems, and understand their applicability based on site-specific conditions and project requirements. 5. Develop the ability to design effective risk management strategies for rock slope engineering projects, including risk assessment, hazard identification, and implementation of risk control measures to ensure the safety of infrastructure, minimize environmental impacts, and optimize project performance.
Course Description	Rock Slope Engineering course offers a comprehensive examination of the principles, methodologies, and practices essential for the assessment, design, and management of rock slopes in various geotechnical and engineering applications. Through a combination of theoretical concepts, practical case studies, and hands-on exercises, students will gain an understanding of the geological factors influencing slope stability, methods for slope assessment and characterization, and techniques for slope stabilization and risk mitigation. Emphasizing a multidisciplinary approach, the course covers topics including rock mechanics, geotechnical investigation, slope stability analysis, monitoring and instrumentation, and the application of engineering principles to mitigate hazards associated with rock slopes. By the conclusion of the course, students will possess the knowledge and skills necessary to effectively evaluate, design, and manage rock slopes to ensure the safety and sustainability of infrastructure projects in challenging terrain.
Course Outline	Principles of rock slope design, Basic mechanics of slope failure, Structural geology and data interpretation, Site investigation and geological data collection, Rock strength properties and their measurement, Plane failure, Wedge failure, circular failure, Toppling failure, Numerical analysis, Stabilization of rock slopes, Movement monitoring
Learning Outcome	At the end of the course, student would be able to: 1. Geotechnical Understanding: Develop a comprehensive grasp of the geological factors influencing rock slope stability, including rock mass properties, weathering processes, and the impact of discontinuities. 2. Risk Assessment and Management: Acquire skills in conducting thorough risk assessments for rock slopes, identifying potential failure modes, and implementing effective risk management strategies to mitigate hazards. 3. Design and Implementation of Stabilization Measures: Learn to design and implement appropriate stabilization measures for rock slopes, including rock bolts, shotcrete, and rockfall protection systems, based on site-specific conditions and project requirements. 4. Application of Analytical Techniques: Gain proficiency in utilizing analytical techniques such as limit equilibrium methods and numerical modeling to assess slope stability and make informed decisions regarding slope design and stabilization measures.
Assessment Method	Assignments, Quizzes, Mid-semester examination and End-semester examination.

Textbooks:

· Duncan C. Wyllie, Chris Mah, Rock Slope Engineering: Fourth Edition, 2004,

· Evert Hoek, Jonathan D. Bray, Rock Slope Engineering, Third Edition, 1974

· Ramamurthy T, Engineering in Rocks for Slopes, Foundations and Tunnels, 2014

Reference books:

· Engineering rock mechanics: Part 1, by John A. Hudson and John P. Harrison

· Engineering rock mechanics: Part 2, by John A. Hudson and John P. Harrison

· Fundamentals of rock mechanics by J. C. Jaeger, N. G. W. Cook, and R. W. Zimmerman

CE6108

Constitutive Modelling in Geotechnics

Course	CE6108: Constitutive Modelling in Geotechnics
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Constitutive Modelling in Geotechnics
Learning Mode	Lectures
Learning Objectives	Complies with PLO- number 1, 2, 3, and 4 1. To understand and analyse the numerical and constitutive modelling and its application in geomaterials to solve the complex geotechnical engineering problems.
Course Description	This course has been designed to provide a fundamental of continuum-mechanics approaches to constitutive and numerical modeling of geomaterials in geotechnical problems. Further, the course aims to provide some knowledge about applications of the constitutive and numerical models within the different existing numerical codes. The various applications, special topics and case studies will be covered in this course to analyse and understand the real geotechnical problems and finding the future solutions.
Course Outline	Introduction and Tensor Analysis, Stresses and strains, Equations of Continuum Mechanics and Thermodynamics, Elasticity, Plasticity and yielding, Introduction to upper and lower bounds, selected boundary value problems, Elastic-plastic model for soils: elastic and plastic volumetric strains, plastic hardening, plastic shear strains, plastic potentials, flow rule. Cam clay model: critical state line, shear strength, stress-dilatancy, index properties, prediction of conventional soil tests. Applications and special topics.
earning Outcome	At the end of the course, student would be able to: 1. Understand the basic of continuum mechanics. 2. Learn the various elastic-plastic model for soils and its applications 3. Comprehend about the cam clay model and its importance in geotechnical engineering. 4. Expose with various case studies and special topics to analyze the real geotechnical problem.
Assessment Method	Assignments, Quizzes, Mid-semester examination and End-semester examination.

Textbooks:

Wood, David Muir. Soil behaviour and critical state soil mechanics. Cambridge university press, 1990.
Atkinson, J. H., and P. L. Bransby. The mechanics of soils, an introduction to critical state soil mechanics. No. Monograph. 1977.
Chan, W.K. and Saleeb, A.F., Constitutive equations for engineering materials, Volume 1: Elasticity and modelling, Elsevier, 1994.
Chan, W.K. and Saleeb, A.F., Constitutive equations for engineering materials, Volume 2: Plasticity and modelling, Elsevier, 1994.

Reference books:

Harr, Milton Edward. Foundations of Theoretical Soil Mechanics. McGraw-Hill, 1966.
Desai, C.S. and Siriwardane, H.J., Constitutive laws for engineering materials with emphasis on geologic materials, Prentice Hall, 1984.

CE6109

Geoenvironmental Engineering

Course	CE6109 Geoenvironmental Engineering
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Geoenvironmental Engineering
Learning Mode	Lectures
Learning Objectives	Complies with PLO- number 1, 2, 3, and 4 1. Understanding methods of waste management and disposal 2. Learning methods of contaminated site characterization 3. Learning methods of remedial measures of a contaminated site 4. Understanding application of unsaturated soil in Geoenvironmental Engineering
Course Description	The course covers the source of various types of waste and its proper disposal, remediation of contamination sites. Municipal solid waste and industrial waste disposal techniques. Role of compacted unsaturated clay as liner material in landfill.
Course Outline	Production and classification of wastes, contaminated site characterization, Selection of waste disposal sites, selection criteria. Design of various landfill components such as liners, covers, leachate collection and removal, gas generation and management, ground water monitoring, stability analysis. Ash disposal facilities, dry disposal, wet disposal, design of ash containment system, stability of ash dykes, mine tailings. Planning, source control, soil washing, bioremediation, stabilization of contaminated soils and risk assessment approaches. Basics of unsaturated soil, soil suction, suction measurement techniques, SWCC, application of unsaturated soil in Geoenvironmental engineering.
Learning Outcome	At the end of the course, student would be able to: 1. Able to manage and dispose particular type of waste 2. Should be able to characterise contaminated site 3. Should be able to take appropriate remedial measures for a contaminated site 4. Should be able to use unsaturated clay as liner material in Geoenvironmental application.
Assessment Method	Assignments, Quizzes, Mid-semester examination and End-semester examination.

Textbooks:

H D Sharma and K R Reddy, “Geoenvironmental Engineering: Site Remediation, waste containment, and emerging waste management technologies”, John Willey and Sons, 2004.
R N. Yong, “Geoenvironmental Engineering: Contaminated Ground: Fate of Pollutions and Remediation”, Thomson Telford, 2000.
D. G. Fredlund and H. Rahardjo, “Soil Mechanics for Unsaturated soils”, Wiley Publication, 1993.

Reference books:

R Kerry Rowe, R M Quigley, Richard W I Brachman and John R Booker, “Barrier Systems for Waste Disposal Facilities”, 2nd edn, CRC press, 2019.

L N Reddy and H.I. Inyang, “Geoenvironmental Engineering: Principles and Applications”, Marcel Dekker, 2000
James K Mitechell, K Soga, “Fundamentals of soil behaviour”, Wiley Publication, 2005.
Charles W.W.Ng, B Menzies, “Advanced unsaturated soil mechanics and engineering”, CRC Press, 2014

CE6110

Biogeotechnics

Course	CE6110: Biogeotechnics
Course Credit (L-T-P-C)	3-0-0-3
Course Title	XX61PQ: Biogeotechnics
Learning Mode	Lectures
Learning Objectives	Complies with PLO- number 1, 3, and 5. The objectives of this course are to 1. Understand the significance of geomicrobiology in geotechnical engineering. 2. Comprehend various biological process in ground/soil improvement. 3. Learn about the testing and instrumentation facilities for biological process and geotechnical behaviour. 4. Apply the knowledge for upscaling to develop sustainable geomaterials.
Course Description	This course combines the principles of environmental biotechnology and geotechnical engineering. Geotechnical engineers design, build, and maintain structures in the subsurface. This course will be able to provide combine and apply basic theory and concepts from soil mechanics and biology in engineering applications. This course also brings an understanding about various geomicrobiological process for soil improvement.
Course Outline	Introduction to Biogeotechnics, Biological process of the subsurface materials, Stoichiometry and kinetics of bio-chemical reactions, Microbially Induced Calcite Precipitation (MICP), Root-Inspired Foundations, Enzymatically Induced Calcite Precipitation (EICP), Self-healing materials, Termite mounds-, Snake- and Ant-Inspired Excavations, Microbial Ecology, Biofilms, and Zeolite Sorption, Production of bio-cements. Instrumentation and testing for evaluating biological process and geotechnical material behaviour, Upscaled model tests and field trails. Special topics and case studies.
Learning Outcome	At the end of the course, student would be able to: 1. Understand the importance of geomicrobiology in geotechnical engineering. 2. Comprehend various bio-chemical reactions and their application in biological process for ground/soil improvement. 3. Investigate biological process and geotechnical behaviour. 4. Apply the knowledge for upscaling to develop sustainable geomaterials.
Assessment Method	Assignments, Quizzes, Term-paper project, Mid-semester examination and End-semester examination.

Textbooks:

Ehrlich, H., Newman, D. Geomicrobiology (5th ed.). Boca Raton: CRC Press (2021).
Hemond, Harold F., and Elizabeth J. Fechner. Chemical fate and transport in the environment. academic press, 2022.
Rittmann, Bruce E., and Perry L. McCarty. "Environmental biotechnology: principles and applications." (No Title) (2001).

Reference books:

Coduto, Donald P., Man-chu Ronald Yeung, and William A. Kitch. Geotechnical engineering: principles and practices. Pearson India (2011).
Zheng, Chunmiao, and Gordon D. Bennett. Applied contaminant transport modeling. Vol. 2. New York: Wiley-Interscience, 2002.
All relevant codes and research papers.

Department Elective - II (Geotechnical Elective)

Department Elective - II (Geotechnical Elective)

Sl. No.

Subject Code

Subject

CE6111

Rock Mechanics

Course	CE6111: Rock Mechanics
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Rock Mechanics
Learning Mode	Lectures
Learning Objectives	Complies with PLO- number 1, 2 and 3 1. Understand the fundamentals of geology. 2. Comprehend and analyse the properties of the intact and jointed rock mass. 3. Recognize and analyse different Rock Mass Classification systems and the stress-strain behaviour, strength and deformability of rock mass. 4. Solve complex engineering problems by applying principles of engineering and mechanics.
Course Description	This course is offered as a core course in department to understand the basics of rock mechanics and behaviors of rocks for various construction purposes such as foundations, underground excavation, landslide etc.
Course Outline	Introduction to Rock Mechanics: Basic knowledge of geology; Problems associated with rock mechanics; General terminologies- Interior of earth, rock forming minerals, identification, intact rock, discontinuities and rock mass; Rock as engineering material. Properties, Mechanics and Classification of Intact Rock; Mechanical properties; Factors affecting strength of rocks; Intact rock classification; Rock cycle; Basic principles- stress and strain; Rock failure criteria. Properties and Mechanics of Rock Discontinuities; Plotting of geological data and its application; Shear behaviour of rock; Shear strength criteria; Flow through discontinuities. Rock mass classification systems; Strength criteria; Time dependent behaviour in rocks; Field investigation; Dynamic and thermal properties of rock.
Learning Outcome	At the end of the course, student would be able to: Understand the basics of rock mechanics Learn and analyze the physical, mechanical, and hydraulic characteristics of the intact and jointed rock mass. Acquaint with different Rock Mass Classification systems. Recognize and analyse the stress-strain behaviour, strength and deformability of rock mass. 5. Solve complex engineering problems by applying principles of engineering and mechanics.
Assessment Method	Assignments, Quizzes, Mid-semester examination and End-semester examination.

Textbooks:

Goodman, R. E. Introduction to rock mechanics, John Wiley and Sons, 1989.
Hudson, J. A., & Harrison, J. P. Engineering rock mechanics: an introduction to the principles, (Vol.: I-IV), Elsevier, 2000.
Harrison, J. P., & Hudson, J. A. Engineering rock mechanics: part 2: illustrative worked examples, Elsevier, 2000.
Ramamurthy, T., Engineering in rocks for slopes, foundations and tunnels, Prentice Hall India, 2010.

References:

Hoek, E., & Bray, J. D. Rock slope engineering, CRC Press, 1981.
Hoek, E, & Brown, E. Underground excavations in rock, CRC Press, 1980. Singh, B., & Goel, R. K. Engineering rock mass classification, Elsevier, 2011.
Mogi, K. Experimental rock mechanics, CRC Press, 2006. Bieniawski, Z. T. Rock mechanics in mining & tunnelling, A.A. Balkema, Rotterdam, 1984.
Jaeger, J. C., Cook, N. G., & Zimmerman, R. Fundamentals of rock mechanics, John Wiley & Sons, 2009.
Debasis, D., & Kumar, V. A. Fundamentals and applications of rock mechanics, PHI Learning Pvt. Ltd. New Delhi, India, 2016.

CE6112

Environmental Rock Engineering

Course	CE6112: Environmental Rock Engineering
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Environmental Rock Engineering
Learning Mode	Lectures and practical
Learning Objectives	Complies with PLO- number 1, 2 and 3 1. Understand Rock Mechanics and Environmental Interactions: Gain foundational knowledge of rock mechanics principles and their environmental implications. 2. Assess and Mitigate Environmental Impacts: Develop skills to assess, design, and implement strategies to mitigate environmental impacts of rock engineering projects. 3. Apply Sustainable and Regulatory Practices: Integrate sustainable engineering practices and ensure compliance with environmental regulations in project planning and execution. 4. Enhance Interdisciplinary and Professional Skills: Cultivate interdisciplinary collaboration, critical thinking, and effective communication to address complex environmental challenges in rock engineering.
Course Description	This course explores the interaction between rock mechanics and environmental considerations. Topics include slope stability, underground excavation, waste disposal, and geohazard mitigation strategies. Students learn principles for sustainable rock engineering practices in various environmental contexts.
Course Outline	Introduction to Rock Mechanics and Environmental Considerations, Geological Hazards and Risk Assessment, Rock Mass Properties and Characterization, Stress-strain behaviour of rocks and rock masses: Elastic, elastoplastic, and brittle, Crack phenomena and mechanisms of rock fracture, Temperature, pressure and water related, problems, Effect of temperature on rock behaviour. Fluid flow through intact and fissured rocks, Time dependent behaviour of rocks: Creep, Viscoelasticity and Viscoplasticity, Continuum and discontinuum theories: Equivalent material, Block and Distinct element Application: Waste disposal, Radioactive and hazardous wastes, repositories, location and design, VLH, VDH and KBS3 concepts. Waste container, barriers, rock structure, embedment, buffers and seals. Performance assessment, quality control and monitoring. Case histories. Hazardous Earth processes, high ground stresses, rock bursts, subsidence. Earthquakes, tectonic stresses, creep, ground motions, damage, prediction. Volcanic activity and hazard. Tsunamis. Case studies. Thermal analysis, Thermo-mechanical analysis, thermo-hydro-mechanical analysis. Rock dynamics. Physical modelling.
Learning Outcome	At the end of the course, student would be able to: 1. Environmental Rock Engineering focuses on understanding the interaction between rock mechanics and the environment. 2. Learners comprehend the effects of natural processes and human activities on rock formations. 3. They develop skills to assess, mitigate, and manage environmental risks related to rock engineering projects. 4. The course equips students to design sustainable solutions for geological hazards and environmental protection.
Assessment Method	Assignments, Quizzes, Mid-semester examination and End-semester examination.

Textbooks:

Goodman, R. E. Introduction to rock mechanics, John Wiley and Sons, 1989.
R. Pusch. Waste Disposal in Rock. Elsevier. 1994
Harrison, J. P., & Hudson, J. A. Engineering rock mechanics: part 2: illustrative worked examples, Elsevier, 2000.

Randall F. Barron and Brian R. Barron. Design for Thermal Stresses. Wiley, 2011
Ramamurthy, T., Engineering in rocks for slopes, foundations and tunnels, Prentice Hall India, 2010.

References:

Hoek, E., & Bray, J. D. Rock slope engineering, CRC Press, 1981.
Hoek, E, & Brown, E. Underground excavations in rock, CRC Press, 1980. Singh, B., & Goel, R. K. Engineering rock mass classification, Elsevier, 2011.
Mogi, K. Experimental rock mechanics, CRC Press, 2006. Bieniawski, Z. T. Rock mechanics in mining & tunnelling, A.A. Balkema, Rotterdam, 1984.
Jaeger, J. C., Cook, N. G., & Zimmerman, R. Fundamentals of rock mechanics, John Wiley & Sons, 2009.
Debasis, D., & Kumar, V. A. Fundamentals and applications of rock mechanics, PHI Learning Pvt. Ltd. New Delhi, India, 2016.

CE6113

Pavement Geotechnics

Course	CE6113 Pavement Geotechnics
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Pavement Geotechnics
Learning Mode	Lectures
Learning Objectives	Complies with program learning outcome 1a; 3a 1. Equip the students with a strong foundation and strengthen their knowledge in pavement geotechnics. 2. The student will be able to apply advanced theory and analysis for problem-solving in pavement geotechnics. 3. The student will prepare for further research and graduate study by critical thinking and improving research skills. 4. The student will be able to apply fundamentals in identifying, formulating, and solving complex engineering problems in pavement geotechnics.
Course Description	This coursework will provide practical insights for students in the field of Pavement Geotechnics. The development of sustainable approaches for green technology-based highways for global road networks is given the highest priority. This coursework will disseminate knowledge to the students in pavement geotechnics. The students will be taught the recent sustainable developments and design principles to face current and future highway problems in relevance with pavement geotechnics.
Course Content	Geotechnical properties of geomaterials such as soil, rock, soil-rock mixture, and alternative geomaterials. Stabilized geomaterials, Various types of pavements, subgrade characterization and geotechnics, challenges faced in constructing subgrades. Subbase, base, and asphalt concrete materials relevant to pavement geotechnics. Elastic theories and stress distribution in pavements. Estimation of resilient modulus of pavements. Geotechnical design parameters for pavements.. Geosynthetic stabilization of constructed layers and interlayers. Asphalt concrete courses and their stabilization technique, Stress distribution of pavement system in stabilized and unstabilized ground conditions. Geosynthetic stabilized pavements, low-carbon cement stabilized pavements, geotechnical parametric studies for AASHTO, MEPDG, and IRC designs. Porous pavement geotechnics, Analysis of pavement distress studies using KENPAVE and IIT Pave. Low-carbon materials and sustainable geosynthetic materials used for pavements. Important concepts on permeable pavements and inverted pavements. Semi and full-depth reclamation techniques of pavements. The waste material used for pavement. Field and case studies.
Learning Outcome	The course structure will impart high-quality knowledge on students to face current and future problems faced by the world’s largest road networks. Students would be able to learn the core principles of pavement designs and advanced sustainable pavement techniques. Exploration of alternative materials, design approaches, and innovation in pavement geotechnics will be disseminated through this course.

Textbooks:

Huang, Y. H. (2004). Pavement analysis and design, Second edition, Upper Saddle River, NJ: Pearson Prentice Hall.
Yoder, E. J., & Witczak, M. W. (1991). Principles of pavement design. John Wiley & Sons.
Mallick, R. B., & El-Korchi, T. (2008). Pavement engineering: principles and practice. CRC Press.
Frost, M. W., Jefferson, I., Faragher, E., Roff, T. E. J., & Fleming, P. R. (Eds.). (2003). Transportation Geotechnics: Proceedings of the Symposium Held at The Nottingham Trent University School of Property and Construction on 11 September 2003. Thomas Telford Publishing.
Ellis, E., Yu, H. S., McDowell, G., Dawson, A. R., & Thom, N. (Eds.). (2008). Advances in Transportation Geotechnics: Proceedings of the International Conference Held in Nottingham, UK, 25-27 August 2008. CRC Press.
Miura, S., Ishikawa, T., Yoshida, N., Hisari, Y., & Abe, N. (Eds.). (2012). Advances in Transportation Geotechnics 2. CRC Press.

Reference books:

Ferguson, B. K., & Ferguson, B. K. (2005). Porous pavements. Boca Raton, FL: Taylor & Francis.
Rogers, M., & Enright, B. (2016). Highway engineering. John Wiley & Sons.
Nikolaides, A. (2014). Highway engineering: Pavements, materials and control of quality. CRC Press.
Babu, G. L. S., Kandhal, P. S., Kottayi, N. M., Mallick, R. B., & Veeraragavan, A. (2019). Pavement Drainage: Theory and Practice. CRC Press.
Babu, G.L.S., (2006). An Introduction to Soil Reinforcement and Geosynthetics, Universities Press (India) Pvt. Ltd.

CE6114

Probalistic Methods in Geotechnical Engineering

Course	CE6114: Probalistic Methods in Geotechnical Engineering
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Probabilistic Methods in Geotechnical Engineering
Learning Mode	Lectures
Learning Objectives	Complies with PLO- number 1, 2, 3, and 4 1. To provide the knowledge of the advanced concept of probabilistic methods in geotechnical engineering. 2. Equip the students with a strong foundation in civil engineering for both research and industrial scenarios.
Course Description	This course intends to bridge the basic concepts with the advanced topics related to the application of probabilistic methods in geotechnical engineering. Topics ranging from risk, uncertainty, Monte Carlo simulation, and FORM are covered. The course started with the basic knowledge gained by the attendee up to undergraduate level regarding the probabilistic methods. Thereafter, the basics and advanced concept related to risk and reliability analysis will be studied by the students.
Course Outline	Introduction: Concept of risk; and uncertainty in geotechnical engineering analysis and design; Fundamental of probability models. Analytical models of random phenomena: Baysian Analysis; Analysis of variance (ANOVA); Application of central limit theorem; confidence interval; expected value; and return period. Application of Monte Carlo simulation (MCS): Determination of function of random variables using MCS methods; Application of MCS in various geotechnical engineering problems. Determination of Probability Distribution Model: Probability paper; testing of goodness-of-fit of distribution models. Methods of risk Analysis: Composite risk analysis; Direct integration method; Method using safety margin; reliability index and safety factor; FORM; SORM; Applications of risk and reliability analysis in engineering systems.
Learning Outcome	At the end of the course, student would be able to: 1. Analyzed structure using various probabilistic methods available along with the method suggested in the Euro code. 2. Perform reliability analysis for various geotechnical problems. 3. Assess composite risk using various techniques to estimate failure of geotechnical structures.
Assessment Method	Assignments, Quizzes, Mid-semester examination and End-semester examination.

Textbooks:

Ang, A. H-S., and Tang, W. H., Probability Concepts in Engineering, Vol. 1, John Wiley and Sons, 2006.
Scheaffer, R. L., Mulekar, M. S. and McClave, J. T., Probability and statistics for Engineers, 5th Edition, Brooks / Cole, Cengage Learning, 2011.

Reference books:

Halder, A and Mahadevan, S., Probability, Reliability and Statistical Methods in Engineering Design, John Wiley and Sons, 2000.

All relevant IS and International Codes.

Department Elective - III (Geotechnical Elective)

Department Elective - III (Geotechnical Elective)

Sl. No.

Subject Code

Subject

CE6206

Geotechnical Earthquake Engineering

Course	CE6206: Geotechnical Earthquake Engineering
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Geotechnical Earthquake Engineering
Learning Mode	Lectures
Learning Objectives	Complies with PLO- number 1, 2, 3, and 4 1. To provide the knowledge of the advanced concept of geotechnical earthquake engineering. 2. Equip the students with a strong foundation in civil engineering for both research and industrial scenarios. 3. Prepares the students to apply knowledge in policy and decision making related to civil engineering infrastructure. 4. Prepare students to attain leadership careers to meet the challenges and demands in civil engineering practice.
Course Description	This course intends to bridge the basic concepts with the advanced topics related to geotechnical engineering. Topics ranging from continental drift, seismic hazard analysis, wave propagation, liquefaction assessment, seismic slope stability and design of retaining structure are covered. The course started with the basic knowledge gained by the attendee during undergraduate level regarding the wave propagation. Therefore, the basics about earthquake engineering will be studied by the students. Introduction to seismic design of retaining structure and slope stability analysis will be also taught in this course.
Course Outline	Introduction, Significant historical earthquakes, Continental drift and plate tectonics, Internal structure of earth, Sources of seismic activity, Size of the earthquake, Strong ground motion and its measurement, Ground motion parameters, Estimation of ground motion parameters, Identification and evaluation of earthquake sources, Seismic hazard analysis, Deterministic seismic hazard analysis, Probabilistic seismic hazard analysis, Wave propagation, Waves in unbounded media, Waves in semi-infinite body, Waves in layered body, Dynamic soil properties and Measurement of dynamic soil properties, Ground response analysis, Local site effects and design of ground motions, Liquefaction, Initiation and effects of liquefaction, Evaluation of liquefaction hazards, Liquefaction susceptibility, Seismic slope stability analysis, and Seismic design of retaining walls.
Learning Outcome	At the end of the course, student would be able to: 1. Design earthquake resistant structure using various methods available along with the method suggested in the IS code. 2. Liquefaction potential assessment using IS code and other methods in practice. 3. Perform seismic hazard analysis for any site. 4. Seismic design of retaining walls considering the dynamic load transferred to the foundation.
Assessment Method	Assignments, Quizzes, Mid-semester examination and End-semester examination.

Textbooks:

Kramar S.L, Geotechnical Earthquake Engineering, Prentice Hall International series, Pearson Education Pvt. Ltd.
J.E. Bowles, Foundation Analysis and Design, McGraw-Hill, 2001.

Reference books:

Ikuo Towhata, Geotechnical Earthquake Engineering, Springer series, 2008.
All relevant IS and International Codes.

CE6207

Soil-Structure Interaction Analysis

Course	CE6207: Soil-Structure Interaction Analysis
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Soil-Structure Interaction
Learning Mode	Lectures
Learning Objectives	Complies with PLO- number 1, 2, 3, and 4 1. To provide the knowledge of the advanced concept of soil and structural interaction. 2. Equip the students with a strong foundation in civil engineering for both research and industrial scenarios. 3. Prepares the students to apply knowledge in policy and decision making related to civil engineering infrastructure under dynamic loading.
Course Description	This course intends to bridge the basic concepts with the advanced topics related to geotechnical engineering. Topics ranging from general concept of soil-structure interaction, beams on elastic foundation, modern concept of analysis of piles and pile groups are covered.
Course Outline	General soil-structure interaction problems. Contact pressures and soil-structure interaction for shallow foundations. Concept of sub grade modulus, effects/parameters influencing subgrade modulus. Analysis of foundations of finite rigidity, Beams on elastic foundation concept, introduction to the solution of beam problems. Curved failure surfaces, their utility and analytical/graphical predictions from Mohr-Coulomb envelope and circle of stresses. Earth pressure computations by friction circle method. Earth pressure distribution on walls with limited/restrained deformations, Dubravo’s analysis. Earth pressures on sheet piles, braced excavations. Design of supporting system of excavations. Arching in soils. Elastic and plastic analysis of stress distribution on yielding bases. Analysis of conduits. Design charts for practical use. Modern concept of analysis of piles and pile groups. Axially, laterally loaded piles and groups. Interaction analysis. Reese and Matlock’s solution. Elastic continuum and elasto-plastic analysis of piles and pile groups. Hrennikoff’s analysis. Ultimate lateral resistance of piles by various approaches. Non-linear load-deflection response. Uplift capacity of piles and anchors.
Learning Outcome	At the end of the course, student would be able to: 1. Design earthquake resistant structure using various methods available along with the method suggested in the IS code. 2. Apply beam on elastic foundation concept in analysis and design of various problem related to geotechnical engineering. 3. Ultimate lateral resistance of piles by various approaches.
Assessment Method	Assignments, Quizzes, Mid-semester examination and End-semester examination.

Textbooks:

J. P. Wolf, “Dynamic Soil-Structure Interaction”, Prentice-Hall, 1985.
S.L. Kramer, Geotechnical Earthquake Engineering, Prentice Hall, 1996.

Reference books:

H. G. Poulos, and E. H. Davis, Pile Foundation Analysis and Design, Krieger Pub Co., 1990.
Structure Soil Interaction- State of Art Report, Institution of Structural Engineers, 1978.
All relevant IS and International Codes.

CE6208

Mine Wastes Generation and Management

Course	CE6208: Mine Wastes Generation and Management
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Mine Wastes Generation and Management
Learning Mode	Lectures
Learning Objectives	Complies with PLO- number 1, 2, 3, 4, and 5 1. Understand and explain the mining operations, regulations and acts. 2. Explain the various types of mine generated wastes, characterizations techniques and application. 3. Describe the mine waste disposal techniques and stability analysis of overburden dumps. 4. Comprehend the mine generated contaminated leachate and ground pollution. 5. Analyse technical strategies, approaches and solutions to engineer's role and responsibility for mine waste management risk analysis, potential application, safety factors for sustainable development.
Course Description	The course covers various mine waste generated during the mining operation and their characteristics, mining regulations and acts, waste disposal, potential application and stability analysis of mine overburden waste, leachate formation and ground contamination. This course deals with geomechanics and rehabilitation techniques of mine generated wastes, valorization of mine wastes, risk analysis and mining safety.
Course Outline	Introduction to mining operations and risk; overview of Indian & international mining regulations and acts; different types of mine waste generated during the mining operation; mine waste disposal & rehabilitation; geochemical compositions, physical & chemical nature of mine wastes; disposal of mine wastes; geomechanics of mine waste disposal & rehabilitation; characterizations and application of mining wastes for infrastructure projects; valorization of mining wastes; leachate formation and ground contamination due to mining wastes; stability analysis of mining wastes overburden dumps, reintegration of mine wastes; mining wastes risk assessment & remedial measures; mining safety.
Learning Outcome	At the end of the course, student would be able to: Describe and explain the mining operations, regulations and acts. Explain the various types of mine generated wastes, characterizations techniques and application. Describe the mine waste disposal techniques and stability analysis of overburden dumps. Understand the mine generated contaminated leachate and ground pollution. 5. Analyze technical strategies, approaches and solutions to engineer's role and responsibility for mine waste management risk analysis, potential application, safety factors for sustainable development.
Assessment Method	Assignments, Quizzes, Term-paper project, Mid-semester examination and End-semester examination.

Textbooks:

Singh, T N. Surface Mining, Lovely Prakashan, India, 2020.
Karra, Ram Chandar, Gayana, B C, Rao, Shubhananda P Mine Waste Utilization, CRC Press, 2022.
Hutchison, Ian P.G. and Ellis, Rechard D., Mine Waste Management, CRC Press, India, 1992.
Lottermoser, Bernd G., Mine Wastes Characterization, Treatment and Environmental Impacts, Springer, 3rd edition, 2010.

References:

Pradhan, S. P., Vishal, V., & Singh, T. N. (Eds.). Landslides: theory, practice and modelling. Springer International Publishing, 2019.
Pathak, Pankaj, Rout, Prangya Ranjan, Urban Mining for Waste Management and Resource Recovery, CRC Press, 2021
Indian and international acts and regulations for mining operations and waste management
Referred journal and publications.

CE6209

Coupled Process in Fractured Geological Media

Course	CE6209: Coupled Process in Fractured Geological Media
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Coupled Process in Fractured Geological Media
Learning Mode	Lectures
Learning Objectives	Complies with PLO- number 1, 2 and 3 1. Understand the coupling mechanisms between various processes (e.g., fluid flow, heat transfer, and mechanical deformation) in fractured geological media. 2. Analyze the impact of fractures on the behavior of fluid flow, heat transfer, and mechanical deformation in geological formations. 3. Apply numerical modeling techniques to simulate coupled processes in fractured media and predict their behavior under different conditions. 4. Develop strategies for managing and controlling coupled processes to optimize resource extraction, geological storage, or environmental remediation in fractured geological environments.
Course Description	The Coupled Processes in Fractured Geological Media course delves into the complex interactions occurring within fractured rock formations. Students explore coupled hydro-mechanical-chemical processes occurring in subsurface environments. Topics include fluid flow, stress distribution, and chemical reactions in fractured media. Emphasis is placed on understanding how these processes affect geotechnical engineering, hydrology, and environmental management. Students learn modeling techniques and practical applications for characterizing and predicting behavior in fractured geological systems.
Course Outline	Introduction to Fractured Geological Media, Rock Mechanics Fundamentals, Hydrological Processes in Fractured Media, Thermal-Hydrological-Mechanical (THM) Coupling, Chemical Processes and Reactive Transport, Geomechanical-Fluid Interaction, Case Studies and Applications.
Learning Outcome	At the end of the course, student would be able to: 1. Students will grasp the complex interactions between fluid flow, heat transfer, and mechanical deformation in fractured geological formations. 2. They will learn to analyze coupled processes influencing subsurface systems such as groundwater flow, geothermal energy, and hydrocarbon reservoirs. 3. Learners will develop skills to model and simulate coupled phenomena to solve real-world problems in fractured media. 4. The course prepares students to address challenges in resource management, environmental remediation, and energy extraction.
Assessment Method	Assignments, Quizzes, Mid-semester examination and End-semester examination.

Textbooks:

Goodman, R. E. Introduction to rock mechanics, John Wiley and Sons, 1989.
R. Pusch. Waste Disposal in Rock. Elsevier. 1994
Coupled Processes Associated with Nuclear Waste Repositories" by Jacques Delay, Peter A. Witherspoon, François X. Dégerine
Randall F. Barron and Brian R. Barron. Design for Thermal Stresses. Wiley, 2011
Fractured Rock Hydrogeology" by John M. Sharp Jr.

References:

Hoek, E., & Bray, J. D. Rock slope engineering, CRC Press, 1981.
Hoek, E, & Brown, E. Underground excavations in rock, CRC Press, 1980.
Singh, B., & Goel, R. K. Engineering rock mass classification, Elsevier, 2011.

"Coupled Processes in Subsurface Deformation, Flow, and Transport" edited by George Pinder, Catherine A. Peters

CE6210

Ground Improvement Techniques

Course	CE6210: Ground Improvement Techniques
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Ground Improvement Techniques
Learning Mode	Lectures
Learning Objectives	Complies with PLO- number 1, 3, 4 & 5 1. Understand the importance of ground improvement for civil engineering structures. 2. Examine the problematic soil and select a suitable ground improvement technique. 3. Analyze and Design the various ground improvement techniques. 4. Understand the construction methodology, equipment and quality control aspects. 5. Know the national and international codal guidelines and provisions.
Course Description	Construction in weak and problematic soil is inevitable nowadays. The course addresses various ground improvement techniques along with principles, design issues and construction procedures. The course has been broadly divided into two modules namely ground improvement techniques and the reinforced earth.
Course Outline	Problematic soil and need for ground improvements, Mechanical modifications using mechanical and dynamic compaction, Accelerated consolidation using preloading and vertical drains, Soil stabilisation using additives and deep soil mixing, Grouting, Vibro techniques, Drainage and dewatering methods; Soil nailing; Soil nailing; Underpinning, Introduction to geo-synthetics and reinforced earth; Applications and advantages of reinforced soil structure; Principles, concepts and mechanism of reinforced soil; Soil-reinforcement interface friction; Behaviour of Reinforced earth walls; Bearing capacity improvement and design of foundations resting on reinforced soil; embankments on soft soils; Design of reinforced soil slopes, Use of geosynthetics for separations, drainage and filtration; practical applications of of geosynthetics; Geosynthetics in landfill system; Use of jute, coir, natural geotextiles, waste products such as scrap tire, LDPE and HDPE strips, as reinforcing material.
Learning Outcome	At the end of the course, student would be able to: 1. Identify the problematic soil and select a suitable ground improvement technique 2. Design the various ground improvement techniques 3. Understand the construction methodology, equipment and quality control aspects 4. Know the national and international codal guidelines and provisions
Assessment Method	Assignments, Quizzes, Mid-semester examination and End-semester examination.

Textbooks:

Manfired R. Hausmann, Engineering Principles of Ground Modification, McGraw-Hill Pub, Co., 1990.
Koerner, R.M. Designing with Geosynthetics, Prentice Hall, New Jersey, USA, 4th edition, 1999.
Jie Han, Principles and Practice of Ground Improvement, Wiley Publishers, 2015.

Reference books:

B.M. Das, Principle of Geotechnical Engineering, Cengage Learning, eighth Edition, 2013.
V. N. S. Murthy, Geotechnical Engineering: Principles and Practices of Soil Mechanics and Foundation Engineering, CRC Press, Taylor & Francis Group, Third Indian Reprint, 2013.
All relevant IS and international codes and relevant research papers/reports

CE6211

Utilization of industrial byproducts for geotechnical applications

Course	CE6211: Utilization of Industrial Byproducts for Geotechnical Applications
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Utilization of industrial byproducts for geotechnical applications
Learning Mode	Lectures
Learning Objectives	Complies with PLO- number 1, 2, 3, 4, and 5 1. Understand various sources and characteristics of industrial byproducts and their application as geomaterials. 2. Explain bulk application of industrial by products for soil stabilization and ground improvement with or without admixtures. 3. Analyse and elucidate the behaviour of industrial byproducts subjected to contamination, various remediation and immobilization techniques. 4. Apply the knowledge for economical, environmental and sustainable infrastructure development.
Course Description	The course covers various sources of industrial byproducts in India, status and potential applications. Further, this course deals with utilization of industrial byproducts as geomaterial for soil stabilization and ground improvement with or without using admixtures. This course also emphasizes the advanced characterizations techniques of industrial by products and behaviour subjected to contamination, various remediation and immobilization techniques.
Course Outline	Introduction to industrial byproducts and its types; characteristics and role of industrial byproducts and admixtures; purpose-based classification of soils; principles of soil stabilization and ground improvement; methods of stabilization using industrial byproducts with or without chemical admixtures such as lime, cement, bitumen and special chemicals; mechanisms, uses and limitations; advanced characterizations technique and use of fly ash, rice husk ash, biochar, marble waste, and quarry generated wastes, mine slurry, slag, and other waste materials for both shallow and deep soil stabilization and ground improvement; potential application of industrial wastes as geomaterials and its behaviour subjected to contamination agents; remediation and immobilization techniques of industrial byproducts; methods and applications of grouting; Application to embankments, excavations, foundations and sensitive soils.
Learning Outcome	At the end of the course, student would be able to: Describe various sources and characteristics of industrial byproducts and their application as geomaterials. Explain bulk application of industrial by products for soil stabilization and ground improvement with or without admixtures. Understand the behaviour of industrial byproducts subjected to contamination, various remediation and immobilization techniques. 4. Apply the knowledge for economical, environmental and sustainable infrastructure development.
Assessment Method	Assignments , Quizzes , Term-paper project, Mid-semester examination and End-semester examination.

Textbooks:

Ingles, O.G. and Metcalf, J.B., Soil Stabilization, Principles and Practice, Butterworths, 1972.
Bowen, R., Grouting in Engineering Practice, Allied Science Publishers Ltd., 1975.
Jie Han, Principles and Practice of Ground Improvement, Wiley Publishers, 2015.

References:

Yong, R. N. and Warkentin, B. P. Soil properties and behaviour, Elsevier, 2012.
Mitchell, J. K. and Soga, K. Fundamentals of soil behaviour, Wiley, New York, 2005.
B.M. Das, Principle of Geotechnical Engineering, Cengage Learning, eighth Edition, 2013.
All relevant IS and international codes and relevant research papers/reports.

CE6212

Rock Engineering for River Valley Projects

Course	CE6212: Rock Engineering for River Valley Projects
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Rock Engineering for River Valley Projects
Learning Mode	Lectures
Learning Objectives	Complies with PLO- number 1, 2 and 3 1. Understand the geological processes shaping river valleys and the behavior of rock masses within them. 2. Analyze and assess geological hazards such as landslides, rockfalls, and erosion affecting river valley infrastructure. 3. Develop skills in rock slope stability analysis, support design, and mitigation measures specific to river valley environments. 4. Apply engineering principles to design sustainable and resilient solutions for infrastructure projects in river valleys, considering geological constraints and environmental impacts.
Course Description	Rock Engineering for River Valley Projects" covers the geotechnical aspects of river valley infrastructure. Students learn about slope stability, rock mechanics, and foundation design tailored to river environments. The course emphasizes risk assessment, mitigation strategies, and engineering solutions for dams, bridges, and other structures in rocky river valleys. Practical applications and case studies provide insights into real-world challenges and solutions.
Course Outline	Introduction to River Valley Projects, Geological Considerations, Rock Mechanics Fundamentals, Design of River Valley Structures, Case Studies, Instrumentation and Monitoring, Construction Techniques and Management, Future Trends and Sustainability
Learning Outcome	At the end of the course, student would be able to: 1. Understand principles of rock mechanics relevant to river valley projects. 2. Analyze geological conditions to design stable structures for dams, tunnels, and slopes. 3. Apply engineering techniques for rock stabilization and slope reinforcement. 4. Develop skills to mitigate geological hazards and ensure the safety and sustainability of river valley infrastructure.
Assessment Method	Assignments, Quizzes, Mid-semester examination and End-semester examination.

Textbooks:

Goodman, R. E. Introduction to rock mechanics, John Wiley and Sons, 1989.
Hoek, E., & Bray, J. D. Rock slope engineering, CRC Press, 1981.
Hoek, E, & Brown, E. Underground excavations in rock, CRC Press, 1980.

References:

Singh, B., & Goel, R. K. Engineering rock mass classification, Elsevier, 2011.
Jaeger, J. C., Cook, N. G., & Zimmerman, R. Fundamentals of rock mechanics, John Wiley & Sons, 2009.
Debasis, D., & Kumar, V. A. Fundamentals and applications of rock mechanics, PHI Learning Pvt. Ltd. New Delhi, India, 2016.

Department Elective - IV (Geotechnical Elective)

Department Elective - IV (Geotechnical Elective)

Sl. No.

Subject Code

Subject

CE6213

Design of Underground Excavations

Course	CE6213: Design of Underground Excavations
Course Credit (L-T-P-C)	3-0-0-4
Course Title	Design of Underground Excavations
Learning Mode	Lectures
Learning Objectives	Complies with PLO- number 1, 2 and 3 1. Understand the principles of underground excavation design, including site investigation and geological mapping. 2. Gain proficiency in analyzing rock mass behavior and selecting appropriate support systems. 3. Learn excavation methods, tunnelling techniques, and their applications in various geological conditions. 4. Develop skills to design safe, cost-effective, and sustainable underground structures while considering geological, geotechnical, and structural factors.
Course Description	This course covers principles of underground excavation design including rock mechanics, support systems, and excavation methods. Topics include ground behavior, stability analysis, tunnelling methods, and practical design considerations. Students learn to develop safe and efficient designs for tunnels, mines, and underground structures.
Course Outline	Introduction to Underground Excavations, Rock Mechanics Fundamentals, Site Investigation and Geotechnical Data Collection, Excavation Methods, Support Systems for Underground Excavations, Tunnel Design, Cavern and Underground Structure Design, Instrumentation and Monitoring, Case Studies and Project Examples
Learning Outcome	At the end of the course, student would be able to: 1. Understanding principles of rock mechanics for underground openings. 2. Ability to analyze and design support systems for stability and safety. 3. Proficiency in assessing geological conditions and their impact on excavation design. 4. Skill development in designing underground excavations for various engineering purposes like tunnels, mines, or underground structures.
Assessment Method	Assignments, Quizzes, Mid-semester examination and End-semester examination.

Textbooks:

Goodman, R. E. Introduction to rock mechanics, John Wiley and Sons, 1989.
Hoek, E., & Bray, J. D. Rock slope engineering, CRC Press, 1981.
Hoek, E, & Brown, E. Underground excavations in rock, CRC Press, 1980.

References:

Singh, B., & Goel, R. K. Engineering rock mass classification, Elsevier, 2011.
Jaeger, J. C., Cook, N. G., & Zimmerman, R. Fundamentals of rock mechanics, John Wiley & Sons, 2009.
Debasis, D., & Kumar, V. A. Fundamentals and applications of rock mechanics, PHI Learning Pvt. Ltd. New Delhi, India, 2016.

CE6214

Special Topics in Geotechnical Engineering

Course	CE6214: Special Topics in Geotechnical Engineering
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Special Topics in Geotechnical Engineering
Learning Mode	Lectures
Learning Objectives	Complies with PLO- number 1, 2, 3, and 4 1. To provide the knowledge of the advanced concept of geotechnical engineering. 2. Equip the students with a strong foundation in various topics in offshore geotechnical engineering. 3. Prepares the students to apply knowledge in policy and decision making related to civil engineering infrastructure.
Course Description	This course intends to bridge the basic concepts with the advanced topics related to geotechnical engineering. Topics ranging from geotechnical earthquake engineering, offshore geotechnical engineering, Tunnels and Earth & Rockfill dams are covered.
Course Outline	Elements of geotechnical earthquake engineering: seismic loading and its effect on earth structures; dynamic response of single, and multi-degree of freedom systems and continuous systems; behaviour of soil under dynamic loading; pore pressure generation and liquefaction effects; seismicity and seismic design parameters; Engineering Seismology and Seismic Microzonation Offshore geotechnical engineering: nature of submarine soils; offshore soil investigations; seabed sediments; wave action on seabed; submarine slope stability; seabed anchor systems Numerical methods in geotechnical engineering: application of finite element method to the solution of stress, deformation, seepage, and consolidation problems; numerical solutions for soil dynamics problems; soil-structure interaction. Tunnels: Drilling and blasting of rocks; Grouting; Instrumentation and measurements in tunnelling, Analysis and Design Earth & Rockfill dams: Analysis and Design, field and laboratory investigations; foundation conditions and treatment; seepage and seepage control; stability analysis; deformation analysis; seismic considerations; instrumentation and monitoring
Learning Outcome	At the end of the course, student would be able to: 1. Design earthquake resistant structure using various methods available along with the method suggested in the IS code. 2. Perform offshore soil investigations and design of offshore structure. 3. Design earth and rockfill dams considering the seepage and seismic loads.
Assessment Method	Assignments, Quizzes, Mid-semester examination and End-semester examination.

Textbooks:

E. Bowles, Engineering Properties of Soils and Their Measurement, McGraw-Hill, 1992.

Kramar S.L, Geotechnical Earthquake Engineering, Prentice Hall International series, Pearson Education Pvt. Ltd.
J.E. Bowles, Foundation Analysis and Design, McGraw-Hill, 2001.

Reference books:

Ikuo Towhata, Geotechnical Earthquake Engineering, Springer series, 2008.
All relevant IS and International Codes.

CE6215

Forensic Geotechnical Engineering

Course	CE6215: Forensic Geotechnical Engineering
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Forensic Geotechnical Engineering
Learning Mode	Lectures
Learning Objectives	Complies with PLO- number 1, 3, & 5. The learning objectives of this course are as follows: 1. To deal with investigations of different failures of engineered projects or facilities or structures related to civil engineering. 2. To analyze failures related to civil engineering, geotechnical, geoenvironmental and geological domains for professional practice, codes of analysis and design and implementation. 3. To apply the knowledge for further design and construction of any structures.
Course Description	This course is designed to understand and examine the various failure of civil and geotechnical engineering project due to different physical, environmental and geological causes. Further, knowledge gathered from this course will help in improving professional practice, developing codal provision and design and implementation.
Course Outline	Introduction, Forensic geotechnical engineering: theory and practice; Types of failure and damages, Preliminary investigations and information, Interaction between neighboring Structures, Planning the investigations, Site investigations and instrumentations, Settlement and failures of sub structures, Foundation design in difficult soil and climatic conditions, Ground water moisture related problems of substructures, Repairs and crack diagnosis, Back analysis in geotechnical engineering, Importance of uncertainty in forensic geotechnical engineering, Ethical and legal issues, Various Case studies of failures of civil engineering structures.
Learning Outcome	At the end of the course, student would be able to: 1. Understand the necessity and importance of forensic investigation in geotechnical engineering for various projects. 2. To deal with investigations of different failures of engineered projects or facilities or structures related to civil engineering. 3. To comprehend the techniques for mitigation of the failure damage. 4. To analyze failures related to civil engineering, geotechnical, geoenvironmental and geological domains for professional practice, codes of analysis and design and implementation.
Assessment Method	Assignments, Quizzes, Mid-semester examination and End-semester examination.

Textbooks:

Rao, V. V. S., and GL Sivakumar Babu, eds. Forensic Geotechnical Engineering. India: Springer India, 2016.
Puzrin, Alexander M., Eduardo E. Alonso, and Núria M. Pinyol. Geomechanics of failures. Dordrecht, The Netherlands: Springer, 2010.
Iwasaki, Y. Instrumentation and Monitoring for Forensic Geotechnical Engineering. Forensic Geotechnical Engineering (2016): 145-163.

Reference books:

Day, Robert W. Forensic geotechnical and foundation engineering. McGraw-Hill, 2011.
Alonso, Eduardo E., Núria M. Pinyol, and Alexander M. Puzrin. Geomechanics of failures: advanced topics. Vol. 277. Berlin: Springer, 2010.
Lacasse, Suzanne. Forensic geotechnical engineering theory and practice. Forensic Geotechnical Engineering (2016): 17-37.
Franck, Harold, and Darren Franck. Forensic engineering fundamentals. Boca Raton, FL: CRC Press, 2013.
All relevant IS and international codes and research articles and reports.

Department Elective - IV (Departmental Electives)

Department Elective - IV (Departmental Electives)

Sl. No.

Subject Code

Subject

CE5217

Geoinformatics for Engineers

Course	CE5217 Geoinformatics for Engineers
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Geoinformatics for Engineers [Even Semester/2^nd Semester, M. Tech
Learning Mode	Lectures
Learning Objectives	Complies with PLOs 1, 2 & 3- 1. To provide fundamental knowledge in the Basics of GIS. 2. Train students to download, process and prepare the GIS data for Water resources applications. 3. Provide scientific and technical knowledge, to prepare students to prepare maps using GIS for Water resources applications.
Course Description	This course will discuss fundamental concepts in GIS. The course will cover theory and real-world practice in map preparation, flood mapping, rivers and canal mapping and GIS software and databases.
Course Outline	Definition – Basic components of GIS – Map projections and coordinate system –Spatial data structure: raster, vector – Spatial Relationship – Topology – Geodata base models: hierarchical, network, relational, object-oriented models – Integrated GIS database -common sources of error – Data quality: Macro, Micro and Usage level components - Meta data - Spatial data transfer standards. Thematic mapping – Measurement in GIS: length, perimeter, and areas – Query analysis– Reclassification – Buffering - Neighbourhood functions - Map overlay: vector and raster overlay – Interpolation – Network analysis –Digital elevation modelling. Analytical Hierarchy Process, – Object oriented GIS – AM/FM/GIS – Web Based GIS Spatial data sources – GIS approach water resources system – Thematic maps -Rainfall-runoff modelling – Groundwater modelling – Water quality modelling – Flood inundation mapping and Modelling – Drought monitoring – Cropping pattern change analysis –Performance evaluation of irrigation commands. Site selection for artificial recharge - Reservoir sedimentation. Introduction to various remote sensing satellite data (Like Landsat, Sentinel, Radar data, DEM, GRACE etc) and their applications for different water resources engineering applications.
Learning Outcome	At the end of the course, student would be able to: 1. Understand technical aspects and properties of GIS. 2. Download and perform GIS based analysis on different satellite data. 3. Basic flood mapping using Optical and SAR data.

Assessment Method

Assignments (10%), Quizzes (10%), Mid-semester examination (30%) and End-semester examination (50%).

REFERENCES:

Lillesand, M. and Kiefer, R.W., Remote Sensing, and Image Interpretation III Edition. John Wiley and Sons, New York. 1993.
Burrough P.A. and McDonnell R.A., Principles of Geographical Information Systems. Oxford University New York. 1998.
Ian Heywood Sarah, Cornelius, and Steve Carver: An Introduction to Geographical Information Pearson Education. New Delhi, 2002.
Jensen, R., Introductory digital image processing: a remote sensing perspective, Fourth Edition, Pearson, 2017
Joseph, G & Jagannathan, , Fundamentals of remote sensing (3^rd edition), The Orient Blackswan, 2018.

CE5218

Groundwater Hydrology

Course	CE5218 Groundwater Hydrology
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Groundwater Hydrology
Learning Mode	Lectures
Learning Objectives	Complies with PLO-1, 2, 3, 4, and 5 1. To provide fundamental knowledge of groundwater hydrology. 2. Train students to plan, design and model groundwater systems. 3. Provide scientific and technical knowledge, to apply the learning in sustainable management of groundwater resources.
Course Description	This course will discuss fundamental concepts of groundwater flow, its occurrence, movement, and flow principles. It will also cover issues related to groundwater management, such as pollution and over-exploitation.
Course Outline	Characteristics of groundwater, Global distribution of water, Role of groundwater in water resources system and their management, groundwater column, aquifers, classification of aquifers. Hydrogeological cycle, water level fluctuations, Groundwater balance. Darcy's Law, Hydraulic conductivity, Aquifer transmissivity and storativity, Dupuit assumptions Storage coefficient - Specific yield Heterogeneity and Anisotropy, Direct and indirect methods for estimation of aquifer parameters. Governing equation for flow and contaminant transport through porous medium - Steady and unsteady state flow - Initial and boundary conditions, solution of flow equations. Tracer techniques using environmental isotopes. Surface water groundwater interaction. Steady and unsteady flow to a well in a confined and unconfined aquifer - Partially penetrating wells - Wells in a leaky confined aquifer - Multiple well systems - Wells near aquifer boundaries - Hydraulics of recharge wells. Dynamic equilibrium in natural aquifers, groundwater budgets, management potential of aquifers, safe yield, seepage from surface water, stream-aquifer interaction, artificial recharge. Hydrodynamic dispersion - occurrence of dispersion phenomena, coefficient of dispersion - Aquifer advection-dispersion equation and parameters - initial and boundary conditions - method of solutions, solution of advection-dispersion equation. Climate change and impact on groundwater. Groundwater monitoring and groundwater sampling techniques. Introduction to sustainable groundwater management.
Learning Outcome	After attending this course, the following outcomes are expected: 1. Student should be able to develop an understanding about the occurrence, movement, and fate of groundwater in aquifer systems. 2. Students comprehend the physical principles of groundwater flow and solute transport processes and can represent those processes through mathematical equations in assessing water quantity and quality in ground-water systems. 3. Students should be able to understand the challenges associated with groundwater resources and apply the scientific method and critical thinking in groundwater quantity and quality management.
Assessment Method	Assignments, Quizzes, Mid-semester examination, and End-semester examination

Text Books/ Reference Book:

Bhagu R Chahar, Groundwater Hydrology, McGraw-Hill Education, 2015
Todd D.K., Ground Water Hydrology, John Wiley and Sons, 2000
Freeze A, Cherry JA, Groundwater, Prentice Hall, 1979.
Bear J., Hydraulics of Groundwater, Dover Publications INC, 1979
Integrated Groundwater Management, Springer Open
Richard W Healey, Estimating Groundwater Recharge, Cambridge University Press

CE5219

Open Channel Hydraulics

Course	CE5219 Open Channel Hydraulics
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Open Channel Hydraulics
Learning Mode	Lectures
Learning Objectives	Complies with PLO 1, 2, 3, 4 and 5 Students will be enabled to understand the fundamental principles governing open channel hydraulics for the design of engineering systems. The course is intended to assist students in developing the skills needed for systematic decomposition and solution of real-world problems.
Course Description	This course covers principles of flow in open channels, conservation laws, critical flow, uniform flow, gradually varied flow, unsteady flow, flow through hydraulic structures, hydraulic jump, and flow routing, analytical and numerical techniques will also be discussed, programming assignments will be carried out in common software and MATLAB.
Course Outline	Difference between Open Channel Flow and Pipe Flow, Types of Channel, Geometric parameters of a channel, Classification of Open Channel Flow, Continuity and Momentum equation. Resistance flow formula, Velocity distribution, Equivalent roughness coefficient, Velocity coefficients, Uniform flow in rigid boundary channel, Uniform flow in mobile boundary channel. Concept of Specific Energy, Critical Depth, Alternate depth, Specific Force, Sequent depth. Governing equation of GVF, Classification of Gradually Varied Flow, Computation of GVF profile, Rapidly Varied Flow, hydraulic Jump, Flow over a Hump, Flow in Channel Transition. Concept of best hydraulic section, Design of rigid boundary canal, design of channel in alluvial formation- Kennedy’s theory, Lacy’s theory, Method of Tractive force, Free-board in canal. Wave and their classification, Celerity of wave, Surges, Characteristic equation.
Learning Outcome	At the end of the course, student would be able to: 1. Learn the form of mass, momentum and energy equations under non hydrostatic pressure distribution and non-uniform velocity profiles. 2. Analyse gradually varied flows numerically. 3. Learn how to analyse rapidly varied flow numerically. 4. Design rigid-boundary and erodible channels. 5. Gain information about the flow through spillways and culverts. 6. Basic components of sediment transport in open channels.
Assessment Method	Assignments, Quizzes, Mid-semester examination, and End-semester examination

Text Books/ Reference Book:

K Subramaniya, Flow in Open Channels, McGraw Hill, 1997.
T. Chow, Open-channel hydraulics, McGraw Hill Publications (1973).
Sturm, 2001, Open-Channel Hydraulics, McGraw Hill.
Chaudhury, Open channel flow, Second Edition. Springer (2008).
Rajesh Srivastava, Flow through open channels, Oxford University Press (2008).

CE6218

Structural Health Monitoring

Course	CE6218 Finite Element Method
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Finite Element Method
Learning Mode	Lectures
Learning Objectives	Objective for learning this course are Lecture: 1. Provide scientific and technical knowledge for the basis for the development of finite element analysis procedure. 2. Equip the students with a strong foundation and understanding for the finite element analysis process of the problems related to various civil and mechanical engineering.
Course Description	The course deals with understanding finite element analysis of various problems. This course provides the students an exposure for topics on analysis of problems related to various civil and mechanical engineering problems which are not covered in undergraduate design courses.
Course Outline	Basic concepts of engineering analysis; Methods of weighted residuals and variational formulations; Finite element discretization; Shape function; Lagrange and serendipity families; Element properties, iso-parametric elements; Criteria for convergence; Numerical evaluation of finite element matrices (Gauss quadrature integration); Assemblage of elements; Analysis of plane stress/strain, axi-symmetric solids; Three dimensional stress analysis; Flow though porous media; Error analyses: estimate of error, error bounds; Solution technique: finite element programming, use of package programs.
Learning Outcome	At the end of the course, student would be able to Lecture: 1. Understand various numerical methods for analysing engineering problems through FEM. 2. Analysis of various civil and mechanical engineering problems. 3. Ability to analyse complex structural system.
Assessment Method	Assignments, Quizzes, Project work, Mid-semester examination and End-semester examination.

Textbooks/ Reference books:

T. R. Chandrapatula and A. D. Belegundu, Introduction to finite elements in engineering, Third Edition, Prentice Hall of India, 2001.
P. Seshu, Text book of finite element analysis, Prentice Hall of India, 2003.
J. N. Reddy, An introduction to the finite element method, McGraw Hill Inc. 1993.
R. D. Cook. D. S. Malkus. M. E. Plesha, and R. J. Witt, Concepts and application of finite element analysis, fourth Edition, John Wiley & Sons, 2002.
O.C. Zienkiewicz and R. L. Taylor, The Finite element method, Butterworth Heinemann (Vol. I and Vol. lI), 2000.
C.S. Krishnamoorthy, Finite Element Analysis, Theory and programming, Tata McGraw Hill, 1994.
K.J. Bathe, Finite Element Procedures in Engg. Analysis, Prentice Hall of India, 1996.
C.S. Desai and T. Kundu, Introduction to finite element method, CRC Press, 2001.

CE6219

Course	CE6219 Structural Health Monitoring
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Structural Health Monitoring
Learning Mode	Lectures
Learning Objectives	Objective for learning this course are Lecture: 1. To develop basic understanding on health monitoring of various civil engineering structures. 2. Become proficient in dealing with commonly used approaches/ algorithms through a fundamental understanding of the basics. 3. Familiar with techniques pertaining to heath assessment of various structures like building, bridge, heritage structures etc. 4. Become acquainted with some advanced techniques line with the state-of-the-art in SHM domain
Course Description	This course explores structural health monitoring methods and technologies for assessing the condition and performance of various structures. Case studies on civil infrastructures will be examined to illustrate SHM principles in practice. Additionally, the course covers emerging trends including advancements in sensor technology and data analytics for predictive maintenance.
Course Outline	Introduction to Structural Health Monitoring (SHM): Definition & requirement for SHM, SHM of a bridge, monitoring historical buildings; Non-Destructive Testing (NDT): Classification of NDT procedures, visual inspection, half-cell electrical potential methods, Schmidt Rebound Hammer Test, resistivity measurement, electro-magnetic methods, radiographic Testing, ultrasonic testing, Infra-Red thermography, ground penetrating radar, radio isotope gauges etc., case studies of a few NDT procedures on bridges; Condition Survey & NDE of Concrete Structures: Definition and objective of Condition survey, stages of condition survey (Preliminary, Planning, Inspection and Testing stages), possible defects in concrete structures, quality control of concrete structures; Vibration-based monitoring: Frequency-domain and time-domain analysis, Experimental modal analysis, application of damage detection methods on civil infrastructures.
Learning Outcome	At the end of the course, student would be able to: Perform sensor deployment, data acquisition, and analysis techniques used to detect and quantify structural damage. Develop proficiency in deploying sensor technologies and data acquisition systems to monitor the health of various structures. To analyse collected data, detect structural damage, and make informed decisions regarding maintenance and safety measures. Use the methods in real-life applications.
Assessment Method	Assignments, Quizzes, Project work, Mid-semester examination and End-semester examination.

Textbooks/ Reference books:

Daniel J. Inman, Charles R. Farrar, Vicente Lopes Junior, Valder Steffen Junior, Damage Prognosis: For Aerospace, Civil and Mechanical Systems, John Wiley & Sons, 2005.
Chee-Kiong Soh, Yaowen Yang, Suresh Bhalla (Eds.), Smart Materials in Structural Health Monitoring, Control and Biomechanics, Springer, 2012.

CE6220

Condition Assessment and Retrofitting of Structures

Course	CE6220 Condition Assessment and Retrofitting of Structures
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Condition Assessment and Retrofitting of Structures
Learning Mode	Lectures
Learning Objectives	Objective for learning this course are Lecture: 1. Understand the background of condition assessment, repair, and strengthening of structures. 2. Understand the strategies of surface repair and retrofitting techniques. 3. Attain knowledge of rehabilitation of existing building.
Course Description	The course deals with the evaluation and strengthening of existing structures. This course provides an understanding of existing non-destructive and destructive methods for condition assessment of structures. The students shall learn about various techniques for the strengthening of structures.
Course Outline	Distress identification and repair management: causes of distress in structures, deterioration model of concrete and moisture effects. Preliminary inspection: planning stage, visual inspection and detailed inspection; Evaluation of concrete buildings: destructive testing systems, non-destructive testing techniques, semi-destructive testing techniques, corrosion potential assessment, half-cell potentiometer test, resistivity measurement, identification and estimation of damage. Evaluation of strength of existing structures and analysis necessary to identify critical sections; Surface repair and retrofitting techniques: strategy and design, selection of repair materials, surface preparation, bonding repair materials to existing concrete, placement methods; Strengthening techniques: beam shear capacity strengthening, shear transfer strengthening between members, column strengthening, flexural strengthening, and crack stabilization. Guidelines for seismic rehabilitation of existing buildings, seismic vulnerability and strategies for seismic retrofit.
Learning Outcome	At the end of the course, student would be able to: Introduce the application of different techniques for evaluation and retrofitting of buildings. Present fundamental principles and methodologies for the design of various retrofitting techniques. Estimate causes for distress and deterioration of structures. NDT techniques for condition assessment of structures for identifying damages in structures. Evaluate properties of distressed structural members. Select retrofitting strategy suitable for distress and formulate guide lines for repair management of deteriorated structures
Assessment Method	Assignments, Quizzes, Project work, Mid-semester examination and End-semester examination.

Textbooks/ Reference books:

ASCE/SEI 41-23 Seismic Evaluation and Retrofit of Existing Buildings. 2023.
Varghese P.C., “Maintenance, Repair & Rehabilitation and Minor Works of Buildings” 1st Edition, PHI Learning Private Ltd., New Delhi., 2014.
Santhakumar A.R., “Concrete Technology” Oxford University Press, 2007, New Delhi
CPWD Handbook on Repair and Rehabilitation of RCC buildings, Govt. of India Press, New Delhi.
Emmons, P.H., “Concrete Repair and Maintenance”, Galgotia Publication. 2001.
Bungey, S., Lillard, G. and Grantham, M.G., “Testing of Concrete in Structures”, Taylor and Francis. 2001.
Malhotra, V.M. and Carino, N.J., “Handbook on Non-destructive Testing of Concrete”, CRC Press. 2004.
Bohni, H., “Corrosion in Concrete Structures”, CRC Press. 2005.
ATC- 40: Seismic Evaluation and Retrofit of Concrete Buildings, Vol. 1 & 2. 1997.
J.N. Priestley, Seible, F. and Calvi, G.M., “Seismic Design and Retrofit of Bridges”, John Wiley. 1996.

CE6223

Uncertainty, Risk and Reliability Analyses in Civil Engineering

Course	CE6223 Uncertainty, Risk and Reliability Analyses in Civil Engineering
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Uncertainty, Risk and Reliability Analyses in Civil Engineering
Learning Mode	Lectures
Learning Objectives	Objective for learning this course are Lecture: 1. Make familiar the concept of probability theory and statistics. 2. Gain knowledge on stochastic simulation methods. 3. Develop knowledge on risk and reliability analysis of structure.
Course Description	The course deals with the risk and reliability analysis and design of different civil engineering infrastructural system. Also, this course discusses about the basic probability theory and random field generation.
Course Outline	Introduction and overview: Review of basic probability, Functions of random variables. Joint probability distribution, conditional distributions, Joint Normal distribution, Baysian Analysis, Analysis of variance (ANOVA), Application of central limit theorem; confidence interval, expected value, and return period, probability paper; testing of goodness-of-fit of distribution models, Random number generation – Monte Carlo simulations, Formulation of structural reliability problems: limit states, composite risk analysis, direct integration method, safety margin method, reliability index and safety factor; FORM and SORM methods, importance sampling and other variance reduction techniques, Reliability – historical development, applications, different measures of reliability; Component reliability - time to failure, Reliability-based maintenance, System reliability - representation of failure, series and parallel systems, redundancy, fault trees, Probability-based acceptance criteria: consequence of failure, concepts of risk, utility, Probability-based design, fragility analysis. Calibration of target reliability: reliability-based design codes.
Learning Outcome	At the end of the course, student would be able to Lecture: 1. Understanding concept of probability theory and application. 2. Risk and reliability analysis of civil engineering infrastructure. 3. Design of civil infrastructure based on risk and reliability.
Assessment Method	Assignments, Quizzes, Project work, Mid-semester examination and End-semester examination.

Textbooks/ Reference books:

A. Haldar and S. Mahadevan, Probability, Reliability, and Statistical Methods in Engineering Design, Wiley, 2000.
H. S. Ang and W. H. Tang, Probability Concepts in Engineering Planning and Design, John Wiley, 1975.
R. Ranganathan, Reliability Analysis and Design of Structures, Tata McGraw Hill, New Delhi, 1990.

CE6228

Analytical Techniques for Infrastructure Systems Analysis

Course Number	CE6228: Analytical Techniques for Infrastructure Systems Analysis
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Analytical Techniques for Infrastructure Systems Analysis
Learning Mode	Theory
Learning Objectives	To provide knowledge of quantitative techniques with application potential for Infrastructure systems.
Course Description	This course provides a comprehensive introduction to the analytical methods and tools used in the analysis of infrastructure (transportation) systems. The course focuses on the application of these techniques to real-world transportation systems and includes a mix of theoretical and practical content. Students will learn about various analytical techniques including but not limited to traffic flow theory, network analysis, demand forecasting, and system optimization. The course will cover both traditional methods such as regression analysis and newer techniques such as machine learning and data analytics. The course will also delve into the use of software tools for transportation analysis and modeling. Students will get hands-on experience with these tools through assignments and projects.
Course Content	Modelling and Simulation: Model Classification, Mathematical; Physical and Analog models, steps involved in simulation, Monte Carlo simulation, validation and verification of simulation models Multivariate Data Analysis: Vectors and Matrices, Simple estimate of centroid, standard deviation, dispersion, variance and co-variance, correlation matrices, principal component analysis Curve Fitting: Method of least squares, curvilinear regression, Multiple regression, checking adequacy of model, correlation, multiple linear regression; Queuing Theory: General structure, operating characteristics, deterministic queuing model, probabilistic queuing models, and simulation of queuing system; Forecasting Models: Moving averages, exponential smoothening, trend projections, causal models, time series analysis of vehicle growth & accidents Neural Networks: Basic concepts; neural network architecture, back propagation networks.
Learning Outcome	The student will be able to 1. Understand and Apply Modelling and Simulation Techniques 2. Perform Curve Fitting 3. Understand and Apply Queuing Theory 4. Perform Multivariate Data Analysis 5. Develop and Use Forecasting Models and Neural networks for the transportation related problems
Assessment Method	Assignments, Quizzes, Mid-semester examination and End-semester examination

References

Vohra, N.D., “Quantitative Techniques in Management”, Tata McGraw Hill, 2001.
Johnson, R. A. and Wichern, D.W., “Applied Multivariate Statistical Analysis”, Prentice Hall., 2003.
Johnson, R., “Probability and Statistics for Engineers”, Prentice Hall. 2009
Hair, J. and Anderson, R., “Multivariate Data Analysis”, Prentice Hall. 2010

CE6229

Advanced Flexible Pavement Analysis and Design

Course	CE6229: Advanced Flexible Pavement Analysis and Design
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Advanced Flexible Pavement Analysis and Design
Learning Mode	Lectures
Learning Objectives	Complies with PLO number – 1, 2 and 4 1. To provide knowledge of recent developments in asphalt material characterization for pavement analysis. 2. Train students to design pavement and overlays. 3. Learn computation of stress distribution and distress mechanisms in pavement. 4. Learn life-cycle analysis of flexible pavements
Course Description	This course will discuss fundamental concepts in design and analysis of flexible pavement. Course will cover Empirical and Mechanistic-Empirical pavement design approaches. Students will learn how to conduct life-cycle cost and environmental analysis for flexible pavements. Students will also learn use of non-destructive tests in pavement condition evaluation and overlay design.
Course Outline	Development of Various Design Methods for Flexible Pavement: Empirical pavement design approach, AASHTO 1993 method, Mechanistic empirical pavement design approach, Asphalt Institute method, IRC Method, MEPDG Method. Theoretical and Numerical Models for Analysis of Flexible Pavement: Axle load configurations, Stresses and strains in pavements, Boussinesq solution, Equivalent Thickness Method, Multi-layer elastic solutions, Multi-layer viscoelastic solutions, 2-D and 3-D Finite element models. Selection of Pavement Design Input Parameters and Pavement Performance Models: Traffic loading, Environmental factors in pavement design, Reliability, Pavement material models for asphalt mix and unbound materials, Pavement performance models, Effects of heavy vehicles on pavement response and performance. Sustainability Analysis: Introduction to sustainability in pavement design, Life-cycle cost analysis, Environmental analysis, Nondestructive testing, Backcalculation of pavement in situ properties, Design of overlays. Software: KENPAVE
Learning Outcome	At the end of the course, student would be able to: 1. Design flexible pavements using Indian Codes and learn best practices. 2. Ability to compute stress-strain distribution in pavement. 3. Identify different type of distresses in pavement and determine condition of pavement using nondestructive testing. 4. Identify factors influencing pavement design. 5. Perform pavement life cycle cost and environmental analysis.
Assessment Method	Assignments , Quizzes , Mid-semester examination and End-semester examination .

Textbooks:

Huang, Y. H. “Pavement analysis and design.” Pearson, 2004.
Papagianna, A. T. and Masad, E. A. “Pavement Design and Materials.” John Wiley & Sons, Inc., 2008.
Chakroborty, P. and Das, A. “Principles of Transportation Engineering.” PHI Learning, 2017.

Reference books:

Ullidtz, P. “Pavement Analysis.” Elsevier, 1987.
Mechanistic-Empirical Pavement Design Guide – A Manual of Practice, AASHTO 2008.

10.

CE6230

Advanced Concrete Pavement Analysis and Design

Course	CE6230: Advanced Concrete Pavement Analysis and Design
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Advanced Concrete Pavement Analysis and Design
Learning Mode	Lectures
Learning Objectives	Complies with PLO number – 1, 2 and 4 1. Differentiate between the various Portland Cement Concrete pavement systems. 2. To provide knowledge of recent developments in concrete material characterization for rigid pavement analysis. 3. Train students to design concrete pavement and overlays. 4. Learn computation of stress distribution and distress mechanisms in rigid pavement. 5. Explain the underlying mechanisms associate with load and material related distresses.
Course Description	This course will discuss fundamental concepts in design and analysis of rigid pavement. Theoretical models for analysis of rigid pavement systems. Evaluation and application of current design practices related to rigid pavements. Course will cover Empirical and Mechanistic-Empirical pavement design approaches. Students will also learn different mechanisms associated with distress in rigid pavements.
Course Outline	INTRODUCTION TO PCC PAVEMENTS: Typical pavement cross-section and plan, Types of PCC pavements, Jointed systems, CRCP, Overlays, 2-lift systems, Precast systems, Prestressed-Post tension systems, Evolution of pavement design, Empirical and Mechanistic-Empirical designs. OVERVIEW OF AASHTO 86/93: Significant inputs needed for the design, Serviceability concept, Impact of inputs on the slab thickness-sensitivity, Limitations of the design process, Need for a systems approach to design-M-E PDG. PCC PAVEMENT DISTRESSES: Functional and structural distress, Load related distress, Material related distress, Underlying mechanism(s) of distresses, Relationship between distress mechanism(s) and design. PCC PAVEMENT RESPONSE: Load related response, Thermal response. Material Characterization: Fresh mixture properties, Mechanical properties, Thermal properties, Fracture properties, Durability properties. Traffic Characterization: ESALs, Load Spectra. PCC Design Methods (New and Overlays): PCA design method, AASHTO’98, M-E PDG. CONSTRUCTION OF PCC PAVEMENTS: Conventional pavement construction, Two-lift construction, Modular pavement construction, Concrete Overlays. SPECIAL TOPICS IN PCC PAVEMENTS: Porous concrete, Pannel concrete, Roller Concrete.
Learning Outcome	At the end of the course, student would be able to: 1. Design rigid pavements using Indian Codes and learn best practices. 2. Ability to compute stress-strain distribution in rigid pavement. 3. Identify different type of distresses in rigid pavement. 4. Identify factors influencing rigid pavement design.
Assessment Method	Assignments, Quizzes , Mid-semester examination and End-semester examination .

Textbooks:

Huang, Y. H. “Pavement analysis and design.” Pearson, 2004.
Papagianna, A. T. and Masad, E. A. “Pavement Design and Materials.” John Wiley & Sons, Inc., 2008.
Chakroborty, P. and Das, A. “Principles of Transportation Engineering.” PHI Learning, 2017.

Reference books:

Ullidtz, P. “Pavement Analysis.” Elsevier, 1987.
Mechanistic-Empirical Pavement Design Guide – A Manual of Practice, AASHTO 2008.

11.

CE6231

Advanced Pavement Material Characterization

Course	CE6231: Advanced Pavement Material Characterization
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Advanced Pavement Material Characterization
Learning Mode	Lectures
Learning Objectives	Complies with PLO number – 1, 2, and 4 To understand characteristic properties of material used in road construction. To understand performance evaluation techniques of road construction materials. To understand design of asphalt mix. To understand different type of waste and recycled materials used in road construction. To understand quality control plan in road construction.
Course Description	This course deals with materials used in road construction. Source, properties and performance evaluation methods of pavement materials are important in selecting them road construction project. The course will help students understand the practices used in road construction industry in selection, design and quality control of pavement materials.
Course Outline	Characterization of Pavement Materials: (1) Asphalt mix: Definitions, Production types and Classification of asphalt mix. (2) Aggregates: Definitions, Sources, Production types, Engineering and Consensus properties. (3) Asphalt binder: Definitions, Sources, Production types, Chemistry and Physical properties, Performance tests and Specifications, Specifications for modified binders. (4) Soil: Definitions, Classification and Engineering properties. (5) Emulsion: Definitions, Classification and Engineering properties; Image based material evaluation, non-destructive testing of material properties. Advance topics in Asphalt Binder and Mixes: Performance grading of asphalt binder, Binder modification, Superpave mix design, Design using recycled materials. Asphalt Mix Modeling: Introduction to viscoelasticity, Rheological properties – viscoelastic models, Viscoplastic models, nonlinear viscoelasticity, Interconversion of viscoelastic properties. Failure Modeling: Fatigue Models, Rutting models, Moisture damage mechanism. Unbound materials: Nonlinearity in fine and coarse grained material; Stabilized granular layer, Design of stabilized materials. Quality Control and Tolerance: Field construction, Quality control plan, Control charts, QA/QC tests. Software: ABAQUS
Learning Outcome	At the end of the course, student would be able to: Understand different conventional and recycled materials used in road construction? Select and design material for road construction. Evaluate pavement material based on performance related properties. Develop quality control plan for pavement materials in road construction projects.
Assessment Method	Assignments, Quizzes , Mid-semester examination and End-semester examination .

Textbooks:

Huang, Y. H. "Pavement analysis and design." Pearson, 2004.
Papagianna, A. T. and Masad, E. A. “Pavement Design and Materials.” John Wiley & Sons, Inc., 2008.

Reference books:

Kim., Y. R. “Modeling of Asphalt Concrete.” McGraw-Hill, 2009, 1st Edition.
National Cooperative Highway Research Program (NCHRP) Reports.
MORTH. “Ministry of Road Transportation & Highways Specifications for Road and Bridge Works.” 2013.

Interdisciplinary Elective (IDE) Course for M. Tech. (Available to students other than CE)

Sl. No.

Subject Code

Subject Name

CE6132

Data Science for Engineers

Course	CE6132: Data Science for Engineers
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Data Science for Engineers
Desirable Prerequisites	Knowledge of Remote Sensing and GIS/Advanced Geomatics, digital image processing, machine learning and AI
Learning Mode	Lectures
Learning Objectives	Complies with PLOs 1, 2 & 3- 1. To provide fundamental knowledge in the basics of Data Science. 2. Train students to understand the various applications of Machine Learning and modelling for research applications. 3. Provide scientific and technical knowledge to the students on Errors and Adjustments.
Course Description	This course will discuss fundamental concepts in data science for Civil Engineers. The course will cover theory and real-world practice in data, errors and adjustments to help deal with various research-related problems.
Course Outline	Overview of probability and statistics; statistical learning: definition, principles and different types of statistical learning, assessing model accuracy, bias-variance tradeoff; regression models: simple linear and multiple linear and non-linear; resampling methods: assessing model prediction quality, cross-validation, bootstrap; model selection and regularisation: dimensionality reduction, ridge and lasso; unsupervised learning: clustering approaches, K-means and hierarchical clustering; supervised learning: classification problem, classification using logistic regression, naive Bayes, classification with Support Vector Machines, neural networks. Background of Errors, Expectations and Error Propagation, Random Errors, Model Development and Problem-solving, Observations and Equations, Conditions and Combined Equations, Errors in Surveying.
Learning Outcome	At the end of the course, students would be able to: 1. Understand technical aspects and properties of Data Science. 2. Perform error adjustments in Civil Engineering problems. 3. Skilled to develop more accurate, robust and error-free predictive and classification models.
Assessment Method	Assignments (10%), Quizzes (10%), Mid-semester examination (30%) and End-semester examination (50%).

REFERENCES:

Gillani, D. Charles, Adjustment Computations: Spatial Data Analysis, 6^th Edition, John Wiley and Sons, 2017.
James, G., Witten, D., Hastie, T., & Tibshirani, R., Introduction to Statistical Learning, Springer, 2^nd Edition, 2013.
Lillesand, M. and Kiefer, R.W., Remote Sensing, and Image Interpretation III Edition. John Wiley and Sons, New York. 1993.
Mehrotra, A.K., Geo-statistics for Beginners, Zorba, 2020.
Ian Heywood Sarah, Cornelius, and Steve Carver: An Introduction to Geographical Information Pearson Education. New Delhi, 2002.
Leick, A., GPS satellite surveying, John Wiley and Sons, 4^th Edition, 2015.
Ogundare, O.J., Precision Surveying: The Principles and Geomatics Practice, John Wiley and Sons, 2015.

M. Tech. in Transportation Engineering

Program Learning Objectives:	Program Learning Outcomes:
Program Goal 1: Equip the students with strong foundation in civil and environmental (Transportation) engineering for both research and industrial scenarios.	Program Learning Outcome 1a: Student develops ability to design and conduct experiments. Program Learning Outcome 1b: Student is able to organize and analyze the experiment data to draw conclusions.
Program Goal 2: Provide scientific and technical knowledge in planning, design, construction, operation and maintenance of civil (transportation) engineering infrastructure.	Program Learning Outcome 2: Students are able to (i) develop material and process specifications, (ii) analyze and design projects, (iii) perform estimate and costing and (iv) manage technical activities.
Program Goal 3: Prepares the students to apply knowledge in policy and decision making related to civil (transportation) engineering infrastructure.	Program Learning Outcome 3a: Student develops understanding of professional and ethical responsibility. Program Learning Outcome 3b: Student is able to consider economic, environmental, and societal contexts while developing engineering solutions.
Program Goal 4: Prepare students to attain leadership careers to meet the challenges and demands in civil (transportation) engineering practice.	Program Learning Outcome 4a: Students is prepared for leading roles/profiles in government sector, construction industry, consultancy services, NGOs, corporate houses and international organizations. Program Learning Outcome 4b: Student develops ability to identify, formulate, and solve engineering problems
Program Goal 5: Nurture interdisciplinary education for finding innovative solutions.	Program Learning Outcome 5: Student is able to solve complex engineering problems by applying principles of engineering and science.

Urban Transportation Planning

Sl. No.

Subject Code

SEMESTER I

HS5111

Technical Writing and Soft Skill

CE5111

Course	CE5111
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Urban Transportation Planning
Learning Mode	Lectures
Learning Objectives	Complies with PLO number – 1, 2, and 4 1. Understand the concept of highway geometry and design controls; 2. Understand the factors influencing road safety; 3. Learn practices and technologies to mitigate road accidents
Course Description	The course mainly focuses on factors influencing road geometry and its relation with road safety. The student will learn design factors that need to be considered in highway geometric design based on different expected road users. Need to understand characteristics of drivers, pedestrians, vehicles and road will be illustrated. Students will learn impact of electric and autonomous vehicles on geometric road design.
Course Outline	Introduction and scope; Definition and basic principles; Transportation problems; Types of models; Planning methodologies; Conventional transportation planning process; Travel demand modelling and forecasting; Trip generation - regression, category analysis; Trip distribution - growth factor, Fratar and Furness methods, calibration of Gravity model, intervening opportunities model, competing opportunities model, LP model; Modal split models - aggregate and disaggregate models, discriminant, logit and probit analysis; Traffic Assignment - route building, capacity restraint, multipath, incremental and equilibrium assignment; Graph theory applications in transport network analysis; Urban goods movement; Land use - transport models: historical development, case studies, ISGLUTI Study, recent developments. Laboratory Component: Solving case study problems in travel demand modelling with the help of transportation planning and econometric packages. Developing computer programs for the calibration of travel demand, land-use and land use-transport models.
Learning Outcome	At the end of the course, student would be able to: 1. Ability to access road safety and prepare road safety audit report. 2. Ability to design road geometry.
Assessment Method	Assignments, Quizzes, Mid-semester examination and End-semester examination.

References:

Hutchinson, B.G., Principles of Urban Transport Systems Planning, McGraw Hill, New York, 1974.
Ortuzar, J. and Willumsen, L.G., Modelling Transport, Wiley, Chinchestor, 1994.
Oppenheim, N., Urban Travel Demand Modeling: From Individual Choices to General Equilibrium, Wiley, New York, 1995.
Thomas, R., Traffic Assignment Techniques, Avebury Technical, Aldershot, 1991.
Taniguchi, E., Thompson, R.G., Yamada, T. and Van Duin, R., City Logistics - Network Modelling and Intelligent Transport Systems, Elsevier, Pergamon, Oxford, 2001.
Bruton, M.J., Introduction to Transportation Planning,
Hutchinson, London, 1985. Dickey, J.W., Metropolitan Transportation Planning, Tata McGraw Hill, New Delhi, 1975.

CE5112

Pavement Analysis and Design

Course	CE5112
Course Credit	3-0-0-3
Course Title	Pavement Analysis and Design
Learning Mode	Lectures
Learning Objectives	Complies with PLO number – 1, 2, and 4 To impart knowledge to students related to analysis and design of flexible and regid pavements for highways.
Course Description	In this course analysis and design of different type of pavements will be covered. The course will help students understand stresses in pavements. The practices used in road construction industry in design and drawing pavement cross-sections.
Course Outline	Introduction: Components of pavement structure, importance of subgrade soil, properties on pavement performance. Functions of subgrade, subbase, base course and wearing course. Stresses in Flexible Pavements:Stresses in homogeneous masses and layered systems, deflections, shear failures, equivalent wheel and axle loads. Design Elements of Flexible Pavements: Loading characteristics-static, impact and repeated loads, effects of dual wheels and tandem axles, area of contact and tyre pressure, modulus or CBR value of different layers, equivalent single wheel load, equivalent stress and equivalent deflection criterion; equivalent wheel load factors, climatic and environmental factors. Design Methods for Flexible Pavements: California bearing ratio (CBR) method, AASHTO 1993 method, Design of flexible pavements IRC 37. Rigid Pavements: Wheel load stresses, Soil subgrade, Westergaard’s analysis, Bradbury’s approach, Arlington test, Pickett’s corner load theory and influence charts. Temperature Stresses: Westergaard’s and Thomlinson’s analysis of warping stresses, Combination of stresses due to different causes, Effect of temperature variation on Rigid Pavements. Reinforced Concrete Slabs: Concrete slabs-general details. Design of Tie Bars and Dowel Bars.Design of Rigid pavements using IRC 58-2015 and AASHTO guidelines.Software: IITPave and ABAQUS
Learning Outcome	At the end of the course, student would be able to: 1. Perform flexible and rigid pavementanalysis and design for highways. 2. Understand various factors influencing pavement design.
Assessment Method	Assignments, Quizzes, Mid-semester examination and End-semester examination.

Textbooks:

Khanna, S.K. and Justo, C.E.G., “Highway Engineering”, Nem Chand Jain & Bros.
Papagianna, A. T. and Masad, E. A. “Pavement Design and Materials.” John Wiley & Sons, Inc., 2008.
Huang, Y. H. "Pavement analysis and design." Pearson, 2004.

Reference books:

Relevant IRC codes.

National Cooperative Highway Research Program (NCHRP) Reports.

CE5113

Traffic Engineering and Management

Course Number	CE5113
Course Credit (L-T-P-C)	3-0-3-4.5
Course Title	Traffic Engineering and Management
Learning Mode	Lectures and Practical
Learning Objectives	Complies with PLO number – 1, 2, and 4 1. To understand various traffic flow parameters 2. To understand how to conduct traffic studies 3. To understand how to design signalized intersections Practical: Complies with PLO number 1 and 4. 1. To understand how to collect, analyze and interpret the traffic data
Course Description	To introduce fundamental knowledge of traffic engineering so that students can understand and be able to deal with traffic issues including safety, planning, design, operation and control. Students will learn and be able to use software such as Highway Capacity Software and Synchro in traffic engineering projects.
Course Content	Introductory concepts of traffic engineering, road user and vehicle characteristics, and Road way geometric characteristics.Traffic Studies – speed, delay, volume, parking, Origin-Destination, capacity, accident, etc. Statistical Applications in Traffic Engineering,Capacity and Level of Service,Traffic control devices: Road Signs, Markings and Islands, Traffic characteristics at unsignalized intersections; Design of signalized intersections; capacity, delay and Level of service at signalized intersection; actuated signal control. Practical on: Spot Speed Study; Measurement of Travel Time and Delay for Congested Corridor; Moving Observer Method Study; Turning Movements and Peak Hour Factor; Plate Method of OD Survey; Acceleration Deceleration Characteristics of Vehicles; Intersection Volume Study; Saturation Flow Measurement; Intersection Delay Measurement; Pedestrian Behaviour Study;
Learning Outcome	At the end of the course, the student will be able to gather the information on 1. Use statistical concepts and applications in traffic engineering. 2. Identify traffic stream characteristics. 3. Understand elements of highway safety and approaches to accident Studies. 4. Design a pre-timed signalized intersection, and determine the signal splits 5. Identify level of services for arterials. 6. Utilize modern software tools (HCS) for network representation and traffic simulation. 7. Utilize modern software tools to estimate traffic measures such as delay and LOS for signalized and unsignalized intersections. 8. Understand, conduct and interpret data for traffic simulation experiments. 9. Understand the contemporary issues related to the use of advanced technology in traffic modeling and control. 10. Design transportation related project in a team of two or three students and submits a final report. 11. Understand Warrants and ability to use them to evaluate intersections.
Assessment Method	Assignments, Quizzes, Mid-semester examination and End-semester Examination

References

Roess, R.P., E.S. Prassas, and W.R. McShane. Traffic Engineering, Fifth Edition, Pearson-Prentice Hall.
Khanna, S.K., C.E.G. Justo, and A. Veraragavan. (2018). Highway Engineering, Tenth Edition, Nem Chand & Bros.
Kadiyali, L. R. (2008). Traffic Engineering and Transportation Planning, Khanna Publishers, India.
Garber, N.J., and L.A. Hoel. (2015). Traffic and Highway Engineering, Fifth Edition Cenage Publications.
Chakraborty, P., and A. Das. (2019). Principles of Transportation Engineering, Second Edition. PHI Learning Private Limited.
Highway Capacity Manual, (2016), TRB, National Research Council, Washington, D. C.
Relevant IRC codes.

4.5

CE51XX/ CE61XX

DE-I (Transportation Elective)

CE51XX/ CE61XX

DE-II (Transportation Elective/ Department Elective)

XX61PQ

IDE

TOTAL

23.5

Computer Applications in Transportation Engineering

Sl. No.

Subject Code

SEMESTER II

CE5212

Highway Materials

Course	CE5212
Course Credit (L-T-P-C)	3-0-3-4.5
Course Title	Highway Materials
Learning Mode	Lectures and Practical
Learning Objectives	Complies with PLO number – 1, 2, and 4 To understand characteristic properties of material used in road construction. To understand performance evaluation techniques of road construction materials. To understand design of asphalt and cement mixes. To understand different type of waste and recycled materials used in road construction. To understand quality control plan in road construction.
Course Description	This course deals with materials used in road construction. Source, properties and performance evaluation methods of pavement materials are important in selecting them road construction project. The course will help students understand the practices used in road construction industry in selection, design and quality control of pavement materials.
Course Outline	Soil:Classification of soil, identification and strength tests – Atterberg limits, compaction tests, California Bearing Ratio (CBR), Unconfined Compressive Strength (UCS), Modulus of subgrade reaction, Resilient Modulus, Permeability, Free Swelling Index (FSI), Deleterious materials, sand equivalent test, Soil stabilization techniques. Aggregates: Origin and classification, physical, mechanical and durability properties, sampling techniques, aggregate texture, polish stone value, alkali-aggregate reactivity. Nonlinearity in fine and coarse grained material. Fillers and its specifications. Binders: Bitumen: Bitumen sources and manufacturing, bitumen constituents and its properties, rheology, bitumen-emulsion, cutback, modified bitumen, separation, long-term and short-term properties. Cement: Manufacturing, composition, type of cement, physical properties of cement: consistence, setting times, soundness and strength. Stabilized materials: Stabilization methods, Subgrade stabilization, Sub-base stabilization, Base stabilisation, Estimation of resilient modulus. Recycled Pavement Materials: Crumbed rubber, Construction and Demolition (C&D) waste, Steel Slag, Recycled Asphalt Pavement (RAP). Mix Designs: Design of granular sub-base and their desirable properties; Design of Wet Mix Macadam and its desirable properties.Bituminous Mix Design and its desirable properties using Marshall Method MS-2.Asphalt cold Mix Design as per MS-14, Asphalt Institute and MORTH. Concrete Mix Design – constituents and their requirements, fresh and hardened concrete properties, factors influencing mix design. Dry lean concrete. Paving quality concrete (PQC). Quality Control and Tolerance: Field construction, Quality control plan, Control charts, QA/QC tests.Laboratory tests in this course will include:Aggregate & Soil Tests: Coarse and Fine Aggregate Specific Gravity; California Bearing Ratio.Binder Test: Penetration Test; Softening Point Test; Ductility Test; Viscosity Test.Asphalt Mix Test: Marshall mix design;Quality Control Tests: Binder Extraction Test; In-situ Density Measurement.
Learning Outcome	At the end of the course, student would be able to: Select and design material for road construction. Evaluate pavement material based on its specification requirements. Understand different conventional and recycled materials used in road construction. Develop quality control plan for pavement materials in road construction projects.
Assessment Method	Assignments, Quizzes, Mid-semester examination and End-semester examination.

Textbooks:

Huang, Y. H. "Pavement analysis and design." Pearson, 2004.
Papagianna, A. T. and Masad, E. A. “Pavement Design and Materials.” John Wiley & Sons, Inc., 2008.

K. Khanna and C. E. G. Justo, Highway Engineering, Nem Chand Bros., 2002.

Reference books:

MORTH. “Ministry of Road Transportation & Highways Specifications for Road and Bridge Works.” 2013.
Kim., Y. R. “Modeling of Asphalt Concrete.” McGraw-Hill, 2009, 1st Edition.
National Cooperative Highway Research Program (NCHRP) Reports.

4.5

CE5213

Railway Engineering

Course	CE5213
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Railway Engineering
Learning Mode	Lectures
Learning Objectives	Complies with PLO- 2 Understand factors that control railway alignment. Understand structural and geometric design of tracks. Understand track maintenance operation.
Course Description	This course deals with the basics of ballasted track design. Course aims to teach factors that govern the structural and geometric design of tracks.
Course Outline	History of Indian railways and importance of railways; Factors controlling railway alignment; Components of railway track, functions and requirements; Geometric design of tracks; Analysis and design of track layers: Axle load, train speed considerations; Factors affecting the performance of ballast and subballast; Track drainage; Train resistance and tractive power requirements.
Learning Outcome	At the end of the course, student would be able to: Factors governing railway alignment Design railway tracks Design track maintenance operation.
Assessment Method	Assignments, Quizzes, Mid-semester examination and End-semester examination

Textbooks/ References:

Coenraad Esveld., “Modern Rail Track Design”, MRT productions.
BuddhimaIndraratna, Wadud Salim, CholachatRujikiatkamjorn, “Advanced Rail Geotechnology - Ballasted Track”, CRC Press, 2011.
S.C. and Arora.S.P, “A Text Book of Railway Engineering”, Dhanpat Rail Publications, 2013.
J S Mundrey, “Railway Track Engineering”, Mc. Graw Hill Education, 2015.

CE5214

Course Number	CE5214
Course Credit (L-T-P-C)	2-1-0-3
Course Title	Computer Applications in Transportation Engineering
Learning Mode	Lectures and Tutorials
LearningObjectives	Complies with PLO number – 1, 2, 3 and 4 To gain basic knowledge on traffic and highway engineering related simulation software and how to build COM interfaces for solving practical problems.
Course Description	This course focuses on the fundamentals behind some of the most popular computer software packages used in the transportation planning, design, operations, and management of transportation systems. Topics include signal optimization and evaluation at various levels of spatiotemporal scales, forecasting of traffic flows and passenger volumes for both long-term and short-term planning, simulation of traffic and transit systems, design and evaluation of Intelligent Transportation Systems.
Course Content	Introduction to a programming language MATLAB, R, Python; Introduction to a microscopic traffic simulation software VISSIM; Introduction to Synchro, HCS, and AIMSUN. Introduction to a highway design software MXROADS Introduction to a pavementanalysis and design software IITPave, KenPave, AASHTOWare, KGPBACK, Abaqus. Airport Pavement Analysis – BAKFAA, FAARFIELD, etc.
Learning Outcome	At the end of the course, the student will be able to 1. Understand how to use microscopic simulation software VISSIM, AIMSUN, optimization software Synchro and analysis software HCS 2. Learn basics of programming languages such as R, python and MATLAB 3. Understand how to frame COM interfaces with various simulation softwares to solve real-world problems.
Assessment Method	Assignments, Quizzes, Mid-semester examination and End-semester examination

References:

Highway Capacity Manual
Indo-Highway Capacity Manual
Relevant Software/Programming manuals

CE52XX/ CE62XX

DE-III (Transportation Elective)

CE52XX/ CE62XX

DE-IV (Transportation Elective/ Department Elective)

RM6201

Course Number	RM6201
Course Credit (L-T-P-C)	3-1-0-4
Course Title	Research Methodology
Learning Mode	Lectures
Learning Objectives	The objective of the course is to train student about the modelling of scalar and multi-objective nonlinear programming problems and various classical and numerical optimization techniques and algorithms to solve these problems
Course Description	Advanced Optimization Techniques, as a subject for postgraduate and PhD students, provides the knowledge of various models of nonlinear optimization problems and different algorithms to solve such problems with its applications in various problems arising in economics, science and engineering.
Course Content	Module I (6 lecture hours) – Research method fundamentals: Definition, characteristics and types, basic research terminology, an overview of research method concepts, research methods vs. method methodology, role of information and communication technology (ICT) in research, Nature and scope of research, information based decision making and source of knowledge. The research process; basic approaches and terminologies used in research. Defining research problem and hypotheses framing to prepare a research plan. Module II (5 lecture hours) - Research problem visualization and conceptualization: Significance of literature survey in identification of a research problem from reliable sources and critical review, identifying technical gaps and contemporary challenges from literature review and research databases, development of working hypothesis, defining and formulating the research problems, problem selection, necessity of defining the problem and conceiving the solution approach and methods. Module III (5 lecture hours) - Research design and data analysis: Research design – basic principles, need of research design and data classification – primary and secondary, features of good design, important concepts relating to research design, observation and facts, validation methods, observation and collection of data, methods of data collection, sampling methods, data processing and analysis, hypothesis testing, generalization, analysis, reliability, interpretation and presentation. Module IV (16 lecture hours) - Qualitative and quantitative analysis: Qualitative Research Plan and designs, Meaning and types of Sampling, Tools of qualitative data Collection; observation depth Interview, focus group discussion, Data editing, processing & categorization, qualitative data analysis, Fundamentals of statistical methods, parametric and nonparametric techniques, test of significance, variables, conjecture, hypothesis, measurement, types of data and scales, sample and sampling techniques, probability and distributions, hypothesis testing, level of significance and confidence interval, t-test, ANOVA, correlation, regression analysis, error analysis, research data analysis and evaluation using software tools (e.g.: MS Excel, SPSS, Statistical, R, etc.). Module V (10 lecture hours) – Principled research: Ethics in research and Ethical dilemma, affiliation and conflict of interest; Publishing and sharing research, Plagiarism and its fallout (case studies), Internet research ethics, data protection and intellectual property rights (IPR) – patent survey, patentability, patent laws and IPR filing process.
Learning Outcome	On successful completion of the course, students should be able to: 1. Understand the terminology and basic concepts of various kinds of nonlinear optimization problems. 2. Develop the understanding about different solution methods to solve nonlinear Programing problems. 3. Apply and differentiate the need and importance of various algorithms to solve scalar and multi-objective optimization problems. 4. Employ programming languages like MATLAB/Python to solve nonlinear programing problems. 5. Model and solve several problems arising in science and engineering as a nonlinear optimization problem.
Assessment Method	Quiz /Assignment/ Project / MSE / ESE

Textbooks & Reference Books:

R. Kothari, Research methodology: Methods and Techniques, 3rd Edn., New age International 2014.
Mark N K. Saunders, Adrian Thornhill, Phkip Lewis, “Research Methods for Studies, 3/c Pearson Education, 2010.
N. Krishnaswamy, apa iyer, siva kumar, m. Mathirajan, “Management Research Methodology”, Pearson Education, 2010.
Ranjit Kumar; “Research Methodology: A Step by Step Guide for Beginners; 2/e; Pearson Education, 2010.

IK6201

IKS

TOTAL

23.5

Sl. No.	Subject Code	SEMESTER III	L	T	P	C
1.	CE6198	Summer Internship/Mini Project*	0	0	12	3
2.	CE6199	Project I	0	0	30	15
	TOTAL		0	0	42	18

*Note: Summer Internship (Credit based)

Department Elective - I (Transportation Elective)

Department Elective - I (Transportation Elective)

Sl. No.

Subject Code

Subject

CE6125

Bituminous Materials

Course	CE6125
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Bituminous Materials
Learning Mode	Lectures
Learning Objectives	Complies with PLO number – 1, 2, and 4 Understand fundamental properties and behavior of asphalt binders. Describe the fundamental properties and behavior of asphalt concrete. Perform Superpave volumetric mixture design Analyze and understand strengths and weaknesses of various performance test methods Understand quality control of bituminous materials in road construction.
Course Description	In this course bituminous materials used in road construction will be covered in detail. Source, properties and performance evaluation methods of bituminous materials are important in selecting them in road construction project. The course will help students understand rheological properties of bituminous materials. The practices used in road construction industry in selection, design and quality control of bituminous materials will be covered.
Course Outline	Introduction to Bituminous Materials Asphalt binder: Definitions, Classification of asphalt paving materials, Sources, Production types, Chemistry and Physical properties, Performance tests and Specifications, Specifications for modified binders.Emulsion: Definitions, Classification and Engineering properties. Introduction to Viscoelasticity Rheological properties – visco-elastic models. Dynamic Shear modulus, Dynamic modulus, Relaxation modulus, Creep compliance, Indirect Tensile Properties. Asphalt binder tests and specifications Rheological properties, high temperature viscosity, low temperature stiffness, fatigue evaluation. Recent tests: RV, DSR, BBR, MSCR, Stress sweep fatigue test. Asphalt mix Rheological properties, Weight-Volume Relationships, Superpave mix design. Image based analyses. Asphalt Mix Design using Recycled Pavement Materials: Crumbed rubber, Construction and Demolition (C&D) waste, Recycled Asphalt Pavement (RAP). Asphalt Mix Performance Modeling: Beam fatigue, Viscoelastic continuum damage, Rutting. Quality Control and Tolerance: Field construction, Quality control plan, Control charts, QA/QC tests. Software: ABAQUS
Learning Outcome	At the end of the course, student would be able to: Perform Superpave volumetric mixture design Understand characteristic properties of bituminous materials. Use recycled materials in bituminous mixes for road construction. Develop quality control plan for bituminous materials in road construction projects.
Assessment Method	Assignments, Quizzes, Mid-semester examination and End-semester examination.

Textbooks:

Kim., Y. R. “Modeling of Asphalt Concrete.” McGraw-Hill, 2009, 1st Edition.
Huang, Y. H. "Pavement analysis and design." Pearson, 2004.
Papagianna, A. T. and Masad, E. A. “Pavement Design and Materials.” John Wiley & Sons, Inc., 2008.
Superpave Mix Design, MS-2, 7^th Edition, Asphalt Institute, 2013.

Reference books:

MORTH. “Ministry of Road Transportation & Highways Specifications for Road and Bridge Works.” 2013.
National Cooperative Highway Research Program (NCHRP) Reports.

CE6126

Intelligent Transportation Systems

Course Number	CE6126
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Intelligent Transportation Systems
Learning Mode	Lectures
Learning Objectives	Complies with PLO number – 1, 2, and 4 To understand various functional areas of ITS and its relevance in smart cities To understand various data collection strategies of ITS To understand various ITS plans around the world To understand ITS user needs and services To understand evaluation of the ITS applications To apply latest technologies in solving congestion related problems
Course Description	Intelligent Transportation Systems (ITS) represent a major transition in transportation on many dimensions. This course considers ITS as a lens through which one can view many transportation issues. ITS is an international program intended to improve the effectiveness and efficiency of surface transportation systems through advanced technologies in information systems, communications, and sensors.
Course Content	Introduction to Intelligent Transportation systems (ITS): Definition, objectives, benefits of ITS. ITS programs in the world – Overview of ITS implementations in developed countries and developing countries. ITS Data Collection Techniques: Intrusive and Non-intrusive, Data Analysis Techniques for ITS: Machine learning techniques, filtering techniques, time series analysis, prediction techniques, optimization. ITS Functional Areas. ITS User Needs and Services: Travel and traffic management; Public transportation management; Electronic payment; Commercial vehicle operations; Information management. ITS Architecture and Standards ITS Architecture: ITS standards, rationale, development process; ITS Policy Issues – institutional, legal etc. User Response and Evaluation: User response to ITS implementations around the world; Evaluation of the ITS implementations
Learning Outcome	At the end of the course, the student will be able to gather the information on 1. What ITS is? 2. Differences between intrusive and non-intrusive technologies 3. Various performance evaluation strategies of ITS applications, 4. Relevance of ITS in the context of developing countries especially with the national mission of smart cities, 5. Understand the differences between various functional areas of ITS etc.
Assessment Method	Assignments, Term Projects, Technical paper presentations, quizzes, mid-semester examination and end-semester examination

References:

Joseph S. Sussman: Perspectives on Intelligent Transportation Systems (ITS), Springer; 2005th edition (April 7, 2005)
Robert Gordon, Intelligent Transportation Systems: Functional Design for Effective Traffic Management, Springer 2016.
Roger W. Vickerman, International Encyclopaedia of Transportation, Elsevier, 2021.
Chowdhury, M. A. and Sadek, A. W., Fundamentals of IntelligentTransportation Systems Planning, Artech House. 2003.
McQueen, B. and McQueen, J., Intelligent Transportation SystemArchitectures, Artech House. 2003
Williams, B., “Intelligent Transportation Systems Standards”, Artech House. 2008
Ghosh, S. and Lee, T., Intelligent Transportation System - New Principles &Architectures, CRC Press.

CE6127

Pavement Management Systems

Course	CE6127
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Pavement Management System
Learning Mode	Lectures
Learning Objectives	Complies with PLO number – 1, 2, and 4 1. Understand the major activities involved in managing highway pavements required for managing pavements and exercised, on a daily basis, by a highway organization. 2. Training on data collection and analysis involved in pavement management system.
Course Description	The course outline the technical activities necessary to set up a pavement management system for an existing pavement network. The data to be collected and the analysis process in the pavement management system.
Course Outline	Introduction to Pavement Management System Commitment for a Pavement Management System, Strategies, policies, specifications and feedback system. Network level and Project level pavement management. Quality control and specifications Pavement Monitoring and Evaluation Pavement surveys. Functional and structural evaluation of the existing highway network. Pavement distresses and durability aspects of pavement design. Pavement condition ratings. Rehabilitation and Maintenance Techniques Restoration, recycling, resurfacing, and routine and major maintenance activities Economic analysis Life cycle cost analysis, Life cycle environmental analysis
Learning Outcome	At the end of the course, student would be able to: 1. Develop an effective project management system for transportation agencies. 2. Design different pavement surveys. 3. Manage pavement data so it can be used effectively.
Assessment Method	Assignments, Quizzes, Mid-semester examination and End-semester examination.

Textbooks:

Relevant IRC and ASTM standards for pavement condition surveys and condition rating
Miller, John S., and William Y. Bellinger. Distress identification manual for the long-term pavement performance program. No. FHWA-RD-03-031. United States. Federal Highway Administration. Office of Infrastructure Research and Development, 2003.
Huang, Y. H. "Pavement analysis and design." Pearson, 2004.
Papagianna, A. T. and Masad, E. A. “Pavement Design and Materials.” John Wiley & Sons, Inc., 2008.
Construction and Rehabilitation of Concrete Pavement. American Concrete Paving Association, Arlington Heights, IL.

Department Elective - II (Transportation Elective/ Department Elective)

Department Elective - II (Transportation Elective/ Department Elective)

Sl. No.

Subject Code

Subject

CE6102

Sampling, Analytical Methods, and Statistics for Environmental Engineering

CE6106

Soil Dynamics

Course	CE6106: Soil Dynamics
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Soil Dynamics
Learning Mode	Lectures
Learning Objectives	Complies with PLO- number 1, 2, 3, and 4 1. To provide the knowledge of the advanced concept of soil dynamics. 2. Equip the students with a strong foundation in civil engineering for both research and industrial scenarios. 3. Prepares the students to apply knowledge in policy and decision making related to civil engineering infrastructure. 4. Prepare students to attain leadership careers to meet the challenges and demands in civil engineering practice.
Course Description	This course intends to bridge the basic concepts with the advanced topics related to soil dynamics. Topics ranging from wave propagation, estimation of dynamic properties and vibration isolation are covered. The course started with the basic knowledge gained by the attendee during undergraduate level regarding the geotechnical engineering. Estimation of dynamic soil properties along with static properties will be covered in this course. The basic concept behind the vibration isolation will also be taught in this course.
Course Outline	Principles of dynamics and vibrations: Vibration of elementary systems-vibratory motion-single and multi-degree of freedom system-free and forced vibration with and without damping. Waves and wave propagation in soil media: Wave propagation in an elastic homogeneous isotropic medium- Raleigh, shear and compression waves. Dynamic properties of soils: Stresses in soil element, coefficient of elastic, uniform and non-uniform compression, shear effect of vibration dissipative properties of soils, Determination of dynamic soil properties, Field tests, Laboratory tests, Model tests, Stress-strain behavior of cyclically loaded soils, Estimation of shear modulus, Modulus reduction curve, Damping ratio, Linear, equivalent-linear and non-linear models, Ranges and applications of dynamic soil tests, Cyclic plate load test, Liquefaction. Vibration isolation: Vibration isolation technique, mechanical isolation, foundation isolation, isolation by location, isolation by barriers, active passive isolation tests.
Learning Outcome	At the end of the course, student would be able to: 1. Estimate dynamic soil properties using various methods available along with the method suggested in the IS code. 2. Understand the basics of wave propagation. 3. Liquefaction potential assessment using IS code and other methods in practice. 4. Vibration isolation of structures using various active and passive isolation technique.
Assessment Method	Assignments, Quizzes, Mid-semester examination and End-semester examination.

Textbooks:

Swami Saran, Soil Dynamics and Machine Foundations, Galgotia Publications Pvt. Ltd, 1999.
B. M. Das and G. V. Ramana, Principles of Soil Dynamics, 2nd edition, Cengage Learning, 2011.

Reference books:

S. Prakesh & V. K. Puri, Foundation for machines, McGraw-Hill 1993.
Kramar S.L, Geotechnical Earthquake Engineering, Prentice Hall International series, Pearson Education (Singapore) Pvt. Ltd.
All relevant IS and International codes.

CE6107

Rock Slope Engineering

Course	CE6107: Rock Slope Engineering
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Rock Slope Engineering
Learning Mode	Lectures
Learning Objectives	Complies with PLO- number 1, 2, 3, and 4 1. Learning Objectives of Rock Slope Engineering: Understand the geological and geotechnical principles governing the stability of rock slopes, including the factors influencing rock mass behavior, such as geological structure, rock type, weathering, and groundwater conditions. 2. Gain proficiency in conducting site investigations and geological mapping to characterize rock slope conditions, identify potential failure mechanisms, and assess the stability of rock slopes using qualitative and quantitative methods. 3. Learn to apply engineering principles and analytical techniques to analyze the stability of rock slopes, including limit equilibrium methods, numerical modeling, and probabilistic approaches, to evaluate factors such as slope geometry, rock strength parameters, and external loading conditions. 4. Acquire knowledge of rock slope stabilization and mitigation techniques, including rock reinforcement, slope scaling, rock bolting, rockfall protection measures, and slope monitoring systems, and understand their applicability based on site-specific conditions and project requirements. 5. Develop the ability to design effective risk management strategies for rock slope engineering projects, including risk assessment, hazard identification, and implementation of risk control measures to ensure the safety of infrastructure, minimize environmental impacts, and optimize project performance.
Course Description	Rock Slope Engineering course offers a comprehensive examination of the principles, methodologies, and practices essential for the assessment, design, and management of rock slopes in various geotechnical and engineering applications. Through a combination of theoretical concepts, practical case studies, and hands-on exercises, students will gain an understanding of the geological factors influencing slope stability, methods for slope assessment and characterization, and techniques for slope stabilization and risk mitigation. Emphasizing a multidisciplinary approach, the course covers topics including rock mechanics, geotechnical investigation, slope stability analysis, monitoring and instrumentation, and the application of engineering principles to mitigate hazards associated with rock slopes. By the conclusion of the course, students will possess the knowledge and skills necessary to effectively evaluate, design, and manage rock slopes to ensure the safety and sustainability of infrastructure projects in challenging terrain.
Course Outline	Principles of rock slope design, Basic mechanics of slope failure, Structural geology and data interpretation, Site investigation and geological data collection, Rock strength properties and their measurement, Plane failure, Wedge failure, circular failure, Toppling failure, Numerical analysis, Stabilization of rock slopes, Movement monitoring
Learning Outcome	At the end of the course, student would be able to: 1. Geotechnical Understanding: Develop a comprehensive grasp of the geological factors influencing rock slope stability, including rock mass properties, weathering processes, and the impact of discontinuities. 2. Risk Assessment and Management: Acquire skills in conducting thorough risk assessments for rock slopes, identifying potential failure modes, and implementing effective risk management strategies to mitigate hazards. 3. Design and Implementation of Stabilization Measures: Learn to design and implement appropriate stabilization measures for rock slopes, including rock bolts, shotcrete, and rockfall protection systems, based on site-specific conditions and project requirements. 4. Application of Analytical Techniques: Gain proficiency in utilizing analytical techniques such as limit equilibrium methods and numerical modeling to assess slope stability and make informed decisions regarding slope design and stabilization measures.
Assessment Method	Assignments, Quizzes, Mid-semester examination and End-semester examination.

Textbooks:

· Duncan C. Wyllie, Chris Mah, Rock Slope Engineering: Fourth Edition, 2004,

· Evert Hoek, Jonathan D. Bray, Rock Slope Engineering, Third Edition, 1974

· Ramamurthy T, Engineering in Rocks for Slopes, Foundations and Tunnels, 2014

Reference books:

· Engineering rock mechanics: Part 1, by John A. Hudson and John P. Harrison

· Engineering rock mechanics: Part 2, by John A. Hudson and John P. Harrison

· Fundamentals of rock mechanics by J. C. Jaeger, N. G. W. Cook, and R. W. Zimmerman

CE6108

Constitutive Modelling in Geotechnics

Course	CE6108: Constitutive Modelling in Geotechnics
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Constitutive Modelling in Geotechnics
Learning Mode	Lectures
Learning Objectives	Complies with PLO- number 1, 2, 3, and 4 1. To understand and analyse the numerical and constitutive modelling and its application in geomaterials to solve the complex geotechnical engineering problems.
Course Description	This course has been designed to provide a fundamental of continuum-mechanics approaches to constitutive and numerical modeling of geomaterials in geotechnical problems. Further, the course aims to provide some knowledge about applications of the constitutive and numerical models within the different existing numerical codes. The various applications, special topics and case studies will be covered in this course to analyse and understand the real geotechnical problems and finding the future solutions.
Course Outline	Introduction and Tensor Analysis, Stresses and strains, Equations of Continuum Mechanics and Thermodynamics, Elasticity, Plasticity and yielding, Introduction to upper and lower bounds, selected boundary value problems, Elastic-plastic model for soils: elastic and plastic volumetric strains, plastic hardening, plastic shear strains, plastic potentials, flow rule. Cam clay model: critical state line, shear strength, stress-dilatancy, index properties, prediction of conventional soil tests. Applications and special topics.
earning Outcome	At the end of the course, student would be able to: 1. Understand the basic of continuum mechanics. 2. Learn the various elastic-plastic model for soils and its applications 3. Comprehend about the cam clay model and its importance in geotechnical engineering. 4. Expose with various case studies and special topics to analyze the real geotechnical problem.
Assessment Method	Assignments, Quizzes, Mid-semester examination and End-semester examination.

Textbooks:

Wood, David Muir. Soil behaviour and critical state soil mechanics. Cambridge university press, 1990.
Atkinson, J. H., and P. L. Bransby. The mechanics of soils, an introduction to critical state soil mechanics. No. Monograph. 1977.
Chan, W.K. and Saleeb, A.F., Constitutive equations for engineering materials, Volume 1: Elasticity and modelling, Elsevier, 1994.
Chan, W.K. and Saleeb, A.F., Constitutive equations for engineering materials, Volume 2: Plasticity and modelling, Elsevier, 1994.

Reference books:

Harr, Milton Edward. Foundations of Theoretical Soil Mechanics. McGraw-Hill, 1966.
Desai, C.S. and Siriwardane, H.J., Constitutive laws for engineering materials with emphasis on geologic materials, Prentice Hall, 1984.

CE6111

Rock Mechanics

Course	CE6111: Rock Mechanics
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Rock Mechanics
Learning Mode	Lectures
Learning Objectives	Complies with PLO- number 1, 2 and 3 1. Understand the fundamentals of geology. 2. Comprehend and analyse the properties of the intact and jointed rock mass. 3. Recognize and analyse different Rock Mass Classification systems and the stress-strain behaviour, strength and deformability of rock mass. 4. Solve complex engineering problems by applying principles of engineering and mechanics.
Course Description	This course is offered as a core course in department to understand the basics of rock mechanics and behaviors of rocks for various construction purposes such as foundations, underground excavation, landslide etc.
Course Outline	Introduction to Rock Mechanics: Basic knowledge of geology; Problems associated with rock mechanics; General terminologies- Interior of earth, rock forming minerals, identification, intact rock, discontinuities and rock mass; Rock as engineering material. Properties, Mechanics and Classification of Intact Rock; Mechanical properties; Factors affecting strength of rocks; Intact rock classification; Rock cycle; Basic principles- stress and strain; Rock failure criteria. Properties and Mechanics of Rock Discontinuities; Plotting of geological data and its application; Shear behaviour of rock; Shear strength criteria; Flow through discontinuities. Rock mass classification systems; Strength criteria; Time dependent behaviour in rocks; Field investigation; Dynamic and thermal properties of rock.
Learning Outcome	At the end of the course, student would be able to: Understand the basics of rock mechanics Learn and analyze the physical, mechanical, and hydraulic characteristics of the intact and jointed rock mass. Acquaint with different Rock Mass Classification systems. Recognize and analyse the stress-strain behaviour, strength and deformability of rock mass. 5. Solve complex engineering problems by applying principles of engineering and mechanics.
Assessment Method	Assignments, Quizzes, Mid-semester examination and End-semester examination.

Textbooks:

Goodman, R. E. Introduction to rock mechanics, John Wiley and Sons, 1989.
Hudson, J. A., & Harrison, J. P. Engineering rock mechanics: an introduction to the principles, (Vol.: I-IV), Elsevier, 2000.
Harrison, J. P., & Hudson, J. A. Engineering rock mechanics: part 2: illustrative worked examples, Elsevier, 2000.
Ramamurthy, T., Engineering in rocks for slopes, foundations and tunnels, Prentice Hall India, 2010.

References:

Hoek, E., & Bray, J. D. Rock slope engineering, CRC Press, 1981.
Hoek, E, & Brown, E. Underground excavations in rock, CRC Press, 1980. Singh, B., & Goel, R. K. Engineering rock mass classification, Elsevier, 2011.
Mogi, K. Experimental rock mechanics, CRC Press, 2006. Bieniawski, Z. T. Rock mechanics in mining & tunnelling, A.A. Balkema, Rotterdam, 1984.
Jaeger, J. C., Cook, N. G., & Zimmerman, R. Fundamentals of rock mechanics, John Wiley & Sons, 2009.
Debasis, D., & Kumar, V. A. Fundamentals and applications of rock mechanics, PHI Learning Pvt. Ltd. New Delhi, India, 2016.

CE6113

Pavement Geotechniques

CE6114

Probalistic Methods in Geotechnical Engineering

Course	CE6114: Probalistic Methods in Geotechnical Engineering
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Probabilistic Methods in Geotechnical Engineering
Learning Mode	Lectures
Learning Objectives	Complies with PLO- number 1, 2, 3, and 4 1. To provide the knowledge of the advanced concept of probabilistic methods in geotechnical engineering. 2. Equip the students with a strong foundation in civil engineering for both research and industrial scenarios.
Course Description	This course intends to bridge the basic concepts with the advanced topics related to the application of probabilistic methods in geotechnical engineering. Topics ranging from risk, uncertainty, Monte Carlo simulation, and FORM are covered. The course started with the basic knowledge gained by the attendee up to undergraduate level regarding the probabilistic methods. Thereafter, the basics and advanced concept related to risk and reliability analysis will be studied by the students.
Course Outline	Introduction: Concept of risk; and uncertainty in geotechnical engineering analysis and design; Fundamental of probability models. Analytical models of random phenomena: Baysian Analysis; Analysis of variance (ANOVA); Application of central limit theorem; confidence interval; expected value; and return period. Application of Monte Carlo simulation (MCS): Determination of function of random variables using MCS methods; Application of MCS in various geotechnical engineering problems. Determination of Probability Distribution Model: Probability paper; testing of goodness-of-fit of distribution models. Methods of risk Analysis: Composite risk analysis; Direct integration method; Method using safety margin; reliability index and safety factor; FORM; SORM; Applications of risk and reliability analysis in engineering systems.
Learning Outcome	At the end of the course, student would be able to: 1. Analyzed structure using various probabilistic methods available along with the method suggested in the Euro code. 2. Perform reliability analysis for various geotechnical problems. 3. Assess composite risk using various techniques to estimate failure of geotechnical structures.
Assessment Method	Assignments, Quizzes, Mid-semester examination and End-semester examination.

Textbooks:

Ang, A. H-S., and Tang, W. H., Probability Concepts in Engineering, Vol. 1, John Wiley and Sons, 2006.
Scheaffer, R. L., Mulekar, M. S. and McClave, J. T., Probability and statistics for Engineers, 5th Edition, Brooks / Cole, Cengage Learning, 2011.

Reference books:

Halder, A and Mahadevan, S., Probability, Reliability and Statistical Methods in Engineering Design, John Wiley and Sons, 2000.

All relevant IS and International Codes.

CE6116

Bridge Engineering and Design

Course	CE6116
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Bridge Engineering and Design
Learning Mode	Lectures
Learning Objectives	Complies with PLO- number 1, 2, and 5 Equip the students with a strong foundation in civil and environmental engineering for both research and industrial scenarios. Provide scientific and technical knowledge in planning, design, construction, operation and maintenance of civil engineering infrastructure. Nurture interdisciplinary education for finding innovative solutions.
Course Description	This course offers a comprehensive exploration of bridge engineering and design, covering fundamental principles, methodologies, and practical applications. This course covers key aspects including structural analysis, material selection, construction techniques, and environmental considerations.
Course Outline	Introduction: Classification of Bridges, General Features of Design, IRC Loading (viz. 70R, Class AA tracked and wheeled vehicle), Design Codes, Working Stress Method, Limit State Method of Design as per IS456:2000 and IRC 112:2020; Analysis & Design: Consideration of various loading (dead load, vehicular load etc.), Slab bridge, Box Culvert, T-beam bridge, Box Girder bridge and Prestressed concrete bridge. Subsoil properties, their uses for substructure design.
Learning Outcome	At the end of the course, student would be able to: 1. Explore structural analysis, materials selection, construction techniques, and sustainability considerations in the context of designing safe, efficient, and resilient bridges. 2. Develop the expertise needed to conceptualize, plan, and execute bridge projects that meet technical standards and address societal needs. 3. Gain knowledge and skills necessary to tackle real-world challenges in bridge engineering, contributing to the development of critical infrastructure systems.
Assessment Method	Assignments, Quizzes, Project work, Mid-semester examination and End-semester examination.

Textbooks/ Reference books:

Swami Saran, Analysis and Design of Substructures: Limit State Design, 28 February 2018.
K. Rakshit, Design and Construction and Highway Bridges.
Raju N. K, Design of Bridges, 5Ed (Pb 2019) – 1 January 2019.
Daniel J. Inman, Charles R. Farrar, Vicente Lopes Junior, Valder Steffen Junior, Damage Prognosis: For Aerospace, Civil and Mechanical Systems, John Wiley & Sons, 2005.

CE6122

Advanced Concrete Technology

Course	CE6122
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Advanced Concrete Technology
Learning Mode	Lectures
Learning Objectives	Lecture: Complies with PLO- number 1, 2, 4 and 5 Equip the students with a strong foundation in civil and environmental engineering for both research and industrial scenarios. Provide scientific and technical knowledge in planning, design, construction, operation and maintenance of civil engineering infrastructure. Prepare students to attain leadership careers to meet the challenges and demands in civil engineering practice. Nurture interdisciplinary education for finding innovative solutions.
Course Description	The course deals with concrete technology. This course provides the students an exposure advanced topic on concrete technology which are not covered in undergraduate design courses.
Course Outline	Cement production and composition Cement chemistry Aggregates for concrete Chemical admixtures Chemical and Mineral admixtures Mineral admixtures High performance concrete mixture proportioning Topics in fresh concrete Topics in hardened concrete Creep and shrinkage Durability of concrete Durability of concrete.
Learning Outcome	At the end of the course, student would be able to 1. Designing high strength concrete. 2. Should be able to understand various types of problems and their solutions in structural concrete.
Assessment Method	Assignments, Quizzes, Project work, Mid-semester examination and End-semester examination.

Textbooks/ Reference books:

Mehta, P. K., and Monteiro, P. J. M., ‘Concrete: Microstructure, Properties, and Materials,’ Fourth Edition (Indian Edition), McGraw Hill, 2014.
Neville, A. M., ‘Properties of Concrete,’ Pitman Publishing, Inc., MA, 1981.
Hewlett, P. C., Ed., ‘Lea’s Chemistry of Cement and Concrete,’ Fourth Edition, Arnold Publishers, NY, 1998.
Bentur, A., Diamond, S., and Berke, N.S., ‘Steel Corrosion in Concrete,’ E&FN Spon, UK, 1997.
Taylor, H. W. F., ‘Cement Chemistry,’ Academic Press, Inc., San Diego, CA, 1990.
Lea, F. M., ‘The Chemistry of Cement and Concrete,’ Chemical Publishing Company, Inc., New York, 1971.
Mindess, S., and Young, J. F., ‘Concrete,’ Prentice Hall, Inc., NJ, 1981.
J. Newman and B. S. Choo, Eds., ‘Advanced Concrete Technology’, Four Volume Set, Elsevier, 2003

10.

CE6128

Highway Geometric Design and Safety

Course	CE6128
Course Credit	3-0-0-3
Course Title	Highway Geometric Design and Safety
Learning Mode	Lectures
Learning Objectives	Complies with PLO number – 1, 2, and 4 1. Understand the concept of highway geometry and design controls; 2. Understand the factors influencing road safety; 3. Learn practices and technologies to mitigate road accidents;.
Course Description	The course mainly focuses on factors influencing road geometry and its relation with road safety. The student will learn design factors that need to be considered in highway geometric design based on different expected road users. Need to understand characteristics of drivers, pedestrians, vehicles and road will be illustrated. Students will learn impact of electric and autonomous vehicles on geometric road design.
Course Outline	Introduction and roadway function. Optimization of highway geometric design for autonomous vehicle. Design controls: vehicles and drivers, speed, volume and access; Practical considerations in fixing the alignments, Route layout, Design of roadway cross-section, Longitudinal drains, Estimate earthwork volumes. Sight distances for road segments and intersections, Fixing of gradients, Design of vertical and horizontal curves. Design speed; Sight distance, horizontal and vertical alignment, Intersection design considerations, Environmental considerations, and context sensitive solutions. Impact of Electric Vehicles on Roads. Highway safety; Safety assessment; Driver behavior and crash causality; Elements of highway safety management systems; Safety counter measures; Safety management process; Crash reporting and collision diagrams; Basics of crash statistics; Before-after methods in crash analysis; Highway geometry and safety; Road safety audits; Crash investigation and analysis.
Learning Outcome	At the end of the course, student would be able to: · Ability to access road safety. · Ability to design road geometry.
Assessment Method	Assignments, Quizzes, Mid-semester examination and End-semester examination.

Textbooks/ Reference books:

H. Banks, Introduction to Transportation Engineering, McGraw-Hill, 2002.
K. Khanna and C. E. G. Justo, Highway Engineering, Nem Chand Bros., 2002.
American Association of State Highway and Transportation Officials (AASHTO), A Policy on Geometric Design of Highways and Streets, 5th Edition, 2004.

11.

CE6129

Airport Engineering

Course Number	CE6129
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Airport Engineering
Learning Mode	Lectures
Learning Objectives	Complies with PLO number – 1, 2, and 4 1. To provide fundamental knowledge in airport engineering. 2. Train students to plan, design and operate airport facilities in industry. 3. To understand design and maintenance of airport runways, taxiways.
Course Description	This course will discuss fundamental concepts in airport engineering. Course will cover planning, design, construction and operation of airport.
Course Content	Basic principles of airport facilities design to include aircraft operational characteristics, noise, site selection, land use compatibility. Airport planning, operational area, ground service areas, airport capacity, runway design, taxiway design, airport pavement analysis and design. Airport pavement material characterization. Airprot pavement structural evaluation and maintenance. ICAO design guidelines, FAA mechanistic-emperical design. Runway and Taxiway signs and markings.
Learning Outcome	At the end of the course, student would be able to: 1. Understand basic airport facilities. 2. Design runway and other airport pavements. 3. Design airport operations.
Assessment Method	Assignments, Quizzes, Mid-semester examination and End-semester examination

Textbooks:

Horonjeff R., McKelvey F.X., Sproule W., Young S. "Planning and Design of Airports", 5th Ed. New York: McGraw-Hill.
Saxena, S.C., "Airport Engineering – Planning and Design", CBS Publishers.
S.C. Rangwala. “Airport Engineering,” 13th edition, Charotar Publishing house, 2013.
Y. H. Huang, Pavement Analysis and Design (2nd Edition), Pearson Education, India
A.T. Papagiannakis and E.A. Masad, Pavement Design and Materials, John Wiley & Sons, Inc.

Reference:

Federal Aviation AdministrationSpecifications.
Inernational Civil Aviation Organisation Specifications.

12.

CE6130

Analytical Methods in Civil Engineering

Course	CE6130
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Analytical Methods in Civil Engineering
Learning Mode	Lectures
Learning Objectives	Complies with PLO number – 1, 2, and 4 Objective for learning this course are: 1. To brush up the undergraduate level understanding in light with some advanced approaches. 2. To develop p proficiency in numerical techniques and algorithms pertaining to various civil engineering problems. 3. To form a stepping stone towards advance understanding of risk and reliability analyses.
Course Description	First part of this course deals with the numerical method for non-linear equation solution, numerical integration, solution of liner system of equations, curve fittings, solution of differential equations. Second part of the course basic concept of probability theory and statistics, estimation of distribution property, stochastic data generation, risk and reliability methods for civil engineering.
Course Outline	Module – I: Linear Algebra and Differential Equation Linear algebra: Rank of a matrix, solutions of linear systems, linear independence and linear transformations,eigenvalues, eigenvectors,matrices similarity, basis of eigenvectors, diagonalization; Differential equations:homogeneous linear equations of second order,second order homogeneous equations with constant coefficients,case of complex roots, complex exponential function,non-homogeneous equations,solution by undetermined coefficients and variation of parameters. Module – II: Numerical Methods Introduction to Numerical Methods: Objectives of numerical methods, Sources of error in numerical solutions: truncation error, round off error, order of accuracy - Taylor series expansion; Roots of equations: Graphical method, Bisection method, Simple fixed-point iteration, Newton-Raphson method, Secant method, Modified secant method; Direct Solution of Linear systems: Naive Gauss elimination, LU decomposition, Gauss-Seidel, Gauss-Jordon, Jacobi iteration, Cholesky decomposition; Curve fitting: linear regression, polynomial regression, interpolation, spline fitting; Numerical Calculus: trapezoidal and Simpson’s rule for integration; Solving differential equation: Euler’s method, Runge-Kutta method, boundary value and eigenvalue problem and their application, solving partial differential equation. Module – III: Probability and Statistics Introduction: concept of risk, uncertainty in engineering analysis and design, fundamental of probability models; Analytical models of random phenomena: Bayesian analysis, analysis of variance (ANOVA), tests of hypothesis, confidence interval, properties of good estimates, interval estimation, maximum likelihood estimates, Sample size determination, central limit theorem, expected value, and return period; Miscellaneous Topics: Fitting theoretical and tests of goodness-of-fit (chi-square test, Kolmogorov-Smirnovtest),identification of outliers,regression with discrete dependent variables; Application of Monte Carlo simulation (MCS): determination of function of random variables using MCS methods, application of MCS in various problems.
Learning Outcome	At the end of the course, student would be able to Lecture: 1. Understand the different numerical methods for solving non-linear equations and numerical integration method. 2. Should be able to solve differential equations numerically. 3. Understand basic concept probability theory and statistics. 4. Should be able to fit statistical distribution and parameter estimation. 5. Should be able to perform MC simulation and preform risk and reliability analysis.
Assessment Method	Assignments, Quizzes, Project work, Mid-semester examination and End-semester examination.

Textbooks/ Reference books:

E. Kreyszig, Advanced Engineering Mathematics, Wiley, 10th edition, 2011.
M. D. Greenberg, Advanced Engineering Mathematics, Pearson, 2nd edition,1998.
S. Chapra and R. Canale, Numerical Methods for Engineers, McGraw Hill, 6th edition, 2010.
S. Guha and R. Srivastava, Numerical Methods: For Engineering and Science, Oxford University Press, 1st edition, 2010.
R. L. Scheaffer, M. S. Mulekar, and J. T. McClave, Probability and statistics for Engineers, Brooks / Cole, Cengage Learning, 5th Edition, 2011.
A. Haldar and S. Mahadevan, Probability, Reliability, and Statistical Methods in Engineering Design, Wiley, 2000.
H. S. Ang and W. H. Tang, Probability Concepts in Engineering Planning and Design, John Wiley, 1975.
J. Benjamin and A. Cornell, Probability, Statistics, and Decision for Civil Engineers, McGraw Hill, 1963.

Department Elective - III (Transportation Elective)

Department Elective - III (Transportation Elective)

Sl. No.

Subject Code

Subject

CE6227

Traffic Flow Theory

CE6228

Analytical Techniques for Infrastructure Systems Analysis

Course Number	CE6228: Analytical Techniques for Infrastructure Systems Analysis
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Analytical Techniques for Infrastructure Systems Analysis
Learning Mode	Theory
Learning Objectives	To provide knowledge of quantitative techniques with application potential for Infrastructure systems.
Course Description	This course provides a comprehensive introduction to the analytical methods and tools used in the analysis of infrastructure (transportation) systems. The course focuses on the application of these techniques to real-world transportation systems and includes a mix of theoretical and practical content. Students will learn about various analytical techniques including but not limited to traffic flow theory, network analysis, demand forecasting, and system optimization. The course will cover both traditional methods such as regression analysis and newer techniques such as machine learning and data analytics. The course will also delve into the use of software tools for transportation analysis and modeling. Students will get hands-on experience with these tools through assignments and projects.
Course Content	Modelling and Simulation: Model Classification, Mathematical; Physical and Analog models, steps involved in simulation, Monte Carlo simulation, validation and verification of simulation models Multivariate Data Analysis: Vectors and Matrices, Simple estimate of centroid, standard deviation, dispersion, variance and co-variance, correlation matrices, principal component analysis Curve Fitting: Method of least squares, curvilinear regression, Multiple regression, checking adequacy of model, correlation, multiple linear regression; Queuing Theory: General structure, operating characteristics, deterministic queuing model, probabilistic queuing models, and simulation of queuing system; Forecasting Models: Moving averages, exponential smoothening, trend projections, causal models, time series analysis of vehicle growth & accidents Neural Networks: Basic concepts; neural network architecture, back propagation networks.
Learning Outcome	The student will be able to 1. Understand and Apply Modelling and Simulation Techniques 2. Perform Curve Fitting 3. Understand and Apply Queuing Theory 4. Perform Multivariate Data Analysis 5. Develop and Use Forecasting Models and Neural networks for the transportation related problems
Assessment Method	Assignments, Quizzes, Mid-semester examination and End-semester examination

References

Vohra, N.D., “Quantitative Techniques in Management”, Tata McGraw Hill, 2001.
Johnson, R. A. and Wichern, D.W., “Applied Multivariate Statistical Analysis”, Prentice Hall., 2003.
Johnson, R., “Probability and Statistics for Engineers”, Prentice Hall. 2009
Hair, J. and Anderson, R., “Multivariate Data Analysis”, Prentice Hall. 2010

CE6229

Advanced Flexible Pavement Analysis and Design

Course	CE6229: Advanced Flexible Pavement Analysis and Design
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Advanced Flexible Pavement Analysis and Design
Learning Mode	Lectures
Learning Objectives	Complies with PLO number – 1, 2 and 4 1. To provide knowledge of recent developments in asphalt material characterization for pavement analysis. 2. Train students to design pavement and overlays. 3. Learn computation of stress distribution and distress mechanisms in pavement. 4. Learn life-cycle analysis of flexible pavements
Course Description	This course will discuss fundamental concepts in design and analysis of flexible pavement. Course will cover Empirical and Mechanistic-Empirical pavement design approaches. Students will learn how to conduct life-cycle cost and environmental analysis for flexible pavements. Students will also learn use of non-destructive tests in pavement condition evaluation and overlay design.
Course Outline	Development of Various Design Methods for Flexible Pavement: Empirical pavement design approach, AASHTO 1993 method, Mechanistic empirical pavement design approach, Asphalt Institute method, IRC Method, MEPDG Method. Theoretical and Numerical Models for Analysis of Flexible Pavement: Axle load configurations, Stresses and strains in pavements, Boussinesq solution, Equivalent Thickness Method, Multi-layer elastic solutions, Multi-layer viscoelastic solutions, 2-D and 3-D Finite element models. Selection of Pavement Design Input Parameters and Pavement Performance Models: Traffic loading, Environmental factors in pavement design, Reliability, Pavement material models for asphalt mix and unbound materials, Pavement performance models, Effects of heavy vehicles on pavement response and performance. Sustainability Analysis: Introduction to sustainability in pavement design, Life-cycle cost analysis, Environmental analysis, Nondestructive testing, Backcalculation of pavement in situ properties, Design of overlays. Software: KENPAVE
Learning Outcome	At the end of the course, student would be able to: 1. Design flexible pavements using Indian Codes and learn best practices. 2. Ability to compute stress-strain distribution in pavement. 3. Identify different type of distresses in pavement and determine condition of pavement using nondestructive testing. 4. Identify factors influencing pavement design. 5. Perform pavement life cycle cost and environmental analysis.
Assessment Method	Assignments , Quizzes , Mid-semester examination and End-semester examination .

Textbooks:

Huang, Y. H. “Pavement analysis and design.” Pearson, 2004.
Papagianna, A. T. and Masad, E. A. “Pavement Design and Materials.” John Wiley & Sons, Inc., 2008.
Chakroborty, P. and Das, A. “Principles of Transportation Engineering.” PHI Learning, 2017.

Reference books:

Ullidtz, P. “Pavement Analysis.” Elsevier, 1987.
Mechanistic-Empirical Pavement Design Guide – A Manual of Practice, AASHTO 2008.

Department Elective - IV (Transportation Elective/ Department Elective)

Department Elective - IV (Transportation Elective/ Department Elective)

Sl. No.

Subject Code

Subject

CE5217

Geoinformatics for Engineers

Course	CE5217 Geoinformatics for Engineers
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Geoinformatics for Engineers [Even Semester/2^nd Semester, M. Tech
Learning Mode	Lectures
Learning Objectives	Complies with PLOs 1, 2 & 3- 1. To provide fundamental knowledge in the Basics of GIS. 2. Train students to download, process and prepare the GIS data for Water resources applications. 3. Provide scientific and technical knowledge, to prepare students to prepare maps using GIS for Water resources applications.
Course Description	This course will discuss fundamental concepts in GIS. The course will cover theory and real-world practice in map preparation, flood mapping, rivers and canal mapping and GIS software and databases.
Course Outline	Definition – Basic components of GIS – Map projections and coordinate system –Spatial data structure: raster, vector – Spatial Relationship – Topology – Geodata base models: hierarchical, network, relational, object-oriented models – Integrated GIS database -common sources of error – Data quality: Macro, Micro and Usage level components - Meta data - Spatial data transfer standards. Thematic mapping – Measurement in GIS: length, perimeter, and areas – Query analysis– Reclassification – Buffering - Neighbourhood functions - Map overlay: vector and raster overlay – Interpolation – Network analysis –Digital elevation modelling. Analytical Hierarchy Process, – Object oriented GIS – AM/FM/GIS – Web Based GIS Spatial data sources – GIS approach water resources system – Thematic maps -Rainfall-runoff modelling – Groundwater modelling – Water quality modelling – Flood inundation mapping and Modelling – Drought monitoring – Cropping pattern change analysis –Performance evaluation of irrigation commands. Site selection for artificial recharge - Reservoir sedimentation. Introduction to various remote sensing satellite data (Like Landsat, Sentinel, Radar data, DEM, GRACE etc) and their applications for different water resources engineering applications.
Learning Outcome	At the end of the course, student would be able to: 1. Understand technical aspects and properties of GIS. 2. Download and perform GIS based analysis on different satellite data. 3. Basic flood mapping using Optical and SAR data.

Assessment Method

Assignments (10%), Quizzes (10%), Mid-semester examination (30%) and End-semester examination (50%).

REFERENCES:

Lillesand, M. and Kiefer, R.W., Remote Sensing, and Image Interpretation III Edition. John Wiley and Sons, New York. 1993.
Burrough P.A. and McDonnell R.A., Principles of Geographical Information Systems. Oxford University New York. 1998.
Ian Heywood Sarah, Cornelius, and Steve Carver: An Introduction to Geographical Information Pearson Education. New Delhi, 2002.
Jensen, R., Introductory digital image processing: a remote sensing perspective, Fourth Edition, Pearson, 2017
Joseph, G & Jagannathan, , Fundamentals of remote sensing (3^rd edition), The Orient Blackswan, 2018.

CE6206

Geotechnical Earthquake Engineering

Course	CE6206: Geotechnical Earthquake Engineering
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Geotechnical Earthquake Engineering
Learning Mode	Lectures
Learning Objectives	Complies with PLO- number 1, 2, 3, and 4 1. To provide the knowledge of the advanced concept of geotechnical earthquake engineering. 2. Equip the students with a strong foundation in civil engineering for both research and industrial scenarios. 3. Prepares the students to apply knowledge in policy and decision making related to civil engineering infrastructure. 4. Prepare students to attain leadership careers to meet the challenges and demands in civil engineering practice.
Course Description	This course intends to bridge the basic concepts with the advanced topics related to geotechnical engineering. Topics ranging from continental drift, seismic hazard analysis, wave propagation, liquefaction assessment, seismic slope stability and design of retaining structure are covered. The course started with the basic knowledge gained by the attendee during undergraduate level regarding the wave propagation. Therefore, the basics about earthquake engineering will be studied by the students. Introduction to seismic design of retaining structure and slope stability analysis will be also taught in this course.
Course Outline	Introduction, Significant historical earthquakes, Continental drift and plate tectonics, Internal structure of earth, Sources of seismic activity, Size of the earthquake, Strong ground motion and its measurement, Ground motion parameters, Estimation of ground motion parameters, Identification and evaluation of earthquake sources, Seismic hazard analysis, Deterministic seismic hazard analysis, Probabilistic seismic hazard analysis, Wave propagation, Waves in unbounded media, Waves in semi-infinite body, Waves in layered body, Dynamic soil properties and Measurement of dynamic soil properties, Ground response analysis, Local site effects and design of ground motions, Liquefaction, Initiation and effects of liquefaction, Evaluation of liquefaction hazards, Liquefaction susceptibility, Seismic slope stability analysis, and Seismic design of retaining walls.
Learning Outcome	At the end of the course, student would be able to: 1. Design earthquake resistant structure using various methods available along with the method suggested in the IS code. 2. Liquefaction potential assessment using IS code and other methods in practice. 3. Perform seismic hazard analysis for any site. 4. Seismic design of retaining walls considering the dynamic load transferred to the foundation.
Assessment Method	Assignments, Quizzes, Mid-semester examination and End-semester examination.

Textbooks:

Kramar S.L, Geotechnical Earthquake Engineering, Prentice Hall International series, Pearson Education Pvt. Ltd.
J.E. Bowles, Foundation Analysis and Design, McGraw-Hill, 2001.

Reference books:

Ikuo Towhata, Geotechnical Earthquake Engineering, Springer series, 2008.
All relevant IS and International Codes.

CE6208

Mine Wastes Generation and Management

Course	CE6208: Mine Wastes Generation and Management
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Mine Wastes Generation and Management
Learning Mode	Lectures
Learning Objectives	Complies with PLO- number 1, 2, 3, 4, and 5 1. Understand and explain the mining operations, regulations and acts. 2. Explain the various types of mine generated wastes, characterizations techniques and application. 3. Describe the mine waste disposal techniques and stability analysis of overburden dumps. 4. Comprehend the mine generated contaminated leachate and ground pollution. 5. Analyse technical strategies, approaches and solutions to engineer's role and responsibility for mine waste management risk analysis, potential application, safety factors for sustainable development.
Course Description	The course covers various mine waste generated during the mining operation and their characteristics, mining regulations and acts, waste disposal, potential application and stability analysis of mine overburden waste, leachate formation and ground contamination. This course deals with geomechanics and rehabilitation techniques of mine generated wastes, valorization of mine wastes, risk analysis and mining safety.
Course Outline	Introduction to mining operations and risk; overview of Indian & international mining regulations and acts; different types of mine waste generated during the mining operation; mine waste disposal & rehabilitation; geochemical compositions, physical & chemical nature of mine wastes; disposal of mine wastes; geomechanics of mine waste disposal & rehabilitation; characterizations and application of mining wastes for infrastructure projects; valorization of mining wastes; leachate formation and ground contamination due to mining wastes; stability analysis of mining wastes overburden dumps, reintegration of mine wastes; mining wastes risk assessment & remedial measures; mining safety.
Learning Outcome	At the end of the course, student would be able to: Describe and explain the mining operations, regulations and acts. Explain the various types of mine generated wastes, characterizations techniques and application. Describe the mine waste disposal techniques and stability analysis of overburden dumps. Understand the mine generated contaminated leachate and ground pollution. 5. Analyze technical strategies, approaches and solutions to engineer's role and responsibility for mine waste management risk analysis, potential application, safety factors for sustainable development.
Assessment Method	Assignments, Quizzes, Term-paper project, Mid-semester examination and End-semester examination.

Textbooks:

Singh, T N. Surface Mining, Lovely Prakashan, India, 2020.
Karra, Ram Chandar, Gayana, B C, Rao, Shubhananda P Mine Waste Utilization, CRC Press, 2022.
Hutchison, Ian P.G. and Ellis, Rechard D., Mine Waste Management, CRC Press, India, 1992.
Lottermoser, Bernd G., Mine Wastes Characterization, Treatment and Environmental Impacts, Springer, 3rd edition, 2010.

References:

Pradhan, S. P., Vishal, V., & Singh, T. N. (Eds.). Landslides: theory, practice and modelling. Springer International Publishing, 2019.
Pathak, Pankaj, Rout, Prangya Ranjan, Urban Mining for Waste Management and Resource Recovery, CRC Press, 2021
Indian and international acts and regulations for mining operations and waste management
Referred journal and publications.

CE6209

Coupled Process in Fractured Geological Media

Course	CE6209: Coupled Process in Fractured Geological Media
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Coupled Process in Fractured Geological Media
Learning Mode	Lectures
Learning Objectives	Complies with PLO- number 1, 2 and 3 1. Understand the coupling mechanisms between various processes (e.g., fluid flow, heat transfer, and mechanical deformation) in fractured geological media. 2. Analyze the impact of fractures on the behavior of fluid flow, heat transfer, and mechanical deformation in geological formations. 3. Apply numerical modeling techniques to simulate coupled processes in fractured media and predict their behavior under different conditions. 4. Develop strategies for managing and controlling coupled processes to optimize resource extraction, geological storage, or environmental remediation in fractured geological environments.
Course Description	The Coupled Processes in Fractured Geological Media course delves into the complex interactions occurring within fractured rock formations. Students explore coupled hydro-mechanical-chemical processes occurring in subsurface environments. Topics include fluid flow, stress distribution, and chemical reactions in fractured media. Emphasis is placed on understanding how these processes affect geotechnical engineering, hydrology, and environmental management. Students learn modeling techniques and practical applications for characterizing and predicting behavior in fractured geological systems.
Course Outline	Introduction to Fractured Geological Media, Rock Mechanics Fundamentals, Hydrological Processes in Fractured Media, Thermal-Hydrological-Mechanical (THM) Coupling, Chemical Processes and Reactive Transport, Geomechanical-Fluid Interaction, Case Studies and Applications.
Learning Outcome	At the end of the course, student would be able to: 1. Students will grasp the complex interactions between fluid flow, heat transfer, and mechanical deformation in fractured geological formations. 2. They will learn to analyze coupled processes influencing subsurface systems such as groundwater flow, geothermal energy, and hydrocarbon reservoirs. 3. Learners will develop skills to model and simulate coupled phenomena to solve real-world problems in fractured media. 4. The course prepares students to address challenges in resource management, environmental remediation, and energy extraction.
Assessment Method	Assignments, Quizzes, Mid-semester examination and End-semester examination.

Textbooks:

Goodman, R. E. Introduction to rock mechanics, John Wiley and Sons, 1989.
R. Pusch. Waste Disposal in Rock. Elsevier. 1994
Coupled Processes Associated with Nuclear Waste Repositories" by Jacques Delay, Peter A. Witherspoon, François X. Dégerine
Randall F. Barron and Brian R. Barron. Design for Thermal Stresses. Wiley, 2011
Fractured Rock Hydrogeology" by John M. Sharp Jr.

References:

Hoek, E., & Bray, J. D. Rock slope engineering, CRC Press, 1981.
Hoek, E, & Brown, E. Underground excavations in rock, CRC Press, 1980.
Singh, B., & Goel, R. K. Engineering rock mass classification, Elsevier, 2011.

"Coupled Processes in Subsurface Deformation, Flow, and Transport" edited by George Pinder, Catherine A. Peters

CE6210

Ground Improvement Techniques

Course	CE6210: Ground Improvement Techniques
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Ground Improvement Techniques
Learning Mode	Lectures
Learning Objectives	Complies with PLO- number 1, 3, 4 & 5 1. Understand the importance of ground improvement for civil engineering structures. 2. Examine the problematic soil and select a suitable ground improvement technique. 3. Analyze and Design the various ground improvement techniques. 4. Understand the construction methodology, equipment and quality control aspects. 5. Know the national and international codal guidelines and provisions.
Course Description	Construction in weak and problematic soil is inevitable nowadays. The course addresses various ground improvement techniques along with principles, design issues and construction procedures. The course has been broadly divided into two modules namely ground improvement techniques and the reinforced earth.
Course Outline	Problematic soil and need for ground improvements, Mechanical modifications using mechanical and dynamic compaction, Accelerated consolidation using preloading and vertical drains, Soil stabilisation using additives and deep soil mixing, Grouting, Vibro techniques, Drainage and dewatering methods; Soil nailing; Soil nailing; Underpinning, Introduction to geo-synthetics and reinforced earth; Applications and advantages of reinforced soil structure; Principles, concepts and mechanism of reinforced soil; Soil-reinforcement interface friction; Behaviour of Reinforced earth walls; Bearing capacity improvement and design of foundations resting on reinforced soil; embankments on soft soils; Design of reinforced soil slopes, Use of geosynthetics for separations, drainage and filtration; practical applications of of geosynthetics; Geosynthetics in landfill system; Use of jute, coir, natural geotextiles, waste products such as scrap tire, LDPE and HDPE strips, as reinforcing material.
Learning Outcome	At the end of the course, student would be able to: 1. Identify the problematic soil and select a suitable ground improvement technique 2. Design the various ground improvement techniques 3. Understand the construction methodology, equipment and quality control aspects 4. Know the national and international codal guidelines and provisions
Assessment Method	Assignments, Quizzes, Mid-semester examination and End-semester examination.

Textbooks:

Manfired R. Hausmann, Engineering Principles of Ground Modification, McGraw-Hill Pub, Co., 1990.
Koerner, R.M. Designing with Geosynthetics, Prentice Hall, New Jersey, USA, 4th edition, 1999.
Jie Han, Principles and Practice of Ground Improvement, Wiley Publishers, 2015.

Reference books:

B.M. Das, Principle of Geotechnical Engineering, Cengage Learning, eighth Edition, 2013.
V. N. S. Murthy, Geotechnical Engineering: Principles and Practices of Soil Mechanics and Foundation Engineering, CRC Press, Taylor & Francis Group, Third Indian Reprint, 2013.
All relevant IS and international codes and relevant research papers/reports

CE6211

Utilization of industrial byproducts for geotechnical applications

Course	CE6211: Utilization of Industrial Byproducts for Geotechnical Applications
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Utilization of industrial byproducts for geotechnical applications
Learning Mode	Lectures
Learning Objectives	Complies with PLO- number 1, 2, 3, 4, and 5 1. Understand various sources and characteristics of industrial byproducts and their application as geomaterials. 2. Explain bulk application of industrial by products for soil stabilization and ground improvement with or without admixtures. 3. Analyse and elucidate the behaviour of industrial byproducts subjected to contamination, various remediation and immobilization techniques. 4. Apply the knowledge for economical, environmental and sustainable infrastructure development.
Course Description	The course covers various sources of industrial byproducts in India, status and potential applications. Further, this course deals with utilization of industrial byproducts as geomaterial for soil stabilization and ground improvement with or without using admixtures. This course also emphasizes the advanced characterizations techniques of industrial by products and behaviour subjected to contamination, various remediation and immobilization techniques.
Course Outline	Introduction to industrial byproducts and its types; characteristics and role of industrial byproducts and admixtures; purpose-based classification of soils; principles of soil stabilization and ground improvement; methods of stabilization using industrial byproducts with or without chemical admixtures such as lime, cement, bitumen and special chemicals; mechanisms, uses and limitations; advanced characterizations technique and use of fly ash, rice husk ash, biochar, marble waste, and quarry generated wastes, mine slurry, slag, and other waste materials for both shallow and deep soil stabilization and ground improvement; potential application of industrial wastes as geomaterials and its behaviour subjected to contamination agents; remediation and immobilization techniques of industrial byproducts; methods and applications of grouting; Application to embankments, excavations, foundations and sensitive soils.
Learning Outcome	At the end of the course, student would be able to: Describe various sources and characteristics of industrial byproducts and their application as geomaterials. Explain bulk application of industrial by products for soil stabilization and ground improvement with or without admixtures. Understand the behaviour of industrial byproducts subjected to contamination, various remediation and immobilization techniques. 4. Apply the knowledge for economical, environmental and sustainable infrastructure development.
Assessment Method	Assignments , Quizzes , Term-paper project, Mid-semester examination and End-semester examination.

Textbooks:

Ingles, O.G. and Metcalf, J.B., Soil Stabilization, Principles and Practice, Butterworths, 1972.
Bowen, R., Grouting in Engineering Practice, Allied Science Publishers Ltd., 1975.
Jie Han, Principles and Practice of Ground Improvement, Wiley Publishers, 2015.

References:

Yong, R. N. and Warkentin, B. P. Soil properties and behaviour, Elsevier, 2012.
Mitchell, J. K. and Soga, K. Fundamentals of soil behaviour, Wiley, New York, 2005.
B.M. Das, Principle of Geotechnical Engineering, Cengage Learning, eighth Edition, 2013.
All relevant IS and international codes and relevant research papers/reports.

CE6213

Design of Underground Excavations

Course	CE6213: Design of Underground Excavations
Course Credit (L-T-P-C)	3-0-0-4
Course Title	Design of Underground Excavations
Learning Mode	Lectures
Learning Objectives	Complies with PLO- number 1, 2 and 3 1. Understand the principles of underground excavation design, including site investigation and geological mapping. 2. Gain proficiency in analyzing rock mass behavior and selecting appropriate support systems. 3. Learn excavation methods, tunnelling techniques, and their applications in various geological conditions. 4. Develop skills to design safe, cost-effective, and sustainable underground structures while considering geological, geotechnical, and structural factors.
Course Description	This course covers principles of underground excavation design including rock mechanics, support systems, and excavation methods. Topics include ground behavior, stability analysis, tunnelling methods, and practical design considerations. Students learn to develop safe and efficient designs for tunnels, mines, and underground structures.
Course Outline	Introduction to Underground Excavations, Rock Mechanics Fundamentals, Site Investigation and Geotechnical Data Collection, Excavation Methods, Support Systems for Underground Excavations, Tunnel Design, Cavern and Underground Structure Design, Instrumentation and Monitoring, Case Studies and Project Examples
Learning Outcome	At the end of the course, student would be able to: 1. Understanding principles of rock mechanics for underground openings. 2. Ability to analyze and design support systems for stability and safety. 3. Proficiency in assessing geological conditions and their impact on excavation design. 4. Skill development in designing underground excavations for various engineering purposes like tunnels, mines, or underground structures.
Assessment Method	Assignments, Quizzes, Mid-semester examination and End-semester examination.

Textbooks:

Goodman, R. E. Introduction to rock mechanics, John Wiley and Sons, 1989.
Hoek, E., & Bray, J. D. Rock slope engineering, CRC Press, 1981.
Hoek, E, & Brown, E. Underground excavations in rock, CRC Press, 1980.

References:

Singh, B., & Goel, R. K. Engineering rock mass classification, Elsevier, 2011.
Jaeger, J. C., Cook, N. G., & Zimmerman, R. Fundamentals of rock mechanics, John Wiley & Sons, 2009.
Debasis, D., & Kumar, V. A. Fundamentals and applications of rock mechanics, PHI Learning Pvt. Ltd. New Delhi, India, 2016.

CE6214

Special Topics in Geotechnical Engineering

Course	CE6214: Special Topics in Geotechnical Engineering
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Special Topics in Geotechnical Engineering
Learning Mode	Lectures
Learning Objectives	Complies with PLO- number 1, 2, 3, and 4 1. To provide the knowledge of the advanced concept of geotechnical engineering. 2. Equip the students with a strong foundation in various topics in offshore geotechnical engineering. 3. Prepares the students to apply knowledge in policy and decision making related to civil engineering infrastructure.
Course Description	This course intends to bridge the basic concepts with the advanced topics related to geotechnical engineering. Topics ranging from geotechnical earthquake engineering, offshore geotechnical engineering, Tunnels and Earth & Rockfill dams are covered.
Course Outline	Elements of geotechnical earthquake engineering: seismic loading and its effect on earth structures; dynamic response of single, and multi-degree of freedom systems and continuous systems; behaviour of soil under dynamic loading; pore pressure generation and liquefaction effects; seismicity and seismic design parameters; Engineering Seismology and Seismic Microzonation Offshore geotechnical engineering: nature of submarine soils; offshore soil investigations; seabed sediments; wave action on seabed; submarine slope stability; seabed anchor systems Numerical methods in geotechnical engineering: application of finite element method to the solution of stress, deformation, seepage, and consolidation problems; numerical solutions for soil dynamics problems; soil-structure interaction. Tunnels: Drilling and blasting of rocks; Grouting; Instrumentation and measurements in tunnelling, Analysis and Design Earth & Rockfill dams: Analysis and Design, field and laboratory investigations; foundation conditions and treatment; seepage and seepage control; stability analysis; deformation analysis; seismic considerations; instrumentation and monitoring
Learning Outcome	At the end of the course, student would be able to: 1. Design earthquake resistant structure using various methods available along with the method suggested in the IS code. 2. Perform offshore soil investigations and design of offshore structure. 3. Design earth and rockfill dams considering the seepage and seismic loads.
Assessment Method	Assignments, Quizzes, Mid-semester examination and End-semester examination.

Textbooks:

E. Bowles, Engineering Properties of Soils and Their Measurement, McGraw-Hill, 1992.

Kramar S.L, Geotechnical Earthquake Engineering, Prentice Hall International series, Pearson Education Pvt. Ltd.
J.E. Bowles, Foundation Analysis and Design, McGraw-Hill, 2001.

Reference books:

Ikuo Towhata, Geotechnical Earthquake Engineering, Springer series, 2008.
All relevant IS and International Codes.

CE6215

Forensic Geotechnical Engineering

Course	CE6215: Forensic Geotechnical Engineering
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Forensic Geotechnical Engineering
Learning Mode	Lectures
Learning Objectives	Complies with PLO- number 1, 3, & 5. The learning objectives of this course are as follows: 1. To deal with investigations of different failures of engineered projects or facilities or structures related to civil engineering. 2. To analyze failures related to civil engineering, geotechnical, geoenvironmental and geological domains for professional practice, codes of analysis and design and implementation. 3. To apply the knowledge for further design and construction of any structures.
Course Description	This course is designed to understand and examine the various failure of civil and geotechnical engineering project due to different physical, environmental and geological causes. Further, knowledge gathered from this course will help in improving professional practice, developing codal provision and design and implementation.
Course Outline	Introduction, Forensic geotechnical engineering: theory and practice; Types of failure and damages, Preliminary investigations and information, Interaction between neighboring Structures, Planning the investigations, Site investigations and instrumentations, Settlement and failures of sub structures, Foundation design in difficult soil and climatic conditions, Ground water moisture related problems of substructures, Repairs and crack diagnosis, Back analysis in geotechnical engineering, Importance of uncertainty in forensic geotechnical engineering, Ethical and legal issues, Various Case studies of failures of civil engineering structures.
Learning Outcome	At the end of the course, student would be able to: 1. Understand the necessity and importance of forensic investigation in geotechnical engineering for various projects. 2. To deal with investigations of different failures of engineered projects or facilities or structures related to civil engineering. 3. To comprehend the techniques for mitigation of the failure damage. 4. To analyze failures related to civil engineering, geotechnical, geoenvironmental and geological domains for professional practice, codes of analysis and design and implementation.
Assessment Method	Assignments, Quizzes, Mid-semester examination and End-semester examination.

Textbooks:

Rao, V. V. S., and GL Sivakumar Babu, eds. Forensic Geotechnical Engineering. India: Springer India, 2016.
Puzrin, Alexander M., Eduardo E. Alonso, and Núria M. Pinyol. Geomechanics of failures. Dordrecht, The Netherlands: Springer, 2010.
Iwasaki, Y. Instrumentation and Monitoring for Forensic Geotechnical Engineering. Forensic Geotechnical Engineering (2016): 145-163.

Reference books:

Day, Robert W. Forensic geotechnical and foundation engineering. McGraw-Hill, 2011.
Alonso, Eduardo E., Núria M. Pinyol, and Alexander M. Puzrin. Geomechanics of failures: advanced topics. Vol. 277. Berlin: Springer, 2010.
Lacasse, Suzanne. Forensic geotechnical engineering theory and practice. Forensic Geotechnical Engineering (2016): 17-37.
Franck, Harold, and Darren Franck. Forensic engineering fundamentals. Boca Raton, FL: CRC Press, 2013.
All relevant IS and international codes and research articles and reports.

10.

CE6218

Structural Health Monitoring

Course	CE6218 Finite Element Method
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Finite Element Method
Learning Mode	Lectures
Learning Objectives	Objective for learning this course are Lecture: 1. Provide scientific and technical knowledge for the basis for the development of finite element analysis procedure. 2. Equip the students with a strong foundation and understanding for the finite element analysis process of the problems related to various civil and mechanical engineering.
Course Description	The course deals with understanding finite element analysis of various problems. This course provides the students an exposure for topics on analysis of problems related to various civil and mechanical engineering problems which are not covered in undergraduate design courses.
Course Outline	Basic concepts of engineering analysis; Methods of weighted residuals and variational formulations; Finite element discretization; Shape function; Lagrange and serendipity families; Element properties, iso-parametric elements; Criteria for convergence; Numerical evaluation of finite element matrices (Gauss quadrature integration); Assemblage of elements; Analysis of plane stress/strain, axi-symmetric solids; Three dimensional stress analysis; Flow though porous media; Error analyses: estimate of error, error bounds; Solution technique: finite element programming, use of package programs.
Learning Outcome	At the end of the course, student would be able to Lecture: 1. Understand various numerical methods for analysing engineering problems through FEM. 2. Analysis of various civil and mechanical engineering problems. 3. Ability to analyse complex structural system.
Assessment Method	Assignments, Quizzes, Project work, Mid-semester examination and End-semester examination.

Textbooks/ Reference books:

T. R. Chandrapatula and A. D. Belegundu, Introduction to finite elements in engineering, Third Edition, Prentice Hall of India, 2001.
P. Seshu, Text book of finite element analysis, Prentice Hall of India, 2003.
J. N. Reddy, An introduction to the finite element method, McGraw Hill Inc. 1993.
R. D. Cook. D. S. Malkus. M. E. Plesha, and R. J. Witt, Concepts and application of finite element analysis, fourth Edition, John Wiley & Sons, 2002.
O.C. Zienkiewicz and R. L. Taylor, The Finite element method, Butterworth Heinemann (Vol. I and Vol. lI), 2000.
C.S. Krishnamoorthy, Finite Element Analysis, Theory and programming, Tata McGraw Hill, 1994.
K.J. Bathe, Finite Element Procedures in Engg. Analysis, Prentice Hall of India, 1996.
C.S. Desai and T. Kundu, Introduction to finite element method, CRC Press, 2001.

11.

CE6219

Course	CE6219 Structural Health Monitoring
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Structural Health Monitoring
Learning Mode	Lectures
Learning Objectives	Objective for learning this course are Lecture: 1. To develop basic understanding on health monitoring of various civil engineering structures. 2. Become proficient in dealing with commonly used approaches/ algorithms through a fundamental understanding of the basics. 3. Familiar with techniques pertaining to heath assessment of various structures like building, bridge, heritage structures etc. 4. Become acquainted with some advanced techniques line with the state-of-the-art in SHM domain
Course Description	This course explores structural health monitoring methods and technologies for assessing the condition and performance of various structures. Case studies on civil infrastructures will be examined to illustrate SHM principles in practice. Additionally, the course covers emerging trends including advancements in sensor technology and data analytics for predictive maintenance.
Course Outline	Introduction to Structural Health Monitoring (SHM): Definition & requirement for SHM, SHM of a bridge, monitoring historical buildings; Non-Destructive Testing (NDT): Classification of NDT procedures, visual inspection, half-cell electrical potential methods, Schmidt Rebound Hammer Test, resistivity measurement, electro-magnetic methods, radiographic Testing, ultrasonic testing, Infra-Red thermography, ground penetrating radar, radio isotope gauges etc., case studies of a few NDT procedures on bridges; Condition Survey & NDE of Concrete Structures: Definition and objective of Condition survey, stages of condition survey (Preliminary, Planning, Inspection and Testing stages), possible defects in concrete structures, quality control of concrete structures; Vibration-based monitoring: Frequency-domain and time-domain analysis, Experimental modal analysis, application of damage detection methods on civil infrastructures.
Learning Outcome	At the end of the course, student would be able to: Perform sensor deployment, data acquisition, and analysis techniques used to detect and quantify structural damage. Develop proficiency in deploying sensor technologies and data acquisition systems to monitor the health of various structures. To analyse collected data, detect structural damage, and make informed decisions regarding maintenance and safety measures. Use the methods in real-life applications.
Assessment Method	Assignments, Quizzes, Project work, Mid-semester examination and End-semester examination.

Textbooks/ Reference books:

Daniel J. Inman, Charles R. Farrar, Vicente Lopes Junior, Valder Steffen Junior, Damage Prognosis: For Aerospace, Civil and Mechanical Systems, John Wiley & Sons, 2005.
Chee-Kiong Soh, Yaowen Yang, Suresh Bhalla (Eds.), Smart Materials in Structural Health Monitoring, Control and Biomechanics, Springer, 2012.

12.

CE6223

Uncertainty, Risk and Reliability Analyses in Civil Engineering

Course	CE6223 Uncertainty, Risk and Reliability Analyses in Civil Engineering
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Uncertainty, Risk and Reliability Analyses in Civil Engineering
Learning Mode	Lectures
Learning Objectives	Objective for learning this course are Lecture: 1. Make familiar the concept of probability theory and statistics. 2. Gain knowledge on stochastic simulation methods. 3. Develop knowledge on risk and reliability analysis of structure.
Course Description	The course deals with the risk and reliability analysis and design of different civil engineering infrastructural system. Also, this course discusses about the basic probability theory and random field generation.
Course Outline	Introduction and overview: Review of basic probability, Functions of random variables. Joint probability distribution, conditional distributions, Joint Normal distribution, Baysian Analysis, Analysis of variance (ANOVA), Application of central limit theorem; confidence interval, expected value, and return period, probability paper; testing of goodness-of-fit of distribution models, Random number generation – Monte Carlo simulations, Formulation of structural reliability problems: limit states, composite risk analysis, direct integration method, safety margin method, reliability index and safety factor; FORM and SORM methods, importance sampling and other variance reduction techniques, Reliability – historical development, applications, different measures of reliability; Component reliability - time to failure, Reliability-based maintenance, System reliability - representation of failure, series and parallel systems, redundancy, fault trees, Probability-based acceptance criteria: consequence of failure, concepts of risk, utility, Probability-based design, fragility analysis. Calibration of target reliability: reliability-based design codes.
Learning Outcome	At the end of the course, student would be able to Lecture: 1. Understanding concept of probability theory and application. 2. Risk and reliability analysis of civil engineering infrastructure. 3. Design of civil infrastructure based on risk and reliability.
Assessment Method	Assignments, Quizzes, Project work, Mid-semester examination and End-semester examination.

Textbooks/ Reference books:

A. Haldar and S. Mahadevan, Probability, Reliability, and Statistical Methods in Engineering Design, Wiley, 2000.
H. S. Ang and W. H. Tang, Probability Concepts in Engineering Planning and Design, John Wiley, 1975.
R. Ranganathan, Reliability Analysis and Design of Structures, Tata McGraw Hill, New Delhi, 1990.

13.

CE6230

Advanced Concrete Pavement Analysis and Design

Course	CE6230: Advanced Concrete Pavement Analysis and Design
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Advanced Concrete Pavement Analysis and Design
Learning Mode	Lectures
Learning Objectives	Complies with PLO number – 1, 2 and 4 1. Differentiate between the various Portland Cement Concrete pavement systems. 2. To provide knowledge of recent developments in concrete material characterization for rigid pavement analysis. 3. Train students to design concrete pavement and overlays. 4. Learn computation of stress distribution and distress mechanisms in rigid pavement. 5. Explain the underlying mechanisms associate with load and material related distresses.
Course Description	This course will discuss fundamental concepts in design and analysis of rigid pavement. Theoretical models for analysis of rigid pavement systems. Evaluation and application of current design practices related to rigid pavements. Course will cover Empirical and Mechanistic-Empirical pavement design approaches. Students will also learn different mechanisms associated with distress in rigid pavements.
Course Outline	INTRODUCTION TO PCC PAVEMENTS: Typical pavement cross-section and plan, Types of PCC pavements, Jointed systems, CRCP, Overlays, 2-lift systems, Precast systems, Prestressed-Post tension systems, Evolution of pavement design, Empirical and Mechanistic-Empirical designs. OVERVIEW OF AASHTO 86/93: Significant inputs needed for the design, Serviceability concept, Impact of inputs on the slab thickness-sensitivity, Limitations of the design process, Need for a systems approach to design-M-E PDG. PCC PAVEMENT DISTRESSES: Functional and structural distress, Load related distress, Material related distress, Underlying mechanism(s) of distresses, Relationship between distress mechanism(s) and design. PCC PAVEMENT RESPONSE: Load related response, Thermal response. Material Characterization: Fresh mixture properties, Mechanical properties, Thermal properties, Fracture properties, Durability properties. Traffic Characterization: ESALs, Load Spectra. PCC Design Methods (New and Overlays): PCA design method, AASHTO’98, M-E PDG. CONSTRUCTION OF PCC PAVEMENTS: Conventional pavement construction, Two-lift construction, Modular pavement construction, Concrete Overlays. SPECIAL TOPICS IN PCC PAVEMENTS: Porous concrete, Pannel concrete, Roller Concrete.
Learning Outcome	At the end of the course, student would be able to: 1. Design rigid pavements using Indian Codes and learn best practices. 2. Ability to compute stress-strain distribution in rigid pavement. 3. Identify different type of distresses in rigid pavement. 4. Identify factors influencing rigid pavement design.
Assessment Method	Assignments, Quizzes , Mid-semester examination and End-semester examination .

Textbooks:

Huang, Y. H. “Pavement analysis and design.” Pearson, 2004.
Papagianna, A. T. and Masad, E. A. “Pavement Design and Materials.” John Wiley & Sons, Inc., 2008.
Chakroborty, P. and Das, A. “Principles of Transportation Engineering.” PHI Learning, 2017.

Reference books:

Ullidtz, P. “Pavement Analysis.” Elsevier, 1987.
Mechanistic-Empirical Pavement Design Guide – A Manual of Practice, AASHTO 2008.

14.

CE6231

Advanced Pavement Material Characterization

Course	CE6231: Advanced Pavement Material Characterization
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Advanced Pavement Material Characterization
Learning Mode	Lectures
Learning Objectives	Complies with PLO number – 1, 2, and 4 To understand characteristic properties of material used in road construction. To understand performance evaluation techniques of road construction materials. To understand design of asphalt mix. To understand different type of waste and recycled materials used in road construction. To understand quality control plan in road construction.
Course Description	This course deals with materials used in road construction. Source, properties and performance evaluation methods of pavement materials are important in selecting them road construction project. The course will help students understand the practices used in road construction industry in selection, design and quality control of pavement materials.
Course Outline	Characterization of Pavement Materials: (1) Asphalt mix: Definitions, Production types and Classification of asphalt mix. (2) Aggregates: Definitions, Sources, Production types, Engineering and Consensus properties. (3) Asphalt binder: Definitions, Sources, Production types, Chemistry and Physical properties, Performance tests and Specifications, Specifications for modified binders. (4) Soil: Definitions, Classification and Engineering properties. (5) Emulsion: Definitions, Classification and Engineering properties; Image based material evaluation, non-destructive testing of material properties. Advance topics in Asphalt Binder and Mixes: Performance grading of asphalt binder, Binder modification, Superpave mix design, Design using recycled materials. Asphalt Mix Modeling: Introduction to viscoelasticity, Rheological properties – viscoelastic models, Viscoplastic models, nonlinear viscoelasticity, Interconversion of viscoelastic properties. Failure Modeling: Fatigue Models, Rutting models, Moisture damage mechanism. Unbound materials: Nonlinearity in fine and coarse grained material; Stabilized granular layer, Design of stabilized materials. Quality Control and Tolerance: Field construction, Quality control plan, Control charts, QA/QC tests. Software: ABAQUS
Learning Outcome	At the end of the course, student would be able to: Understand different conventional and recycled materials used in road construction? Select and design material for road construction. Evaluate pavement material based on performance related properties. Develop quality control plan for pavement materials in road construction projects.
Assessment Method	Assignments, Quizzes , Mid-semester examination and End-semester examination .

Textbooks:

Huang, Y. H. "Pavement analysis and design." Pearson, 2004.
Papagianna, A. T. and Masad, E. A. “Pavement Design and Materials.” John Wiley & Sons, Inc., 2008.

Reference books:

Kim., Y. R. “Modeling of Asphalt Concrete.” McGraw-Hill, 2009, 1st Edition.
National Cooperative Highway Research Program (NCHRP) Reports.
MORTH. “Ministry of Road Transportation & Highways Specifications for Road and Bridge Works.” 2013.

Interdisciplinary Elective (IDE) Course for M. Tech. (Available to students other than CE)

Sl. No.

Subject Code

Subject Name

CE6132

Data Science for Engineers

Course	CE6132: Data Science for Engineers
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Data Science for Engineers
Desirable Prerequisites	Knowledge of Remote Sensing and GIS/Advanced Geomatics, digital image processing, machine learning and AI
Learning Mode	Lectures
Learning Objectives	Complies with PLOs 1, 2 & 3- 1. To provide fundamental knowledge in the basics of Data Science. 2. Train students to understand the various applications of Machine Learning and modelling for research applications. 3. Provide scientific and technical knowledge to the students on Errors and Adjustments.
Course Description	This course will discuss fundamental concepts in data science for Civil Engineers. The course will cover theory and real-world practice in data, errors and adjustments to help deal with various research-related problems.
Course Outline	Overview of probability and statistics; statistical learning: definition, principles and different types of statistical learning, assessing model accuracy, bias-variance tradeoff; regression models: simple linear and multiple linear and non-linear; resampling methods: assessing model prediction quality, cross-validation, bootstrap; model selection and regularisation: dimensionality reduction, ridge and lasso; unsupervised learning: clustering approaches, K-means and hierarchical clustering; supervised learning: classification problem, classification using logistic regression, naive Bayes, classification with Support Vector Machines, neural networks. Background of Errors, Expectations and Error Propagation, Random Errors, Model Development and Problem-solving, Observations and Equations, Conditions and Combined Equations, Errors in Surveying.
Learning Outcome	At the end of the course, students would be able to: 1. Understand technical aspects and properties of Data Science. 2. Perform error adjustments in Civil Engineering problems. 3. Skilled to develop more accurate, robust and error-free predictive and classification models.
Assessment Method	Assignments (10%), Quizzes (10%), Mid-semester examination (30%) and End-semester examination (50%).

REFERENCES:

Gillani, D. Charles, Adjustment Computations: Spatial Data Analysis, 6^th Edition, John Wiley and Sons, 2017.
James, G., Witten, D., Hastie, T., & Tibshirani, R., Introduction to Statistical Learning, Springer, 2^nd Edition, 2013.
Lillesand, M. and Kiefer, R.W., Remote Sensing, and Image Interpretation III Edition. John Wiley and Sons, New York. 1993.
Mehrotra, A.K., Geo-statistics for Beginners, Zorba, 2020.
Ian Heywood Sarah, Cornelius, and Steve Carver: An Introduction to Geographical Information Pearson Education. New Delhi, 2002.
Leick, A., GPS satellite surveying, John Wiley and Sons, 4^th Edition, 2015.
Ogundare, O.J., Precision Surveying: The Principles and Geomatics Practice, John Wiley and Sons, 2015.

M. Tech. in Environmental Engineering

Program Learning Objectives:	Program Learning Outcomes:
Program Goal 1: Equip the students with strong foundation in civil and environmental engineering for both research and industrial scenarios.	Program Learning Outcome 1a: Student develops ability to design and conduct experiments. Program Learning Outcome 1b: Student is able to organize and analyze the experiment data to draw conclusions.
Program Goal 2: Provide scientific and technical knowledge in planning, design, construction, operation and maintenance of civil engineering infrastructure.	Program Learning Outcome 2: Students are able to (i) develop material and process specifications, (ii) analyze and design projects, (iii) perform estimate and costing and (iv) manage technical activities.
Program Goal 3: Prepares the students to apply knowledge in policy and decision making related to civil engineering infrastructure.	Program Learning Outcome 3a: Student develops understanding of professional and ethical responsibility. Program Learning Outcome 3b: Student is able to consider economic, environmental, and societal contexts while developing engineering solutions.
Program Goal 4: Prepare students to attain leadership careers to meet the challenges and demands in civil engineering practice.	Program Learning Outcome 4a: Students is prepared for leading roles/profiles in government sector, construction industry, consultancy services, NGOs, corporate houses and international organizations. Program Learning Outcome 4b: Student develops ability to identify, formulate, and solve engineering problems.
Program Goal 5: Nurture interdisciplinary education for finding innovative solutions.	Program Learning Outcome 5: Student is able to solve complex engineering problems by applying principles of engineering and science.

Sl. No.	Subject Code	SEMESTER I	L	T	P	C
1.	HS5111	Technical Writing and Soft Skill	1	2	2	4
2.	CE5101	Chemistry for Environmental Engineers	3	0	2	4
3.	CE5102	Physico-Chemical Principles and Processes	3	0	0	3
4.	CE5103	Solid and Hazardous Waste Management	3	0	0	3
5.	CE51XX/ CE61XX	DE-I (Environmental Elective)	3	0	0	3
6.	CE51XX/ CE61XX	DE-II (Environmental / Department Elective)	3	0	0	3
7.	XX61PQ	IDE	3	0	0	3
	TOTAL		19	2	4	23
IDE (Inter Disciplinary electives) in the curriculum aims to create multitasking professionals/ scientists with learning opportunities for students across disciplines/aptitude of their choice by opting level (5 or 6) electives, as appropriate, listed in the approved curriculum.

Sl. No.

Subject Code

SEMESTER II

CE5201

Biological Principles and Processes

CE5202

Air Pollution and Control

CE5203

Environmental Impact Assessment

CE52XX/ CE62XX

DE-III (Environmental Elective)

CE52XX/ CE62XX

DE-IV (Environmental Elective/ Department Elective)

RM6201

Course Number	RM6201
Course Credit (L-T-P-C)	3-1-0-4
Course Title	Research Methodology
Learning Mode	Lectures
Learning Objectives	The objective of the course is to train student about the modelling of scalar and multi-objective nonlinear programming problems and various classical and numerical optimization techniques and algorithms to solve these problems
Course Description	Advanced Optimization Techniques, as a subject for postgraduate and PhD students, provides the knowledge of various models of nonlinear optimization problems and different algorithms to solve such problems with its applications in various problems arising in economics, science and engineering.
Course Content	Module I (6 lecture hours) – Research method fundamentals: Definition, characteristics and types, basic research terminology, an overview of research method concepts, research methods vs. method methodology, role of information and communication technology (ICT) in research, Nature and scope of research, information based decision making and source of knowledge. The research process; basic approaches and terminologies used in research. Defining research problem and hypotheses framing to prepare a research plan. Module II (5 lecture hours) - Research problem visualization and conceptualization: Significance of literature survey in identification of a research problem from reliable sources and critical review, identifying technical gaps and contemporary challenges from literature review and research databases, development of working hypothesis, defining and formulating the research problems, problem selection, necessity of defining the problem and conceiving the solution approach and methods. Module III (5 lecture hours) - Research design and data analysis: Research design – basic principles, need of research design and data classification – primary and secondary, features of good design, important concepts relating to research design, observation and facts, validation methods, observation and collection of data, methods of data collection, sampling methods, data processing and analysis, hypothesis testing, generalization, analysis, reliability, interpretation and presentation. Module IV (16 lecture hours) - Qualitative and quantitative analysis: Qualitative Research Plan and designs, Meaning and types of Sampling, Tools of qualitative data Collection; observation depth Interview, focus group discussion, Data editing, processing & categorization, qualitative data analysis, Fundamentals of statistical methods, parametric and nonparametric techniques, test of significance, variables, conjecture, hypothesis, measurement, types of data and scales, sample and sampling techniques, probability and distributions, hypothesis testing, level of significance and confidence interval, t-test, ANOVA, correlation, regression analysis, error analysis, research data analysis and evaluation using software tools (e.g.: MS Excel, SPSS, Statistical, R, etc.). Module V (10 lecture hours) – Principled research: Ethics in research and Ethical dilemma, affiliation and conflict of interest; Publishing and sharing research, Plagiarism and its fallout (case studies), Internet research ethics, data protection and intellectual property rights (IPR) – patent survey, patentability, patent laws and IPR filing process.
Learning Outcome	On successful completion of the course, students should be able to: 1. Understand the terminology and basic concepts of various kinds of nonlinear optimization problems. 2. Develop the understanding about different solution methods to solve nonlinear Programing problems. 3. Apply and differentiate the need and importance of various algorithms to solve scalar and multi-objective optimization problems. 4. Employ programming languages like MATLAB/Python to solve nonlinear programing problems. 5. Model and solve several problems arising in science and engineering as a nonlinear optimization problem.
Assessment Method	Quiz /Assignment/ Project / MSE / ESE

Textbooks & Reference Books:

R. Kothari, Research methodology: Methods and Techniques, 3rd Edn., New age International 2014.
Mark N K. Saunders, Adrian Thornhill, Phkip Lewis, “Research Methods for Studies, 3/c Pearson Education, 2010.
N. Krishnaswamy, apa iyer, siva kumar, m. Mathirajan, “Management Research Methodology”, Pearson Education, 2010.
Ranjit Kumar; “Research Methodology: A Step by Step Guide for Beginners; 2/e; Pearson Education, 2010.

IK6201

IKS

TOTAL

Sl. No.	Subject Code	SEMESTER III	L	T	P	C
1.	CE6198	Summer Internship/Mini Project*	0	0	12	3
2.	CE6199	Project I **	0	0	30	15
	TOTAL		0	0	42	18

*Note: Summer Internship (Credit based)

Department Elective -I: Environmental Elective Course

Department Elective -I: Environmental Elective Course
Sl. No.	Subject Code	Subject	L	T	P	C
1.	CE6101	Atmospheric Physics and Chemistry	3	0	0	3
2.	CE6102	Sampling, Analytical Methods, and Statistics for Environmental Engineering	3	0	0	3
3.	CE6103	Environmental Toxicology and Risk Assessment	3	0	0	3
4.	CE6104	Environmental Hydraulics	3	0	0	3
5.	CE6105	Atmospheric Science and Climate Change	3	0	0	3

Department Elective -II: Department Elective Course

Department Elective -II: Department Elective Course

Sl. No.

Subject Code

Subject

CE5117

Water Resources Management

CE6109

Geoenvironmental Engineering

Course	CE6109 Geoenvironmental Engineering
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Geoenvironmental Engineering
Learning Mode	Lectures
Learning Objectives	Complies with PLO- number 1, 2, 3, and 4 1. Understanding methods of waste management and disposal 2. Learning methods of contaminated site characterization 3. Learning methods of remedial measures of a contaminated site 4. Understanding application of unsaturated soil in Geoenvironmental Engineering
Course Description	The course covers the source of various types of waste and its proper disposal, remediation of contamination sites. Municipal solid waste and industrial waste disposal techniques. Role of compacted unsaturated clay as liner material in landfill.
Course Outline	Production and classification of wastes, contaminated site characterization, Selection of waste disposal sites, selection criteria. Design of various landfill components such as liners, covers, leachate collection and removal, gas generation and management, ground water monitoring, stability analysis. Ash disposal facilities, dry disposal, wet disposal, design of ash containment system, stability of ash dykes, mine tailings. Planning, source control, soil washing, bioremediation, stabilization of contaminated soils and risk assessment approaches. Basics of unsaturated soil, soil suction, suction measurement techniques, SWCC, application of unsaturated soil in Geoenvironmental engineering.
Learning Outcome	At the end of the course, student would be able to: 1. Able to manage and dispose particular type of waste 2. Should be able to characterise contaminated site 3. Should be able to take appropriate remedial measures for a contaminated site 4. Should be able to use unsaturated clay as liner material in Geoenvironmental application.
Assessment Method	Assignments, Quizzes, Mid-semester examination and End-semester examination.

Textbooks:

H D Sharma and K R Reddy, “Geoenvironmental Engineering: Site Remediation, waste containment, and emerging waste management technologies”, John Willey and Sons, 2004.
R N. Yong, “Geoenvironmental Engineering: Contaminated Ground: Fate of Pollutions and Remediation”, Thomson Telford, 2000.
D. G. Fredlund and H. Rahardjo, “Soil Mechanics for Unsaturated soils”, Wiley Publication, 1993.

Reference books:

R Kerry Rowe, R M Quigley, Richard W I Brachman and John R Booker, “Barrier Systems for Waste Disposal Facilities”, 2nd edn, CRC press, 2019.

L N Reddy and H.I. Inyang, “Geoenvironmental Engineering: Principles and Applications”, Marcel Dekker, 2000
James K Mitechell, K Soga, “Fundamentals of soil behaviour”, Wiley Publication, 2005.
Charles W.W.Ng, B Menzies, “Advanced unsaturated soil mechanics and engineering”, CRC Press, 2014

CE6114

Probalistic Methods in Geotechnical Engineering

Course	CE6114: Probalistic Methods in Geotechnical Engineering
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Probabilistic Methods in Geotechnical Engineering
Learning Mode	Lectures
Learning Objectives	Complies with PLO- number 1, 2, 3, and 4 1. To provide the knowledge of the advanced concept of probabilistic methods in geotechnical engineering. 2. Equip the students with a strong foundation in civil engineering for both research and industrial scenarios.
Course Description	This course intends to bridge the basic concepts with the advanced topics related to the application of probabilistic methods in geotechnical engineering. Topics ranging from risk, uncertainty, Monte Carlo simulation, and FORM are covered. The course started with the basic knowledge gained by the attendee up to undergraduate level regarding the probabilistic methods. Thereafter, the basics and advanced concept related to risk and reliability analysis will be studied by the students.
Course Outline	Introduction: Concept of risk; and uncertainty in geotechnical engineering analysis and design; Fundamental of probability models. Analytical models of random phenomena: Baysian Analysis; Analysis of variance (ANOVA); Application of central limit theorem; confidence interval; expected value; and return period. Application of Monte Carlo simulation (MCS): Determination of function of random variables using MCS methods; Application of MCS in various geotechnical engineering problems. Determination of Probability Distribution Model: Probability paper; testing of goodness-of-fit of distribution models. Methods of risk Analysis: Composite risk analysis; Direct integration method; Method using safety margin; reliability index and safety factor; FORM; SORM; Applications of risk and reliability analysis in engineering systems.
Learning Outcome	At the end of the course, student would be able to: 1. Analyzed structure using various probabilistic methods available along with the method suggested in the Euro code. 2. Perform reliability analysis for various geotechnical problems. 3. Assess composite risk using various techniques to estimate failure of geotechnical structures.
Assessment Method	Assignments, Quizzes, Mid-semester examination and End-semester examination.

Textbooks:

Ang, A. H-S., and Tang, W. H., Probability Concepts in Engineering, Vol. 1, John Wiley and Sons, 2006.
Scheaffer, R. L., Mulekar, M. S. and McClave, J. T., Probability and statistics for Engineers, 5th Edition, Brooks / Cole, Cengage Learning, 2011.

Reference books:

Halder, A and Mahadevan, S., Probability, Reliability and Statistical Methods in Engineering Design, John Wiley and Sons, 2000.

All relevant IS and International Codes.

CE6130

Analytical Methods in Civil Engineering

Course	CE6130
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Analytical Methods in Civil Engineering
Learning Mode	Lectures
Learning Objectives	Complies with PLO number – 1, 2, and 4 Objective for learning this course are: 1. To brush up the undergraduate level understanding in light with some advanced approaches. 2. To develop p proficiency in numerical techniques and algorithms pertaining to various civil engineering problems. 3. To form a stepping stone towards advance understanding of risk and reliability analyses.
Course Description	First part of this course deals with the numerical method for non-linear equation solution, numerical integration, solution of liner system of equations, curve fittings, solution of differential equations. Second part of the course basic concept of probability theory and statistics, estimation of distribution property, stochastic data generation, risk and reliability methods for civil engineering.
Course Outline	Module – I: Linear Algebra and Differential Equation Linear algebra: Rank of a matrix, solutions of linear systems, linear independence and linear transformations,eigenvalues, eigenvectors,matrices similarity, basis of eigenvectors, diagonalization; Differential equations:homogeneous linear equations of second order,second order homogeneous equations with constant coefficients,case of complex roots, complex exponential function,non-homogeneous equations,solution by undetermined coefficients and variation of parameters. Module – II: Numerical Methods Introduction to Numerical Methods: Objectives of numerical methods, Sources of error in numerical solutions: truncation error, round off error, order of accuracy - Taylor series expansion; Roots of equations: Graphical method, Bisection method, Simple fixed-point iteration, Newton-Raphson method, Secant method, Modified secant method; Direct Solution of Linear systems: Naive Gauss elimination, LU decomposition, Gauss-Seidel, Gauss-Jordon, Jacobi iteration, Cholesky decomposition; Curve fitting: linear regression, polynomial regression, interpolation, spline fitting; Numerical Calculus: trapezoidal and Simpson’s rule for integration; Solving differential equation: Euler’s method, Runge-Kutta method, boundary value and eigenvalue problem and their application, solving partial differential equation. Module – III: Probability and Statistics Introduction: concept of risk, uncertainty in engineering analysis and design, fundamental of probability models; Analytical models of random phenomena: Bayesian analysis, analysis of variance (ANOVA), tests of hypothesis, confidence interval, properties of good estimates, interval estimation, maximum likelihood estimates, Sample size determination, central limit theorem, expected value, and return period; Miscellaneous Topics: Fitting theoretical and tests of goodness-of-fit (chi-square test, Kolmogorov-Smirnovtest),identification of outliers,regression with discrete dependent variables; Application of Monte Carlo simulation (MCS): determination of function of random variables using MCS methods, application of MCS in various problems.
Learning Outcome	At the end of the course, student would be able to Lecture: 1. Understand the different numerical methods for solving non-linear equations and numerical integration method. 2. Should be able to solve differential equations numerically. 3. Understand basic concept probability theory and statistics. 4. Should be able to fit statistical distribution and parameter estimation. 5. Should be able to perform MC simulation and preform risk and reliability analysis.
Assessment Method	Assignments, Quizzes, Project work, Mid-semester examination and End-semester examination.

Textbooks/ Reference books:

E. Kreyszig, Advanced Engineering Mathematics, Wiley, 10th edition, 2011.
M. D. Greenberg, Advanced Engineering Mathematics, Pearson, 2nd edition,1998.
S. Chapra and R. Canale, Numerical Methods for Engineers, McGraw Hill, 6th edition, 2010.
S. Guha and R. Srivastava, Numerical Methods: For Engineering and Science, Oxford University Press, 1st edition, 2010.
R. L. Scheaffer, M. S. Mulekar, and J. T. McClave, Probability and statistics for Engineers, Brooks / Cole, Cengage Learning, 5th Edition, 2011.
A. Haldar and S. Mahadevan, Probability, Reliability, and Statistical Methods in Engineering Design, Wiley, 2000.
H. S. Ang and W. H. Tang, Probability Concepts in Engineering Planning and Design, John Wiley, 1975.
J. Benjamin and A. Cornell, Probability, Statistics, and Decision for Civil Engineers, McGraw Hill, 1963.

CE6131

Sustainability of Water Resources System

Department Elective -III: Environmental Elective Course

Department Elective -III: Environmental Elective Course
Sl. No.	Subject Code	Subject	L	T	P	C
1.	CE6201	E-waste Management for Circular Economy	3	0	0	3
2.	CE6202	Industrial Pollution Control and Prevention	3	0	0	3
3.	CE6203	Water Supply and Sewerage Network Design	3	0	0	3
4.	CE6204	Design of Water and Wastewater Treatment Facilities	3	0	0	3
5.	CE6205	Advanced Water and Wastewater Engineering	3	0	0	3

Department Elective - IV: Department Elective Course

Department Elective - IV: Department Elective Course

Sl. No.

Subject Code

Subject

CE5217

Geoinformatics for Engineers

Course	CE5217 Geoinformatics for Engineers
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Geoinformatics for Engineers [Even Semester/2^nd Semester, M. Tech
Learning Mode	Lectures
Learning Objectives	Complies with PLOs 1, 2 & 3- 1. To provide fundamental knowledge in the Basics of GIS. 2. Train students to download, process and prepare the GIS data for Water resources applications. 3. Provide scientific and technical knowledge, to prepare students to prepare maps using GIS for Water resources applications.
Course Description	This course will discuss fundamental concepts in GIS. The course will cover theory and real-world practice in map preparation, flood mapping, rivers and canal mapping and GIS software and databases.
Course Outline	Definition – Basic components of GIS – Map projections and coordinate system –Spatial data structure: raster, vector – Spatial Relationship – Topology – Geodata base models: hierarchical, network, relational, object-oriented models – Integrated GIS database -common sources of error – Data quality: Macro, Micro and Usage level components - Meta data - Spatial data transfer standards. Thematic mapping – Measurement in GIS: length, perimeter, and areas – Query analysis– Reclassification – Buffering - Neighbourhood functions - Map overlay: vector and raster overlay – Interpolation – Network analysis –Digital elevation modelling. Analytical Hierarchy Process, – Object oriented GIS – AM/FM/GIS – Web Based GIS Spatial data sources – GIS approach water resources system – Thematic maps -Rainfall-runoff modelling – Groundwater modelling – Water quality modelling – Flood inundation mapping and Modelling – Drought monitoring – Cropping pattern change analysis –Performance evaluation of irrigation commands. Site selection for artificial recharge - Reservoir sedimentation. Introduction to various remote sensing satellite data (Like Landsat, Sentinel, Radar data, DEM, GRACE etc) and their applications for different water resources engineering applications.
Learning Outcome	At the end of the course, student would be able to: 1. Understand technical aspects and properties of GIS. 2. Download and perform GIS based analysis on different satellite data. 3. Basic flood mapping using Optical and SAR data.

Assessment Method

Assignments (10%), Quizzes (10%), Mid-semester examination (30%) and End-semester examination (50%).

REFERENCES:

Lillesand, M. and Kiefer, R.W., Remote Sensing, and Image Interpretation III Edition. John Wiley and Sons, New York. 1993.
Burrough P.A. and McDonnell R.A., Principles of Geographical Information Systems. Oxford University New York. 1998.
Ian Heywood Sarah, Cornelius, and Steve Carver: An Introduction to Geographical Information Pearson Education. New Delhi, 2002.
Jensen, R., Introductory digital image processing: a remote sensing perspective, Fourth Edition, Pearson, 2017
Joseph, G & Jagannathan, , Fundamentals of remote sensing (3^rd edition), The Orient Blackswan, 2018.

CE5218

Groundwater Hydrology

Course	CE5218 Groundwater Hydrology
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Groundwater Hydrology
Learning Mode	Lectures
Learning Objectives	Complies with PLO-1, 2, 3, 4, and 5 1. To provide fundamental knowledge of groundwater hydrology. 2. Train students to plan, design and model groundwater systems. 3. Provide scientific and technical knowledge, to apply the learning in sustainable management of groundwater resources.
Course Description	This course will discuss fundamental concepts of groundwater flow, its occurrence, movement, and flow principles. It will also cover issues related to groundwater management, such as pollution and over-exploitation.
Course Outline	Characteristics of groundwater, Global distribution of water, Role of groundwater in water resources system and their management, groundwater column, aquifers, classification of aquifers. Hydrogeological cycle, water level fluctuations, Groundwater balance. Darcy's Law, Hydraulic conductivity, Aquifer transmissivity and storativity, Dupuit assumptions Storage coefficient - Specific yield Heterogeneity and Anisotropy, Direct and indirect methods for estimation of aquifer parameters. Governing equation for flow and contaminant transport through porous medium - Steady and unsteady state flow - Initial and boundary conditions, solution of flow equations. Tracer techniques using environmental isotopes. Surface water groundwater interaction. Steady and unsteady flow to a well in a confined and unconfined aquifer - Partially penetrating wells - Wells in a leaky confined aquifer - Multiple well systems - Wells near aquifer boundaries - Hydraulics of recharge wells. Dynamic equilibrium in natural aquifers, groundwater budgets, management potential of aquifers, safe yield, seepage from surface water, stream-aquifer interaction, artificial recharge. Hydrodynamic dispersion - occurrence of dispersion phenomena, coefficient of dispersion - Aquifer advection-dispersion equation and parameters - initial and boundary conditions - method of solutions, solution of advection-dispersion equation. Climate change and impact on groundwater. Groundwater monitoring and groundwater sampling techniques. Introduction to sustainable groundwater management.
Learning Outcome	After attending this course, the following outcomes are expected: 1. Student should be able to develop an understanding about the occurrence, movement, and fate of groundwater in aquifer systems. 2. Students comprehend the physical principles of groundwater flow and solute transport processes and can represent those processes through mathematical equations in assessing water quantity and quality in ground-water systems. 3. Students should be able to understand the challenges associated with groundwater resources and apply the scientific method and critical thinking in groundwater quantity and quality management.
Assessment Method	Assignments, Quizzes, Mid-semester examination, and End-semester examination

Text Books/ Reference Book:

Bhagu R Chahar, Groundwater Hydrology, McGraw-Hill Education, 2015
Todd D.K., Ground Water Hydrology, John Wiley and Sons, 2000
Freeze A, Cherry JA, Groundwater, Prentice Hall, 1979.
Bear J., Hydraulics of Groundwater, Dover Publications INC, 1979
Integrated Groundwater Management, Springer Open
Richard W Healey, Estimating Groundwater Recharge, Cambridge University Press

CE5219

Open Channel Hydraulics

Course	CE5219 Open Channel Hydraulics
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Open Channel Hydraulics
Learning Mode	Lectures
Learning Objectives	Complies with PLO 1, 2, 3, 4 and 5 Students will be enabled to understand the fundamental principles governing open channel hydraulics for the design of engineering systems. The course is intended to assist students in developing the skills needed for systematic decomposition and solution of real-world problems.
Course Description	This course covers principles of flow in open channels, conservation laws, critical flow, uniform flow, gradually varied flow, unsteady flow, flow through hydraulic structures, hydraulic jump, and flow routing, analytical and numerical techniques will also be discussed, programming assignments will be carried out in common software and MATLAB.
Course Outline	Difference between Open Channel Flow and Pipe Flow, Types of Channel, Geometric parameters of a channel, Classification of Open Channel Flow, Continuity and Momentum equation. Resistance flow formula, Velocity distribution, Equivalent roughness coefficient, Velocity coefficients, Uniform flow in rigid boundary channel, Uniform flow in mobile boundary channel. Concept of Specific Energy, Critical Depth, Alternate depth, Specific Force, Sequent depth. Governing equation of GVF, Classification of Gradually Varied Flow, Computation of GVF profile, Rapidly Varied Flow, hydraulic Jump, Flow over a Hump, Flow in Channel Transition. Concept of best hydraulic section, Design of rigid boundary canal, design of channel in alluvial formation- Kennedy’s theory, Lacy’s theory, Method of Tractive force, Free-board in canal. Wave and their classification, Celerity of wave, Surges, Characteristic equation.
Learning Outcome	At the end of the course, student would be able to: 1. Learn the form of mass, momentum and energy equations under non hydrostatic pressure distribution and non-uniform velocity profiles. 2. Analyse gradually varied flows numerically. 3. Learn how to analyse rapidly varied flow numerically. 4. Design rigid-boundary and erodible channels. 5. Gain information about the flow through spillways and culverts. 6. Basic components of sediment transport in open channels.
Assessment Method	Assignments, Quizzes, Mid-semester examination, and End-semester examination

Text Books/ Reference Book:

K Subramaniya, Flow in Open Channels, McGraw Hill, 1997.
T. Chow, Open-channel hydraulics, McGraw Hill Publications (1973).
Sturm, 2001, Open-Channel Hydraulics, McGraw Hill.
Chaudhury, Open channel flow, Second Edition. Springer (2008).
Rajesh Srivastava, Flow through open channels, Oxford University Press (2008).

CE6208

Mine Wastes Generation and Management

Course	CE6208: Mine Wastes Generation and Management
Course Credit (L-T-P-C)	3-0-0-3
Course Title	Mine Wastes Generation and Management
Learning Mode	Lectures
Learning Objectives	Complies with PLO- number 1, 2, 3, 4, and 5 1. Understand and explain the mining operations, regulations and acts. 2. Explain the various types of mine generated wastes, characterizations techniques and application. 3. Describe the mine waste disposal techniques and stability analysis of overburden dumps. 4. Comprehend the mine generated contaminated leachate and ground pollution. 5. Analyse technical strategies, approaches and solutions to engineer's role and responsibility for mine waste management risk analysis, potential application, safety factors for sustainable development.
Course Description	The course covers various mine waste generated during the mining operation and their characteristics, mining regulations and acts, waste disposal, potential application and stability analysis of mine overburden waste, leachate formation and ground contamination. This course deals with geomechanics and rehabilitation techniques of mine generated wastes, valorization of mine wastes, risk analysis and mining safety.
Course Outline	Introduction to mining operations and risk; overview of Indian & international mining regulations and acts; different types of mine waste generated during the mining operation; mine waste disposal & rehabilitation; geochemical compositions, physical & chemical nature of mine wastes; disposal of mine wastes; geomechanics of mine waste disposal & rehabilitation; characterizations and application of mining wastes for infrastructure projects; valorization of mining wastes; leachate formation and ground contamination due to mining wastes; stability analysis of mining wastes overburden dumps, reintegration of mine wastes; mining wastes risk assessment & remedial measures; mining safety.
Learning Outcome	At the end of the course, student would be able to: Describe and explain the mining operations, regulations and acts. Explain the various types of mine generated wastes, characterizations techniques and application. Describe the mine waste disposal techniques and stability analysis of overburden dumps. Understand the mine generated contaminated leachate and ground pollution. 5. Analyze technical strategies, approaches and solutions to engineer's role and responsibility for mine waste management risk analysis, potential application, safety factors for sustainable development.
Assessment Method	Assignments, Quizzes, Term-paper project, Mid-semester examination and End-semester examination.

Textbooks:

Singh, T N. Surface Mining, Lovely Prakashan, India, 2020.
Karra, Ram Chandar, Gayana, B C, Rao, Shubhananda P Mine Waste Utilization, CRC Press, 2022.
Hutchison, Ian P.G. and Ellis, Rechard D., Mine Waste Management, CRC Press, India, 1992.
Lottermoser, Bernd G., Mine Wastes Characterization, Treatment and Environmental Impacts, Springer, 3rd edition, 2010.

References:

Pradhan, S. P., Vishal, V., & Singh, T. N. (Eds.). Landslides: theory, practice and modelling. Springer International Publishing, 2019.
Pathak, Pankaj, Rout, Prangya Ranjan, Urban Mining for Waste Management and Resource Recovery, CRC Press, 2021
Indian and international acts and regulations for mining operations and waste management
Referred journal and publications.

CE6211

Utilization of industrial Byproducts for Geotechnical Application

CE6218