Course Number

EC5101

Course Credit (L-T-P-C)

3-1-0-4

Course Title

Information Theory and Coding

Learning Mode

Lectures

Learning Objectives

After learning this course, the students will be able

1. to know about the fundamental concepts of information theory

2. to understand coding, quantification, storage, and communication of information

3. to analyse source coding and channel coding

4. to get familiar with the basics of the error control coding

Course Description

This course deals with the Information theory and coding.

Course Outline

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. Maximum entropy distributions for continuous and discrete cases.

Error Control Coding: Introduction, Forward & Backward error Correction, Hamming Weight and Hamming Distance, Hamming Codes, Linear Block Codes, Encoding and decoding of Linear Block-codes, Parity Check Matrix, Syndrome Decoding.

Convolutional and Turbo Codes: Introduction, Polynomial description of Convolutional Codes, Generating function, Matrix description of Convolutional Codes, Viterbi Decoding of Convolutional codes, Turbo Codes, Turbo Encoder and Decoder, LDPC, Encoder and Decoder of LDPC.

Learning Outcome

Complies with PLO 1b, 2a and 4a

Assessment Method

Quiz, Assignments, and Exams

Suggested Readings

Text Books:
1. Joy A. Thomas and Thomas M. Cover, Elements of Information Theory, 2nd Edition, 2006, John Wiley & Sons.
2. Shu Lin and Daniel Costello, Error Control Coding: Fundamentals and Applications, 2nd Edition, 2004, Pearson.

References Books:
1. R. Bose, Information Theory and applications, 3rd Edition, 2017, McGraw Hill Education India Private Limited
2. San Ling and Chaoping Xing, Coding Theory: A First Course, 2004, Cambridge University Press.
3. Todd K. Moon, Error Correction Coding: Mathematical Methods and Algorithms, 1st Edition, 2005, John Wiley & Sons.

Course Number

EC5102

Course Credit (L-T-P-C)

3-0-2-4

Course Title

Advanced Digital Signal Processing

Learning Mode

Lectures and Practical

Learning Objectives

After learning this course, the students will be able

1. to recognize some of the most important advanced signal processing techniques, including multirate processing and its applications.

2. to use time-frequency analysis techniques for audio signals and its applications.

3. to explain the relationship between time and frequency domain interpretations and implementations of signal processing algorithms.

4. to describe fundamental statistical signal processing concepts of signal detection and parameter estimation.

Course Description

This course deals with the Advanced Digital Signal Processing.

Course Outline

Multi-rate Digital Signal Processing: Brief review of digital signal processing, discrete cosine transforms (DCT), Multi-rate digital signal processing, Sampling rate conversion of bandpass signals, Filter design and implementation for sampling rate conversion, Multistage implementation of sampling rate conversion, Applications of multi-rate signal processing.

Time-Frequency Methods: STFT, Wigner-Ville transformation, Introduction to wavelets, The Haar wavelet, The Haar multi-resolution analysis, The Haar filter bank, Haar bank filter bank analysis and synthesis, Z-domain analysis of multi-rate filter bank, Two channel filter bank, Perfect reconstruction, Brief introduction to Daubechies wavelets.

The uncertainty principle, Spectral Estimation and Linear Prediction: Non-parametric spectrum estimation, Periodogram, Bartlett, Welch and Blackman, Tukey methods, Parametric methods for rational spectra (ARMA, MA, AR processes) and line spectra, Autocorrelation and model parameters.

Linear prediction, Forward and backward linear prediction, Solution of the normal equations, Properties of linear prediction-error Filter, AR lattice and ARMA Lattice-Ladder filters, AR (Auto-Regressive) process and linear prediction, Yule-Walker, Burg and Least squares methods, Sequential estimation, Moving average (MA) and ARMA models.

Subspace algorithms - MUSIC, ESPRIT, Root-MUSIC, Minimum variance method, Piscaranko’s harmonic decomposition methods.

Experiments:
1. Filtering for Audio/Speech/Biomedical Signals and Image, real-time speech/audio/colored signal processing.
2. Applications of sampling rate conversion
3. AR, MA, ARMA Process
4. Linear prediction (fixed and adaptive)
5. Prediction Error Filter for decorrelation applications
6. System/Plant Identification
7. Noise Cancellation
8. Real-time experiment using dSPACE/Microlabbox

Learning Outcome

Complies with PLO 1b, 2a and 4a

Assessment Method

Quiz, Assignments, and Exams

Suggested Readings

Text Books:
1. J. G. Proakis and D. G. Manolakis, Digital Signal Processing Principles, Algorithms and Application, 4th Edition, 2007, Pearson Education India.
2. A. V. Openheim and R. W. Schafer, Discrete Time Signal Processing, 3rd Edition, 2009, Pearson.
3. P. Stoica and R. Moses, Spectral Analysis of Signals, 1st Edition, 2005, Prentice Hall.

Reference Books:
1. P. Sircar, Mathematical Aspects of Signal Processing, 1st Edition, 2016, Cambridge University Press.
2. P. P. Vaidyanathan, Signals, Systems, and Signal Processing, 1st Edition, 2024, Cambridge University Press.
3. L. R. Rabiner and B. Gold, Theory and Application of Digital Signal Processing, 1st Edition, 1975, Prentice Hall.

Course Number

EC5103

Course Credit (L-T-P-C)

3-0-2-4

Course Title

Antenna and Microwave Devices

Learning Mode

Lectures and Practical

Learning Objectives

Course Learning Outcome (CLO): After successful completion of this course, the students will

1. Understand the basic principles of electromagnetic theory as they apply to antennas and microwave devices.

2. Define and explain key antenna parameters such as gain, directivity, efficiency, bandwidth, and polarization.

3. Describe the function and characteristics of various microwave components, including passive and active devices.

4. Use simulation software (e.g., HFSS, CST, ADS) for modeling and optimizing antennas and microwave systems.

5. Solve complex engineering problems related to antenna and microwave device design, considering practical constraints and requirements.

Course Description

This course deals with the Antenna and Microwave Devices.

Course Outline

Maxwell Equations, Generation and Propagation of EM Waves in Guided and Unguided Media, Transmission Lines, Microstrip Lines, RF Fabrication Techniques. Network Parameters, High Frequency Network Parameters, Scattering Parameters, Smith Chart Concepts, Impedance Matching, matching using stubs, Microstrip Line Designing and Characterization. Microstrip Filter Design by Insertion Loss Method, Introduction to microwave devices and systems, Microwave link budget analysis.

Radiation principle of EM wave, boundary conditions, antenna parameters, polarization of waves.

Antenna parameters: gain, directivity, efficiency, bandwidth, and polarization.

Antenna types: dipole, monopole, loop, patch, and array antennas, Antenna impedance and matching techniques. Antenna synthesis, Antenna analysis.

Microwave source: Klystron, Magnetron, Travelling Wave Tube, Backward Wave Oscillator, IMPATT Diode, TRAPATT Diode, GUNN Diode, Schottky/Esaki Diode.

Experiments:
1. Designing, development and characterisation of
• Microstrip line
• Low pass filter
• Coupled line filters
• Power dividers
• Microstrip patch antenna
2. Measurement of scattering parameters of two-port network

Learning Outcome

Complies with PLO 1b, 2a and 4a

Assessment Method

Quiz, Assignments, and Exams

Suggested Readings

Text Books:
1. David M. Pozar, “Microwave Engineering”, 4th Edition, 2011, Wiley.
2. Robert E. Collin, “Foundations for Microwave Engineering”, 2nd Edition, 2007, Wiley.

Reference Books:
1. Constantine A. Balanis, “Antenna Theory: Analysis and Design", 3rd Edition, 2009, Wiley.
2. Samuel Y. Liao, “Microwave devices and circuits”. 3rd Edition, 2003, Pearson Education India.
3. Mongia, R. K., Hong, J., Bhartia, P., & Bahl, I. J., “RF and microwave coupled-line circuits” 2007, Artech house, 2007.
4. Stephen A. Maas, “The RF and microwave circuit design cookbook”, 1998, Artech House.

Course Number

EC5201

Course Credit (L-T-P-C)

3-0-2-4

Course Title

Wireless Communication Systems

Learning Mode

Lectures and Practical

Learning Objectives

After learning this course, the students will be able:

1. to understand the concepts of the cellular architecture.

2. to understand the basics of radio propagation.

3. to develop mathematical skills to model communication system.

4. to understand the challenges associated with data transmission over wireless medium.

5. to get familiar with the mitigation strategies to achieve reliable communication over wireless medium.

Course Description

This course deals with the wireless communication system.

Course Outline

Random Signal Theory: Joint Probability, Statistical independence, Cumulative Distribution function and Probability Density function, Error function, Rayleigh and Gaussian Probability Density, Stationary and Ergodic Process, Power Spectral Density of digital data.

Propagation & Fading: Free-space propagation model, Outdoor propagation models (Okumura model & Hata model), Indoor propagation model, Shadow fading and outage analysis, Multipath fading, time dispersive and frequency dispersive channels, delay spread and coherence bandwidth, LCR and ADF.

Multiple-Input Multiple-Output (MIMO): Zero Forcing Receiver, MIMO MMSE Receiver, SVD and MIMO Capacity, Asymptotic MIMO Capacity, Alamouti and Space-time Codes, OSTBC and V-Blast Receivers. MIMO-OFDM.

Evolution of wireless technologies: 1G to 5G and Beyond.

Code Division Multiple Access (CDMA): Spreading Codes, Pseudo-Noise (PN) Sequence, Multi-Users CDMA, Near-Far Problem, Power Control and Advantages of CDMA.

Orthogonal Frequency-Division Multiplexing (OFDM): Overview of Multicarrier Communications, Cyclic Prefix, Bit Error Rate, Frequency Offset, Peak-to-Average Power Ratio (PAPR), and SC-FDMA.

Experiments:

1. Simulation in Wireless Communication Using MATLAB

2. Effect of fading on the performance of M-PSK and M-QAM modulation.

3. Effect of fading on the capacity of additive white Gaussian noise (AWGN) channel.

4. Implementation of optimal power allocation Water-Filling Algorithm.

5. Implementation of diversity techniques to mitigate the effect of multipath fading

6. MIMO receiver design

7. Implementation of BPSK modulation using Software Defined Radio (SDR)

8. Characterization of small-scale fading using SDR

Learning Outcome

Complies with PLO 1b, 2a and 4a

Assessment Method

Quiz, Assignments, and Exams

Suggested Readings

Text Books:
1. A. Goldsmith, Wireless Communications, 2009, Cambridge University Press.
2. D. Tse, P. Viswanath, Fundamentals of Wireless Communications, 2005, Cambridge Press.

Reference Books:
1. G. L. Stuber, Principles of Mobile Communication, 1996, Kluwer Acdemic.
2. J. G. Proakis, Digital Communications, 1995, McGraw-Hill.
3. T. S. Rappaport, Wireless Communications: Principles and Practice, 1996, Prentice Hall.
4. A. J. Viterbi, CDMA Systems: Principles of Spread Spectrum Communication, 1995, Addison Wesley.

Course Number

EC5202

Course Credit (L-T-P-C)

3-0-2-4

Course Title

Advanced Communication Systems

Learning Mode

Lectures and Practical

Learning Objectives

After learning this course, the students will be able:

1. to understand different modulation schemes and their application to the real world.

2. to understand the various degradations caused at the receiver side.

3. to design the optimal receivers and their performance evaluation.

4. to understand the basics of channel estimation.

Course Description

This course deals with the Advanced Communication 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

Receiver Synchronization: Signal Parameter Estimation, Carrier Phase Estimation, Symbol Timing Estimation, Joint Estimation of Carrier Phase and Symbol Timing

Channel Estimation and Equalization: Zero-Forcing Algorithm, Least-Mean-Square (LMS) Algorithm, Recursive Least Square Algorithms, Linear and Decision Feedback Equalization, Channel Impulse Response, Pilot Symbol, Non-Data-aided and Data-aided Channel Estimation.

Introduction to Quantum Communications, Aerial Communications, Haptics Communications, and Holographic Communications.

Experiments:

• Simulation-based experiment
  • Different modulation formats, signal generation
  • Noise generation : white and color
  • BER and SER analysis of different modulation format
  • Design of digital receivers: ZF, Matched
  • Realization of different channels: wireless, optical, wired
• Hardware Kit/SDR-based modulation format generation
• Hardware Kit/SDR-based signal reception

Learning Outcome

Complies with PLO 1b, 2a and 4a

Assessment Method

Quiz, Assignments, and Exams

Suggested Readings

Text Books:
1. A. Goldsmith, Wireless Communications, 2009, Cambridge University Press.
2. D. Tse, P. Viswanath, Fundamentals of Wireless Communications, 2005, Cambridge Press.

Reference Books:
1. G. L. Stuber, Principles of Mobile Communication, 1996, Kluwer Acdemic.
2. J. G. Proakis, Digital Communications, 1995, McGraw-Hill.
3. T. S. Rappaport, Wireless Communications: Principles and Practice, 1996, Prentice Hall.
4. A. J. Viterbi, CDMA Systems: Principles of Spread Spectrum Communication, 1995, Addison Wesley.

Course Number

EE6107

Course Credit (L-T-P-C)

3-0-0-3

Course Title

Renewable Energy Sources

Learning Mode

Lectures

Learning Objectives

Complies with Program Goals 1 and 2

Course Description

The course is designed to meet the requirements of M. Tech. The course aims at giving a broad overview of various Renewable Energy Sources.

Course Outline

General Overview of electricity demand and supply, and industry structure: Vertically integrated electricity supply industry, Restructuring, Electric energy management in restructured environment, Electricity markets.

Distributed generation technologies for increased efficiency: Distributed generation technologies, Integration issues, Future network architectures with DGs, Microgrids, Economics of distributed resources.

Wind turbine generation systems: Types, Power in the wind, Impact of tower height, Rotor efficiency, Wind turbine generators, Speed control, Performance of grid connected WTG, Economics, Environmental impacts.

Solar resources and photovoltaic (PV) systems: Solar spectrum, Insolation measurement, Photovoltaic systems and its engineering aspects, Standalone and grid connected PV systems.

Other renewable energy sources: Elementary concepts of fuel cell, Biomass, Tidal energy, Microturbines and their analysis for engineering application.

Energy Storage: Lead acid batteries, Ultra capacitors, Fly wheels, Superconducting magnetic storage, Pumped hydro electric storage, Compressed air energy storage.

Demand side management: Application of smart devices, Distribution automation, Demand Optimization.

Learning Outcomes

Complies with PLOs 1a, 2a, and 3a

Assessment Method

Quizzes/Assignments, Mid Sem, and End Sem

Suggested Reading

Texts:
1. N. Jenkins, J.B. Ekanayake, G. Strbac, Distributed Generation, IET, Renewable Energy Series, 2010
2. Gilbert M. Masters, Renewable and Efficient Electric Power Systems, Wiley, 2004.

References:
1. A. Keyhani, M.N. Marwali, Integration of Green and Renewable Energy in Electric Power Systems; Wiley, 2010.
2. F.A. Farret, M. Godoy Simoes, Integration of Alternative Sources of Energy; IEEE Press, 2006.
3. L. Freris, D. Infield, Renewable Energy in Power Systems; Wiley, 2008.
4. D. Pimentel, Biofuels, Solar and Wind as Renewable Energy Systems; Springer, 2008.
5. P. A. Rizzi, Wind and Solar Power Systems: Design, Analysis and Operation; 2/e, Taylor & Francis, 2006.

Course Number

EC5107

Course Credit (L-T-P-C)

3-0-0-3

Course Title

RF and Microwave Active Circuits

Learning Mode

Lectures

Learning Objectives

1. Proficiency in Active Circuit Design and Analysis: Students will develop proficiency in analyzing and designing active circuits commonly used in RF and microwave applications, including amplifiers, oscillators, mixers, and modulators.

2. Knowledge of RF Transceiver Architecture: Students will gain insight into RF transceiver architectures used in communication systems, including receiver front-end design, transmitter driver stages, frequency synthesis, and frequency conversion.

Course Description

This course deals with the RF Microwave Active Circuits.

Course Outline

Network Parameters, High Frequency Network Parameters, Scattering Parameters, Signal Flow Graphs, Smith Chart Concepts, Impedance Matching, Microstrip Line Designing and Characterization. Microstrip Design- Filter Design by Insertion Loss Method, Fundamentals of Power Divider and Branch Line Coupler. Small Signal Amplifiers- low noise, maximum gain, stability, Broad band amplifiers- matching circuits, travelling wave amplifiers. Power Amplifiers- Efficiency, CAD, device modeling, measurement. Mixers- Single ended, balanced, double balanced, different configurations for microstrip, waveguide etc., noise properties, simulation using harmonic balance.

Learning Outcome

Complies with PLO 1b, 2a and 4a

Assessment Method

Quiz, Assignments, and Exams

Suggested Readings

Text Books:
1. David M. Pozar, “Microwave Engineering”, Wiley, 4th Edition.
2. Robert E. Collin, “Foundations for Microwave Engineering”, Wiley, 2nd Edition.

Reference Books:
1. Liao, Samuel Y. Microwave devices and circuits. Pearson Education India, 1989.
2. Mongia, R. K., Hong, J., Bhartia, P., & Bahl, I. J., “RF and microwave coupled-line circuits” Artech house, 2007.
3. Maas, Stephen A., “The RF and microwave circuit design cookbook” Artech House, 1998.
4. Gilmore, Rowan, and Les Besser, “Practical RF Circuit Design for Modern Wireless Systems: Active Circuits and Systems”, Volume 2. Vol. 1. Artech House, 2003.
5. Howe, Harlan. Stripline circuit design. Dedham, MA: Artech House, 1974

Course Number

EC5108

Course Credit (L-T-P-C)

3-0-0-3

Course Title

Internet of Things (IoT) Networks

Learning Mode

Lectures

Learning Objectives

After the successful completion of the course the students will be able to:

1. Fundamentally understand the building blocks of the Internet of Things networks.

2. Learning and implementation of various algorithms in the course

3. Design an IoT based network for a given application

Course Description

This course deals with the IoT Networks.

Course Outline

Things and Internet: Overview of Things/Sensors, Smart Object, Open Systems Interconnection (OSI) Model, Transmission Control Protocol/Internet Protocol (TCP/IP) Model, IPv4 & IPv6 Addressing, Routing, Delays in the Networks

Markov Chain: Discrete-Time Markov Chain, Continuous-Time Markov Chain, Types of Markov Chain, Time Reversed Markov Chain, Applications

Queuing Theory: Little’s Formula, Queuing Model - Single and Multiple Servers (M/M/1, M/M/c), Finite and Infinite Queue Length/Population, M/G/1 model, Burke’s Theorem, Jackson’s Networks

IoT Protocols: Short-Range and Long Range Protocols - Bluetooth, Zigbee, Wireless LAN, and LoRaWAN. Application Protocols.

Smart Device Localization: Range-based Localization using Received Signal Strength (RSS), Time and Angle Measurements. Range-Free Localization. Performance Metrics. Applications

IoT Analytics: Bayesian Fusion, Dempster-Shafer Fusion, Decision Fusion, Types of Learning. Artificial Neural Networks, Bias–Variance Tradeoff, Applications

Fog Computing: Cloud Computing, Fog Computing, Edge Computing, Technology for Fog Computing, Task Offloading, Applications

Learning Outcome

Complies with PLO 1b, 2a and 4a

Assessment Method

Quiz, Assignments, and Exams

Suggested Readings

Texts Books:
1. Rayes, A., and Salam, S., Internet of Things from hype to reality: the road to digitization, 2nd Edition, 2016, Springer International Publishing.
2. Kumar S., Fundamentals of Internet of Things, 1st Edition, 2021, Chapman and Hall/CRC.
3. Bertsekas, D.P., and Gallager, R. G., Data Networks, 2nd Edition, 2021, Athena Scientific.

Reference Books:
1. Raj, P., and Raman, A.C., The Internet of Things: Enabling technologies, platforms, and use cases, 1st Edition, 2017, Auerbach Publications.

Course Number

EC5109

Course Credit (L-T-P-C)

3-0-0-3

Course Title

Radio Frequency Integrated Circuits

Learning Mode

Lectures

Learning Objectives

After studying this course, the students will be able to:

1. Analyse a wireless transceiver and its various sub components like LNAs, Mixers, VCOs, RF Power amplifiers.

2. Design the transceivers for wireless communication. Additionally, they will be able to design many standalone circuits like Low noise and power amplifiers, Oscillators and Frequency synthesizers, PLL.

Course Description

This course deals with the RFIC.

Course Outline

Prerequisite: Basic Electronics and Basic Electromagnetic Engineering.

Introduction: Basic concepts in RFIC Design – 1dB point and IIP3; Receiver and Transmitter Architectures.

Low Noise RF Amplifiers (LNAs) – Electrical Noises, Two port Noise theory, LNA characteristic parameters and basic topologies, Input impedance and Noise Factors of various LNAs, Differential and Broadband Amplifier, Stability;

Mixers – Mixer Operation and linearity, Passive and Active Mixers, Single & Double-Balanced Mixers, Conversion Gain and Port-to-Port Feedthrough (or leakage), Image Reject and Single Sideband Mixers, Noise in Mixers;

Oscillators – Oscillator as a Feedback System, Negative Resistance Oscillator, Colpitts, Hartley, Clapp, Pierce crystal Oscillators, Quadrature Oscillators, Voltage Controlled-Oscillator, Phase Noise in Oscillators;

Frequency Synthesizers – Phase Locked Loop (PLL), Analysis of PLL Synthesizers blocks – PD, PFD, Charge Pump, Phase Noise in PLL Synthesis, PLL Frequency Synthesizers, Integer-N and Fractional-N PLL Synthesizers, PLL System of second order, its frequency response, bandwidth, Designing a PLL of 2nd order;

RF Power Amplifiers – Efficiency, Analysis of Basic Classes – A, AB, B, C, Class B Push-Pull Arrangements, Switch mode Classes – D, E, F Amplifiers, Doherty Power Amplifier, Linearization Techniques.

Learning Outcome

Complies with PLO 1b, 2a and 4a

Assessment Method

Quiz, Assignments, and Exams

Suggested Readings

Text Books:
1. Thomas H Lee, The Design of CMOS Radio Frequency Integrated Circuits, 2/e, 2003, Cambridge University Press
2. Behzad Razavi, RF Microelectronics, 2/e, 2012, Pearson India
3. Steven Cripps, RF Power amplifier for wireless communications, 2/e, 2006, Artech House
4. Herbert Krauss, Charles Bostian, and Frederick Raab, Solid state radio engineering, 1/e, 1980, John Wiley and Sons
5. Andrei Grebennikov, Marc J. Franco, Switchmode RF and Microwave Power Amplifiers, 3/e, 2021, Academic Press

References:
1. Richard C-H Li, RF Circuit Design, 2/e, 2012, John Wiley
2. Ronald E Best, Phase-locked Loops, 6/e, 2007, McGraw Hill
3. John W M Rogers and Calvin Plett, Radio Frequency Circuit Design, 2/e, 2010, Artech House
4. Les Besser and Rowan Gilmore, Practical RF Circuit Design for Modern Wireless Systems, vol. 2, 1/e, 2003, Artech House

Course Number

EC5110

Course Credit (L-T-P-C)

3-0-0-3

Course Title

Advanced Digital Image Processing

Learning Mode

Lectures

Learning Objectives

Complies with Program Goals 1 and 2

Course Description

Advanced Digital Image Processing involves the manipulation and analysis of digital images using computational algorithms. The course covers topics such as image enhancement, restoration, segmentation, feature extraction, and compression. It also includes applications in fields such as medical imaging, remote sensing, robotics, and multimedia systems.

Course Outline

DIGITAL IMAGE FUNDAMENTALS: Elements of Visual Perception; Image Sensing and Acquisition; Image Sampling and Quantization; Basic Relationships between Pixels; Monochromatic Vision Models; Colour Vision Models; Colour Fundamentals; Colour Models; Conversion of Colour Models; Colour Transformations.

ENHANCEMENT & RESTORATION : Homomorphic filtering, inverse and minimum error filtering, Noise types and related filtering.

IMAGE ANALYSIS AND REPRESENATION: Introduction; Image Segmentation - Point, Line, Edge, Boundary Detection; Colour Image Segmentation; Thresholding- Basic Global Thresholding, Multiple Thresholding, Variable Thresholding; Region Based Segmentation; Representation: Chain codes, Signatures, Boundary segments, Skeletons, Description: Boundary Descriptors, Regional Descriptors.

MORPHOLOGICAL PROCESSING & COMPRESSION: Morphological Image Processing – Logic Operations involving Binary Images; Dilation and Erosion; Basic Morphological Algorithms – Boundary Extraction, Region Filling, Thickening

Image Compression – Compression Model, Different Coding schemes like Arithmetic Coding, LZW coding etc. Baseline jpeg, jpeg 2000, Mpeg etc.

CLASSIFICATION AND APPLICATIONS of Object Recognition and Classification, Statistical classification, Structural /Syntactic Classification, 3D Image Processing, 3D Visualization: Surface rendering, Volume rendering;

Applications: Motion Analysis, Image Fusion, Image super resolution

Learning Outcome

Complies with PLOs 1a, 1b, 2 and 3a

Assessment Method

Quizzes/Assignments, Mid Sem, and End Sem

Suggested Readings

Text/References:
1. Rafael C. Gonzalez and Richard E. Woods, Digital Image Processing, Pearson
2. Milan Sonka, Vaclav Hlavac and Roger Boyle, Image Processing, Analysis and Machine Vision, Springer
3. Anil K. Jain, Fundamentals of Digital Image Processing, Prentice Hall

Course Number

EC6101

Course Credit (L-T-P-C)

3-0-0-3

Course Title

Advance Antenna and Microwave Devices

Learning Mode

Lectures

Learning Objectives

After successful completion of this course, the students will

1. Understand the basic principles of electromagnetic theory as they apply to antennas and microwave devices.

2. Define and explain key antenna parameters such as gain, directivity, efficiency, bandwidth, and polarization.

3. Describe the function and characteristics of various microwave components, including passive and active devices.

4. Use simulation software (e.g., HFSS, CST, ADS) for modeling and optimizing antennas and microwave systems.

5. Solve complex engineering problems related to antenna and microwave device design, considering practical constraints and requirements.

Course Description

This course deals with the Advance antenna and devices.

Course Outline

Pre-requisites: Engineering Electromagnetics

Microwave Network Theory, Equations, Wave Propagation, Medium and free space, Transmission Lines, Microstrip Lines, RF Fabrication Techniques. High power microwave devices, two port, three port and 4 port microwave devices, design analysis and fabrication. Microwave link budget analysis.

Antenna principle and Antenna Types, Designs and analysis, Antenna Simulation and Measurement techniques, Antenna optimizations, boundary conditions, antenna parameters, Impedance matching and modal analysis, equivalent circuit analysis. Antenna synthesis, Antenna analysis.

Advance Antennas- Transmitarray antennas, reflector antennas, lens antennas, slot antennas, Leaky wave antenna, reconfigurable antennas.

Metamaterial inspired antennas, wave propagation in metamaterial, design and analysis of metamaterial, frequency selective surfaces, EBG Structures, metasurfaces, reconfigurable metasurfaces.

Selected advanced topics in Antennas and Microwave Technology.

Learning Outcome

Complies with PLO 1b, 2a and 4a

Assessment Method

Quiz, Assignments, and Exams

Suggested Readings

Text Books:
1. David M. Pozar, “Microwave Engineering”, 4th Edition, 2011, Wiley.
2. Robert E. Collin, “Foundations for Microwave Engineering”, 2nd Edition, 2007, Wiley.
3. Constantine A. Balanis, “Antenna Theory: Analysis and Design", 3rd Edition, 2009, Wiley.

Reference Books:
1. Samuel Y. Liao, “Microwave devices and circuits”. 3rd Edition, 2003, Pearson Education India.
2. Mongia, R. K., Hong, J., Bhartia, P., & Bahl, I. J., “RF and microwave coupled-line circuits” 2007, Artech house, 2007.
3. Stephen A. Maas, “The RF and microwave circuit design cookbook”, 1998, Artech House.

Course Number

EC5111

Course Credit (L-T-P-C)

3-0-0-3

Course Title

VLSI Architectural Design and Implementation

Learning Mode

Lectures

Learning Objectives

Complies with Program Goals 1 and 2

Course Description

VLSI Architectural Design and Implementation covers the principles of designing and implementing efficient VLSI architectures. The course includes topics such as pipeline design, data path optimization, and hardware description languages.

Course Outline

Introduction to VLSI System Design and Implementation; Architectural mapping with case studies: Data path, Control path Synthesis; Control Strategies: Hardware implementation of various control structures; Micro-program control techniques; Design issues: Timing, Area, power analysis; FSM Architecture and Synthesis, HDL design and implementation of VLSI architecture;

Semiconductor Memory and Peripheral Architectures; Computer arithmetic architecture design and analysis: Introduction to integer and floating-point arithmetic, Adders, Subtractors, Sequential and Array multipliers & dividers, square root, Absolute Difference Value, CORDIC.

Hardware architecture design and performance analysis: Sequential/Folding architectures; bit and word serial architecture; High performance architectures: pipelined, parallel and Systolic Array with examples; Architectural performance Analysis: Throughput and Latency; Low Power VLSI Architectures; Basic Hardware Architectures for Digital Signal processing and machine learning algorithms.

Introduction to VLSI Chip testing methods and Architectures: Introduction to Chip Fault Model, DFT Architecture, BIST Architecture.

Learning Outcomes

Complies with PLOs 1a, 1b, 2 and 3a

Assessment Method

Quizzes/Assignments, Mid Sem, and End Sem

Suggested Readings

Text/References:
1. Peter Pirsch, "Architectures for Digital Signal Processing", John Willy & sons,2nd Edition,2014.
2. K. K. Parhi, " VLSI Digital Signal Processing Systems: Design and Implementation", A Wiley-Interscience publications,2011.
3. Behrooz Parhami, " Computer Arithmetic: Algorithm and Hardware Design", Behrooz Parhami, Oxford University Press, 2nd Edition,2009.
4. A. Bellaouar, M. I. Elmarsny, "Low Power Digital VLSI Design", A. Bellaouar, M. I. Elmarsny, Kluwe academic Publication,1995.
5. DSP Integrated Circuit, L. Wamhammer, Academic Press,1999.

Course Number

EC5112

Course Credit (L-T-P-C)

3-0-0-3

Course Title

Bio Sensors and Circuits

Learning Mode

Lectures

Learning Objectives

Complies with Program Goals 1 and 2

Course Description

Bio Sensors and Circuits focus on the design and implementation of circuits and systems for biological sensing applications. The course covers topics such as sensor technologies (e.g., optical, electrochemical), signal conditioning, data acquisition, and integration with biological systems for healthcare monitoring, environmental sensing, and biomedical research.

Course Outline

Transducers Principles, Biochemical Transducers: Electrode theory, electrode impedance, metal-electrolyte interface and electrode-tissue interface, Bio-potential electrodes: microelectrodes, body surface electrodes, needle electrodes, electrodes for ECG, EEG, and EMG. Electrodes: hydrogen electrodes, Ag/AgCl electrodes, Calomel electrodes, specific ion electrodes, pH electrode, O2 and CO2 electrode, Optical Sensor and Radiation Detectors: Principles of optical sensors and types of optical sensors, Optical fibers, LASERs, Radiation detectors: Proportional counter, Gas-ionization chamber, Geiger counters, Scintillation detectors., Biological Sensors: Receptors in the human body, Ion exchange membrane electrodes, enzymatic biosensors, Basic principles of MOSFET biosensors & BIOMEMS, basic idea about Smart sensors, Biomedical Measurement

Learning Outcomes

Complies with PLOs 1a, 1b, 2 and 3a

Assessment Method

Quizzes/Assignments, Mid Sem, and End Sem

Suggested Readings

Text/References:
1. Josheph J. Carr and John M. Brown, “Introduction to Biomedical Equipment Technology”, 4th Edition, Pearson Education, 2001.
2. John. G. Webster, “Medical Instrumentation- Application and Design”, 4th Edition, John Wiley & Sons, 2010.
3. Willis J. Tompkins, “Biomedical Digital Signal Processing” Prentice-Hall of India, 1993.
4. Rangraj M. Rangayyan, “Biomedical Signal analysis- A Case Study Approach”, Wiley India Pvt. Ltd., 2009.
5. Suresh R. Devashahayan, “Signals and Systems in Biomedical Engineering”, Revised 2nd Edition, Kluwer academics/ Plenum publication, 2013.
6. Josheph J. Carr and John M. Brown, “Introduction to Biomedical Equipment Technology”,4th Edition, Prentice Hall, 2000.
7. Leslie Cromwell, Fred J. Weibell, and Erich A. Pfeiffer “Biomedical Instrumentation and Measurements”, 2nd Edition, Prentice-Hall of India, 2000.

Course Number

EC5113

Course Credit (L-T-P-C)

3-0-0-3

Course Title

Quantum Computing

Learning Mode

Lectures

Learning Objectives

1. Analyze and explain the key components and architecture of quantum computing systems, including qubits, quantum gates, and quantum circuits.

2. Develop the ability to implement and analyze quantum algorithms, such as Shor's algorithm for factoring and Grover's algorithm for search problems.

3. Evaluate and apply quantum error correction techniques to mitigate the impact of errors in quantum computations.

4. Investigate and comprehend quantum communication protocols, including quantum key distribution (QKD) and teleportation, and their applications in secure communication.

5. Comprehend the principles of quantum information theory, including quantum entanglement, quantum entropy, and quantum teleportation.

Course Description

This course deals with the Quantum Computing.

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 Circuits: Single qubits and multiple quibits operations, architecture, quantum gates; quantum teleportation, quantum Fourier Transform: phase estimation

Quantum Algorithms: Deutsch’s algorithm, Deutsch-Jozsa algorithm, Simon’s algorithm, Grover algorithm and Shor’s factoring algorithm

Quantum Information and Error Corrections: Classical vs quantum noise, quantum operations, quantum error correction, entropy and information

Quantum Tools and Applications: Goal Challenges, Lights and Photon, Decoherence, Ion Trap, Linear Optics, NMR, Quantum Simulation

Learning Outcome

Complies with PLO 1b, 2a and 4a

Assessment Method

Quiz, Assignments, and Exams

Suggested Readings

Text Books:
1. Nielsen, Michael A., and Isaac L. Chuang. Quantum computation and quantum information. Cambridge university press, 2010.
2. Johnston, Eric R., Nic Harrigan, and Mercedes Gimeno-Segovia. Programming quantum computers: essential algorithms and code samples. O'Reilly Media, 2019.

Course Number

EC6102

Course Credit (L-T-P-C)

3-0-0-3

Course Title

Computer Vision

Learning Mode

Lectures

Learning Objectives

1. Understanding geometric transformations, Learning how to extract 3D information from 2D images.

2. Understanding knowledge of edge enhancement, filtering and stereo graphics projection etc.

3. Gaining hands-on experience with image segmentation useful for higher order image analysis and understanding.

4. Different types of clustering, object detection etc. Neural network fundamentals for AI applications etc.

Course Description

This course deals with the Computer Vision.

Course Outline

Computer vision introduction, image formation, perspective projection, camera response & HDR imaging, nature of image sensors, image filtering, template matching, Fourier transform, convolution and deconvolution, edge and corner detection, canny edge detection, Active contours, Hough transform, SIFT detector and descriptor, Image homography, Image warping and image blending, Face detection, nearest neighbor classifier, support vector machine, Radiometry and reflectance property, Photometric stereo, reflectance map, shape from normal, shape from shading, stereographic projection, shading illusion, dept from focus and depth from defocus, photometric stereo systems, camera calibration, simple stereo, uncalibrated stereo, epipolar geometry, stereo vision in nature, optical flow, Lucas Kanade method, structure from motion, object tracking, gaussian mixture model, feature detection for tracking, image segmentation by k-means, mean-shift and graph cut based methods. PCA and SVD and shape verses appearance. Neural network, Gradient descent, back propagation algorithm.

Learning Outcome

Complies with PLO 1b, 2a and 4a

Assessment Method

Quiz, Assignments, and Exams

Suggested Readings

Text Books:
1. Computer Vision - A modern approach, by D. Forsyth and J. Ponce, Prentice Hall Robot Vision, by B. K. P. Horn, McGraw-Hill.

Reference Books:
1. Computer Vision: Algorithms and Applications: Richard Szeliski, 2011, Springer.
2. Antonio Torralba, Phillip Isola, and William T. Freeman, “Foundations of Computer Vision”, 2024, The MIT Press.

Course Number

EC6103

Course Credit (L-T-P-C)

3-0-0-3

Course Title

Radio Frequency Design and Technology

Learning Mode

Lectures

Learning Objectives

The students will be able to learn the design of microwave coupler and power dividers, filters and their implementation, microwave amplifiers, active microwave devices, oscillators and mixers. A slight introduction of network analysis is covered. It also highlights the distortions caused by the noise in microwave circuits. Microwave systems are also discussed.

Course Description

This course deals with the RFDT.

Course Outline

Planar Transmission Lines and Lumped Elements for MICs: Fundamentals of the theory of transmission lines, Foundations of Microstrip lines, Striplines, Higher modes in microstrips and striplines, Slotlines, Coplanar waveguides. Microstrip Transmission line, propagation module, Scattering parameters.

Microwave Planar Filters: Periodic structures, Filter design by the Image Parameter method, Filter design by the Insertion Loss method, Filter transformations, Filter implementation, Stepped-Impedance Low-Pass filters, Coupled line filters, Filters using coupled resonators.

3-Port Network Design: Power Divider network.

4-Port Network Design: Introduction; Even-and odd-mode analysis; Branch-line couple, Branch-line coupler with improved coupling performance, Branch-line coupler with multiple sections.

Measurement Fundamentals, VNA, Spectrum analyzer, and techniques.

Software defined Radio, design and analysis. Radar systems, graphene, and metamaterials.

Learning Outcome

Complies with PLO 1b, 2a and 4a

Assessment Method

Quiz, Assignments, and Exams

Suggested Readings

Text Books:
1. David M. Pozar, Microwave Engineering, Wiley India Private Limited; Fourth edition (14 May 2013).
2. C. A. Balanis: Antenna Theory: Analysis and Design, John Wiley, 2005, 3/e.

Reference Books:
1. R. E. Collin, Foundations for Microwave Engineering, Wiley-Blackwell; 2nd Edition
2. D. M. Sullivan: Electromagnetic Simulation using the FDTD Method, Wiley-IEEE, 2000, 1/e.
3. B. S. Guru & H. R. Hiziroglu: Electromagnetic Field Theory Fundamentals, Thomson, 1997, 1/e

Course Number

EC6104

Course Credit (L-T-P-C)

3-0-0-3

Course Title

VLSI Signal Processing

Learning Mode

Lectures

Learning Objectives

Complies with Program Goals 1 and 2

Course Description

VLSI Signal Processing involves the design and implementation of signal processing algorithms and systems using Very-Large-Scale Integration (VLSI) technology. The course covers topics such as digital signal processing (DSP) algorithms, efficient hardware implementations, optimization techniques, and applications in areas such as telecommunications, audio processing, image processing, and biomedical signal processing.

Course Outline

Introduction to DSP systems: Representation of DSP algorithms; Iteration Bound: Definition, Examples, Algorithms for computing Iteration bound; Pipelining and Parallel Processing: Definitions, Pipelining and parallel processing of FIR filters, Pipelining and parallel processing for low power; Retiming: Definitions and Properties, Solving system of Inequalities, Retiming techniques; Unfolding: Definition, An algorithm for unfolding, Applications of unfolding; Folding: Definition, Folding transformations, Register minimization techniques, Register minimization in folded architectures; Systolic Architecture Design: Introduction, Systolic array design methodology, FIR systolic arrays, Selection of scheduling vector, Matrix-Matrix multiplication and 2D systolic array design; CORDIC based Implementations: Architecture, Implementation of FIR filter and FFT algorithm; Bit-Level arithmetic architectures: Parallel multipliers, Bit-serial multipliers, Bit-Serial FIR filter design and Implementation; Redundant arithmetic: Redundant number representation, Carry-free radix-2 addition and subtraction, radix-2 hybrid redundant multiplication architectures; Low-power design: Theoretical background, Scaling versus power consumption, Power analysis, Power reduction techniques, Power estimation approaches.

Learning Outcomes

Complies with PLOs 1a, 1b, 2 and 3a

Assessment Method

Quizzes/Assignments, Mid Sem, and End Sem

Suggested Readings

Text/References:
1. U. Meyer-Baese, “DSP with FPGA”, Springer,4th Edition, 2014.
2. K. K. Parhi, “VLSI DSP Systems”, Wiley, 2003.
3. R.G. Lyons, “Understanding Digital Signal Processing”, Pearson Education,3rd Edition, 2011.

Course Number

EC5205

Course Credit (L-T-P-C)

3-0-0-3

Course Title

Pattern Recognition and Machine Learning

Learning Mode

Lectures

Learning Objectives

Course Learning Outcome (CLO): After learning this course, the students will be able:

1. to know various tools and techniques of pattern recognition.

2. to develop skills to characterize and implement big data analytics.

3. to understand the application of pattern recognition in different real-life problems.

Course Description

This course deals with the Pattern recognition and ML.

Course Outline

Introduction: Feature extraction and Pattern Representation, Concept of Supervised and Unsupervised Classification, Introduction to Application Areas. Statistical Pattern Recognition: Bayes Decision Theory, Minimum Error and Minimum Risk Classifiers, Discriminant Function and Decision Boundary, Normal Density, Discriminant Function for Discrete Features, Parameter Estimation. Dimensionality Problem: Dimensionality Reduction, Fisher Linear Discriminant and Multiple Discriminant Analysis. Nonparametric Pattern Classification: Density Estimation, Nearest Neighbour Rule, Fuzzy Classification. Linear Discriminant Functions: Separability, Two Category and Multi Category Classification, Linear Discriminators, Perceptron Criterion, Relaxation Procedure, Minimum Square Error Criterion, Widrow-Hoff Procedure, HoKashyap Procedure, Kesler’s Construction. Neural Network Classifier: Single and Multilayer Perceptron, Back Propagation, Learning Hopfield, Network Fuzzy and Neural Network. Time Varying Pattern Recognition: First Order Hidden Markov, Model Evaluation, Decoding Learning. Unsupervised Classification: Clustering, Hierarchical Clustering, Graph Based Method, Sum of Squared Error Technique, Iterative Optimization.

Learning Outcome

Complies with PLO 1b, 2a and 4a

Assessment Method

Quiz, Assignments, and Exams

Suggested Readings

Texts/References:
1. Richard O. Duda, Peter E. Hart and David G. Stork, Pattern Classification, John Wiley & Sons, 2001.
2. Earl Gose, Richard Johsonbaugh and Steve Jost, Pattern Recognition and Image Analysis, Prentice Hall, 1999.

Course Number

EC5206

Course Credit (L-T-P-C)

3-0-0-3

Course Title

Multimedia Communication

Learning Mode

Lectures

Learning Objectives

After learning this course, the students will be able:

1. to understand the fundamental knowledge on multimedia system and Multimedia Communication.

2. to understand the knowledge on Multimedia Information Systems.

3. to understand the real-time constraints in Multimedia Communication.

4. to develop problem statement on Multimedia Communication for research direction

Course Description

This course deals with the Multimedia Communication.

Course Outline

Introduction to Multimedia System: Architecture and components, Multimedia distributed processing model, Synchronization, Orchestration and Quality of Service (QOS) architecture. Audio and Speech: Data acquisition, Sampling and Quantization, Human Speech production mechanism, Digital model of speech production, Analysis and synthesis, Psycho-acoustics, low bit rate speech compression, MPEG audio compression. Images and Video: Image acquisition and representation, Composite video signal NTSC, PAL and SECAM video standards, Bilevel image compression standards: ITU (formerly CCITT) Group III and IV standards, JPEG image compression standards, MPEG video compression standards. Multimedia Communication: Fundamentals of data communication and networking, Bandwidth requirements of different media, Real time constraints: Audio latency, Video data rate, multimedia over LAN and WAN, Multimedia conferencing. Hypermedia presentation: Authoring and Publishing, Linear and non-linear presentation, Structuring Information, Different approaches of authoring hypermedia documents, Hyper-media data models and standards. Multimedia Information Systems: Operating system support for continuous media applications: limitations is usual OS, New OS support, Media stream protocol, file system support for continuous media, data models for multimedia and hypermedia information, content based retrieval of unstructured data.

Learning Outcome

Complies with PLO 1b, 2a and 4a

Assessment Method

Quiz, Assignments, and Exams

Suggested Readings

Text Books:
1. J. D. Gibson, Multimedia Communications: Directions and Innovations, 2000, Elsevier.
2. A. Puri and T. Chen, Multimedia Systems, Standards, and Networks, 1st Edition, 2000, CRC Press.
3. Iain E.G. Richardson, H.264 and MPEG-4 Video Compression, 2004, John Wiley & Sons.

Reference Books:
1. Ralf Steinmetz and Klara Nahrstedt, Multimedia Systems, 2004, Springer.
2. K. Sayood, Introduction to Data Compression, 2017, Morgan-Kaufmann.
3. Borivoje Furht, Handbook of Multimedia Computing, 1998, CRC Press

Course Number

EC5207

Course Credit (L-T-P-C)

3-0-0-3

Course Title

Optical Communication

Learning Mode

Lectures

Learning Objectives

After learning this course, the students will be able:

1. to know the basic elements of optical fiber transmission link.

2. to know about the design and operating principle of modern optical communication systems.

3. to get familiar with various components used in optical communication systems.

4. to understand the different kind of losses, distortions, and other degradation factors caused during signal transmission and their mitigation techniques for high speed communication.

Course Description

This course deals with the Optical Communication.

Course Outline

Introduction: Fiber optic communication, Free space optical communication, Visible light communication, chip-to-chip optical communication.

Optical fiber fundamentals: Light propagation, types of optical fibers, Numerical aperture, step index fiber, graded index fiber, concept of modes, single mode fiber, multimode fiber.

Bit rate distance product in multimode fiber. Impairment in optical fiber: Loss, chromatic dispersion, Polarization mode dispersion.

Bit rate distance product in single mode fiber.

Fiber Nonlinearity: Self phase modulation, cross phase modulation, four wave mixing.

Passive optical components. Optical Sources and Detectors: LED, LD, DFB-laser, PIN photodetector, APD.

Optical Modulator. Wavelength division Multiplexing (WDM).

Modulation formats: OOK, DPSK, DQPSK, PDM-QPSK. Optical System Performance Metrics: Eye opening penalty, Q, BER, OSNR.

Link Analysis: Single channel point to point, WDM point-to-point. Optically Amplified Systems.

Learning Outcome

Complies with PLO 1b, 2a and 4a

Assessment Method

Quiz, Assignments, and Exams

Suggested Readings

Text Books:
1. G. P. Agrawal, Fiber-optic communication systems, 3rd Edition, 2007, Wiley India Pvt Ltd.
2. P Bhattacharya, Semiconductor optoelectronic devices, 2nd Edition, Phi Le

Reference Books:
1. R. Ramaswami, K. N. Sivarajan, G. H. Sasaki, Optical Networks: A Practical Perspective, 2009, Elsevier direct.
2. G. P. Agrawal, Nonlinear fiber optics, 4th Edition, Academic Press.
3. M. Cvijetic, Optical transmission systems engineering, 2004, Artech House Publishers.
4. A Gumaste, T Antony, DWDM network designs and engineering solutions, 2002, Cisco Press.

Course Number

EC6201

Course Credit (L-T-P-C)

3-0-0-3

Course Title

RF and Microwave Measurement Techniques

Learning Mode

Lectures

Learning Objectives

Throughout the course, emphasis is placed on both theoretical understanding and practical skills development to enable students to proficiently perform RF and microwave measurements in professional settings.

Course Description

This course deals with the microwave measurement.

Course Outline

Prerequisite: Engineering Electromagnetics

Network Parameters: High Frequency Network Parameters, Scattering Parameters. Measurement Fundamentals: Basics of RF parameters and terminology, impedance matching and reflection coefficient measurements, VNA & SA: Theory of operation of network analyzer, and spectrum analyzer, Basics of spectrum analysis and measurements using spectrum analyzers. VNA calibration, Calibration techniques (e.g., TRL, SOLT), synthesized signal generation, noise measurement, Measurement of antenna properties: resonance, bandwidth, radiation pattern, gain,

Learning Outcome

Complies with PLO 1b, 2a and 4a

Assessment Method

Quiz, Assignments, and Exams

Suggested Readings

Text Books:
1. Joel P. Dunsmore, Handbook of Microwave Component Measurements: With Advanced VNA Techniques, 2012 Wiley USA.
2. A. Mariscotti, RF and Microwave Measurements: Device Characterization, Signal Integrity, and Spectrum Analysis, 2015 ASTM USA.

Reference Books:
1. Inder Bahl, Kai Chang, Vijay Nair, RF and Microwave Circuit and Component Design for Wireless Systems, 2002 Wiley USA.
2. A.S. Khan, Microwave Engineering: Concepts and Fundamentals, 2014 CRC Press USA.

Course Number

EC6202

Course Credit (L-T-P-C)

3-0-0-3

Course Title

Smart Antenna: From Theory to Practice

Learning Mode

Lectures

Learning Objectives

Successful completion of this will be helpful to design an antenna array whose beam can be controlled and directed at a targeted user/direction. After successful completion of this course the students will have:

1. Understanding the concept of Phased Array.

2. Understanding the concept of multipath propagation.

3. Understanding the concept of Angle of Arrival estimation.

4. Understanding the concept of beam forming.

5. Implementation based on computer based simulations and learning of various algorithms.

Course Description

This course deals with the smart antenna.

Course Outline

Prerequisite: Engineering Electromagnetic, Matrix Algebra

Introduction, antenna basic parameters, Friis-formula, vector potential, linear antenna, loop antenna. Linear array, Array weighting, circular array, planar array, fixed beam array, fixed sidelobe canceling, retro-directive array. Propagation channel characteristic: Flat earth model, multipath propagation mechanism, propagation channel basics. Improving signal quality: equalization, diversity, channel coding, MIMO. Fundamental of matrix algebra: Array correlation matrix, AOA estimation: Bartlett, Capon, Linear prediction, maximum entropy, MUSIC, ESPRIT. Fixed weight beamforming algorithm: Maximum likelihood, minimum variance. Adaptive beamforming: least mean square, simple matrix inversion, recursive least square, conjugate gradient method.

Learning Outcome

Complies with PLO 1b, 2a and 4a

Assessment Method

Quiz, Assignments, and Exams

Suggested Readings

Text books:
1. Frank B. Gross, Smart Antennas for Wireless Communication, 1999 McGraw Hill USA.
2. Constantine A. Balanis, Antenna Theory: Analysis & Design, 3rd Edition 2009 Wiley USA.

Reference Book:
1. T.S. Rappaport & J.C. Liberti, Smart Antennas for Wireless Communication, 1999 Prentice Hall USA.
2. R. Janaswamy, Radio Wave Propagation and Smart Antennas for Wireless Communication, 2001 Kluwer USA.

Course Number

EC5209

Course Credit (L-T-P-C)

3-0-0-3

Course Title

Communication Networks

Learning Mode

Lectures

Learning Objectives

Course Learning Outcome (CLO): The students will be able to understand:

1. the network layered architecture and the protocol stack

2. the principles upon which the Internet and other computer networks are built;

3. how those principles translate into deployed protocols

Course Description

This course deals with the Communication Networks.

Course Outline

Overview of Communications Networks — Introduction to Internet, Layering Concept, OSI Model, TCP/IP Model, Introduction to Protocols, Topology, Performance Metrics, Devices at different layers

Overview of Data link Control and Media access control: Ethernet, Wireless LANs, Bluetooth, WiFi, 6LowPAN, Zigbee. Packet and Circuit Switching, Queuing Theory, Stop and wait protocol, sliding window protocol, Medium access protocols: Aloha, slotted aloha, CSMA, CSMA CD, and collision - free protocols, FDDI, token ring

Routing: Protocols, Types of Routing, Algorithms, IP Protocol, Addressing: IPV4 Address, IPv6 Addressing, Transition from IPv4 to IPv6

Transport Layer: Protocols - User Datagram Protocols (UDP) and Transmission Control Protocols (TCP), Flow, Error and Congestion Control: Congestion avoidance, QoS in networks

Application Layer: Client Server Model, World Wide Web and HTTP, DNS, Electronic Mail

Selected advanced topics in Antennas and Microwave Technology.

Learning Outcome

Complies with PLO 1b, 2a and 4a

Assessment Method

Quiz, Assignments, and Exams

Suggested Readings

Text Books:
1. Alberto Leon-Garcia and Indra Widjaja, Communication Networks, 2nd Edition, 2017, McGraw Hill Education.
2. A. S. Tanenbaum, Computer Networks, 5th edition, Prentice-Hall, Inc., 2010.

Reference Books:
1. J. Kurose and K. Ross, "Computer Networking: A Top-Down Approach Featuring the Internet"
2. W. Stallings, Data and Computer Communications, 10th edition, Prentice-Hall, Inc., 2013.
3. R. Gallager and D. P. Bertsekas, Data Networks, 2nd edition, Prentice-Hall, Inc., 1991.

Course Number

EC5210

Course Credit (L-T-P-C)

3-0-0-3

Course Title

Advanced Biomedical Signal Processing

Learning Mode

Lectures

Learning Objectives

Course Learning Outcome (CLO): Course training via lectures, tutorial & workshop sessions enable with.

1. Various Biomedical Signal Processing and Monitoring Tasks.

2. Ability to understand and analyze machine and deep learning biomedical models.

3. Competence to take logical, scientific and correct decisions while predicting model outcomes.

4. Aptitude and ability of performance measurement and management of various biomedical instruments.

Course Description

This course deals with the Advanced Biomedical Signal Processing.

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.

Machine Learning Tools for Medical Signal Classification: Support Vector Machine, Hidden Markov Model, Neural Networks. Medical Applications: Application of Event Related Potential in understanding human psychology, Cognitive neuroscience and higher order brain function: Attention, language, memory and executive functions and damage to the nervous system, Application of EEG and ECG signal processing over different cognitive and physical task

Learning Outcome

Complies with PLO 1b, 2a and 4a

Assessment Method

Quiz, Assignments, and Exams

Suggested Readings

Text Books
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.

Course Number

EC5211

Course Credit (L-T-P-C)

3-0-0-3

Course Title

Silicon Photonics

Learning Mode

Lectures

Learning Objectives

After learning this course, the students will be able:

1. to understand the fundamental concepts and operating principles of silicon photonic devices and circuits.

2. to design primary passive and active silicon photonic integrated circuits and interconnects.

3. to get familiar with different applications of silicon photonic devices.

Course Description

This course deals with the Silicon Photonics.

Course Outline

Introduction to Silicon Photonics. SOI platform. SOI, SiN, InP, and LNOI platforms.

Guided modes in Silicon Photonic Waveguides. Concept of the effective index.

Coupled Mode theory. Coupling of light to waveguides: grating couplers, butt coupling, mode transformers, inverted tapers.

Waveguides loss mechanisms: absorption scattering. Plasma dispersion effect, thermo-optic effect, and stress-optic effect.

Passive silicon photonic devices: Mach Zehnder interferometer, ring resonator, directional couplers, waveguide bends, multiplexers.

Active silicon photonic devices: Source, Modulators, photodetector.

Fundamentals of silicon photonics device fabrication and integration.

Applications of silicon photonic devices: Communication, Sensing, Neuromorphic computing.

Types of Satellite Networks: Fixed Point Satellite Network, INTELSAT, Mobile Satellite Network, INMARSAT, Low Earth Orbit and Medium Earth Orbit Satellite Systems, Very Small Aperture Terminal (VSAT) Network, Direct Broadcast Satellite Systems, Global Positioning System.

Learning Outcome

Complies with PLO 1b, 2a and 4a

Assessment Method

Quiz, Assignments, and Exams

Suggested Readings

Text Books:
1. G T Reed & AP Knights, Silicon Photonics: An Introduction, 2004, Wiley.
2. G T Reed, Silicon Photonics: The state of the art, 2008, Wiley.
3. M J Deen and P K Basu, Silicon Photonics: Fundamentals and Devices, 2012, Wiley

Reference Books:
1. L. Pavesi and D J Lockwoodt, Silicon Photonics, 2004, Springer
2. L Pavesi and David J. Lockwood, Silicon Photonics III Systems and Applications, 2016, Springer
3. J Ahmed, M Y Siyal, F Adeel, A Hussain, Optical Signal Processing by Silicon Photonics, 2013, Springer
4. A Yariv and P Yeh, Photonics, Sixth Edition, Oxford University Press

Course Number

EC6206

Course Credit (L-T-P-C)

3-0-0-3

Course Title

Optimization Theory and Techniques for Electrical Engineering

Learning Mode

Lectures

Learning Objectives

A student who successfully completes this course will be able to:

1. Understand the mathematical concepts underlying the optimization problems and their solutions

2. Classify optimization problems according to their mathematical properties

3. Formulate and solve the optimization problems

4. Design computationally-efficient solutions for difficult optimization problems

Course Description

This course deals with the Optimization theory.

Course Outline

Introduction of Optimization Theory; Introduction to Linear Programming; Convex Optimization Problem: Convex Sets, Convex Functions, Convex Optimization Problems: LP, QCQP, SOCP; Duality Theory, KKT Conditions. Numerical Optimization Techniques: Bisection Method, Golden Section Method, Newton Rapson Method, Interior Point Method. Introduction to Multi-objective Optimization Problems. Combinatorial Optimization: Integer Programming, Graphs and Graph Algorithms, Hard Problems, Heuristics, and Approximations. Application of Optimization Theory in Communication Systems, Signal Processing, Network Design, and Power & Control Systems.

Learning Outcome

Complies with PLO 1b, 2a and 4a

Assessment Method

Quiz, Assignments, and Exams

Suggested Readings

Text Books:
1. Stephen Boyd and Lieven Vandenberghe, Convex Optimization, Cambridge University Press.
2. C. H. Papadimitriou and Kenneth Steiglitz. Combinatorial optimization: algorithms and complexity, 1998, Courier Corporation.
3. M.S.Bazaraa , H.D.Sherali and C.Shetty , Nonlinear Programming, Theory and Algorithms, John Wiley and Sons, New York, 1993.

Reference Books:
1. Dimitri P. Bertsekas, Convex Analysis and Optimization, Athena-Scientific, 2003.
2. D. P. Palomar, Y. C. Eldar, Convex Optimization in Signal Processing and Communications, Cambridge Press, 2010.
3. Dimitris Bertsimas and John N. Tsitsiklis, Introduction to Linear Optimization, Athena-Scientific, 2003.
4. Suresh Chandra, Jayadeva and Aparna Mehra, Numerical Optimization with Applications, Alpha Science International 2009.
5. D. B. West, Introduction to graph theory, 2nd Edition, 2001, Prentice hall.
6. Charles Byrne, A First Course in Optimization, 1st edition, 2014, Chapman and Hall/CRC.

Course Number

EC6207

Course Credit (L-T-P-C)

3-0-0-3

Course Title

Microwave and Millimetre Wave Integrated Circuits (MMIC)

Learning Mode

Lectures

Learning Objectives

Course Learning Outcome (CLO): Students will gain a comprehensive understanding of the fundamental principles of MMICs, including their structure, fabrication techniques, and advantages over discrete component-based circuits in microwave and millimeter-wave applications. Students will develop proficiency in designing MMICs for specific microwave and millimeter-wave applications

Course Description

This course deals with the MMIC.

Course Outline

Prerequisite: Engineering Electromagnetics

Introduction to Microwaves and Millimeter Waves, Transmission Lines for Microwave and Millimeter Waves- Microstrip, Suspended Microstrip, Suspended Stripline, Fin-lines, Dielectric Integrated Guides. Microwave and Millimeter wave Switches, P-i-n diode switches: basic configurations, Insertion loss and isolation of series and shunt switches, Series and shunt switches in microstrip, Device reactance compensation, Isolation improvement techniques, SPDT switches, Application of p-i-n diode switches, Design Examples. Microwave and Millimeter Wave Phase Shifters- Analog versus digital Phase Shifters, Principle of ferrite Phase Shifters, Reciprocal versus non-reciprocal phase shifters, Different types of p-i-n diode phase shifters. Small Signal Amplifiers, Low Noise, Maximum Gain, Stability, Narrow band Design, Broadband Design, Noise Analysis. Microwave and Millimeter Wave Mixers, Millimeter Wave Transceiver Design.

Learning Outcome

Complies with PLO 1b, 2a and 4a

Assessment Method

Quiz, Assignments, and Exams

Suggested Readings

Text Books:
1. B. Bhat and Shiban K Koul, Strip line like Transmission line for Microwave Integrated Circuits, 1989 New Age Publishers, India Delhi.
2. D.M. Pozar, Microwave Engineering, 4th Edition, 2013 John Wiley, USA.
3. T.C. Edwards, Foundations for Microstrip Circuit Design, 1981 John Wiley, USA.

Reference Books:
1. T.T. Ha, Solid-State Microwave Amplifier Design, 1981 John Wiley, USA.
2. G. Gonzales, Microwave Transistor Amplifiers: Analysis and Design, 1997 Prentice Hall, USA.
3. Shiban K Koul and B. Bhat, Microwave Phase shifters, Volume-I and II, 1992 Artech House, USA.

Course Number

EC6208

Course Credit (L-T-P-C)

3-0-0-3

Course Title

Generative AI for Video Surveillance System

Learning Mode

Lectures

Learning Objectives

Complies with Program Goals 6 and 7

Course Description

This course introduces students to the theoretical foundations and practical applications of generative artificial intelligence (AI) in video surveillance systems. Students will learn about various generative models and their applications in video synthesis, anomaly detection, and activity recognition within surveillance scenarios.

Course Outline

Module 1: Image and Video Processing

• Basics of Image Processing
• Basics of Video Compression and Motion Analysis
• Background Modelling
• Object detection and classification
• Human Activity Recognition
• Video Object Tracking

Module 2: Video Surveillance Systems

• Foreground and Background Detection
• Segmentation and Tracking
• Behaviour analysis of individuals and groups
• Static and Dynamic analysis of crowds

Module 3: Introduction to Generative AI

• Overview of generative AI and its applications
• Introduction to generative models
• Key concepts: generative models vs. discriminative models, probability distributions

Module 4: Fundamentals of Deep Learning

• Introduction to deep learning and neural networks
• Training neural networks: backpropagation, optimization algorithms
• Regularization techniques: dropout, L1/L2 regularization
• Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs) and Long and Short Term Memory (LSTM) for generative tasks

Module 5: Variational Autoencoders (VAEs)

• Introduction to autoencoders
• Understanding VAEs: encoder, decoder, and latent space
• Variational inference and the reparameterization trick
• Applications of VAEs: image generation, data compression

Module 6: Generative Adversarial Networks (GANs)

• Introduction to GANs and their components (generator, discriminator)
• GAN training process: minimax game, adversarial loss
• Architectural variations: DCGAN, WGAN, Conditional GAN, SR GAN, Cycle GAN
• GAN applications: image synthesis, style transfer, super resolution

Module 7: Transformers

• Introduction and Evolution: Explore Transformer evolution and key components.
• Transformer Architecture: Study encoder-decoder stacks and attention mechanisms.
• Training Strategies: Compare pre-training, fine-tuning, and optimization techniques.
• Applications: Examine text, image, and video generation tasks.
• Recent Trends: Review Vision Transformers, Video Vision Transformers, GPT, DALL-E and BERT.

Module 8: Hands-on Projects and Case Studies

• Practical implementation of generative AI models using popular frameworks (e.g., TensorFlow, PyTorch)
• Guided projects and assignments to reinforce concepts learned
• Case studies showcasing real-world applications of generative AI

Learning Outcomes

Complies with PLOs 6a, 6b, 7 and 8a

Assessment Method

Quizzes/Assignments, Mid Sem, and End Sem

Suggested Readings

Text and References
1. M. H. Kolekar, “Intelligent video surveillance systems: an algorithmic approach”, Chapman and Hall/CRC; 2018 Jun 27.
2. F. Chollet, “Deep learning with Python”, Simon and Schuster; 2021 Dec 7.
3. J. Babcock, R. Bali, “Generative AI with Python and TensorFlow 2: Create images, text, and music with VAEs, GANs, LSTMs, Transformer models”, Packt Publishing Ltd; 2021 Apr 30.