Syllabus

Module-I: Introduction to Advanced Signal Processing [3 hr]
Overview of Signal Processing- Emerging Trends in Signal Processing- Course Structure and Objectives- Applications and Future Directions in Signal Processing

Module-II: Mathematical Preliminaries on Advanced Signal Processing [6hr]
Linear Algebra for Signal Processing- Probability Theory and Random Processes- Fourier Transforms (Continuous and Discrete)- Optimization Techniques in Signal Processing- Basic Concepts in Signal Estimation and Filtering

Module-III: Signal Models and Non-Linear Systems [4hr]
Signal Representation and Modelling- Non-linear Signal Models- Nonlinear Systems and Their Behaviour- Chaos and Nonlinear Dynamics in Signal Processing- Applications of Nonlinear Systems in Signal Processing

Module-IV: Non-Uniform Sampling and Non-Harmonic Fourier Analysis [6hr]
Non-uniform Sampling Techniques- Theoretical Foundations of Non-uniform Sampling- Non-Harmonic Fourier Transform- Signal Reconstruction from Non-uniform Samples- Applications in Signal Reconstruction and Data Acquisition

Module-V: Compressed and Adaptive Sensing [4hr]
Introduction to Compressed Sensing- Theory of Sparse Signals and Sampling- Adaptive Sensing Techniques- Signal Reconstruction in Compressed Sensing- Practical Applications in Data Acquisition and Wireless Communications

Module-VI: Non-Gaussian Random Processes and Applications [4hr]
Non-Gaussian Processes and Their Properties- Stochastic Models in Signal Processing- Applications of Non-Gaussian Processes- Noise Modeling and Mitigation Techniques

Module-VII: Quantum Signal Processing [4hr]
Introduction to Quantum Computing and Signal Processing- Quantum Algorithms in Signal Processing- Quantum Information Theory- Applications of Quantum Signal Processing in Communication and Data Analysis

Module-VIII: Chaotic Signal Processing [4hr]
Basics of Chaos Theory- Chaotic Systems in Signal Processing- Modeling Chaotic Signals- Applications of Chaos in Secure Communications and Data Encryption- Chaos-Based Signal Processing Techniques

To aware the students on the signal processing domain and beyond convention

To aware the students on processing signals with non-linear systems or technique

To aware the students on development of algorithms meeting application specific performance criteria

To aware the students on implemention nonlinear signal processing system in software/Hardware

The student will be able to
CO1 Analyse and visualize the domain of signal processing beyond conventional techniques.
CO2 Apply signal processing methods to non-linear systems.
CO3 Design and develop algorithms meeting application-specific performance criteria.
CO4 Implement a nonlinear signal processing system in software or hardware.
CO5 Evaluate and compare the effectiveness of different nonlinear signal processing approaches.

Volker Pohl, and Holger Boche, Advanced Topics in System and Signal Theory, Springer , 2009 or latest Ed.

Y. C. Eldar and G. Kutyniok, Compressed Sensing: Theory and Applications, CUP , 2012

Robert M. Young, An Introduction to Non-harmonic Fourier Series, Academic Press , 2001 or latest Ed.

Gonzalo R. Arce, Nonlinear signal processing: a statistical approach, JOHN WILEY & SONS , 2005 or latest Ed.