Course Details

Subject {L-T-P / C} : EE6103 : Advanced Signal Processing {3-0-0 / 3}
Subject Nature : Theory
Coordinator : Dr. Supratim Gupta

Syllabus

Mathematical Preliminaries, Signal/System Model and Learning, Data Structure in Signal Processing, Spectral Representation and Analysis, Sparse Signal Processing, Blind Signal Processing, Signal Processing on Graph

Course Objectives

  1. To aware the students to visualize the domain of signal processing and recent development
  2. To make the students skilled to analyze different classes of signals and their representations
  3. To make the students skilled to develop algorithms meeting application specific performance criteria
  4. To make the students to implement signal processing system in software/Hardware

Course Outcomes

• The student will be aware and able to visualize the domain of signal processing
• The student will be able to group different classes of signals with an abstract representation
• The student will be able to develop algorithms meeting application specific performance criteria
• The student will be able to implement the signal processing system in software/Hardware
• The student will be able to use modern tools for design, simulation, & realization of systems

Essential Reading

  1. I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning, The MIT Press , 2015
  2. Simon Foucart, Holger Rauhut, A Mathematical Introduction to Compressive Sensing, Springer New York , 2013

Supplementary Reading

  1. Radomir S. Stankovic, Claudio Moraga, Jaakko Astola, Fourier Analysis on Finite Groups with Applications in Signal Processing and System Design, John Wiley & Sons , 2005
  2. Piet Van Mieghem, Graph Spectra for Complex Networks, Cambridge University Press , 2011