Course Details
Subject {L-T-P / C} : EE6103 : Advanced Signal Processing { 3-0-0 / 3}
Subject Nature : Theory
Coordinator : Dr. Supratim Gupta
Syllabus
Module-I: Introduction to Signal Processing and Applications [3 hr.]
Module-II: Mathematical Methods for Signal Processing [8 hr.]
Module-III: Linear Adaptive Filters and Applications [8 hr.]
Module-IV: Non-Linear Adaptive Filters and Applications [8 hr.]
Module-V: Realizing Adaptive Systems in Software ( PYTHON/MATLAB/SCILAB) [6 hr.]
Course Objectives
- To understand the broader scope of signal processing domain and the mathematical methods in-depth and its use in signal processing
- To understand the adaptive signal processing with linear system model
- To understand the adaptive signal processing with non-linear system model like Convolution Neural Network, Deep Neural Network
- To learn coding in PYTHON/MATLAB/SCILAB for signal processing
Course Outcomes
At the end of the course, students will be able to <br /> <br />CO1: Explain scope of the signal processing domain with abstract framework <br />CO2: Analyse and formulate application problems mathematically <br />CO3: Develop algorithms meeting application specific performance criteria <br />CO4: Implement and test performance of the signal processing system in software <br />CO5: Work with modern tools for design, simulation, & realization of systems like <br /> Python/MATLAB/SCILAB
Essential Reading
- D. G. Manolakis, V. K. Ingle, and S. M. Kogon, Statistical and Adaptive Signal Processing: Spectral Estimation, Signal Modeling, Adaptive Filtering and Array Processing, Artech House , 2005 or latest Ed.
- François Chollet, Deep Learning with Python, Manning Publications , 2017 or latest Ed.
Supplementary Reading
- G. Strang, Linear Algebra and its Applications, THE , 2006 or latest Ed.
- Dr. Ana Bell Prof. Eric Grimson Prof. John Guttag, INTRODUCTION TO COMPUTER SCIENCE AND PROGRAMMING IN PYTHON, Massachusetts Institute of Technology , https://ocw.mit.edu/courses/6-0001-introduction-to-computer-science-and-programming-in-python-fall-2016/video_galleries/lecture-videos/