Basics of Signal Processing [E9 207 (3:0) – Spring 2018]

Shayan G. Srinivasa,

[Tuesday, Thursday 11:30 am – 1:00 pm]


Undergraduate Signals and Systems, Digital Signal Processing, Some background in linear algebra and probability

Course Syllabus:

  • Introduction to probability and random processes: basic definitions, discrete, continuous and mixed random variables, probability density function, cumulative density function, various notions of stationarity, ergodicity, filtering noise through linear systems, Signal spaces and signal geometry,
  • Topics in sampling: Shannon sampling theorem for bandlimited and random signals, basic ideas on compressive sampling,
  • Sampling rate conversion: decimation, expansion and rational fractional rate conversion, filter banks and applications.
  • Introduction to transform methods: Fourier transforms and convergence issues, wavelets and algorithms for fast decomposition.

Reference Books:

  • Moon & Stirling, Mathematical Methods and Algorithms for Signal Processing, Prentice Hall, 2000.
  • P. P. Vaidyanathan, Multirate systems and filterbanks, Prentice Hall Signal Processing Series
  • Lecture notes

Grading Policy:

  • Homeworks : 25%
  • Mid Term Exams : 25%
  • Project : 25%
  • Final Exam : 25%