**Instructor: **

**Shayan G. Srinivasa,**

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

**Pre-requisities:**

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%

**Homeworks:**