**Instructor: **

**Shayan G. Srinivasa**

**Pre-requisities:
**UG in Digital Signal Processing, familiarity with Probability and Linear Algebra

**Course Syllabus:**

- Review of basic signals, systems and signal space: Review of 1-D signals and systems, review of random signals, multi-dimensional signals, review of vector spaces, inner product spaces, orthogonal projections and related concepts.
- Sampling theorems (a peek into Shannon and compressive sampling), Basics of multi-rate signal processing: sampling, decimation and interpolation, sampling rate conversion (integer and rational sampling rates), oversampled processing (A/D and D/A conversion), and introduction to filter banks.
- Signal representation: Transform theory and methods (FT and variations, KLT), other transform methods including convergence issues.
- Wavelets: Characterization of wavelets, wavelet transform, multi-resolution analysis.

**Reference Books:**

- Moon & Stirling, Mathematical Methods and Algorithms for Signal Processing, Prentice Hall, 2000. (required)
- P. P. Vaidyanathan, Multirate systems and filter banks, Prentice Hall, 2000. (required)
- A. Boggess & F. J. Narcowich, A First Course in Wavelets with Fourier Analysis, Prentice Hall, 2001.
- G. Strang, Introduction to Linear Algebra, 2016.
- H. Stark & J. W. Woods, Probability and Random Processes with Applications to Signal Processing, 2014.
- Class notes

**Grading Policy:**

Assignments: 25%

Final exam: 75%

**Assignments**