**Instructor: **

**Shayan G. Srinivasa,**

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

**Pre-requisities: **

Digital signal processing at the undergrad level.

**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.
- 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 (FFT and variations, KLT), other transform methods.
- Statistical signal modeling: The least squares method, Pade’s approximation, Prony’s method, Shanks’ method, iterative pre-filtering, all-pole modeling and linear prediction, autocorrelation and covariance methods, FIR least squares inverse filter design, applications and examples.
- Inverse problems (signal reconstruction): underdetermined least squares, pseudo-inverse (SVD), min-norm solutions, regularized methods, reconstruction from projections, iterative methods such as projection onto convex sets, expectation-maximization and simulated annealing.

**Reference Books:**

- Moon & Stirling, Mathematical Methods and Algorithms for Signal Processing, Prentice Hall, 2000. (required)
- Monson Hayes, Statistical Digital Signal Processing and Modeling, John Wiley and Sons, 1996. (optional)
- Class notes

**Grading Policy: **

Homeworks: 25%

Mid-term exam: 25%

Project: 25%

Final exam: 25%

**Homeworks **

**Exams**

**Project**

**Course announcements**

**Final exam:**3rd December 2014

**Project report due date:**3rd December 2014