E9 203: Compressive Sensing and Sparse Signal Processing

Instructor: Chandra R. Murthy (cmurthy at ece)
Class hours: MW 11am-12.30pm. First class: 07 Jan. 2015.
Class location: ECE Golden Jubilee Hall
TA: Ms. Geethu Joseph (geethu at ece)

Textbooks:
1. M. Elad, “Sparse and Redundant Representations”, Springer, 2010.
2. H. Rauhut, “Compressive Sensing and Structured Random Matrices,” Radon Series Comp. Appl. Math., 2011.
3. M. A. Davenport, M. F. Duarte, Y. C. Eldar, G. Kutyniok, “Introduction to Compressed Sensing,” available here.
4. http://dsp.rice.edu/cs
5. S. Foucart and H. Rauhut, “A mathematical introduction to compressive sensing,” Birkhauser Press.

Prerequisites: Random processes (E2-202 or equivalent), Matrix theory (E2-212 or equivalent).

The goal of this course is to provide an overview of the recent advances in compressed sensing and sparse signal processing. We start with a discussion of classical techniques to solve undetermined linear systems, and then introduce the lo norm minimization problem as the central problem of compressed sensing. We then discuss the theoretical underpinnings of sparse signal representations and uniqueness of recovery in detail. We study the popular sparse signal recovery algorithms and their performances guarantees. We will also cover signal processing interpretations of sparse signal recovery in terms of MAP and NMSE estimation.

Homeworks: 15% Due 2 weeks after the date the homework is announced
Exam 1: Mar. 04, 2015, in class: 30%
Exam 2: Apr. 30, 2015, 10am-1pm, EC1.07: 40%
Project presentation and report (this year, the project is HW6): 15%

Homeworks and assignments are posted here.

The project this year is HW6.