E9 203: Compressive Sensing and Sparse Signal Processing

Instructor: Chandra R. Murthy (cmurthy at ece)
Class hours: WF 10.00am-11.30am. First class: Wednesday, Jan. 08, 2014.
Class location: EC1.08
TA: Abhay Sharma.

Textbooks/Reading Material:
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. Research papers (references will be provided in class).

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: 25% Due 2 weeks after the date the homework is announced
Initial project presentation: 25% Mar. 5th and 7th, 2014
Project presentation: 25% Last 2 weeks of classes
Project report: 25% Due Tuesday, Apr. 30th, 2014, 10am

Homeworks and assignments are posted here.

Projects will be posted here.