Differences
This shows you the differences between two versions of the page.
|
courses:sp15:e9-203:index [2014/12/30 10:45] cmurthy |
courses:sp15:e9-203:index [2015/04/28 10:13] (current) cmurthy |
||
|---|---|---|---|
| Line 2: | Line 2: | ||
| =====Logistics===== | =====Logistics===== | ||
| **Instructor:** Chandra R. Murthy (cmurthy at ece) \\ | **Instructor:** Chandra R. Murthy (cmurthy at ece) \\ | ||
| - | **Class hours:** MW 11am-12.30pm. First class: 05 Jan. 2015.\\ | + | **Class hours:** MW 11am-12.30pm. First class: 07 Jan. 2015.\\ |
| - | **Class location:** EC1.07\\ | + | **Class location:** ECE Golden Jubilee Hall\\ |
| - | **TA**: TBD. | + | **TA**: Ms. Geethu Joseph (geethu at ece) |
| **Textbooks:** \\ | **Textbooks:** \\ | ||
| Line 10: | Line 10: | ||
| 2. H. Rauhut, “Compressive Sensing and Structured Random Matrices,” Radon Series Comp. Appl. Math., 2011.\\ | 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," [[http://www-stat.stanford.edu/~markad/publications/ddek-chapter1-2011.pdf|available here]].\\ | 3. M. A. Davenport, M. F. Duarte, Y. C. Eldar, G. Kutyniok, "Introduction to Compressed Sensing," [[http://www-stat.stanford.edu/~markad/publications/ddek-chapter1-2011.pdf|available here]].\\ | ||
| - | 4. http://dsp.rice.edu/cs | + | 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). | **Prerequisites:** Random processes (E2-202 or equivalent), Matrix theory (E2-212 or equivalent). | ||
| - | |||
| ====Overview:==== | ====Overview:==== | ||
| 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. | 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. | ||
| =====Grading===== | =====Grading===== | ||
| - | Homeworks: 25% **Due 2 weeks after the date the homework is announced**\\ | + | Homeworks: 15% **Due 2 weeks after the date the homework is announced**\\ |
| - | Two exams: 25% each.\\ | + | Exam 1: Mar. 04, 2015, in class: 30%\\ |
| - | Project presentation and report: 25% | + | Exam 2: Apr. 30, 2015, 10am-1pm, EC1.07: 40%\\ |
| + | Project presentation and report (this year, the project is HW6): 15% | ||
| =====Homeworks and Assignments===== | =====Homeworks and Assignments===== | ||
| - | Homeworks and assignments are posted [[courses:sp13:e9-203:homeworks|here]]. | + | Homeworks and assignments are posted [[courses:sp15:e9-203:homeworks|here]]. |
| =====Project===== | =====Project===== | ||
| - | Projects will be announced. | + | The project this year is HW6. |
| ====Announcements===== | ====Announcements===== | ||
