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courses:sp14:e9-203:index [2013/12/09 04:47] cmurthy [Grading] |
courses:sp14:e9-203:index [2014/03/04 09:58] (current) cmurthy [Logistics] |
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| =====Logistics===== | =====Logistics===== | ||
| **Instructor:** Chandra R. Murthy (cmurthy at ece) \\ | **Instructor:** Chandra R. Murthy (cmurthy at ece) \\ | ||
| - | **Class hours:** WF 11.30am-1pm. First class: **Wednesday, Jan. 08, 2014.**\\ | + | **Class hours:** WF 10.00am-11.30am. First class: **Wednesday, Jan. 08, 2014.**\\ |
| - | **Class location:** EC1.07\\ | + | **Class location:** EC1.08\\ |
| **TA**: Abhay Sharma. | **TA**: Abhay Sharma. | ||
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| **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: 25% **Due 2 weeks after the date the homework is announced**\\ | ||
| Initial project presentation: 25% **Mar. 5th and 7th, 2014** \\ | Initial project presentation: 25% **Mar. 5th and 7th, 2014** \\ | ||
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| - | ====Announcements===== | + | =====Announcements====== |
| - | * **The first class** will be held at EC1.07, 11.30am-1pm, on Wednesday, Jan. 08, 2014. | + | |
