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courses:sp20:e9-203:index [2020/01/02 03:54] cmurthy created |
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| =====Logistics===== | =====Logistics===== | ||
| **Instructor:** Chandra R. Murthy (cmurthy at iisc dot ac dot in) \\ | **Instructor:** Chandra R. Murthy (cmurthy at iisc dot ac dot in) \\ | ||
| - | **Class hours:** TuTh 11am-12.30pm, MP30. \\ | + | **Class hours:** MWF 8-9am, MP30. **Make-up classes:** S 1.30-2.30pm, also at MP30\\ |
| - | **TA**: Chandrasekhar S. (chandrashekhars at iisc)\\ | + | **TA**: Chandrasekhar S. (chandrasekhars at iisc)\\ |
| - | **TA Hours**: TBD\\ | + | **TA Hours**: Tue 5:30-6:30pm, Wed 11am-12pm and Fri 12-1pm. Venue: SPW204.\\ |
| **Textbooks:** \\ | **Textbooks:** \\ | ||
| 1. M. Elad, “Sparse and Redundant Representations”, Springer, 2010. \\ | 1. M. Elad, “Sparse and Redundant Representations”, Springer, 2010. \\ | ||
| 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,” available here. \\ | + | 3. M. A. Davenport, M. F. Duarte, Y. C. Eldar, G. Kutyniok, “Introduction to Compressed Sensing,” [[http://www.ecs.umass.edu/~mduarte/images/IntroCS.pdf|available here.]] \\ |
| - | 4. http://dsp.rice.edu/cs 5. S. Foucart and H. Rauhut, “A mathematical introduction to compressive sensing,” Birkhauser Press.\\ | + | 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 l0 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. |
