[et_pb_section fb_built=”1″ fullwidth=”on” _builder_version=”4.27.0″ background_image=”https:\/\/ece.iisc.ac.in\/~cmurthy\/wp-content\/uploads\/2024\/09\/GOPR0015-scaled.jpg” global_colors_info=”{}”][et_pb_fullwidth_header title=”E9 203: Compressive Sensing and Sparse Signal Processing” content_max_width=”none” module_class=”sub_header” _builder_version=”4.27.0″ title_font=”|on|||” title_text_color=”#FFFFFF” title_font_size=”50px” background_color=”rgba(255, 255, 255, 0)” background_image=”https:\/\/ece.iisc.ac.in\/~cmurthy\/wp-content\/uploads\/2024\/09\/abstract-1438441.jpg” parallax=”on” button_one_letter_spacing_hover=”0″ button_two_letter_spacing_hover=”0″ global_colors_info=”{}” button_one_text_size__hover_enabled=”off” button_two_text_size__hover_enabled=”off” button_one_text_color__hover_enabled=”off” button_two_text_color__hover_enabled=”off” button_one_border_width__hover_enabled=”off” button_two_border_width__hover_enabled=”off” button_one_border_color__hover_enabled=”off” button_two_border_color__hover_enabled=”off” button_one_border_radius__hover_enabled=”off” button_two_border_radius__hover_enabled=”off” button_one_letter_spacing__hover_enabled=”on” button_one_letter_spacing__hover=”0″ button_two_letter_spacing__hover_enabled=”on” button_two_letter_spacing__hover=”0″ button_one_bg_color__hover_enabled=”off” button_two_bg_color__hover_enabled=”off”][\/et_pb_fullwidth_header][\/et_pb_section][et_pb_section fb_built=”1″ _builder_version=”4.27.0″ _module_preset=”default” global_colors_info=”{}”][et_pb_row _builder_version=”4.27.0″ _module_preset=”default” custom_padding=”|||0px||” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.27.0″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.27.0″ _module_preset=”default” text_orientation=”justified” custom_padding=”|||11px||” hover_enabled=”0″ global_colors_info=”{}” sticky_enabled=”0″]<\/p>\n
Instructor:<\/strong>\u00a0Chandra R. Murthy (cmurthy at iisc dot ac dot in) Textbooks:<\/strong> Prerequisites:<\/strong>\u00a0Random processes (E2-202 or equivalent), Matrix theory (E2-212 or equivalent).<\/p>\n Overview:<\/strong> Homeworks:\u00a0due 2 weeks after the date the homework is announced:<\/strong>\u00a015%
Class hours:<\/strong>\u00a0MWF 8-9am, MP30.\u00a0Make-up classes:<\/strong>\u00a0S 1.30-2.30pm, also at MP30
TA<\/strong>: Chandrasekhar S. (chandrasekhars at iisc)
TA Hours<\/strong>: Tue 5:30-6:30pm, Wed 11am-12pm and Fri 12-1pm. Venue: SPW204.<\/p>\n
1. M. Elad, \u201cSparse and Redundant Representations\u201d, Springer, 2010.
2. H. Rauhut, \u201cCompressive Sensing and Structured Random Matrices,\u201d Radon Series Comp. Appl. Math., 2011.
3. M. A. Davenport, M. F. Duarte, Y. C. Eldar, G. Kutyniok, \u201cIntroduction to Compressed Sensing,\u201d\u00a0available here.<\/a>
4.\u00a0http:\/\/dsp.rice.edu\/cs<\/a>
5. S. Foucart and H. Rauhut, \u201cA mathematical introduction to compressive sensing,\u201d Birkhauser Press.<\/p>\n
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.<\/p>\n<\/div>\n\u00a0<\/h3>\n
Syllabus:<\/h3>\n
\n\n
\n S. No.<\/th>\n Topic<\/th>\n Num. Lectures<\/th>\n<\/tr>\n \n 1<\/td>\n Introduction and math review<\/td>\n 2<\/td>\n<\/tr>\n \n 2<\/td>\n Uniqueness and uncertainty principles<\/td>\n 4<\/td>\n<\/tr>\n \n 3<\/td>\n Recovery algorithms – greedy and convex<\/td>\n 6<\/td>\n<\/tr>\n \n 4<\/td>\n The theory of compressed sensing<\/td>\n 6<\/td>\n<\/tr>\n \n 5<\/td>\n Stable recovery<\/td>\n 4<\/td>\n<\/tr>\n \n 6<\/td>\n Approximate recovery algorithms<\/td>\n 4<\/td>\n<\/tr>\n \n 7<\/td>\n Bayesian recovery algorithms<\/td>\n 4<\/td>\n<\/tr>\n \n 8<\/td>\n Extensions and applications<\/td>\n 2<\/td>\n<\/tr>\n \n \u00a0<\/td>\n Total<\/strong><\/td>\n 32<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div>\n \u00a0<\/h2>\n
Grading<\/h2>\n
Exam 1: Date TBD, in class: 25%.
Exam 2: Date TBD, in class: 25%.
Initial project presentations: date TBD: 10%
Final project presentations and report: date TBD: 25%
Note:\u00a0there will be no makeup exams.<\/strong><\/p>\n<\/div>\n\u00a0<\/h2>\n
Homeworks<\/h2>\n