Course
PostgraduateSemester
ElectivesSubject Code
AVD881Subject Title
Compressed Sensing and Sparse Signal ProcessingSyllabus
Introduction-Recovery algorithms - greedy and convex-The theory of compressed sensing
Introduction Sparse Signal Recovery via Basis Pursuit with RIP- Sparse Signal Recovery via Basis Pursuit without RIP-Sparse Signal Recovery via Greedy Algorithms- Concentration inequalities
Matrix Completion -Robust PCA-Atomic Norms-new advances and Application
Text Books
Same as Reference
References
1. M. Elad, Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing, Springer, 2010.
2. J.L.Starck, F. Murtagh and J. M. Fadili, Sparse Image and Signal Processing: Wavelets, Curvelets, Morphological Diversity, CUP, 2010.
3. G. Strang, Linear Algebra and Its Applications, 4th Ed., Cengage, 2006.
4. G. Grimmett and D. Stirzaker, Probability and Random Processes, OUP, 2001.
5. S. Boyd and L. Vandenberghe, Convex Optimization, CUP, 2004.
Course Outcomes (COs):
CO1: Understand the fundamentals of compressive sensing
CO2: Apply signal recovery algorithms to real world problems
CO3: Understand significance of matrix completion and PCA