EC879 Compressed Sensing and Sparse Signal Processing
Course Name:
EC879 Compressed Sensing and Sparse Signal Processing
Programme:
M.Tech(SPML)
Category:
Elective (Ele)
Credits (L-T-P):
(3-0-2) 4
Content:
Basis and Frames, Low dimensional signal models, Sensing matrices, Signal recovery via l1 minimization, Necessary and sufficient conditions for L0-L1 equivalence. RIP and random matrices. Johnson-Lindenstrauss Lemma, Stable signal recovery and restricted eigenvalue property. Recovery algorithms and their performance guarantees. Multiple measurement models.
References:
M. Elad, “Sparse and Redundant Representations” Springer 2010 H.
Rauhut, “Compressive Sensing and structured random matrices”,Radon series, Comp. applied math.
Compressive Sensing Resources - http://dsp.rice.edu/cs/
Department:
Electronics and Communication Engineering(ECE)