EC370N Sparse Representations and Compressive Sensing
Course Name:
EC370N Sparse Representations and Compressive Sensing
Programme:
B.Tech (ECE)
Category:
Programme Specific Electives (PSE)
Credits (L-T-P):
(3-0-0) 3
Content:
Introduction, mathematical preliminaries, 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 eigen value property. Recovery algorithms and their performance guarantees. Multiple measurement models and Applications.
References:
S. Foucart and H. Rauhut, “A mathematical introduction to compressive sensing,” Birkhauser Press, 2013.
M. Elad, “Sparse and Redundant Representations,” Springer, 2010.
H. Rauhut, “Compressive Sensing and structured random matrices”, Radon series, Comp. Applied math. 2011.
Compressive Sensing Resources - http://dsp.rice.edu/cs/
Department:
Electronics and Communication Engineering(ECE)