Deterministic Compressed Sensing Matrices: Construction via Euler Squares and Applications

Naidu, R. Ramu and Jampana, Phanindra Varma and Sastry, Challa Subrahmanya (2016) Deterministic Compressed Sensing Matrices: Construction via Euler Squares and Applications. IEEE Transactions on Signal Processing, 64 (14). pp. 3566-3575. ISSN 1053-587X

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In compressed sensing the matrices that satisfy the Restricted Isometry Property (RIP) play an important role. To date, however, very few results for designing such matrices are available. For applications such as multiplier-less data compres- sion, binary sensing matrices are of interest. The present paper constructs deterministic and binary sensing matrices using Euler Squares. In particular, given a positive integer m different from p, p 2 for a prime p , we show that it is possible to construct a binary sensing matrix of size m × c ( mμ ) 2 ,where μ is the coher- ence parameter of the matrix and c ∈ [1 , 2) . The matrices that we construct have small density (that is, percentage of nonzero en- tries in the matrix is small) with no function evaluation in their construction, which support algorithms with low computational complexity. Through experimental work, we show that our binary sensing matrices can be used for such applications as content based image retrieval. Our simulation results demonstrate that the Eu- ler Square based CS matrices give better performance than their Gaussian counterparts.

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IITH Creators:
IITH CreatorsORCiD
Jampana, Phanindra Varma
Sastry, Challa SubrahmanyaUNSPECIFIED
Item Type: Article
Uncontrolled Keywords: Compressed Sensing, Coherence, RIP, Binary sensing matrices, Euler Squares, CBIR
Subjects: Mathematics
Chemical Engineering
Divisions: Department of Chemical Engineering
Depositing User: Team Library
Date Deposited: 21 Dec 2017 10:49
Last Modified: 21 May 2018 11:11
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