Computer Science – Information Theory
Scientific paper
2009-08-05
Computer Science
Information Theory
8 pages, 2 figures, 1 table
Scientific paper
In this paper we introduce deterministic $m\times n$ RIP fulfilling $\pm 1$ matrices of order $k$ such that $\frac{\log m}{\log k}\approx \frac{\log(\log_2 n)}{\log(\log_2 k)}$. The columns of these matrices are binary BCH code vectors that their zeros are replaced with -1 (excluding the normalization factor). The samples obtained by these matrices can be easily converted to the original sparse signal; more precisely, for the noiseless samples, the simple Matching Pursuit technique, even with less than the common computational complexity, exactly reconstructs the sparse signal. In addition, using Devore's binary matrices, we expand the binary scheme to matrices with $\{0,1,-1\}$ elements.
Amini Arash
Marvasti Farokh
No associations
LandOfFree
Deterministic Construction of Compressed Sensing Matrices using BCH Codes does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Deterministic Construction of Compressed Sensing Matrices using BCH Codes, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Deterministic Construction of Compressed Sensing Matrices using BCH Codes will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-254551