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Recent dictionary training algorithms for sparse representation like K-SVD, MOD, and their variation are reminiscent of K-means clustering, and this letter investigates such algorithms from that viewpoint. It shows: though K-SVD is sequential like K-means, it fails to simplify to K-means by destroying the structure in the sparse coefficients. In contrast,(More)
This letter presents a variant of Orthogonal Matching Pursuit (OMP) method, called Backtracking-based Adaptive OMP (BAOMP), for compressive sensing and sparse signal reconstruction. As an extension of the OMP algorithm, the BAOMP method incorporates a simple backtracking technique to detect the previous chosen atoms' reliability and then deletes the(More)
The decoding of a class of discrete cosine transform (DCT) and discrete sine transform (DST) codes that are maximum distance separable codes (MDS) is considered in this paper. These class of codes are considered for error correction over real fields. All the existing algebraic decoding algorithms are capable of decoding only a subclass of these codes [which(More)
This correspondence extends the theory and lattice factorizations for <i>M</i>-channel linear phase perfect reconstruction filter banks (LPPRFBs). We deal with FIR FBs with real-valued filter coefficients in which all filters have the same <i>arbitrary</i> length L = KM + beta (0 les beta &lt; M) and same symmetry center, in contrast to traditional(More)
We introduce a new class of real number codes derived from DFT matrix, Hermitian symmetric DFT (HSDFT) codes. We propose a new decoding algorithm based on coding-theoretic as well as subspace based approach. Decoding of HSDFT codes requires only real arithmetic operations and smaller dimension matrices compared to the decoding of the state-of-art real BCH(More)