Proceedings ArticleDOI
Radial basis function cascade network for Sparse signal Recovery (RASR)
V. Vivekanand,L. Vidya,U. Shyam Kumar,Deepak Mishra +3 more
- pp 1-5
TLDR
The proposed algorithm Radial basis function cascade network for Sparse signal Recovery (RASR) uses the L0 norm optimization, L2 least square method and feedback network model to improve the signal recovery performance and computational time over the existing ANN based CSIANN and relaxation based SL0 algorithms.Abstract:
The use of cascade network consisting of RBF nodes and least square error minimization block to Compressed Sensing for recovery of sparse signals is explored in this paper to improve the computation time and convergence. The proposed algorithm Radial basis function cascade network for Sparse signal Recovery (RASR) uses the L 0 norm optimization, L 2 least square method and feedback network model to improve the signal recovery performance and computational time over the existing ANN based CSIANN and relaxation based SL0 algorithms. The simulation results and experimental evluation of algorithm performance are presented here.read more
Citations
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Proceedings ArticleDOI
Noise immunity analysis of compressed sensing recovery algorithms
TL;DR: The relaxation based algorithms are found to have better recovery precision, when the CS measurement is noisy; and the performance of ANN based RASR algorithm is found to be comparable to basis pursuit and IRLS algorithms.
Proceedings ArticleDOI
Compressed sensing recovery using polynomial approximated l 0 minimization of signal and error
V. Vivekanand,L. Vidya +1 more
TL;DR: The development of a computationally optimal Compressed Sensing recovery algorithm based on polynomial approximated L0 minimization of the objective signal x and the error projection, is discussed in this paper.
Proceedings ArticleDOI
Analysis of RBF cascade network for sparse signal recovery and application in telemetry
V. Vivekanand,L. Vidya +1 more
TL;DR: The proposed algorithm radial basis function cascade network for sparse signal recovery uses the L0 norm optimization, L2 least square method and feedback network model to improve the signal recovery performance and computational time over the existing ANN based and SL0 algorithms.
References
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Book
Compressed sensing
TL;DR: It is possible to design n=O(Nlog(m)) nonadaptive measurements allowing reconstruction with accuracy comparable to that attainable with direct knowledge of the N most important coefficients, and a good approximation to those N important coefficients is extracted from the n measurements by solving a linear program-Basis Pursuit in signal processing.
Journal ArticleDOI
Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information
TL;DR: In this paper, the authors considered the model problem of reconstructing an object from incomplete frequency samples and showed that with probability at least 1-O(N/sup -M/), f can be reconstructed exactly as the solution to the lscr/sub 1/ minimization problem.
Journal ArticleDOI
Atomic Decomposition by Basis Pursuit
TL;DR: Basis Pursuit (BP) is a principle for decomposing a signal into an "optimal" superposition of dictionary elements, where optimal means having the smallest l1 norm of coefficients among all such decompositions.
Journal ArticleDOI
An Introduction To Compressive Sampling
TL;DR: The theory of compressive sampling, also known as compressed sensing or CS, is surveyed, a novel sensing/sampling paradigm that goes against the common wisdom in data acquisition.
Journal ArticleDOI
Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit
Joel A. Tropp,Anna C. Gilbert +1 more
TL;DR: It is demonstrated theoretically and empirically that a greedy algorithm called orthogonal matching pursuit (OMP) can reliably recover a signal with m nonzero entries in dimension d given O(m ln d) random linear measurements of that signal.