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In this paper we propose a robust iterative hard thresholding (IHT) algorithm for reconstructing sparse signals in the presence of impulsive noise. To address this problem, we use a Lorentzian cost function instead of the L2 cost function employed by the traditional IHT algorithm. The derived algorithm is comparable in computational load to the least squares based IHT. Analysis of the proposed method...
Recent works in modified compressed sensing (CS) show that reconstruction of sparse or compressible signals with partially known support yields better results than traditional CS. In this paper, we extend the ideas of these works to modify the iterative hard thresholding (IHT) algorithm to incorporate known support in the recovery process. We present a theoretical analysis that shows that including...
Compressive sensing (CS) is a new approach for the acquisition and recovery of sparse signals that enables sampling rates significantly below the classical Nyquist rate. Based on the fact that electrocardiogram (ECG) signals can be approximated by a linear combination of a few coefficients taken from a Wavelet basis, we propose a compressed sensing-based approach for ECG signal compression. ECG signals...
Compressed sensing shows that a sparse or compressible signal can be reconstructed from a few incoherent measurements. Noting that sparse signals can be well modeled by algebraic-tailed impulsive distributions, in this paper, we formulate the sparse recovery problem in a Bayesian framework using algebraic-tailed priors from the generalized Cauchy distribution (GCD) family for the signal coefficients...
Compressed sensing (CS) can be applied in distributed scenarios, where the objective is to independently compress several signals that are characterized by presenting a sparse correlation. In this case, the compressed version of each signal is produced without knowledge of the other signals. The decoder has access to the compressed versions of all the signals of interest and recovers them by exploiting...
Recent works in modified compressed sensing (CS) show that reconstruction of sparse or compressible signals with partially known support yields better results than traditional CS. In this paper, we extend the ideas of these works to modify three iterative algorithms to incorporate the known support in the recovery process. The performance and effect of the prior information are studied through simulations...
Statistical modeling is at the heart of many engineering problems. The importance of statistical modeling emanates not only from the desire to accurately characterize stochastic events, but also from the fact that distributions are the central models utilized to derive sample processing theories and methods. The generalized Cauchy distribution (GCD) family has a closed-form pdf expression across the...
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