[PDF] Top 20 Compressive sensing for sparse approximations: constructions, algorithms, and analysis

... Motivations Compressive sensing, also referred to as compressed sensing or compressive sampling, is an emerging area in signal processing and information theory which has attracted a lot of ... See full document

... way, compressive sensing promises to recover the high-dimensional signals exactly or accurately, by using a much smaller number of non-adaptive linear samplings or ...often sparse or compressible ... See full document

... the algorithms can be largely ...the sensing matrix is very large and can only be accessed one row per ...the sensing matrix at each iteration of the ... See full document

... the sensing matrix but only need functions that implement the matrix-vector multiplication by and T at each ...the algorithms can be largely ...the sensing matrix is very large and can only be ... See full document

... structured sparse signals with length N and blocks K = 2 (or K = ...generated sparse signal θ on sensing matrix ...the algorithms are optimally set to ˆ S = S, ˆ K = K and the size of clusters ... See full document

... One-bit compressive sensing has extended the scope of sparse recovery by showing that sparse signals can be accurately reconstructed even when their linear measurements are subject to the ... See full document

... 59 6.1. Experimental results for solution A As was exposed previously, CS does not provide any advantage when the signal is not sparse. Then, it is necessary to establish a threshold to decide when CS is used or ... See full document

... reconstruction algorithms have been proposed to find the suboptimal sparse ...a sparse signal. Many nonlinear sparse sensing (NSS) algorithms and their variants have been ... See full document

... genetic algorithms, simulated annealing or other similar optimization ...of compressive sensing and a formulation based on a modified l 1 norm is ... See full document

... structured sparse signals, of which significant coefficients appear in clustered ...of sparse pattern is often exploited in many concrete applications, such as multi-band signals, gene expression levels, ... See full document

... For the prior knowledge has a crucial influence on the performance of the image reconstruction algorithm, design- ing an effective regularization term is beneficial to make full use of image prior information and further ... See full document

... Abstract—When compressive sensing (CS) was employed to solve electromagnetic scattering problems over wide incident angles, the selection of sparse transform strongly affects the efficiency of the CS ... See full document

... the sparse representation and its effective ...the sparse representation as it has due advantage the large amount of data turns to be non ...estimation algorithms have ... See full document

... 4.1.5 Analysis and discussion: recovery without noise For on grid single target cases, CS with the MFOCUSS algorithm is perfectly capable to estimate the DOA of a target ... See full document

... and Compressive Sampling Matching Pursuit (COSAMP) reconstruction algorithms, which improve the measurement efficiency and possess greater advantages and more development potential than traditional antenna ... See full document

... called sparse multi-path ...channels, Compressive Sensing (CS) based channel estimation method provides better estimation of sparse channel than the conventional estimation methods which are ... See full document

... Secondly, motivated by compressive sensing of images, we examine linear embeddings of datasets containing points that are sparse in the pixel basis, with the goal o[r] ... See full document

... Convergence Analysis Lastly, we have conducted experiments to show the convergence of our method using the Toy and Indian Pines dataset as examples under different sampling rates and different ... See full document

... GAs are stochastic global search and optimization methods inspired by natural biological evolution. The core of a GA is a population of potential solutions, named individuals, to a given optimization problem as well as a ... See full document

... from compressive measurements from the Bayesian ...other algorithms that were discussed here all provide a point-estimate of the sparse state x, Bayesian Compressive Sensing provides an ... See full document