Theory of compressed sensing applications in information processing 翻译结果2复制译文编辑译文朗读译文返回顶部 Application of compressed sensing theory in information processing 翻译结果3复制译文编辑译文朗读译文返回顶部 Application of compressed sensing theory in information processing 翻译结果4复制译文编辑译文朗读...
This chapter provides the use of Bayesian inference in compressive sensing (CS), a method in signal processing. Among the recovery methods used in CS literature, the convex relaxation methods are reformulated again using the Bayesian framework and this method is applied in different CS applications ...
Compressed sensing is a new concept in signal processing where one seeks to minimize the number of measurements to be taken from signals while still retaining the information necessary to approximate them well. The ideas have their origins in certain abstract results from functional analysis and appro...
Slavakis, "Sparsity-aware learning and compressed sensing: An overview," arXiv preprint arXiv:1211.5231, 2012.S. Theodoridis, Y. Kopsinis, and K. Slavakis, "Sparsity-aware learning and compressed sensing: An overview," in Academic Press Library in Signal Processing: Singal Processing Theory ...
Compressed sensing is an exciting, rapidly growing field, attracting considerable attention in electrical engineering, applied mathematics, statistics and computer science. This book provides the first detailed introduction to the subject, highlighting recent theoretical advances and a range of applications, ...
In the past few decades, with the growing popularity of compressed sensing (CS) in the signal processing field, the quantization step in CS has received significant attention. Current research generally considers multi-bit quantization. For systems employing quantization with a sufficient number of bit...
a recent significant advance in signal processing known as compressed sensing (CS) [cf.Donoho, D. (2006). IEEE Trans. Inf. Theory 52, 1289-1306]. ... R Arora,RA Lutfi - 《Journal of the Acoustical Society of America》 被引量: 10发表: 2009年 214 Sparse Coding and Its Applications in...
Albert Cohen, Wolfgang Dahmen, and Ronald DeVore July 13, 2006 Abstract Compressed sensing is a new concept in signal processing where one seeks to minimize the number of measurements to be taken from signals while still retaining the information necessary to approximate them well. The ideas have...
Recently, in information science, information processing based on signal sparsity has been intensively investigated. This is because a quite impactive idea for reconstruction of sparse signal, called compressed sensing, was proposed about ten years ago, which has been widely spread onto variety of fiel...
Compressed sensing is an advanced method of acquiring and processing signals, and it was first proposed by Donoho [1,2]. It can accurately recover the original signal from a few incoherent measurements. In CS, fewer samples are required to reconstruct sparse or compressible signals, which breaks...