Open AccessProceedings Article
Compressive sensing
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This paper overviews the recent work on compressive sensing, a new approach to data acquisition in which analog signals are digitized for processing not via uniform sampling but via measurements using more general, even random, test functions.Abstract:
This paper overviews the recent work on compressive sensing, a new approach to data acquisition in which analog signals are digitized for processing not via uniform sampling but via measurements using more general, even random, test functions. In stark contrast with conventional wisdom, the new theory asserts that one can combine "low-rate sampling" with digital computational power for efficient and accurate signal acquisition. Compressive sensing systems directly translate analog data into a compressed digital form; all we need to do is "decompress" the measured data through an optimization on a digital computer. The implications of compressive sensing are promising for many applications and enable the design of new kinds of analog-to-digital converters, cameras, and imaging systems.read more
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A wavelet tour of signal processing
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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.
Posted Content
Stable Signal Recovery from Incomplete and Inaccurate Measurements
TL;DR: It is shown that it is possible to recover x0 accurately based on the data y from incomplete and contaminated observations.
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Approximate nearest neighbors: towards removing the curse of dimensionality
Piotr Indyk,Rajeev Motwani +1 more
TL;DR: In this paper, the authors present two algorithms for the approximate nearest neighbor problem in high-dimensional spaces, for data sets of size n living in R d, which require space that is only polynomial in n and d.
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The Dantzig selector: Statistical estimation when p is much larger than n
Emmanuel J. Candès,Terence Tao +1 more
TL;DR: In many important statistical applications, the number of variables or parameters p is much larger than the total number of observations n as discussed by the authors, and it is possible to estimate β reliably based on the noisy data y.