Compressive Sensing Using Iterative Hard Thresholding with Low Precision Data Representation: Theory and Applications

Modern scientific instruments produce vast amounts of data, which can overwhelm the processing ability of computer systems. Lossy compression of data is an intriguing solution, but comes with its own drawbacks, such as potential signal loss, and the need for careful optimization of the compression r...

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Bibliographic Details
Published in:	IEEE transactions on signal processing Vol. 68; p. 1
Main Authors:	Gurel, Nezihe Merve, Kara, Kaan, Stojanov, Alen, Smith, Tyler, Lemmin, Thomas, Alistarh, Dan, Puschel, Markus, Zhang, Ce
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.01.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Algorithms Compressed sensing Compression ratio Compressive sensing Data compression Extraterrestrial measurements Instruments Iterative methods Magnetic resonance imaging Mathematical analysis Matrix algebra Matrix methods Medical imaging Noise measurement normalized IHT Optimization Quantization (signal) Radio astronomy Representations stochastic quantization Thresholding (Imaging)
ISSN:	1053-587X, 1941-0476
Online Access:	Get full text
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