K-bit Hamming compressed sensing

Zhou, T; Tao, D

K-bit Hamming compressed sensing

Zhou, T Tao, D

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Publication Type:: Conference Proceeding
Citation:: IEEE International Symposium on Information Theory - Proceedings, 2013, pp. 679 - 683
Issue Date:: 2013-12-19

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Field	Value	Language
dc.contributor.author	Zhou, T	en_US
dc.contributor.author	Tao, D https://orcid.org/0000-0001-7225-5449	en_US
dc.date.issued	2013-12-19	en_US
dc.identifier.citation	IEEE International Symposium on Information Theory - Proceedings, 2013, pp. 679 - 683	en_US
dc.identifier.isbn	9781479904464	en_US
dc.identifier.issn	2157-8095	en_US
dc.identifier.uri	http://hdl.handle.net/10453/28348
dc.description.abstract	We consider recovering d-level quantization of a signal from k-level quantization of linear measurements. This problem has great potential in practical systems, but has not been fully addressed in compressed sensing (CS). We tackle it by proposing k-bit Hamming compressed sensing (HCS). It reduces the decoding to a series of hypothesis tests of the bin where the signal lies in. Each test equals to an independent nearest neighbor search for a histogram estimated from quantized measurements. This method is based on that the distribution of the ratio between two random projections is defined by their intersection angle. Compared to CS and 1-bit CS, k-bit HCS leads to lower cost in both hardware and computation. It admits a trade-off between recovery/measurement resolution and measurement amount and thus is more flexible than 1-bit HCS. A rigorous analysis shows its error bound. Extensive empirical study further justifies its appealing accuracy, robustness and efficiency. © 2013 IEEE.	en_US
dc.relation.ispartof	IEEE International Symposium on Information Theory - Proceedings	en_US
dc.relation.isbasedon	10.1109/ISIT.2013.6620312	en_US
dc.title	K-bit Hamming compressed sensing	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Istanbul, Turkey	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

We consider recovering d-level quantization of a signal from k-level quantization of linear measurements. This problem has great potential in practical systems, but has not been fully addressed in compressed sensing (CS). We tackle it by proposing k-bit Hamming compressed sensing (HCS). It reduces the decoding to a series of hypothesis tests of the bin where the signal lies in. Each test equals to an independent nearest neighbor search for a histogram estimated from quantized measurements. This method is based on that the distribution of the ratio between two random projections is defined by their intersection angle. Compared to CS and 1-bit CS, k-bit HCS leads to lower cost in both hardware and computation. It admits a trade-off between recovery/measurement resolution and measurement amount and thus is more flexible than 1-bit HCS. A rigorous analysis shows its error bound. Extensive empirical study further justifies its appealing accuracy, robustness and efficiency. © 2013 IEEE.

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http://hdl.handle.net/10453/28348