1-bit Hamming compressed sensing

UTSePress Research/Manakin Repository

Search UTSePress Research

Advanced Search


My Account

Show simple item record

dc.contributor.author Zhou, Tianyi en_US
dc.contributor.author Tao, Dacheng en_US
dc.contributor.editor NA en_US
dc.date.accessioned 2014-04-03T01:23:26Z
dc.date.available 2014-04-03T01:23:26Z
dc.date.issued 2012 en_US
dc.identifier 2012004232 en_US
dc.identifier.citation Tao, Dacheng and Zhou, Tianyi 2012, '1-bit Hamming compressed sensing', IEEE, Piscataway, USA, pp. 1862-1866. en_US
dc.identifier.issn 978-1-4673-2580-6 en_US
dc.identifier.other E1 en_US
dc.identifier.uri http://hdl.handle.net/10453/22945
dc.description.abstract Compressed sensing (CS) and 1-bit CS cannot directly recover quantized signals preferred in digital systems and require time consuming recovery. In this paper, we introduce 1-bit Hamming compressed sensing (HCS) that directly recovers a k-bit quantized signal of dimension n from its 1-bit measurements via invoking n times of Kullback-Leibler divergence based nearest neighbor search. Compared to CS and 1-bit CS, 1-bit HCS allows the signal to be dense, takes considerably less (linear and non-iterative) recovery time and requires substantially less measurements. Moreover, 1-bit HCS can accelerate 1bit CS recover. We study a quantized recovery error bound of 1-bit HCS for general signals. Extensive numerical simulations verify the appealing accuracy, robustness, efficiency and consistency of 1-bit HCS. en_US
dc.language en_US
dc.publisher IEEE en_US
dc.relation.isbasedon http://dx.doi.org/10.1109/ISIT.2012.6283603 en_US
dc.title 1-bit Hamming compressed sensing en_US
dc.parent IEEE International Symposium on Information Theory - Proceedings en_US
dc.journal.volume en_US
dc.journal.number en_US
dc.publocation Piscataway, USA en_US
dc.identifier.startpage 1862 en_US
dc.identifier.endpage 1866 en_US
dc.cauo.name FEIT.Faculty of Engineering & Information Technology en_US
dc.conference Verified OK en_US
dc.for 080100 en_US
dc.personcode 11201340 en_US
dc.personcode 111502 en_US
dc.percentage 100 en_US
dc.classification.name Artificial Intelligence and Image Processing en_US
dc.classification.type FOR-08 en_US
dc.edition en_US
dc.custom IEEE International Symposium on Information Theory en_US
dc.date.activity 20120701 en_US
dc.location.activity Cambridge, USA en_US
dc.description.keywords NA en_US
dc.staffid en_US
dc.staffid 111502 en_US

Files in this item

This item appears in the following Collection(s)

Show simple item record