Universal Distributed Sensing via Random Projections

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dc.citation.bibtexNameinproceedingsen_US
dc.citation.conferenceNameInternational Symposium on Integrated Processing in Sensor Networksen_US
dc.citation.firstpage177en_US
dc.citation.lastpage185en_US
dc.citation.locationNashville, Tennesseeen_US
dc.contributor.authorWakin, Michaelen_US
dc.contributor.authorDuarte, Marco F.en_US
dc.contributor.authorBaraniuk, Richard G.en_US
dc.contributor.authorBaron, Droren_US
dc.contributor.orgDigital Signal Processing (http://dsp.rice.edu/)en_US
dc.date.accessioned2007-10-31T00:43:08Zen_US
dc.date.available2007-10-31T00:43:08Zen_US
dc.date.issued2006-04-01en_US
dc.date.modified2006-07-17en_US
dc.date.note2006-07-17en_US
dc.date.submitted2006-04-01en_US
dc.descriptionConference Paperen_US
dc.description.abstractThis paper develops a new framework for distributed coding and compression in sensor networks based on distributed compressed sensing (DCS). DCS exploits both intra-signal and inter-signal correlations through the concept of joint sparsity; just a few measurements of a jointly sparse signal ensemble contain enough information for reconstruction. DCS is well-suited for sensor network applications, thanks to its simplicity, universality, computational asymmetry, tolerance to quantization and noise, robustness to measurement loss, and scalability. It also requires absolutely no inter- sensor collaboration. We apply our framework to several real world datasets to validate the framework.en_US
dc.identifier.citationM. Wakin, M. F. Duarte, R. G. Baraniuk and D. Baron, "Universal Distributed Sensing via Random Projections," 2006.en_US
dc.identifier.doihttp://dx.doi.org/10.1145/1127777.1127807en_US
dc.identifier.urihttps://hdl.handle.net/1911/19866en_US
dc.language.isoengen_US
dc.subjectlinear programmingen_US
dc.subjectcompressed sensingen_US
dc.subjectSparsityen_US
dc.subjectgreedy algorithmsen_US
dc.subjectsensor networksen_US
dc.subjectcorrelationen_US
dc.subject.keywordlinear programmingen_US
dc.subject.keywordcompressed sensingen_US
dc.subject.keywordSparsityen_US
dc.subject.keywordgreedy algorithmsen_US
dc.subject.keywordsensor networksen_US
dc.subject.keywordcorrelationen_US
dc.subject.otherDSP for Communicationsen_US
dc.titleUniversal Distributed Sensing via Random Projectionsen_US
dc.typeConference paperen_US
dc.type.dcmiTexten_US
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