Overview of compressed sensing: Sensing model, reconstruction algorithm, and its applications

dc.bibliographicCitation.firstPage5909
dc.bibliographicCitation.issue17
dc.bibliographicCitation.journalTitleApplied Scienceseng
dc.bibliographicCitation.volume10
dc.contributor.authorLi, Lixiang
dc.contributor.authorFang, Yuan
dc.contributor.authorLiu, Liwei
dc.contributor.authorPeng, Haipeng
dc.contributor.authorKurths, Jürgen
dc.contributor.authorYang, Yixian
dc.date.accessioned2022-12-08T07:12:01Z
dc.date.available2022-12-08T07:12:01Z
dc.date.issued2020
dc.description.abstractWith the development of intelligent networks such as the Internet of Things, network scales are becoming increasingly larger, and network environments increasingly complex, which brings a great challenge to network communication. The issues of energy-saving, transmission efficiency, and security were gradually highlighted. Compressed sensing (CS) helps to simultaneously solve those three problems in the communication of intelligent networks. In CS, fewer samples are required to reconstruct sparse or compressible signals, which breaks the restrict condition of a traditional Nyquist-Shannon sampling theorem. Here, we give an overview of recent CS studies, along the issues of sensing models, reconstruction algorithms, and their applications. First, we introduce several common sensing methods for CS, like sparse dictionary sensing, block-compressed sensing, and chaotic compressed sensing. We also present several state-of-the-art reconstruction algorithms of CS, including the convex optimization, greedy, and Bayesian algorithms. Lastly, we offer recommendation for broad CS applications, such as data compression, image processing, cryptography, and the reconstruction of complex networks. We discuss works related to CS technology and some CS essentials. © 2020 by the authors.eng
dc.description.versionpublishedVersion
dc.identifier.urihttps://oa.tib.eu/renate/handle/123456789/10538
dc.identifier.urihttp://dx.doi.org/10.34657/9574
dc.language.isoeng
dc.publisherBasel : MDPI
dc.relation.doihttps://doi.org/10.3390/app10175909
dc.relation.essn2076-3417
dc.rights.licenseCC BY 4.0 Unported
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subject.ddc600
dc.subject.otherComplex networkeng
dc.subject.otherCompressed sensingeng
dc.subject.otherReconstruction algorithmeng
dc.subject.otherSensing methodeng
dc.titleOverview of compressed sensing: Sensing model, reconstruction algorithm, and its applicationseng
dc.typeArticleeng
dc.typeTexteng
tib.accessRightsopenAccess
wgl.contributorPIK
wgl.subjectInformatikger
wgl.subjectIngenieurwissenschaftenger
wgl.typeZeitschriftenartikelger
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