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dc.contributor.authorThesing, Lauraen
dc.contributor.authorHansen, Andersen
dc.date.accessioned2018-09-27T14:13:05Z
dc.date.available2018-09-27T14:13:05Z
dc.identifier.issn1096-603X
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/282849
dc.description.abstractThis paper is concerned with the problem of reconstructing an infinite-dimensional signal from a limited number of linear measurements. In particular, we show that for binary measurements (modelled with Walsh functions and Hadamard matrices) and wavelet reconstruction the stable sampling rate is linear. This implies that binary measurements are as efficient as Fourier samples when using wavelets as the reconstruction space. Powerful techniques for reconstructions include generalized sampling and its compressed versions, as well as recent methods based on data assimilation. Common to these methods is that the reconstruction quality depends highly on the subspace angle between the sampling and the reconstruction space, which is dictated by the stable sampling rate. As a result of the theory provided in this paper, these methods can now easily use binary measurements and wavelet reconstruction bases.
dc.description.sponsorshipThis work of LT was supported by the UK Engineering and Physical Sciences Research Council (EPSRC) grant EP/L016516/1 for the University of Cambridge Centre for Doctoral Training, the Cambridge Centre for Analysis. ACH acknowledges support from Royal Society University Research Fellowship as well as the UK Engineering and Physical Sciences Research Council (EPSRC) grant EP/L003457/1.
dc.publisherElsevier
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.subjectsampling theoryen
dc.subjectgeneralized samplingen
dc.subjectwaveletsen
dc.subjectWalsh functionsen
dc.subjectstable sampling rateen
dc.subjectdata assimilationen
dc.subjectHilbert spacesen
dc.titleOn the stable sampling rate for binary measurements and wavelet reconstructionen
dc.typeArticle
prism.publicationNameApplied and Computational Harmonic Analysisen
dc.identifier.doi10.17863/CAM.30213
dcterms.dateAccepted2018-08-17en
rioxxterms.versionofrecord10.1016/j.acha.2018.08.004en
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/en
rioxxterms.licenseref.startdate2018-08-17en
dc.contributor.orcidThesing, Laura [0000-0002-3712-3819]
dc.identifier.eissn1096-603X
rioxxterms.typeJournal Article/Reviewen
pubs.funder-project-idEPSRC (EP/L016516/1)
pubs.funder-project-idEPSRC (EP/L003457/1)
pubs.funder-project-idRoyal Society (UF160716)
cam.issuedOnline2018-08-23en
rioxxterms.freetoread.startdate2019-08-23


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