Zobrazit minimální záznam

dc.contributor.authorNovosadová, Michaelacs
dc.contributor.authorRajmic, Pavelcs
dc.contributor.authorŠorel, Michalcs
dc.date.accessioned2019-01-29T07:57:11Z
dc.date.available2019-01-29T07:57:11Z
dc.date.issued2019-01-25cs
dc.identifier.citationEURASIP Journal on Advances in Signal Processing. 2019, vol. 2019, issue 6, p. 1-15.en
dc.identifier.issn1687-6172cs
dc.identifier.other153383cs
dc.identifier.urihttp://hdl.handle.net/11012/137441
dc.description.abstractSegmentation and denoising of signals often rely on the polynomial model which assumes that every segment is a polynomial of a certain degree and that the segments are modeled independently of each other. Segment borders (breakpoints) correspond to positions in the signal where the model changes its polynomial representation. Several signal denoising methods successfully combine the polynomial assumption with sparsity. In this work, we follow on this and show that using orthogonal polynomials instead of other systems in the model is beneficial when segmenting signals corrupted by noise. The switch to orthogonal bases brings better resolving of the breakpoints, removes the need for including additional parameters and their tuning, and brings numerical stability. Last but not the least, it comes for free!en
dc.formattextcs
dc.format.extent1-15cs
dc.format.mimetypeapplication/pdfcs
dc.language.isoencs
dc.publisherSpringer Opencs
dc.relation.ispartofEURASIP Journal on Advances in Signal Processingcs
dc.relation.urihttps://asp-eurasipjournals.springeropen.com/articles/10.1186/s13634-018-0598-9cs
dc.rightsCreative Commons Attribution 4.0 Internationalcs
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/cs
dc.subjectSignal segmentationen
dc.subjectSignal smoothingen
dc.subjectSignal approximationen
dc.subjectDenoisingen
dc.subjectPiecewise polynomialsen
dc.subjectOrthogonalityen
dc.subjectSparsityen
dc.subjectProximal splittingen
dc.subjectConvex optimizationen
dc.titleOrthogonality is superiority in piecewise-polynomial signal segmentation and denoisingen
thesis.grantorVysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií. Ústav telekomunikacícs
sync.item.dbidVAV-153383en
sync.item.dbtypeVAVen
sync.item.insts2019.02.20 16:44:28en
sync.item.modts2019.02.20 16:22:23en
dc.coverage.issue6cs
dc.coverage.volume2019cs
dc.identifier.doi10.1186/s13634-018-0598-9cs
dc.rights.accessopenAccesscs
dc.rights.sherpahttp://www.sherpa.ac.uk/romeo/issn/1687-6172/cs
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen


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Zobrazit minimální záznam

Creative Commons Attribution 4.0 International
Kromě případů, kde je uvedeno jinak, licence tohoto záznamu je Creative Commons Attribution 4.0 International