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A method for sub-sample computation of time displacements between discrete signals based only on discrete correlation sequences

dc.contributor.authorTeixeira, Cesar A.
dc.contributor.authorMendes, Luis
dc.contributor.authorGraca Ruano, Maria
dc.contributor.authorPereira, Wagner C. A.
dc.date.accessioned2019-11-20T15:08:07Z
dc.date.available2019-11-20T15:08:07Z
dc.date.issued2017-01
dc.description.abstractIn this paper, we propose a new method for sub-sample computation of time displacements between two sampled signals. The new algorithm is based on sampled auto- and cross-correlation sequences and takes into account only the sampled signals without the need for the customary interpolation and fitting procedures. The proposed method was evaluated and compared with other methods, in simulated and real signals. Four other methods were used for comparison: two based on cross-correlation plus fitting, one method based on spline fitting over the input signals, and another based on phase demodulation. With simulated signals, the proposed approach presented similar or better performance, concerning bias and variance, in almost all the tested conditions. The exception was signals with very low SNRs (<10 dB), for which the methods based on phase demodulation and spline fitting presented lower variances. Considering only the two methods based on cross-correlation, our approach presented improved results with signals with high and moderate noise levels. The proposed approach and other three out of the four methods used for comparison are robust in real data. The exception is the phase demodulation method, which may fail when applied to signals collected from real-world scenarios because it is very sensitive to phase changes caused by other oscillations not related to the main echoes. This paper introduced a new class of methods, demonstrating that it is possible to estimate sub-sample delay, based on discrete cross-correlations sequences without the need for interpolation or fitting over the original sampled signals. The proposed approach was robust when applied to real-world signals and presented a moderated computational complexity when compared to the other tested algorithms. Although the new method was tested using ultrasound signals, it can be applied to any time-series with observable events. (C) 2016 Elsevier Ltd. All rights reserved.
dc.description.sponsorshipFundacao para a Ciencia e a Tecnologia
dc.description.sponsorshipCAPES/FCT [FCT/CAPES: 10172/13-0]
dc.description.versioninfo:eu-repo/semantics/publishedVersion
dc.identifier.doi10.1016/j.bspc.2016.09.024
dc.identifier.issn1746-8094
dc.identifier.issn1746-8108
dc.identifier.urihttp://hdl.handle.net/10400.1/13368
dc.language.isoeng
dc.peerreviewedyes
dc.publisherElsevier Sci Ltd
dc.relationLINKING RINGS INTO COMPLEX STRUCTURES
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectTemperature estimation
dc.subjectUltrasonic signals
dc.subjectPerformance
dc.titleA method for sub-sample computation of time displacements between discrete signals based only on discrete correlation sequences
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleLINKING RINGS INTO COMPLEX STRUCTURES
oaire.awardURIinfo:eu-repo/grantAgreement/EC/FP7/219588/EU
oaire.citation.endPage568
oaire.citation.startPage560
oaire.citation.titleBiomedical Signal Processing and Control
oaire.citation.volume31
oaire.fundingStreamFP7
person.familyNameRuano
person.givenNameMaria
person.identifier.ciencia-id9811-A0DD-D5A5
person.identifier.orcid0000-0002-0014-9257
person.identifier.ridA-8321-2011
person.identifier.scopus-author-id7004483805
project.funder.identifierhttp://doi.org/10.13039/501100008530
project.funder.nameEuropean Commission
rcaap.rightsrestrictedAccess
rcaap.typearticle
relation.isAuthorOfPublication61fc8492-d73f-46ca-a3a3-4cd762a784e6
relation.isAuthorOfPublication.latestForDiscovery61fc8492-d73f-46ca-a3a3-4cd762a784e6
relation.isProjectOfPublicationd328ccc1-3e87-4012-a63c-3dbcbf5c71e6
relation.isProjectOfPublication.latestForDiscoveryd328ccc1-3e87-4012-a63c-3dbcbf5c71e6

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