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dc.contributor.authorRodríguez Cuevas, Alberto
dc.contributor.authorFontana, Marco
dc.contributor.authorRodríguez Cobo, Luis 
dc.contributor.authorLomer Barboza, Mauro Matías 
dc.contributor.authorLópez Higuera, José Miguel 
dc.contributor.otherUniversidad de Cantabriaes_ES
dc.date.accessioned2019-05-07T17:03:58Z
dc.date.issued2018-09-01
dc.identifier.issn0733-8724
dc.identifier.issn1558-2213
dc.identifier.otherTEC2016-76021-C2-2-Res_ES
dc.identifier.urihttp://hdl.handle.net/10902/16224
dc.description.abstractFiber Specklegram Sensors (FSSs) are highly sensitive to external perturbations, however, trying to locate perturbation's position remains as a barely addressed study. In this work, a system able to classify perturbations according to the place they have been caused along a multimode optical fiber has been designed. As proof of concept, a multimode optical fiber has been perturbated in different points, recording the videos of the perturbations in the speckle pattern, processing these videos, training with them a machine learning algorithm, and classifying further perturbations based on the spatial locations they were generated. The results show classifications up to 99% when the system has to categorize among three different locations lowering to 71% when the locations rise to ten.es_ES
dc.description.sponsorshipThis work was supported by the Spanish Government through the Ministry of Economy and Competitiveness project TEC2016-76021-C2-2-R (AEI/FEDER, UE).es_ES
dc.format.extent6 p.es_ES
dc.language.isoenges_ES
dc.publisherOSA - IEEEes_ES
dc.rights© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.es_ES
dc.sourceJournal of Lightwave Technology, 2018, 36(17), 3733-3738es_ES
dc.subject.otherFiber optic sensorses_ES
dc.subject.otherMultimode waveguideses_ES
dc.subject.otherNeural networkses_ES
dc.subject.otherPattern recognitiones_ES
dc.subject.otherSpecklees_ES
dc.subject.otherSpeckle interferometryes_ES
dc.titleMachine learning for turning optical Fiber Specklegram Sensor into a spatially-resolved sensing system. Proof of conceptes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.relation.publisherVersionhttps://doi.org/10.1109/JLT.2018.2850801es_ES
dc.rights.accessRightsopenAccesses_ES
dc.identifier.DOI10.1109/JLT.2018.2850801
dc.type.versionacceptedVersiones_ES


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