Machine learning for turning optical Fiber Specklegram Sensor into a spatially-resolved sensing system. Proof of concept

Fiber 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 fi...

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Autores: Rodríguez Cuevas, Alberto, Fontana, Marco, Rodríguez Cobo, Luis|||0000-0002-2068-2956, Lomer Barboza, Mauro Matías|||0000-0002-4721-4247, López Higuera, José Miguel|||0000-0002-8615-8487
Tipo de recurso: artículo
Fecha de publicación:2018
País:España
Institución:Universidad de Cantabria (UC)
Repositorio:UCrea Repositorio Abierto de la Universidad de Cantabria
Idioma:inglés
OAI Identifier:oai:repositorio.unican.es:10902/16224
Acceso en línea:http://hdl.handle.net/10902/16224
Access Level:acceso abierto
Palabra clave:Fiber optic sensors
Multimode waveguides
Neural networks
Pattern recognition
Speckle
Speckle interferometry
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spelling Machine learning for turning optical Fiber Specklegram Sensor into a spatially-resolved sensing system. Proof of conceptRodríguez Cuevas, AlbertoFontana, MarcoRodríguez Cobo, Luis|||0000-0002-2068-2956Lomer Barboza, Mauro Matías|||0000-0002-4721-4247López Higuera, José Miguel|||0000-0002-8615-8487Fiber optic sensorsMultimode waveguidesNeural networksPattern recognitionSpeckleSpeckle interferometryFiber 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.This work was supported by the Spanish Government through the Ministry of Economy and Competitiveness project TEC2016-76021-C2-2-R (AEI/FEDER, UE).OSA - IEEEUniversidad de Cantabria20182018-09-01journal articlehttp://purl.org/coar/resource_type/c_6501NAhttp://purl.org/coar/version/c_be7fb7dd8ff6fe43info:eu-repo/semantics/articlehttp://hdl.handle.net/10902/16224Journal of Lightwave Technology, 2018, 36(17), 3733-3738reponame:UCrea Repositorio Abierto de la Universidad de Cantabriainstname:Universidad de Cantabria (UC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:repositorio.unican.es:10902/162242026-06-02T12:39:31Z
dc.title.none.fl_str_mv Machine learning for turning optical Fiber Specklegram Sensor into a spatially-resolved sensing system. Proof of concept
title Machine learning for turning optical Fiber Specklegram Sensor into a spatially-resolved sensing system. Proof of concept
spellingShingle Machine learning for turning optical Fiber Specklegram Sensor into a spatially-resolved sensing system. Proof of concept
Rodríguez Cuevas, Alberto
Fiber optic sensors
Multimode waveguides
Neural networks
Pattern recognition
Speckle
Speckle interferometry
title_short Machine learning for turning optical Fiber Specklegram Sensor into a spatially-resolved sensing system. Proof of concept
title_full Machine learning for turning optical Fiber Specklegram Sensor into a spatially-resolved sensing system. Proof of concept
title_fullStr Machine learning for turning optical Fiber Specklegram Sensor into a spatially-resolved sensing system. Proof of concept
title_full_unstemmed Machine learning for turning optical Fiber Specklegram Sensor into a spatially-resolved sensing system. Proof of concept
title_sort Machine learning for turning optical Fiber Specklegram Sensor into a spatially-resolved sensing system. Proof of concept
dc.creator.none.fl_str_mv Rodríguez Cuevas, Alberto
Fontana, Marco
Rodríguez Cobo, Luis|||0000-0002-2068-2956
Lomer Barboza, Mauro Matías|||0000-0002-4721-4247
López Higuera, José Miguel|||0000-0002-8615-8487
author Rodríguez Cuevas, Alberto
author_facet Rodríguez Cuevas, Alberto
Fontana, Marco
Rodríguez Cobo, Luis|||0000-0002-2068-2956
Lomer Barboza, Mauro Matías|||0000-0002-4721-4247
López Higuera, José Miguel|||0000-0002-8615-8487
author_role author
author2 Fontana, Marco
Rodríguez Cobo, Luis|||0000-0002-2068-2956
Lomer Barboza, Mauro Matías|||0000-0002-4721-4247
López Higuera, José Miguel|||0000-0002-8615-8487
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Universidad de Cantabria
dc.subject.none.fl_str_mv Fiber optic sensors
Multimode waveguides
Neural networks
Pattern recognition
Speckle
Speckle interferometry
topic Fiber optic sensors
Multimode waveguides
Neural networks
Pattern recognition
Speckle
Speckle interferometry
description Fiber 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.
publishDate 2018
dc.date.none.fl_str_mv 2018
2018-09-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
NA
http://purl.org/coar/version/c_be7fb7dd8ff6fe43
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10902/16224
url http://hdl.handle.net/10902/16224
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv OSA - IEEE
publisher.none.fl_str_mv OSA - IEEE
dc.source.none.fl_str_mv Journal of Lightwave Technology, 2018, 36(17), 3733-3738
reponame:UCrea Repositorio Abierto de la Universidad de Cantabria
instname:Universidad de Cantabria (UC)
instname_str Universidad de Cantabria (UC)
reponame_str UCrea Repositorio Abierto de la Universidad de Cantabria
collection UCrea Repositorio Abierto de la Universidad de Cantabria
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,301603

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