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

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URI: http://hdl.handle.net/10902/16224
ISSN: 0733-8724
ISSN: 1558-2213
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Autoría
Rodríguez Cuevas, Alberto; Fontana, Marco; Rodríguez Cobo, LuisAutoridad Unican; Lomer Barboza, Mauro MatíasAutoridad Unican; López Higuera, José MiguelAutoridad Unican
Fecha
2018-09-01
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© 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.
Publicado en
Journal of Lightwave Technology, 2018, 36(17), 3733-3738
Editorial
OSA - IEEE
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Palabras clave
Fiber optic sensors
Multimode waveguides
Neural networks
Pattern recognition
Speckle
Speckle interferometry
Resumen/Abstract
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.
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