Buscar

 Detalle_Publicacion

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

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.

 Autoría: Cuevas A., Fontana M., Rodriguez-Cobo L., Lomer M., Lopez-Higuera J.,

 Fuente: Journal of Lightwave Technology, 2018, 36(17), 3733-3738

Editorial: OSA e IEEE

 Fecha de publicación: 01/09/2018

Nº de páginas: 6

Tipo de publicación: Artículo de Revista

DOI: 10.1109/JLT.2018.2850801

ISSN: 0733-8724,1558-2213

Proyecto español: TEC2016-76021-C2-2-R

Url de la publicación: https://doi.org/10.1109/JLT.2018.2850801

Autores/as

ALBERTO RODRIGUEZ CUEVAS

MARCO FONTANA

LUIS RODRIGUEZ COBO