@article{10902/16224,
year = {2018},
month = {9},
url = {http://hdl.handle.net/10902/16224},
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.},
organization = {This work was supported by the Spanish Government through the Ministry of Economy and Competitiveness project TEC2016-76021-C2-2-R (AEI/FEDER, UE).},
publisher = {OSA - IEEE},
publisher = {Journal of Lightwave Technology, 2018, 36(17), 3733-3738},
title = {Machine learning for turning optical Fiber Specklegram Sensor into a spatially-resolved sensing system. Proof of concept},
author = {Rodríguez Cuevas, Alberto and Fontana, Marco and Rodríguez Cobo, Luis and Lomer Barboza, Mauro Matías and López Higuera, José Miguel},
}