Journal
JOURNAL OF LIGHTWAVE TECHNOLOGY
Volume 36, Issue 17, Pages 3733-3738Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JLT.2018.2850801
Keywords
Fiber optic sensors; multimode waveguides; neural networks; pattern recognition; speckle; speckle interferometry
Funding
- Spanish Government through the Ministry of Economy and Competitiveness [TEC2016-76021-C2-2-R]
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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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