Machine learning for turning optical Fiber Specklegram Sensor into a spatially-resolved sensing system. Proof of concept
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AuthorRodríguez Cuevas, Alberto; Fontana, Marco; Rodríguez Cobo, Luis; Lomer Barboza, Mauro Matías; López Higuera, José Miguel
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.