Fiber Specklegram Sensors (FSSs) are highly sensitive to external perturbations. However, the detection perturbation's position remains as a barely addressed study to date. 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 at different points, and the output specklegrams have been analyzed using machine learning algorithms.

Using Machine Learning to Turn Optical Fiber Specklegram Sensor into a Spatially Resolved Sensing System

Fontana, Marco
2018/2019

Abstract

Fiber Specklegram Sensors (FSSs) are highly sensitive to external perturbations. However, the detection perturbation's position remains as a barely addressed study to date. 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 at different points, and the output specklegrams have been analyzed using machine learning algorithms.
2018-04-17
fiber optics, sensors, polymer, optical, spatial resolution, specklegram, artificial neural networks, deep learing
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12608/27004