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

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.