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A flexible quasi-distributed liquid level sensor based on the changes in the light transmittance in a plastic optical fibre (POF) cable is proposed. The measurement points are constituted by small areas created by side-polishing on a curved fibre and the removal of a portion of the core. These points are distributed on each full turn of a coil of fibre built on a cylindrical tube vertically positioned in a tank. The changes between the refractive indices of air and liquid generate a signal power proportional to the position and level of the liquid. The sensor system has been successfully demonstrated in the laboratory, and experimental results of two prototypes with 15 and 18 measurement points in a range of 33 mm and 39 mm respectively and a resolution of 0.08 mm with bend radii of 5 mm and 8 mm are presented.

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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 design. 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.