TY - GEN
T1 - Sectorial-Perturbation analysis of fiber specklegram using machine learning techniques
AU - Fontana, M.
AU - Rodríguez-Cuevas, A.
AU - Rodríguez-Cobo, L.
AU - Mateo, J.
AU - Lomer, M.
PY - 2017
Y1 - 2017
N2 - Fiber-optic specklegram patterns are highly sensitive to external perturbations such as vibrations, temperature, or strain. However, due to its highly random behaviour using this kind of system for distributed sensing remains. In this work, a distributed sensing speckle system has been designed. This proof-of-concept has been developed by perturbating a multimode plastic optical fiber in three different places, recording the videos of these perturbations and using them for training and testing machine learning algorithms. The results show classifications over 99% of accuracy when testing new data under certain conditions.
AB - Fiber-optic specklegram patterns are highly sensitive to external perturbations such as vibrations, temperature, or strain. However, due to its highly random behaviour using this kind of system for distributed sensing remains. In this work, a distributed sensing speckle system has been designed. This proof-of-concept has been developed by perturbating a multimode plastic optical fiber in three different places, recording the videos of these perturbations and using them for training and testing machine learning algorithms. The results show classifications over 99% of accuracy when testing new data under certain conditions.
UR - https://www.scopus.com/pages/publications/85035782496
M3 - Conference publication
AN - SCOPUS:85035782496
T3 - 26th International Conference on Plastic Optical Fibres, POF 2017 - Proceedings
BT - 26th International Conference on Plastic Optical Fibres, POF 2017 - Proceedings
A2 - Rocha, Ana Maria
A2 - Nogueira, Rogerio Nunes
T2 - 26th International Conference on Plastic Optical Fibres, POF 2017 - Proceedings
Y2 - 13 September 2017 through 15 September 2017
ER -