@conference{10902/18281,
year = {2019},
month = {10},
url = {http://hdl.handle.net/10902/18281},
abstract = {In this work, a deep convolutional adaptive filter is proposed to enhance the performance of a Raman based distributed temperature sensor system by the application of domain randomization methods for its training. The improvement of the signal-to-noise ratio in the Raman backscattered signals in the training process and translation to a real scenario is demonstrated. The ability of the proposed technique to reduce signal noise effectively is proved independently of the sensor configuration and without degradation of temperature accuracy or spatial resolution of these systems. Moreover, using single trace to noise reduction in the ROTDR signals accelerates the system response avoiding the employment of many averages in a unique measurement.},
organization = {This work has been supported by Spanish CICYT (TEC2016-76021-C2-2-R), by ISCIII (DTS17-00055, INTRACARDIO) co-funded by EU-FEDER FUNDS and by the Spanish Ministry of Education, Culture and Sports through FPU16/05705.},
publisher = {SPIE Society of Photo-Optical Instrumentation Engineers},
publisher = {Proceedings of SPIE, 2019, 11199, 111993N},
publisher = {Seventh European Workshop on Optical Fibre Sensors (EWOFS 2019), Limassol, Chipre, 2019},
title = {ROTDR signal enhancement via deep convolutional denoising autoencoders trained with domain randomization},
author = {Laarossi, Ismail and Pardo Franco, Arturo and Conde Portilla, Olga María and Quintela Incera, María Ángeles and López Higuera, José Miguel},
}