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ROTDR signal enhancement via deep convolutional denoising autoencoders trained with domain randomization
Author(s): I. Laarossi; A. Pardo Franco; O. M. Conde; M. A. Quintela; José Miguel López-Higuera
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Paper 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

Paper Details

Date Published: 14 October 2019
PDF: 4 pages
Proc. SPIE 11199, Seventh European Workshop on Optical Fibre Sensors, 111993N (14 October 2019); doi: 10.1117/12.2540012
Show Author Affiliations
I. Laarossi, Univ. de Cantabria (Spain)
A. Pardo Franco, Univ. de Cantabria (Spain)
O. M. Conde, Univ. de Cantabria (Spain)
CIBER-bbn, Institituo de Salud Carlos III (Spain)
M. A. Quintela, Univ. de Cantabria (Spain)
José Miguel López-Higuera, Univ. de Cantabria (Spain)
CIBER-bbn, Institituo de Salud Carlos III (Spain)
Instituto de Investigación Sanitaria Valdecilla (IDIVAL) (Spain)


Published in SPIE Proceedings Vol. 11199:
Seventh European Workshop on Optical Fibre Sensors
Kyriacos Kalli; Sinead O. O'Keeffe; Gilberto Brambilla, Editor(s)

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