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In this work, the potential of fiber Bragg gratings (FBGs) in low-loss perfluorinated polymer optical fibers (PF-POFs) is explored. The FBG is femtosecond-inscribed in a commercial multi-mode (MM) PF-POF based on Cytop® polymer.
Femtosecond inscription leads to creation of a highly saturated grating with a number of higher order reflection Peaks visible throughout the visible and near-infrared spectral region. For 2 mm long FBG having a pitch of 2.2895 μm, a total of nine higher-order MM reflection bands are visible spanning from 1548 nm (4th order) to 520 nm (12th order). Strain sensitivity was measured for 6 peak bands in 500-900 nm region, where relatively low cost CCD based spectrometers and broadband LEDs are available. Strain sensitivity increases almost linearly with increasing initial peak wavelength, growing from 4.82 ± 0.02 nm/% measured for 12th order peak at 517 nm to 8.12 ± 0.04 nm/% measured for 7th order peak at 883 nm. These values correspond to roughly 20 % higher sensitivity than silica FBGs exhibit in this spectral range. The gratings in PF-POFs combine the higher strain sensitivity and low-loss operation while maintaining the mechanical advantages of polymer optical fibers. Therefore, they hold a high potential for considerable broadening of polymer optical fiber Bragg gratings application range.
Short presentation of the PhD project in machine learning based Brillouin distributed sensing. Machine learning can be used to enhance the performance of BOFDA and reduce considerably the measurement time. Apart from this, ML can also be used to extract more information from the Brillouin gain spectrum in order to render the temperature and strain discrimination possible
Brillouin Optical Frequency Domain Analysis (BOFDA) is a powerful and well-established method for static distributed sensing of temperature and strain. Recently, we demonstrated a BOFDA system based on convolutional neural network which shortens the measurement time considerably. In this paper, we apply leave-one-out cross validation to evaluate the generalization performance and provide an unbiased and reliable machine learning model for a time-efficient BOFDA system.