Machine Learning Approach to Data Processing of TFBG-Assisted SPR Sensors

Fiber optic sensors are applied in industry, remote sensing, environmental monitoring and healthcare. A special place is occupied by tilted fiber Bragg gratings, which can significantly expand the capabilities provided by standard Bragg sensors. But these gratings have complex spectral responses, th...

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Vydané v:Journal of lightwave technology Ročník 40; číslo 9; s. 3046 - 3054
Hlavní autori: Chubchev, Eugeny, Tomyshev, Kirill, Nechepurenko, Igor, Dorofeenko, Alexander, Butov, Oleg
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 01.05.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Bragg gratings
Data processing
Environmental monitoring
Fiber optics
Machine learning
machine learning algorithm
Machine learning algorithms
optical fiber devices
Optical fiber sensors
Optical fibers
Plasmons
Refractive index
Refractivity
Remote monitoring
Remote sensing
Remote sensors
Sensors
Temperature sensors
ISSN:0733-8724, 1558-2213
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Shrnutí:Fiber optic sensors are applied in industry, remote sensing, environmental monitoring and healthcare. A special place is occupied by tilted fiber Bragg gratings, which can significantly expand the capabilities provided by standard Bragg sensors. But these gratings have complex spectral responses, therefore, data processing becomes a critical task for achieving maximum performance. In this paper, machine learning methods for processing spectral data of a plasmonic fiber sensor based on a tilted fiber Bragg grating were applied for the first time for the measurement of small refractive index changes. The responses of two similar but not identical sensors were measured in two independent experiments. The model trained on the data of the first sensor was used to analyze data obtained with another sensor. The best resolution achieved in our experiments was <inline-formula><tex-math notation="LaTeX">9 \times {10^{ - 6}}</tex-math></inline-formula> RIU.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0733-8724
1558-2213
DOI:10.1109/JLT.2022.3148533