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The Bauman University will launch contract production of quantum processors
The goal is to meet the growing need in the Russian Federation for next generation supercomputers
New feature in IEEE Sensors Journal
10 june 2020
Machine Learning for Optical Gas Sensing: A Leaky-Mode Humidity Sensor as Example
Optical gas sensing attracts growing attention in the recent years. This is governed by progressive availability of optical nanostructures fabrication and complex techniques of optical spectrum processing. In the present paper, a room-temperature optical humidity sensor based on a hydrophilic polymer Nafion is theoretically and experimentally investigated. Sensor geometry is optimized for maximum sensitivity of an angle-resolved ATR dip in the Kretschmann configuration.
The reflectance dip is attributed to the 2nd order Nafion layer leaky waveguide mode hybridized with the surface plasmon-polariton at the silver/Nafion interface.
Results of the relative humidity (RH) retrieval with the regression of the physical model parameters is compared to these obtained with different machine learning (ML) techniques. It is shown that a limited raw data set is enough for using ML algorithms. Accuracy of 0.3% has been demonstrated in RH measurements.
Kornienko VV, Nechepurenko IA, Tananaev PN, Chubchev ED, Baburin AS, Echeistov VV, Zverev AV, Novoselov II, Kruglov IA, Rodionov IA and Baryshev AV.
IEEE Sensors Journal 20, 13, 2020 (ImF 3,076)
https://ieeexplore.ieee.org/document/9026912
Optical gas sensing attracts growing attention in the recent years. This is governed by progressive availability of optical nanostructures fabrication and complex techniques of optical spectrum processing. In the present paper, a room-temperature optical humidity sensor based on a hydrophilic polymer Nafion is theoretically and experimentally investigated. Sensor geometry is optimized for maximum sensitivity of an angle-resolved ATR dip in the Kretschmann configuration.
The reflectance dip is attributed to the 2nd order Nafion layer leaky waveguide mode hybridized with the surface plasmon-polariton at the silver/Nafion interface.
Results of the relative humidity (RH) retrieval with the regression of the physical model parameters is compared to these obtained with different machine learning (ML) techniques. It is shown that a limited raw data set is enough for using ML algorithms. Accuracy of 0.3% has been demonstrated in RH measurements.
IEEE Sensors Journal 20, 13, 2020 (ImF 3,076)
https://ieeexplore.ieee.org/document/9026912
