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Optics, Volume 5, Issue 2 (June 2024) – 3 articles

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10 pages, 3124 KiB  
Communication
Multipolar Analysis in Symmetrical Meta-Atoms Sustaining Fano Resonances
by Vittorio Bonino and Angelo Angelini
Optics 2024, 5(2), 238-247; https://doi.org/10.3390/opt5020017 - 15 Apr 2024
Viewed by 334
Abstract
We present an optical metasurface with symmetrical individual elements sustaining Fano resonances with high Q-factors. This study combines plane-wave illumination and modal analysis to investigate the resonant behavior that results in a suppression of the forward scattering, and we investigate the role of [...] Read more.
We present an optical metasurface with symmetrical individual elements sustaining Fano resonances with high Q-factors. This study combines plane-wave illumination and modal analysis to investigate the resonant behavior that results in a suppression of the forward scattering, and we investigate the role of the lattice constant on the excited multipoles and on the spectral position and Q-factor of the Fano resonances, revealing the nonlocal nature of the resonances. The results show that the intrinsic losses play a crucial role in modulating the resonance amplitude in specific conditions and that the optical behavior of the device is extremely sensitive to the pitch of the metasurface. The findings highlight the importance of near-neighbor interactions to achieve high Q resonances and offer an important tool for the design of spectrally tunable metasurfaces using simple geometries. Full article
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15 pages, 3575 KiB  
Article
Enhancing Microwave Photonic Interrogation Accuracy for Fiber-Optic Temperature Sensors via Artificial Neural Network Integration
by Roman Makarov, Mohammed R. T. M. Qaid, Alaa N. Al Hussein, Bulat Valeev, Timur Agliullin, Vladimir Anfinogentov and Airat Sakhabutdinov
Optics 2024, 5(2), 223-237; https://doi.org/10.3390/opt5020016 - 10 Apr 2024
Viewed by 363
Abstract
In this paper, an application of an artificial neural network algorithm is proposed to enhance the accuracy of temperature measurement using a fiber-optic sensor based on a Fabry–Perot interferometer (FPI). It is assumed that the interrogation of the FPI is carried out using [...] Read more.
In this paper, an application of an artificial neural network algorithm is proposed to enhance the accuracy of temperature measurement using a fiber-optic sensor based on a Fabry–Perot interferometer (FPI). It is assumed that the interrogation of the FPI is carried out using an optical comb generator realizing a microwave photonic approach. Firstly, modelling of the reflection spectrum of a Fabry–Perot interferometer is implemented. Secondly, probing of the obtained spectrum using a comb-generator model is performed. The resulting electrical signal of the photodetector is processed and is used to create a sample for artificial neural network training aimed at temperature detection. It is demonstrated that the artificial neural network implementation can predict temperature variations with an accuracy equal to 0.018 °C in the range from −10 to +10 °C and 0.147 in the range from −15 to +15 °C. Full article
(This article belongs to the Special Issue Optical Sensing and Optical Physics Research)
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16 pages, 1066 KiB  
Article
Wavelet-Based Machine Learning Algorithms for Photoacoustic Gas Sensing
by Artem Kozmin, Evgenii Erushin, Ilya Miroshnichenko, Nadezhda Kostyukova, Andrey Boyko and Alexey Redyuk
Optics 2024, 5(2), 207-222; https://doi.org/10.3390/opt5020015 - 03 Apr 2024
Viewed by 497
Abstract
The significance of intelligent sensor systems has grown across diverse sectors, including healthcare, environmental surveillance, industrial automation, and security. Photoacoustic gas sensors are a promising type of optical gas sensor due to their high sensitivity, enhanced frequency selectivity, and fast response time. However, [...] Read more.
The significance of intelligent sensor systems has grown across diverse sectors, including healthcare, environmental surveillance, industrial automation, and security. Photoacoustic gas sensors are a promising type of optical gas sensor due to their high sensitivity, enhanced frequency selectivity, and fast response time. However, they have limitations such as dependence on a high-power light source, a requirement for a high-quality acoustic signal detector, and sensitivity to environmental factors, affecting their accuracy and reliability. Machine learning has great potential in the analysis and interpretation of sensor data as it can identify complex patterns and make accurate predictions based on the available data. We propose a novel approach that utilizes wavelet analysis and neural networks with enhanced architectures to improve the accuracy and sensitivity of photoacoustic gas sensors. Our proposed approach was experimentally tested for methane concentration measurements, showcasing its potential to significantly advance the field of gas detection and analysis, providing more accurate and reliable results. Full article
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