The application of artificial neural networks in solid-state photoacoustics for the recognition of microphone response effects in the frequency domain
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2020
Authors
Jordović-Pavlović, Miroslava I.Stanković, Milena M.
Popović, Marica N.

Ćojbašić, Žarko М.

Galović, Slobodanka

Markushev, Dragan D.
Article (Published version)

© 2020, Springer Science+Business Media, LLC, part of Springer Nature
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An analysis of the application of neural networks as a reliable, precise, and fast tool in open-cell photoacoustics setups for the recognition of microphone effects in the frequency domain from 10 Hz to 100 × 104 Hz is presented. The network is trained to achieve simultaneous recognition of microphone characteristics, which are the most important parameters leading to the distortion of photoacoustic signals in both amplitude and phase. The training is carried out using a theoretically obtained database of amplitudes and phases as the input and five microphone characteristics as the output, based on transmission measurements obtained using an open photoacoustic cell setup. The results show that the network can precisely and reliably interpolate the output to recognize microphone characteristics including electronic effects in the low and acoustic effects in the high frequency domain. The simulations reveal that the network is not capable of interpolating an input including modulation fr...equencies. Consequently, in real applications, the network training must be adapted to the experimental frequencies, or vice versa. The total number of frequencies used in the experiment must also be in accordance with the total number of frequencies used in the network training.
Keywords:
Neural networks / Intelligent instruments / Photoacoustics / Microphone response / Modulation frequencySource:
Journal of Computational Electronics, 2020, 19, 3, 1268-1280Funding / projects:
- Atomic collision processes and photoacoustic spectroscopy of molecules and solids (RS-171016)
- Functional, Functionalized and Advanced Nanomaterials (RS-45005)
DOI: 10.1007/s10825-020-01507-4
ISSN: 1569-8025
WoS: 000530177800001
Scopus: 2-s2.0-85085096151
Institution/Community
VinčaTY - JOUR AU - Jordović-Pavlović, Miroslava I. AU - Stanković, Milena M. AU - Popović, Marica N. AU - Ćojbašić, Žarko М. AU - Galović, Slobodanka AU - Markushev, Dragan D. PY - 2020 UR - https://vinar.vin.bg.ac.rs/handle/123456789/9005 AB - An analysis of the application of neural networks as a reliable, precise, and fast tool in open-cell photoacoustics setups for the recognition of microphone effects in the frequency domain from 10 Hz to 100 × 104 Hz is presented. The network is trained to achieve simultaneous recognition of microphone characteristics, which are the most important parameters leading to the distortion of photoacoustic signals in both amplitude and phase. The training is carried out using a theoretically obtained database of amplitudes and phases as the input and five microphone characteristics as the output, based on transmission measurements obtained using an open photoacoustic cell setup. The results show that the network can precisely and reliably interpolate the output to recognize microphone characteristics including electronic effects in the low and acoustic effects in the high frequency domain. The simulations reveal that the network is not capable of interpolating an input including modulation frequencies. Consequently, in real applications, the network training must be adapted to the experimental frequencies, or vice versa. The total number of frequencies used in the experiment must also be in accordance with the total number of frequencies used in the network training. T2 - Journal of Computational Electronics T1 - The application of artificial neural networks in solid-state photoacoustics for the recognition of microphone response effects in the frequency domain VL - 19 IS - 3 SP - 1268 EP - 1280 DO - 10.1007/s10825-020-01507-4 ER -
@article{ author = "Jordović-Pavlović, Miroslava I. and Stanković, Milena M. and Popović, Marica N. and Ćojbašić, Žarko М. and Galović, Slobodanka and Markushev, Dragan D.", year = "2020", abstract = "An analysis of the application of neural networks as a reliable, precise, and fast tool in open-cell photoacoustics setups for the recognition of microphone effects in the frequency domain from 10 Hz to 100 × 104 Hz is presented. The network is trained to achieve simultaneous recognition of microphone characteristics, which are the most important parameters leading to the distortion of photoacoustic signals in both amplitude and phase. The training is carried out using a theoretically obtained database of amplitudes and phases as the input and five microphone characteristics as the output, based on transmission measurements obtained using an open photoacoustic cell setup. The results show that the network can precisely and reliably interpolate the output to recognize microphone characteristics including electronic effects in the low and acoustic effects in the high frequency domain. The simulations reveal that the network is not capable of interpolating an input including modulation frequencies. Consequently, in real applications, the network training must be adapted to the experimental frequencies, or vice versa. The total number of frequencies used in the experiment must also be in accordance with the total number of frequencies used in the network training.", journal = "Journal of Computational Electronics", title = "The application of artificial neural networks in solid-state photoacoustics for the recognition of microphone response effects in the frequency domain", volume = "19", number = "3", pages = "1268-1280", doi = "10.1007/s10825-020-01507-4" }
Jordović-Pavlović, M. I., Stanković, M. M., Popović, M. N., Ćojbašić, Ž. М., Galović, S.,& Markushev, D. D.. (2020). The application of artificial neural networks in solid-state photoacoustics for the recognition of microphone response effects in the frequency domain. in Journal of Computational Electronics, 19(3), 1268-1280. https://doi.org/10.1007/s10825-020-01507-4
Jordović-Pavlović MI, Stanković MM, Popović MN, Ćojbašić ŽМ, Galović S, Markushev DD. The application of artificial neural networks in solid-state photoacoustics for the recognition of microphone response effects in the frequency domain. in Journal of Computational Electronics. 2020;19(3):1268-1280. doi:10.1007/s10825-020-01507-4 .
Jordović-Pavlović, Miroslava I., Stanković, Milena M., Popović, Marica N., Ćojbašić, Žarko М., Galović, Slobodanka, Markushev, Dragan D., "The application of artificial neural networks in solid-state photoacoustics for the recognition of microphone response effects in the frequency domain" in Journal of Computational Electronics, 19, no. 3 (2020):1268-1280, https://doi.org/10.1007/s10825-020-01507-4 . .