Computationally intelligent description of a photoacoustic detector
Само за регистроване кориснике
2020
Аутори
Jordović-Pavlović, Miroslava I.Kupusinac, Aleksandar
Đorđević, Katarina Lj.
Galović, Slobodanka
Markushev, Dragan D.
Nešić, Mioljub V.
Popović, Marica N.
Чланак у часопису (Објављена верзија)
,
© 2020, Springer Science+Business Media, LLC, part of Springer Nature
Метаподаци
Приказ свих података о документуАпстракт
In this article, a method for determination of photoacoustic detector transfer function as an accurate representation of microphone frequency response is presented. The method is based on supervised machine learning techniques, classification and regression, performed by two artificial neural networks. The transfer function is obtained by determining the microphone type and characteristic parameters closely related to its filtering properties. This knowledge is crucial within the signal correction procedure. The method is carefully designed in order to maintain requirements of photoacoustic experiment accuracy, reliability and real-time performance. The networks training is performed using large base of theoretical signals simulating frequency response of three types of commercial electret microphones frequently used in photoacoustic measurements extended with possible flat response of the so-called ideal microphone. The method test is performed with simulated and experimental signals ...assuming the usage of open-cell photoacoustic set-up. Experimental testing leads to the microphone transfer function determination used to correct the experimental signals, targeting the “true” undistorted photoacoustic response which can be further used in material characterization process.
Кључне речи:
Photoacoustic / Artificial neural networks / Microphone / Classification / RegressionИзвор:
Optical and Quantum Electronics, 2020, 52, 5, 246-Финансирање / пројекти:
- Функционални, функционализовани и усавршени нано материјали (RS-MESTD-Integrated and Interdisciplinary Research (IIR or III)-45005)
- Атомски сударни процеси и фотоакустичка спектрометрија молекула и чврстих тела (RS-MESTD-Basic Research (BR or ON)-171016)
- Репрезентације логичких структура и формалних језика и њихове примене у рачунарству (RS-MESTD-Basic Research (BR or ON)-174026)
- Развој нових информационо-комуникационих технологија, коришћењем напредних математичких метода, са применама у медицини, телекомуникацијама, енергетици, заштитити националне баштине и образовању (RS-MESTD-Integrated and Interdisciplinary Research (IIR or III)-44006)
DOI: 10.1007/s11082-020-02372-y
ISSN: 0306-8919
WoS: 000529535400001
Scopus: 2-s2.0-85084007075
Институција/група
VinčaTY - JOUR AU - Jordović-Pavlović, Miroslava I. AU - Kupusinac, Aleksandar AU - Đorđević, Katarina Lj. AU - Galović, Slobodanka AU - Markushev, Dragan D. AU - Nešić, Mioljub V. AU - Popović, Marica N. PY - 2020 UR - https://vinar.vin.bg.ac.rs/handle/123456789/8982 AB - In this article, a method for determination of photoacoustic detector transfer function as an accurate representation of microphone frequency response is presented. The method is based on supervised machine learning techniques, classification and regression, performed by two artificial neural networks. The transfer function is obtained by determining the microphone type and characteristic parameters closely related to its filtering properties. This knowledge is crucial within the signal correction procedure. The method is carefully designed in order to maintain requirements of photoacoustic experiment accuracy, reliability and real-time performance. The networks training is performed using large base of theoretical signals simulating frequency response of three types of commercial electret microphones frequently used in photoacoustic measurements extended with possible flat response of the so-called ideal microphone. The method test is performed with simulated and experimental signals assuming the usage of open-cell photoacoustic set-up. Experimental testing leads to the microphone transfer function determination used to correct the experimental signals, targeting the “true” undistorted photoacoustic response which can be further used in material characterization process. T2 - Optical and Quantum Electronics T1 - Computationally intelligent description of a photoacoustic detector VL - 52 IS - 5 SP - 246 DO - 10.1007/s11082-020-02372-y ER -
@article{ author = "Jordović-Pavlović, Miroslava I. and Kupusinac, Aleksandar and Đorđević, Katarina Lj. and Galović, Slobodanka and Markushev, Dragan D. and Nešić, Mioljub V. and Popović, Marica N.", year = "2020", abstract = "In this article, a method for determination of photoacoustic detector transfer function as an accurate representation of microphone frequency response is presented. The method is based on supervised machine learning techniques, classification and regression, performed by two artificial neural networks. The transfer function is obtained by determining the microphone type and characteristic parameters closely related to its filtering properties. This knowledge is crucial within the signal correction procedure. The method is carefully designed in order to maintain requirements of photoacoustic experiment accuracy, reliability and real-time performance. The networks training is performed using large base of theoretical signals simulating frequency response of three types of commercial electret microphones frequently used in photoacoustic measurements extended with possible flat response of the so-called ideal microphone. The method test is performed with simulated and experimental signals assuming the usage of open-cell photoacoustic set-up. Experimental testing leads to the microphone transfer function determination used to correct the experimental signals, targeting the “true” undistorted photoacoustic response which can be further used in material characterization process.", journal = "Optical and Quantum Electronics", title = "Computationally intelligent description of a photoacoustic detector", volume = "52", number = "5", pages = "246", doi = "10.1007/s11082-020-02372-y" }
Jordović-Pavlović, M. I., Kupusinac, A., Đorđević, K. Lj., Galović, S., Markushev, D. D., Nešić, M. V.,& Popović, M. N.. (2020). Computationally intelligent description of a photoacoustic detector. in Optical and Quantum Electronics, 52(5), 246. https://doi.org/10.1007/s11082-020-02372-y
Jordović-Pavlović MI, Kupusinac A, Đorđević KL, Galović S, Markushev DD, Nešić MV, Popović MN. Computationally intelligent description of a photoacoustic detector. in Optical and Quantum Electronics. 2020;52(5):246. doi:10.1007/s11082-020-02372-y .
Jordović-Pavlović, Miroslava I., Kupusinac, Aleksandar, Đorđević, Katarina Lj., Galović, Slobodanka, Markushev, Dragan D., Nešić, Mioljub V., Popović, Marica N., "Computationally intelligent description of a photoacoustic detector" in Optical and Quantum Electronics, 52, no. 5 (2020):246, https://doi.org/10.1007/s11082-020-02372-y . .