Computationally intelligent description of a photoacoustic detector
Samo za registrovane korisnike
2020
Autori
Jordović-Pavlović, Miroslava I.Kupusinac, Aleksandar
Đorđević, Katarina Lj.
Galović, Slobodanka
Markushev, Dragan D.
Nešić, Mioljub V.
Popović, Marica N.
Članak u časopisu (Objavljena verzija)
,
© 2020, Springer Science+Business Media, LLC, part of Springer Nature
Metapodaci
Prikaz svih podataka o dokumentuApstrakt
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.
Ključne reči:
Photoacoustic / Artificial neural networks / Microphone / Classification / RegressionIzvor:
Optical and Quantum Electronics, 2020, 52, 5, 246-Finansiranje / projekti:
- Funkcionalni, funkcionalizovani i usavršeni nano materijali (RS-MESTD-Integrated and Interdisciplinary Research (IIR or III)-45005)
- Atomski sudarni procesi i fotoakustička spektrometrija molekula i čvrstih tela (RS-MESTD-Basic Research (BR or ON)-171016)
- Reprezentacije logičkih struktura i formalnih jezika i njihove primene u računarstvu (RS-MESTD-Basic Research (BR or ON)-174026)
- Razvoj novih informaciono-komunikacionih tehnologija, korišćenjem naprednih matematičkih metoda, sa primenama u medicini, telekomunikacijama, energetici, zaštititi nacionalne baštine i obrazovanju (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
Institucija/grupa
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 . .