Jordović-Pavlović, Miroslava I.

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  • Jordović-Pavlović, Miroslava I. (4)
Projects

Author's Bibliography

Deep Neural Network Application in the Phase-Match Calibration of Gas–Microphone Photoacoustics

Jordović-Pavlović, Miroslava I.; Markushev, Dragan D.; Kupusinac, Aleksandar; Đorđević, Katarina Lj.; Nešić, Mioljub V.; Galović, Slobodanka; Popović, Marica N.

(2020)

TY  - JOUR
AU  - Jordović-Pavlović, Miroslava I.
AU  - Markushev, Dragan D.
AU  - Kupusinac, Aleksandar
AU  - Đorđević, Katarina Lj.
AU  - Nešić, Mioljub V.
AU  - Galović, Slobodanka
AU  - Popović, Marica N.
PY  - 2020
UR  - https://vinar.vin.bg.ac.rs/handle/123456789/8912
AB  - In this paper, a methodology for the application of neural networks in phase-match calibration of gas–microphone photoacoustics in frequency domain is developed. A two-layer deep neural network is used to determine, in real-time, reliably and accurately, the phase transfer function of the used microphone, applying the photoacoustic response of aluminum as reference material. This transfer function was used to correct the photoacoustic response of laser-sintered polyamide and to compare it with theoretical predictions. The obtained degree of correlation of the corrected and theoretical signal tells us that our method of phase-match calibration in photoacoustics can be generalized to a photoacoustic response coming from a solid sample made of different materials.
T2  - International Journal of Thermophysics
T1  - Deep Neural Network Application in the Phase-Match Calibration of Gas–Microphone Photoacoustics
VL  - 41
IS  - 6
SP  - 73
DO  - 10.1007/s10765-020-02650-7
ER  - 
@article{
author = "Jordović-Pavlović, Miroslava I. and Markushev, Dragan D. and Kupusinac, Aleksandar and Đorđević, Katarina Lj. and Nešić, Mioljub V. and Galović, Slobodanka and Popović, Marica N.",
year = "2020",
url = "https://vinar.vin.bg.ac.rs/handle/123456789/8912",
abstract = "In this paper, a methodology for the application of neural networks in phase-match calibration of gas–microphone photoacoustics in frequency domain is developed. A two-layer deep neural network is used to determine, in real-time, reliably and accurately, the phase transfer function of the used microphone, applying the photoacoustic response of aluminum as reference material. This transfer function was used to correct the photoacoustic response of laser-sintered polyamide and to compare it with theoretical predictions. The obtained degree of correlation of the corrected and theoretical signal tells us that our method of phase-match calibration in photoacoustics can be generalized to a photoacoustic response coming from a solid sample made of different materials.",
journal = "International Journal of Thermophysics",
title = "Deep Neural Network Application in the Phase-Match Calibration of Gas–Microphone Photoacoustics",
volume = "41",
number = "6",
pages = "73",
doi = "10.1007/s10765-020-02650-7"
}
Jordović-Pavlović, M. I., Markushev, D. D., Kupusinac, A., Đorđević, K. Lj., Nešić, M. V., Galović, S.,& Popović, M. N. (2020). Deep Neural Network Application in the Phase-Match Calibration of Gas–Microphone Photoacoustics.
International Journal of Thermophysics, 41(6), 73.
https://doi.org/10.1007/s10765-020-02650-7
Jordović-Pavlović MI, Markushev DD, Kupusinac A, Đorđević KL, Nešić MV, Galović S, Popović MN. Deep Neural Network Application in the Phase-Match Calibration of Gas–Microphone Photoacoustics. International Journal of Thermophysics. 2020;41(6):73
Jordović-Pavlović Miroslava I., Markushev Dragan D., Kupusinac Aleksandar, Đorđević Katarina Lj., Nešić Mioljub V., Galović Slobodanka, Popović Marica N., "Deep Neural Network Application in the Phase-Match Calibration of Gas–Microphone Photoacoustics" International Journal of Thermophysics, 41, no. 6 (2020):73,
https://doi.org/10.1007/s10765-020-02650-7 .
1

Photoacoustic optical semiconductor characterization based on machine learning and reverse-back procedure

Đorđević, Katarina Lj.; Galović, Slobodanka; Jordović-Pavlović, Miroslava I.; Nešić, Mioljub V.; Popović, Marica N.; Ćojbašić, Žarko М.; Markushev, Dragan D.

(2020)

TY  - JOUR
AU  - Đorđević, Katarina Lj.
AU  - Galović, Slobodanka
AU  - Jordović-Pavlović, Miroslava I.
AU  - Nešić, Mioljub V.
AU  - Popović, Marica N.
AU  - Ćojbašić, Žarko М.
AU  - Markushev, Dragan D.
PY  - 2020
UR  - https://vinar.vin.bg.ac.rs/handle/123456789/8972
AB  - This paper introduces the possibility of the determination of optical absorption and reflexivity coefficient of silicon samples using neural networks and reverse-back procedure based on the photoacoustics response in the frequency domain. Differences between neural network predictions and parameters obtained with standard photoacoustic signal correction procedures are used to adjust our experimental set-up due to the instability of the optical excitation source and the state (contamination) of the illuminated surface. It has been shown that the changes of the optical absorption values correspond to the light source wavelength fluctuations, while changes in the reflexivity coefficient, obtained in this way, correspond to the small effect of the ultrathin layer formation of SiO2 due to the natural process of surface oxidation.
T2  - Optical and Quantum Electronics
T1  - Photoacoustic optical semiconductor characterization based on machine learning and reverse-back procedure
VL  - 52
IS  - 5
SP  - 247
DO  - 10.1007/s11082-020-02373-x
ER  - 
@article{
author = "Đorđević, Katarina Lj. and Galović, Slobodanka and Jordović-Pavlović, Miroslava I. and Nešić, Mioljub V. and Popović, Marica N. and Ćojbašić, Žarko М. and Markushev, Dragan D.",
year = "2020",
url = "https://vinar.vin.bg.ac.rs/handle/123456789/8972",
abstract = "This paper introduces the possibility of the determination of optical absorption and reflexivity coefficient of silicon samples using neural networks and reverse-back procedure based on the photoacoustics response in the frequency domain. Differences between neural network predictions and parameters obtained with standard photoacoustic signal correction procedures are used to adjust our experimental set-up due to the instability of the optical excitation source and the state (contamination) of the illuminated surface. It has been shown that the changes of the optical absorption values correspond to the light source wavelength fluctuations, while changes in the reflexivity coefficient, obtained in this way, correspond to the small effect of the ultrathin layer formation of SiO2 due to the natural process of surface oxidation.",
journal = "Optical and Quantum Electronics",
title = "Photoacoustic optical semiconductor characterization based on machine learning and reverse-back procedure",
volume = "52",
number = "5",
pages = "247",
doi = "10.1007/s11082-020-02373-x"
}
Đorđević, K. Lj., Galović, S., Jordović-Pavlović, M. I., Nešić, M. V., Popović, M. N., Ćojbašić, Ž. М.,& Markushev, D. D. (2020). Photoacoustic optical semiconductor characterization based on machine learning and reverse-back procedure.
Optical and Quantum Electronics, 52(5), 247.
https://doi.org/10.1007/s11082-020-02373-x
Đorđević KL, Galović S, Jordović-Pavlović MI, Nešić MV, Popović MN, Ćojbašić ŽМ, Markushev DD. Photoacoustic optical semiconductor characterization based on machine learning and reverse-back procedure. Optical and Quantum Electronics. 2020;52(5):247
Đorđević Katarina Lj., Galović Slobodanka, Jordović-Pavlović Miroslava I., Nešić Mioljub V., Popović Marica N., Ćojbašić Žarko М., Markushev Dragan D., "Photoacoustic optical semiconductor characterization based on machine learning and reverse-back procedure" Optical and Quantum Electronics, 52, no. 5 (2020):247,
https://doi.org/10.1007/s11082-020-02373-x .
1
1

Computationally intelligent description of a photoacoustic detector

Jordović-Pavlović, Miroslava I.; Kupusinac, Aleksandar; Đorđević, Katarina Lj.; Galović, Slobodanka; Markushev, Dragan D.; Nešić, Mioljub V.; Popović, Marica N.

(2020)

TY  - 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",
url = "https://vinar.vin.bg.ac.rs/handle/123456789/8982",
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.
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. Optical and Quantum Electronics. 2020;52(5):246
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" Optical and Quantum Electronics, 52, no. 5 (2020):246,
https://doi.org/10.1007/s11082-020-02372-y .
1
1

The application of artificial neural networks in solid-state photoacoustics for the recognition of microphone response effects in the frequency domain

Jordović-Pavlović, Miroslava I.; Stanković, Milena M.; Popović, Marica N.; Ćojbašić, Žarko М.; Galović, Slobodanka; Markushev, Dragan D.

(2020)

TY  - 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",
url = "https://vinar.vin.bg.ac.rs/handle/123456789/9005",
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.
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. Journal of Computational Electronics. 2020;19(3):1268-1280
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" Journal of Computational Electronics, 19, no. 3 (2020):1268-1280,
https://doi.org/10.1007/s10825-020-01507-4 .