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dc.creatorJordović-Pavlović, Miroslava I.
dc.creatorKupusinac, Aleksandar
dc.creatorĐorđević, Katarina Lj.
dc.creatorGalović, Slobodanka
dc.creatorMarkushev, Dragan D.
dc.creatorNešić, Mioljub V.
dc.creatorPopović, Marica N.
dc.date.accessioned2021-02-24T07:38:14Z
dc.date.available2021-02-24T07:38:14Z
dc.date.issued2020
dc.identifier.issn0306-8919
dc.identifier.urihttps://vinar.vin.bg.ac.rs/handle/123456789/8982
dc.description.abstractIn 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.en
dc.language.isoen
dc.relationinfo:eu-repo/grantAgreement/MESTD/Integrated and Interdisciplinary Research (IIR or III)/45005/RS//
dc.relationinfo:eu-repo/grantAgreement/MESTD/Basic Research (BR or ON)/171016/RS//
dc.relationinfo:eu-repo/grantAgreement/MESTD/Basic Research (BR or ON)/174026/RS//
dc.relationinfo:eu-repo/grantAgreement/MESTD/Integrated and Interdisciplinary Research (IIR or III)/44006/RS//
dc.rightsrestrictedAccess
dc.sourceOptical and Quantum Electronics
dc.subjectPhotoacousticen
dc.subjectArtificial neural networksen
dc.subjectMicrophoneen
dc.subjectClassificationen
dc.subjectRegressionen
dc.titleComputationally intelligent description of a photoacoustic detectoren
dc.typearticleen
dc.rights.licenseARR
dcterms.abstractНешић, М В; Купусинац, A. Д.; Маркусхев, Д.; Ђорђевић, К Љ.; Поповић, Марица Н.; Јордовић-Павловић, Мирослава И.; Галовић, Слободанка П.;
dc.rights.holder© 2020, Springer Science+Business Media, LLC, part of Springer Nature
dc.citation.volume52
dc.citation.issue5
dc.citation.spage246
dc.identifier.wos000529535400001
dc.identifier.doi10.1007/s11082-020-02372-y
dc.type.versionpublishedVersion
dc.identifier.scopus2-s2.0-85084007075


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