The Methods of Choice the Wavelets for One Dimensional Signals Processing

Simple item page
dc.citation.epage90
dc.citation.issue2
dc.citation.journalTitleAdvances in Cyber-Physical Systems
dc.citation.spage84
dc.contributor.affiliationLviv Polytechnic National University
dc.contributor.authorLagun, Ilona
dc.coverage.placenameЛьвів
dc.coverage.placenameLviv
dc.date.accessioned2020-06-16T08:12:15Z
dc.date.available2020-06-16T08:12:15Z
dc.date.created2019-02-26
dc.date.issued2019-02-26
dc.description.abstractThe paper describes the problems of the effectiveness increasing in the selection of base functions for the processing of different types of one-dimensional signals in the wavelet domain. The efficiency of representing signals in the wavelet domain has been shown; their analysis and processing are related to the choice of base functions. The basic methods and algorithms for selecting base functions are defined, in which the choice of optimal wavelets has been carried out according to a particular criterion for certain types of signals. Methods have been presented for assessing the efficiency of the choice of base wavelets by the criterion for the ratio of the energy of the wavelet coefficients to the entropy of energy distribution of wavelet coefficients, the criterion for estimating the correlation coefficient, and the information criterion. The universal index of quality of the signal has been proposed and substantiated for the first time as a new criterion for choosing a wavelet and the method has been improved for the choice of base wavelets using a genetic algorithm according to the universal signal quality index criterion. The method of multi-criteria optimization of the choice of base wavelet for the processing one-dimensional nonperiodic signals based on the tools of fuzzy logic has been proposed and developed, which made it possible to improve the efficiency of signal processing.
dc.format.extent84-90
dc.format.pages7
dc.identifier.citationLagun I. The Methods of Choice the Wavelets for One Dimensional Signals Processing / Ilona Lagun // Advances in Cyber-Physical Systems. — Lviv : Lviv Politechnic Publishing House, 2019. — Vol 4. — No 2. — P. 84–90.
dc.identifier.citationenLagun I. The Methods of Choice the Wavelets for One Dimensional Signals Processing / Ilona Lagun // Advances in Cyber-Physical Systems. — Lviv : Lviv Politechnic Publishing House, 2019. — Vol 4. — No 2. — P. 84–90.
dc.identifier.urihttps://ena.lpnu.ua/handle/ntb/52230
dc.language.isoen
dc.publisherВидавництво Львівської політехніки
dc.publisherLviv Politechnic Publishing House
dc.relation.ispartofAdvances in Cyber-Physical Systems, 2 (4), 2019
dc.relation.ispartofAdvances in Cyber-Physical Systems, 2 (4), 2019
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dc.relation.references[2] Smolentsev N. K. Osnovy teorii veyvletov. Veyvlety v MATLAB. Moskva: DMK Press, 2014. 628 p.
dc.relation.references[3] Cunha, Caio F. and Carvalho, André and Petraglia, M. R. and Lima, Antonio C. S. An improved scale dependent wavelet selection for data denoising of partial discharge measurement. Proceedings of IEEE International Conference on Solid Dielectrics, ICSD, 2013. Pp. 100–104.
dc.relation.references[4] Yang, Q.; Wang, J. Multi-Level Wavelet Shannon Entropy-Based Method for Single-Sensor Fault Location. Entropy. 17, 2015. Pp. 7101–7117.
dc.relation.references[5] J. Rafiee, M. A. Rafiee, N. Prause, M. P. Schoen. Wavelet basis functions in biomedical signal processing. Expert Systems with Applications. 38(5), 2011. Pp. 6190–620.
dc.relation.references[6] T. M. Cover; J. A. Thomas. Elements of Information Theory, 2nd ed. Wiley-Interscience: Hoboken, NJ, USA, 2006.
dc.relation.references[7] N. Wu, Y. Q. Wei. Research on Wavelet Energy Entropy and its application to harmonic detection in power system. International Journal of Applied Physics and Mathematics. 3(1), 2013. Pp. 31–33.
dc.relation.references[8] H. Hong, Y. Tan, Y. Wang. Optimal base wavelet selection for ECG noise reduction using a Comprehensive Entropy Criterion. Entropy. Vol. 17, Issue 9, 2015. Pp. 6093–6109.
dc.relation.references[9] Dyakonov V. P. Veyvlety. Ot teorii k praktike. Moskva: SOLON-R, 2002. 448 p.
dc.relation.references[10] I. Lagun, A. Nakonechnyi. Selection of wavelet basis for the effectiveness processing of signals. Vestnik Brestskogo gosudarstvennogo tekhnicheskogo universiteta, No. 5, 2016. Pp. 69–73.
dc.relation.references[11] Shtoyyer R. Mnogokriterialnaya optimizatsiya. Teoriya, vychisleniya, i prilozheniya. Moskva: Radio i svyaz, 1992. 504 p.
dc.relation.references[12] Saati T. Prinyatiye resheniy. Metod analiza iyerarkhiy. Moskva: Radio i svyaz, 1993. 320 p.
dc.relation.references[13] Shtovba S. D. Proyektirovaniye nechetkikh sistem sredstvami MATLAB. Moskva: Goryachaya liniya – Telekom, 2007. 288 p.
dc.relation.references[14] Lotfi A Zadeh. Fuzzy Sets. Information and Control, No. 8, 1965. Pp. 338–353.
dc.relation.references[15] Z. Wang, A. C. Bovik. A universal image quality index. IEEE Signal Processing Letters, vol. 9, No. 3, 2002. Pp. 81–84.
dc.relation.references[16] I. I. Lahun, A. Y. Nakonechnyy, R. I. Stakhiv. Vykorystannya universalnoho indeksu yakosti syhnalu pry vybori optymalnykh malokhvylovykh bazovykh funktsiy. Materialy 3-yi Mizhnarodnoyi konferentsiyi z avtomatychnoho upravlinnya ta informatsiynykh tekhnolohiy, ICACIT-2015, Kyiv, 11–13 December 2015. Kyiv, 2015. Pp. 132–135.
dc.relation.references[17] Burakov M. V. Geneticheskiy algoritm: teoriya i praktika: uchebnoye posobiye. Sankt Peterburg: GUAP, 2008. 164 p.
dc.relation.references[18] Lahun I. I., Nakonechnyy R. A. Optymizatsiya poshuku bazovykh malokhvylovykh funktsiy z vykorystannyam henetychnoho alhorytmu. Visnyk Natsionalnoho universytetu “Lvivska politekhnika”. Avtomatyka, vymiryuvannya ta keruvannya. Lviv: NU “Lvivs’ka politekhnika”, 2015. No. 821. Pp. 30–36.
dc.relation.referencesen[1] Nakonechnyy A. Y. Teoriya malokhvylovoho (wavelet) peretvorennya ta yiyi zastosuvannya. Lviv: Feniks, 2001. 278 p.
dc.relation.referencesen[2] Smolentsev N. K. Osnovy teorii veyvletov. Veyvlety v MATLAB. Moskva: DMK Press, 2014. 628 p.
dc.relation.referencesen[3] Cunha, Caio F. and Carvalho, André and Petraglia, M. R. and Lima, Antonio C. S. An improved scale dependent wavelet selection for data denoising of partial discharge measurement. Proceedings of IEEE International Conference on Solid Dielectrics, ICSD, 2013. Pp. 100–104.
dc.relation.referencesen[4] Yang, Q.; Wang, J. Multi-Level Wavelet Shannon Entropy-Based Method for Single-Sensor Fault Location. Entropy. 17, 2015. Pp. 7101–7117.
dc.relation.referencesen[5] J. Rafiee, M. A. Rafiee, N. Prause, M. P. Schoen. Wavelet basis functions in biomedical signal processing. Expert Systems with Applications. 38(5), 2011. Pp. 6190–620.
dc.relation.referencesen[6] T. M. Cover; J. A. Thomas. Elements of Information Theory, 2nd ed. Wiley-Interscience: Hoboken, NJ, USA, 2006.
dc.relation.referencesen[7] N. Wu, Y. Q. Wei. Research on Wavelet Energy Entropy and its application to harmonic detection in power system. International Journal of Applied Physics and Mathematics. 3(1), 2013. Pp. 31–33.
dc.relation.referencesen[8] H. Hong, Y. Tan, Y. Wang. Optimal base wavelet selection for ECG noise reduction using a Comprehensive Entropy Criterion. Entropy. Vol. 17, Issue 9, 2015. Pp. 6093–6109.
dc.relation.referencesen[9] Dyakonov V. P. Veyvlety. Ot teorii k praktike. Moskva: SOLON-R, 2002. 448 p.
dc.relation.referencesen[10] I. Lagun, A. Nakonechnyi. Selection of wavelet basis for the effectiveness processing of signals. Vestnik Brestskogo gosudarstvennogo tekhnicheskogo universiteta, No. 5, 2016. Pp. 69–73.
dc.relation.referencesen[11] Shtoyyer R. Mnogokriterialnaya optimizatsiya. Teoriya, vychisleniya, i prilozheniya. Moskva: Radio i svyaz, 1992. 504 p.
dc.relation.referencesen[12] Saati T. Prinyatiye resheniy. Metod analiza iyerarkhiy. Moskva: Radio i svyaz, 1993. 320 p.
dc.relation.referencesen[13] Shtovba S. D. Proyektirovaniye nechetkikh sistem sredstvami MATLAB. Moskva: Goryachaya liniya – Telekom, 2007. 288 p.
dc.relation.referencesen[14] Lotfi A Zadeh. Fuzzy Sets. Information and Control, No. 8, 1965. Pp. 338–353.
dc.relation.referencesen[15] Z. Wang, A. C. Bovik. A universal image quality index. IEEE Signal Processing Letters, vol. 9, No. 3, 2002. Pp. 81–84.
dc.relation.referencesen[16] I. I. Lahun, A. Y. Nakonechnyy, R. I. Stakhiv. Vykorystannya universalnoho indeksu yakosti syhnalu pry vybori optymalnykh malokhvylovykh bazovykh funktsiy. Materialy 3-yi Mizhnarodnoyi konferentsiyi z avtomatychnoho upravlinnya ta informatsiynykh tekhnolohiy, ICACIT-2015, Kyiv, 11–13 December 2015. Kyiv, 2015. Pp. 132–135.
dc.relation.referencesen[17] Burakov M. V. Geneticheskiy algoritm: teoriya i praktika: uchebnoye posobiye. Sankt Peterburg: GUAP, 2008. 164 p.
dc.relation.referencesen[18] Lahun I. I., Nakonechnyy R. A. Optymizatsiya poshuku bazovykh malokhvylovykh funktsiy z vykorystannyam henetychnoho alhorytmu. Visnyk Natsionalnoho universytetu "Lvivska politekhnika". Avtomatyka, vymiryuvannya ta keruvannya. Lviv: NU "Lvivs’ka politekhnika", 2015. No. 821. Pp. 30–36.
dc.rights.holder© Національний університет “Львівська політехніка”, 2019
dc.rights.holder© Lagun I., 2019
dc.subjectwavelet
dc.subjectbase wavelets
dc.subjectoptimal wavelets
dc.subjectselection criteria
dc.subjectmulti-criterion optimization
dc.titleThe Methods of Choice the Wavelets for One Dimensional Signals Processing
dc.typeArticle

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Advances In Cyber-Physical Systems. – 2019. – Vol. 4, No. 2