Utilizza questo identificativo per citare o creare un link a questo documento: http://elartu.tntu.edu.ua/handle/lib/36036

Record completo di tutti i metadati
Campo DCValoreLingua
dc.contributor.authorЛупенко, Сергій Анатолійович
dc.contributor.authorЛитвиненко, Ярослав Володимирович
dc.contributor.authorГотович, Володимир Анатолійович
dc.contributor.authorЗозуля, Андрій Миколайович
dc.contributor.authorНнамене, Чізоба
dc.contributor.authorВоляник, Олександр Вадимович
dc.contributor.authorLupenko, Serhii
dc.contributor.authorLytvynenko, Iaroslav
dc.contributor.authorHotovych, Volodymyr
dc.contributor.authorZozulia, Andrii
dc.contributor.authorChizoba, Nnamene
dc.contributor.authorVolyanyk, Oleksandr
dc.date.accessioned2021-12-11T09:03:25Z-
dc.date.available2021-12-11T09:03:25Z-
dc.date.created2021-06-22
dc.date.issued2021-06-22
dc.date.submitted2021-03-11
dc.identifier.citationConcept of design, requirements and generalized architectures of components of the integrated onto-oriented information environment of simulation and processing of cyclic signals / Serhii Lupenko, Iaroslav Lytvynenko, Volodymyr Hotovych, Andrii Zozulia, Nnamene Chizoba, Oleksandr Volyanyk // Scientific Journal of TNTU. — Tern. : TNTU, 2021. — Vol 102. — P. 147–160.
dc.identifier.issn2522-4433
dc.identifier.urihttp://elartu.tntu.edu.ua/handle/lib/36036-
dc.description.abstractОбґрунтовано актуальність розроблення, а також формулювання загальних вимог та побудову узагальненої архітектури інтегрованого онтоорієнтованого інформаційного середовища моделювання й опрацювання циклічних сигналів на базі теорії циклічних функціональних відношень. Це дозволило ефективно системно вирішити цілий спектр важливих методологічних, методичних та технологічних завдань у галузі моделювання та опрацювання циклічних сигналів. Зокрема, суттєво спростило, інтенсифікувало (автоматизувало) та підвищило ступінь достовірності процедури розроблення математичного й програмного забезпечення інтелектуалізованих систем для потреб медицини, техніки та економіки. Сформульовано основні завдання розроблення та розроблено узагальнену архітектуру інтегрованого онтоорієнтованого інформаційного середовища моделювання й опрацювання циклічних сигналів. Сформульовано вимоги та розроблено узагальнені архітектури таких складових інтегрованого онтоорієнтованого інформаційного середовища моделювання та опрацювання циклічних сигналів: інформаційної онтоорієнтованої довідкової системи в галузі моделювання та опрацювання циклічних сигналів; бази знань інтегрованого інформаційного середовища, ядром якої є онтологія предметної області «Моделювання та опрацювання циклічних сигналів у рамках теорії циклічних функціональних відношень»; експертної онтоорієнтованої системи підтримки прийняття рішень у галузі моделювання й опрацювання циклічних сигналів, інформаційної системи з онтоорієнтованою архітектурою для моделювання та опрацювання циклічних сигналів.
dc.description.abstractThe article gives the reasoning to the relevance of developing a generalized architecture of integrated onto-oriented information environment for simulation and processing of cyclic signals based on the theory of cyclic functional relations, as well as formulates the general requirements to it and its developingt. The research deals with statement of and creating the generalized architectures of the components of the integrated onto-oriented information environment for simulation and processing of cyclic signals, namely, for information-oriented reference system in the field of simulation and processing of cyclic signals; knowledge base of the integrated information environment, the core of which is the corresponding ontology; onto-oriented expert decision support system in the field of simulation and processing of cyclic signals; information system with onto-oriented architecture for simulation and processing of cyclic signals.
dc.format.extent147-160
dc.language.isoen
dc.publisherТНТУ
dc.publisherTNTU
dc.relation.ispartofВісник Тернопільського національного технічного університету (102), 2021
dc.relation.ispartofScientific Journal of the Ternopil National Technical University (102), 2021
dc.relation.urihttps://doi.org/10.1016/j.sigpro.2005.06.016
dc.relation.urihttps://doi.org/10.1109/18.212283
dc.relation.urihttps://doi.org/10.1016/0165-1684(91)90005-4
dc.relation.urihttps://doi.org/10.1016/j.cmpb.2016.10.014
dc.relation.urihttps://doi.org/10.1016/j.hrthm.2017.09.003
dc.relation.urihttps://doi.org/10.1016/j.compbiomed.2018.10.009
dc.relation.urihttps://doi.org/10.1016/j.sigpro.2015.09.013
dc.relation.urihttps://doi.org/10.1002/9781118731543.ch1
dc.relation.urihttps://doi.org/10.3389/fpubh.2017.00258
dc.relation.urihttps://doi.org/10.1016/j.bspc.2018.03.003
dc.relation.urihttps://doi.org/10.1016/j.hrthm.2015.03.016
dc.relation.urihttps://doi.org/10.1002/9781118231296
dc.relation.urihttps://doi.org/10.1016/B978-0-12-386981-4.50011-4
dc.relation.urihttps://doi.org/10.1109/WCITCA.2015.7367032
dc.relation.urihttps://doi.org/10.1109/IranianCEE.2016.7585836
dc.relation.urihttps://doi.org/10.1016/j.bspc.2017.09.018
dc.relation.urihttp://ceur-ws.org/Vol-2753/short8.pdf
dc.relation.urihttps://doi.org/10.33108/visnyk_tntu
dc.relation.urihttps://doi.org/10.33108/visnyk_tntu2020.01.110
dc.relation.urihttps://doi.org/10.20998/2522-9052.2020.2.08
dc.relation.urihttps://doi.org/10.3390/ma13214983
dc.relation.urihttps://doi.org/10.1007/s11003-016-9933-1
dc.subjectонтологія
dc.subjectінтегроване онтоорієнтоване інформаційне середовище
dc.subjectмоделювання
dc.subjectметоди опрацювання
dc.subjectциклічні сигнали
dc.subjectontology
dc.subjectintegrated onto-oriented information environment
dc.subjectsimulation
dc.subjectprocessing methods
dc.subjectcyclic signals
dc.titleConcept of design, requirements and generalized architectures of components of the integrated onto-oriented information environment of simulation and processing of cyclic signals
dc.title.alternativeКонцепція побудови, вимоги та узагальнені архітектури складових інтегрованого онтоорієнтованого інформаційного середовища моделювання та опрацювання циклічних сигналів
dc.typeArticle
dc.rights.holder© Тернопільський національний технічний університет імені Івана Пулюя, 2021
dc.coverage.placenameТернопіль
dc.coverage.placenameTernopil
dc.format.pages14
dc.subject.udc004.652
dc.relation.references1. Gardner W., Napolitano A., L. Paura Cyclostationarity: Half a century of research. Signal Processing. 2005. Vol. 86. Р. 639–697. DOІ: https://doi.org/10.1016/j.sigpro.2005.06.016
dc.relation.references2. Gardner W., Archer T. Exploitation of cyclostationarity for identifying the Volterra kernels of non–linear systems. IEEE Transactions on Information Theory. 1993. Nо. 39 (2). P. 535–542. DOІ: https://doi.org/10.1109/18.212283
dc.relation.references3. Gardner W., Brown W. Fraction of time probability for time-series that exhibit cyclostationarity. Signal Processing. 1991. Vol. 23. P. 273–292. DOІ: https://doi.org/10.1016/0165-1684(91)90005-4
dc.relation.references4. Israa Shaker Tawfic, Sema Koc Kayhan. (2017) Improving recovery of ECG signal with deterministic guarantees using split signal for multiple supports of matching pursuit (SSMSMP) algorithm, Computer Methods and Programs in Biomedicine. Vol. 139. 2017. P. 39–50. DOI: https://doi.org/10.1016/j.cmpb.2016.10.014
dc.relation.references5. Fumagalli F., Silver A. E., Tan Q., Zaidi N., Ristagno G. (2018), Cardiac rhythm analysis during ongoing cardiopulmonary resuscitation using the Analysis During Compressions with Fast Reconfirmation technology, Heart Rhythm, 15 (2). P. 248–255. DOI: https://doi.org/10.1016/j.hrthm.2017.09.003
dc.relation.references6. Napoli N. J., Demas M. W., Mendu S., Stephens C. L., Kennedy K. D, Harrivel A. R, Bailey R. E., Barnes L.E. (2018), Uncertainty in heart rate complexity metrics caused by R-peak perturbations, Computers in Biology and Medicine. 103. P. 198–207. DOI: https://doi.org/10.1016/j.compbiomed.2018.10.009
dc.relation.references7. Napolitano A. (2016) Cyclostationarity: Limits and generalizations. Signal Processing. Vol. 120. March 2016. P. 323–347. DOI: https://doi.org/10.1016/j.sigpro.2015.09.013
dc.relation.references8. Lepage R., Boucher J., Blan J. and Cornilly J., «ECG segmentation and p-wave feature extraction: application to patients prone to atrial fibrillation», IEEE EMBS 2001; 1:298–301.
dc.relation.references9. Moody B. G. and Mark R. G., “A new method for detecting atrial fibrillation using R-R intervals”, IEEE Computers in Cardiology 1983; 10:227-230.
dc.relation.references10. Cerutti S., Mainardi L. T., Porta A. and Bianchi A. M., «Analysis of the Dynamics of RR Interval Series for the Detection of Atrial Fibrillation Episodes», IEEE Computers in Cardiology 1997; 24:77-80.
dc.relation.references11. Tateno K. and Glass L., «A Method for Detection of Atrial Fibrillation Using RR intervals», IEEE Computers in Cardiology 2000; 27:391-394.
dc.relation.references12. Karyotis V., Khouzani M. H. R. (2016) Malware Diffusion Models for Modern Complex Networks. Theory and Applications. USA. 2016. P. 324. ISBN 978-0-12-802714-1.
dc.relation.references13. Sericola B. (2013) Markov Chains: Theory and Applications. London, July 2013. P. 416. ISBN: 978-1-848-21493-4. DOI: https://doi.org/10.1002/9781118731543.ch1
dc.relation.references14. Shaffer F., Ginsberg J. P. (2017), An Overview of Heart Rate Variability Metrics and Norms, Frontiers in Public Health. Volume 5. Article 258. September 2017. P. 1–17. DOI: https://doi.org/10.3389/fpubh.2017.00258
dc.relation.references15. Berkaya S. K., Uysal A. K., Gunal E. S, Ergin S., Gunal S., Gulmezoglu M. B. (2018), A survey on ECG analysis. Biomedical Signal Processing and Control, 43, 216–235. DOI: https://doi.org/10.1016/j.bspc.2018.03.003
dc.relation.references16. Shen C., Yu Z., Liu Z. (2015), The use of statistics in heart rhythm research: a review, Heart Rhythm, 12 (6), 1376–1386. DOI: https://doi.org/10.1016/j.hrthm.2015.03.016
dc.relation.references17. Peter Olofsson, Mikael Andersson. Probability, Statistics, and Stochastic Processes. John Wiley & Sons, INC, 2012. USA. P. 553. DOI: https://doi.org/10.1002/9781118231296
dc.relation.references18. Athanasios Christou Micheas. Theory of Stochastic Objects Probability, Stochastic Processes and Inference. Chapman and Hall/CRC. January 24. 2018. P. 408. ISBN: 9781466515215.
dc.relation.references19. Hisashi Kobayashi, Brian L. Mark, William Turin. Probability, Random Processes, and Statistical Analysis. USA by Cambridge University Press, New York 2012. P. 812. Hardback 978-0-521-89544-6.
dc.relation.references20. Oliver C. Ibe. Fundamentals of Applied Probability and Random Processes. 2nd Edition. Academic Press is an imprint of Elsevier. USA 2014. P. 431.
dc.relation.references21. Scott Miller, Donald Childers Probability and Random Processes: With Applications to Signal Processing and Communications. 2nd Edition. Academic Press is an imprint of Elsevier. USA 2012. P.593. DOI: https://doi.org/10.1016/B978-0-12-386981-4.50011-4
dc.relation.references22. Ben Salah R., Hadidi T. and Chabchoub S., «Intelligent diagnosis method of cardiovascular anomalies using medical signal processing,» 2015 World Congress on Information Technology and Computer Applications (WCITCA), Hammamet, 2015, P. 1–5. DOI: https://doi.org/10.1109/WCITCA.2015.7367032
dc.relation.references23. Rahimpour M., Asl M. E. and Merati M. R., «ECG fiducial points extraction using QRS morphology and adaptive windowing for real-time ECG signal analysis,» 2016 24th Iranian Conference on Electrical Engineering (ICEE). Shiraz. 2016. P. 1925–1930. DOI: https://doi.org/10.1109/IranianCEE.2016.7585836
dc.relation.references24. Ciucurel C., Georgescu L., Iconaru E. I. (2018), ECG response to submaximal exercise from the perspective of Golden Ratio harmonic rhythm, Biomedical Signal Processing and Control, 40, 156–162. DOI: https://doi.org/10.1016/j.bspc.2017.09.018
dc.relation.references25. Onyskiv P., Lupenko S., Lytvynenko I., Zozulia A. Mathematical modeling and processing of high resolution rhythmocardio signal based on a vector of stationary and stationary related random sequences. IDDM’2020: 3rd International Conference on Informatics & Data-Driven Medicine, November 19–21, 2020, Växjö, Sweden. CEUR Workshop Proceedings, 2020, 2753, pp. 149–155. URL: http://ceur-ws.org/Vol-2753/short8.pdf.
dc.relation.references26. Горкуненко A. Б., Лупенко С. A. Обгрунтування діагностичних і прогностичних ознак в інформаційних системах аналізу та прогнозування випадкових економічних процесів. Науковий вісник НЛТУ України: збірник науково-теҳнічних праць. 2012. № 22.9. 347−352 с.
dc.relation.references27. Lupenko S., Lytvynenko I., Stadnyk N. Method for reducing the computational complexity of processing discrete cyclic random processes in digital data analysis systems Scientific Journal of the Ternopil national technical university. 2020. Vol. 97. No. 1. P. 110–121. URL: https://doi.org/10.33108/visnyk_tntu. DOI: https://doi.org/10.33108/visnyk_tntu2020.01.110
dc.relation.references28. Lupenko S., Lytvynenko I., Stadnyk N., Zozulia A. та інші Mathematical model of rhythmocardiosignal in vector view of stationary and stationary-related case sequences. Advanced Information Systems. National Technical University «Kharkiv Polytechnic Institute». 2020. Vol. 4. No. 2. P. 42–46. DOI: https://doi.org/10.20998/2522-9052.2020.2.08
dc.relation.references29. Hutsaylyuk V., Lytvynenko I., Maruschak P., Schnell, G. та інші.A new method for modeling the cyclic structure of the surface microrelief of titanium alloy ti6al4v after processing with femtosecond pulses. Materials, 2020, 13(21), pp. 1–8, 4983. DOI: https://doi.org/10.3390/ma13214983
dc.relation.references30. Marushak P. O., Lytvynenko I. O., Lupenko S. A., Popovych P. V. Modeling of the Ordered Surface Topography of Statically Deformed Aluminum Alloy. Materials Science. 2016. Vol. 52. No. 1. P. 113–122. DOI: https://doi.org/10.1007/s11003-016-9933-1
dc.relation.referencesen1. Gardner W., Napolitano A., L. Paura Cyclostationarity: Half a century of research. Signal Processing. 2005. Vol. 86. Р. 639–697. DOІ: https://doi.org/10.1016/j.sigpro.2005.06.016
dc.relation.referencesen2. Gardner W., Archer T. Exploitation of cyclostationarity for identifying the Volterra kernels of non–linear systems. IEEE Transactions on Information Theory. 1993. Nо. 39 (2). P. 535–542. DOІ: https://doi.org/10.1109/18.212283
dc.relation.referencesen3. Gardner W., Brown W. Fraction of time probability for time-series that exhibit cyclostationarity. Signal Processing. 1991. Vol. 23. P. 273–292. DOІ: https://doi.org/10.1016/0165-1684(91)90005-4
dc.relation.referencesen4. Israa Shaker Tawfic, Sema Koc Kayhan. (2017) Improving recovery of ECG signal with deterministic guarantees using split signal for multiple supports of matching pursuit (SSMSMP) algorithm, Computer Methods and Programs in Biomedicine. Vol. 139. 2017. P. 39–50. DOI: https://doi.org/10.1016/j.cmpb.2016.10.014
dc.relation.referencesen5. Fumagalli F., Silver A. E., Tan Q., Zaidi N., Ristagno G. (2018), Cardiac rhythm analysis during ongoing cardiopulmonary resuscitation using the Analysis During Compressions with Fast Reconfirmation technology, Heart Rhythm, 15 (2). P. 248–255. DOI: https://doi.org/10.1016/j.hrthm.2017.09.003
dc.relation.referencesen6. Napoli N. J., Demas M. W., Mendu S., Stephens C. L., Kennedy K. D, Harrivel A. R, Bailey R. E., Barnes L.E. (2018), Uncertainty in heart rate complexity metrics caused by R-peak perturbations, Computers in Biology and Medicine. 103. P. 198–207. DOI: https://doi.org/10.1016/j.compbiomed.2018.10.009
dc.relation.referencesen7. Napolitano A. (2016) Cyclostationarity: Limits and generalizations. Signal Processing. Vol. 120. March 2016. P. 323–347. DOI: https://doi.org/10.1016/j.sigpro.2015.09.013
dc.relation.referencesen8. Lepage R., Boucher J., Blan J. and Cornilly J., “ECG segmentation and p-wave feature extraction: application to patients prone to atrial fibrillation”, IEEE EMBS 2001; 1:298–301.
dc.relation.referencesen9. Moody B. G. and Mark R. G., “A new method for detecting atrial fibrillation using R-R intervals”, IEEE Computers in Cardiology 1983; 10:227-230.
dc.relation.referencesen10. Cerutti S., Mainardi L. T., Porta A. and Bianchi A. M., “Analysis of the Dynamics of RR Interval Series for the Detection of Atrial Fibrillation Episodes”, IEEE Computers in Cardiology 1997; 24:77-80.
dc.relation.referencesen11. Tateno K. and Glass L., “A Method for Detection of Atrial Fibrillation Using RR intervals”, IEEE Computers in Cardiology 2000; 27:391-394.
dc.relation.referencesen12. Karyotis V., Khouzani M. H. R. (2016) Malware Diffusion Models for Modern Complex Networks. Theory and Applications. USA. 2016. P. 324. ISBN 978-0-12-802714-1.
dc.relation.referencesen13. Sericola B. (2013) Markov Chains: Theory and Applications. London, July 2013. P. 416. ISBN: 978-1-848-21493-4. DOI: https://doi.org/10.1002/9781118731543.ch1
dc.relation.referencesen14. Shaffer F., Ginsberg J. P. (2017), An Overview of Heart Rate Variability Metrics and Norms, Frontiers in Public Health. Volume 5. Article 258. September 2017. P. 1–17. DOI: https://doi.org/10.3389/fpubh.2017.00258
dc.relation.referencesen15. Berkaya S. K., Uysal A. K., Gunal E. S, Ergin S., Gunal S., Gulmezoglu M. B. (2018), A survey on ECG analysis. Biomedical Signal Processing and Control, 43, 216–235. DOI: https://doi.org/10.1016/j.bspc.2018.03.003
dc.relation.referencesen16. Shen C., Yu Z., Liu Z. (2015), The use of statistics in heart rhythm research: a review, Heart Rhythm, 12 (6), 1376–1386. DOI: https://doi.org/10.1016/j.hrthm.2015.03.016
dc.relation.referencesen17. Peter Olofsson, Mikael Andersson. Probability, Statistics, and Stochastic Processes. John Wiley & Sons, INC, 2012. USA. P. 553. DOI: https://doi.org/10.1002/9781118231296
dc.relation.referencesen18. Athanasios Christou Micheas. Theory of Stochastic Objects Probability, Stochastic Processes and Inference. Chapman and Hall/CRC. January 24. 2018. P. 408. ISBN: 9781466515215.
dc.relation.referencesen19. Hisashi Kobayashi, Brian L. Mark, William Turin. Probability, Random Processes, and Statistical Analysis. USA by Cambridge University Press, New York 2012. –p.812. Hardback 978-0-521-89544-6.
dc.relation.referencesen20. Oliver C. Ibe. Fundamentals of Applied Probability and Random Processes. 2nd Edition. Academic Press is an imprint of Elsevier. USA 2014. P. 431.
dc.relation.referencesen21. Scott Miller, Donald Childers Probability and Random Processes: With Applications to Signal Processing and Communications. 2nd Edition. Academic Press is an imprint of Elsevier. USA 2012. P. 593. DOI: https://doi.org/10.1016/B978-0-12-386981-4.50011-4
dc.relation.referencesen22. Ben Salah R., Hadidi T. and Chabchoub S., “Intelligent diagnosis method of cardiovascular anomalies using medical signal processing,” 2015 World Congress on Information Technology and Computer Applications (WCITCA), Hammamet, 2015, P. 1–5. DOI: https://doi.org/10.1109/WCITCA.2015.7367032
dc.relation.referencesen23. Rahimpour M., Asl M. E. and Merati M. R., “ECG fiducial points extraction using QRS morphology and adaptive windowing for real-time ECG signal analysis,” 2016 24th Iranian Conference on Electrical Engineering (ICEE). Shiraz. 2016. P. 1925–1930. DOI: https://doi.org/10.1109/IranianCEE.2016.7585836
dc.relation.referencesen24. Ciucurel C., Georgescu L., Iconaru E. I. (2018), ECG response to submaximal exercise from the perspective of Golden Ratio harmonic rhythm, Biomedical Signal Processing and Control, 40, 156–162. DOI: https://doi.org/10.1016/j.bspc.2017.09.018
dc.relation.referencesen25. Onyskiv P., Lupenko S., Lytvynenko I., Zozulia A. Mathematical modeling and processing of high resolution rhythmocardio signal based on a vector of stationary and stationary related random sequences. IDDM’2020: 3rd International Conference on Informatics & Data-Driven Medicine, November 19–21, 2020, Växjö, Sweden. CEUR Workshop Proceedings, 2020, 2753, pp. 149–155. URL: http://ceur-ws.org/Vol-2753/short8.pdf.
dc.relation.referencesen26. Gorkunenko A. B., Lupenko S. A. Obgʼruntuvannja diagnostychnyh i prognostychnyh oznak v informacijnyh systemah analizu ta prognozuvannja cyklichnyh ekonomichnyh procesiv. Naukovyj visnyk NLTU Ukrainy: zbirnyk naukovo-tehnichnyh prac. Lʼviv. 2012. No. 22.9. P. 347−352. [In Ukrainian].
dc.relation.referencesen27. Lupenko S., Lytvynenko I., Stadnyk N. Method for reducing the computational complexity of processing discrete cyclic random processes in digital data analysis systems Scientific Journal of the Ternopil national technical university. 2020. Vol. 97. No. 1. P. 110–121. URL: https://doi.org/10.33108/visnyk_tntu. DOI: https://doi.org/10.33108/visnyk_tntu2020.01.110
dc.relation.referencesen28. Lupenko S., Lytvynenko I., Stadnyk N., Zozulia A. та інші Mathematical model of rhythmocardiosignal in vector view of stationary and stationary-related case sequences. Advanced Information Systems. National Technical University “Kharkiv Polytechnic Institute”. 2020. Vol. 4. No. 2. P. 42–46. DOI: https://doi.org/10.20998/2522-9052.2020.2.08
dc.relation.referencesen29. Hutsaylyuk V., Lytvynenko I., Maruschak P., Schnell, G. та інші.A new method for modeling the cyclic structure of the surface microrelief of titanium alloy ti6al4v after processing with femtosecond pulses. Materials, 2020, 13(21), pp. 1–8, 4983. DOI: https://doi.org/10.3390/ma13214983
dc.relation.referencesen30. Marushak P. O., Lytvynenko I. O., Lupenko S. A., Popovych P. V. Modeling of the Ordered Surface Topography of Statically Deformed Aluminum Alloy. Materials Science. 2016. Vol. 52. No. 1. P. 113–122. DOI: https://doi.org/10.1007/s11003-016-9933-1
dc.identifier.citationenLupenko S., Lytvynenko I., Hotovych V., Zozulia A., Chizoba N., Volyanyk O. (2021) Concept of design, requirements and generalized architectures of components of the integrated onto-oriented information environment of simulation and processing of cyclic signals. Scientific Journal of TNTU (Tern.), vol. 102, pp. 147-160.
dc.identifier.doihttps://doi.org/10.33108/visnyk_tntu2021.02.147
dc.contributor.affiliationТернопільський національний технічний університет імені Івана Пулюя, Тернопіль, Україна
dc.contributor.affiliationІнститут телекомунікацій і глобального інформаційного простору Національної академії наук України, Київ, Україна
dc.contributor.affiliationTernopil Ivan Pului National Technical Univesity, Ternopil, Ukraine
dc.contributor.affiliationInstitute of Telecommunications and Global Information Space of National Academy of Sciences of Ukraine, Kyiv, Ukraine
dc.citation.journalTitleВісник Тернопільського національного технічного університету
dc.citation.volume102
dc.citation.spage147
dc.citation.epage160
È visualizzato nelle collezioni:Вісник ТНТУ, 2021, № 2 (102)



Tutti i documenti archiviati in DSpace sono protetti da copyright. Tutti i diritti riservati.