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dc.contributor.authorSkeivalas, Jonas
dc.contributor.authorParšeliūnas, Eimuntas Kazimieras
dc.contributor.authorParšeliūnas, Audrius
dc.contributor.authorŠlikas, Dominykas
dc.date.accessioned2023-09-18T16:39:31Z
dc.date.available2023-09-18T16:39:31Z
dc.date.issued2023
dc.identifier.urihttps://etalpykla.vilniustech.lt/handle/123456789/115533
dc.description.abstractThis paper analyses the structures of covariance functions of digital electroencephalography measurement vectors and digital vectors of two coronavirus images. For this research, we used the measurement results of 30-channel electroencephalography (E1–E30) and digital vectors of images of two SARS-CoV-2 variants (cor2 and cor4), where the magnitudes of intensity of the electroen-cephalography parameters and the parameters of the digital images of coronaviruses were en-coded. The estimators of cross-covariance functions of the digital electroencephalography meas-urements’ vectors and the digital vectors of the coronavirus images and the estimators of au-to-covariance functions of separate vectors were derived by applying random functions con-structed according to the vectors’ parameter measurement data files. The estimators of covariance functions were derived by changing the values of the quantised interval k on the time and image pixel scales. The symmetric matrices of correlation coefficients were calculated to estimate the level of dependencies between the electroencephalography measurement results’ vectors and the digital vectors of the coronavirus images. The graphical images of the normalised cross-covariance func-tions for the electroencephalography measurement results’ vectors and the digital vectors of the coronavirus images within the period of all measurements are asymmetric. For all calculations, a computer program was developed by applying a package of Matlab procedures. A probabilistic interdependence between the results of the electroencephalography measurements and the pa-rameters of the coronavirus vectors, as well as their variation on the time and image pixel scales, was established.eng
dc.formatPDF
dc.format.extentp. 1-12
dc.format.mediumtekstas / txt
dc.language.isoeng
dc.relation.isreferencedbyScience Citation Index Expanded (Web of Science)
dc.relation.isreferencedbyScopus
dc.rightsLaisvai prieinamas internete
dc.source.urihttps://www.mdpi.com/2073-8994/15/7/1330
dc.source.urihttps://talpykla.elaba.lt/elaba-fedora/objects/elaba:170795392/datastreams/MAIN/content
dc.titleAnalysis of results of digital electroencephalography and digital vectors of Coronavirus images upon applying the theory of covariance functions
dc.typeStraipsnis Web of Science DB / Article in Web of Science DB
dcterms.accessRightsThis article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).
dcterms.licenseCreative Commons – Attribution – 4.0 International
dcterms.references19
dc.type.pubtypeS1 - Straipsnis Web of Science DB / Web of Science DB article
dc.contributor.institutionVilniaus Gedimino technikos universitetas
dc.contributor.institutionLietuvos sveikatos mokslų universitetas
dc.contributor.facultyAplinkos inžinerijos fakultetas / Faculty of Environmental Engineering
dc.subject.researchfieldT 010 - Matavimų inžinerija / Measurement engineering
dc.subject.researchfieldM 001 - Medicina / Medicine
dc.subject.vgtuprioritizedfieldsSD05 - Geodezinės technologijos / Geodetic technologies
dc.subject.ltspecializationsL102 - Energetika ir tvari aplinka / Energy and a sustainable environment
dc.subject.enelectroencephalography
dc.subject.enSARS-CoV-2
dc.subject.encovariance function
dc.subject.enquantised interval
dcterms.sourcetitleSymmetry
dc.description.issueiss. 7
dc.description.volumevol. 15
dc.publisher.nameMDPI
dc.publisher.cityBasel
dc.identifier.doi001036537400001
dc.identifier.doi10.3390/sym15071330
dc.identifier.elaba170795392


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