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dc.contributor.authorGerasimov, Vadim
dc.contributor.authorJonaitis, Gintaras
dc.contributor.authorJonkus, Vytautas
dc.date.accessioned2023-09-18T17:08:15Z
dc.date.available2023-09-18T17:08:15Z
dc.date.issued2015
dc.identifier.issn2029-1035
dc.identifier.urihttps://etalpykla.vilniustech.lt/handle/123456789/120077
dc.description.abstractDesign of electronic system devoted for processing and recognition of myoelectric signals were observed. Description of used embedded systems were presented and analyzed.eng
dc.format.extentp. 13-17
dc.format.mediumtekstas / txt
dc.language.isoeng
dc.source.urihttp://journal.kolegija.lt/iitsbe/2015/Gerasimov-application2-IITSBE-2015-1(18)-13-17.pdf
dc.titleApplication of artificial neural networks for human muscle signal analysis and mechanical equipment control. 2. Hardware, software, interfaces
dc.typeStraipsnis kitame recenzuotame leidinyje / Article in other peer-reviewed source
dcterms.references11
dc.type.pubtypeS4 - Straipsnis kitame recenzuotame leidinyje / Article in other peer-reviewed publication
dc.contributor.institutionVilniaus universitetas
dc.contributor.institutionVilniaus Gedimino technikos universitetas
dc.contributor.facultyMechanikos fakultetas / Faculty of Mechanics
dc.contributor.departmentBiomechanikos katedra / Department of Biomechanics
dc.subject.researchfieldN 002 - Fizika / Physics
dc.subject.researchfieldT 001 - Elektros ir elektronikos inžinerija / Electrical and electronic engineering
dc.subject.enArtificial Neural Networks
dc.subject.enANN
dc.subject.enrecognition of myoelectric signals
dc.subject.enelectromyogram
dc.subject.enlinear regression method
dc.subject.ensupervised learning
dc.subject.enlogistic regression
dc.subject.enartificial neuron.
dcterms.sourcetitleInnovative infotechnologies for science, business and education
dc.description.volumeVol. 1 (18)
dc.publisher.nameVilniaus verslo kolegija
dc.publisher.cityVilnius
dc.identifier.elaba26997674


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