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dc.contributor.authorDiez Pastor, José Francisco 
dc.contributor.authorLatorre Carmona, Pedro 
dc.contributor.authorGarrido Labrador, José Luis 
dc.contributor.authorRamírez Sanz, José Miguel 
dc.contributor.authorRodríguez Diez, Juan José 
dc.date.accessioned2023-01-31T11:46:41Z
dc.date.available2023-01-31T11:46:41Z
dc.date.issued2021-07
dc.identifier.urihttp://hdl.handle.net/10259/7352
dc.description.abstractRadar technology has evolved considerably in the last few decades. There are many areas where radar systems are applied, including air traffic control in airports, ocean surveillance, and research systems, to cite a few. Other types of sensors have recently appeared, which allow tracking sub-millimeter motion with high speed and accuracy rates. These millimeter-wave radars are giving rise to myriad new applications, from the recognition of the material close objects are made, to the recognition of hand gestures. They have also been recently used to identify how a person interacts with digital devices through the physical environment (Tangible User Interfaces, TUIs). In this case, the radar is used to detect the orientation, movement, or distance from the objects to the user’s hands or the digital device. This paper presents a thoughtful comparative analysis of different feature extraction techniques and classification strategies applied on a series of datasets that cover problems such as the identification of materials, element counting, or determining the orientation and distance of objects to the sensor. The results outperform previous works using these datasets, especially when the accuracy was lowest, showing the benefits feature extraction techniques have on classification performance.en
dc.description.sponsorshipThis work was supported by the Spanish Ministry of Science and Innovation under project PID2020-119894GB-I00, Junta de Castilla y León under project BU055P20 (JCyL/FEDER, UE) co-financed through European Union FEDER funds. José Luis Garrido-Labrador was supported by the predoctoral grant (BDNS 510149) awarded by the Universidad de Burgos, Spain.en
dc.format.mimetypeapplication/pdf
dc.language.isoenges
dc.publisherMDPIes
dc.relation.ispartofApplied sciences. 2021, V. 11, n. 15, 6745es
dc.rightsAtribución 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectRadar signalen
dc.subjectFeature extractionen
dc.subjectClassificationen
dc.subjectStackingen
dc.subjectTangible user interfacesen
dc.subject.otherInformáticaes
dc.subject.otherComputer scienceen
dc.titleExperimental Assessment of Feature Extraction Techniques Applied to the Identification of Properties of Common Objects, Using a Radar Systemen
dc.typeinfo:eu-repo/semantics/articlees
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.relation.publisherversionhttps://doi.org/10.3390/app11156745es
dc.identifier.doi10.3390/app11156745
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-119894GB-I00/ES/APRENDIZAJE AUTOMATICO CON DATOS ESCASAMENTE ETIQUETADOS PARA LA INDUSTRIA 4.0/es
dc.relation.projectIDinfo:eu-repo/grantAgreement/Junta de Castilla y León//BU055P20//Métodos y Aplicaciones Industriales del Aprendizaje Semisupervisado/en
dc.identifier.essn2076-3417
dc.journal.titleApplied Scienceses
dc.volume.number11es
dc.issue.number15es
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersiones


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