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dc.rights.licenseAttribution 4.0 International*
dc.contributor.authorGorospe Jauregui, Joseba
dc.contributor.otherMulero Martínez, Rubén
dc.contributor.otherArbelaitz Gallego, Olatz
dc.contributor.otherMuguerza Rivero, Javier
dc.contributor.otherAnton Gonzalez, Miguel Angel
dc.date.accessioned2021-02-16T13:37:08Z
dc.date.available2021-02-16T13:37:08Z
dc.date.issued2021
dc.identifier.issn1424-8220en
dc.identifier.otherhttps://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=162624en
dc.identifier.urihttp://hdl.handle.net/20.500.11984/5225
dc.description.abstractDeep learning techniques are being increasingly used in the scientific community as a consequence of the high computational capacity of current systems and the increase in the amount of data available as a result of the digitalisation of society in general and the industrial world in particular. In addition, the immersion of the field of edge computing, which focuses on integrating artificial intelligence as close as possible to the client, makes it possible to implement systems that act in real time without the need to transfer all of the data to centralised servers. The combination of these two concepts can lead to systems with the capacity to make correct decisions and act based on them immediately and in situ. Despite this, the low capacity of embedded systems greatly hinders this integration, so the possibility of being able to integrate them into a wide range of micro-controllers can be a great advantage. This paper contributes with the generation of an environment based on Mbed OS and TensorFlow Lite to be embedded in any general purpose embedded system, allowing the introduction of deep learning architectures. The experiments herein prove that the proposed system is competitive if compared to other commercial systems.en
dc.description.sponsorshipGobierno de Españaes
dc.language.isoengen
dc.publisherMDPIen
dc.rights© 2021 by the authors. Licensee MDPIen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectedge computingen
dc.subjectdeep learningen
dc.subjectquantisationen
dc.subjectcomputer visionen
dc.titleA Generalization Performance Study Using Deep Learning Networks in Embedded Systemsen
dc.typeinfo:eu-repo/semantics/articleen
dcterms.accessRightsinfo:eu-repo/semantics/openAccessen
dcterms.sourceSensorsen
dc.description.versioninfo:eu-repo/semantics/publishedVersionen
local.contributor.groupTeoría de la señal y comunicacioneses
local.description.peerreviewedtrueen
local.identifier.doihttps://doi.org/10.3390/s21041031en
local.relation.projectIDGE/Programa estatal de fomento de la investigación científica y técnica de excelencia, subprograma estatal de generación del conocimiento, en el marco del plan estatal de investigación científica y técnica y de innovación 2013-2016, convocatoria 2017/TIN2017-85409-P/ES/Aplicación de aprendizaje automático a señales fisiológicas para facilitar la interacción de usuario y el control de dispositivos/en
local.rights.publicationfeeAPCen
local.rights.publicationfeeamount1880 EUR 2200 CHFen
local.contributor.otherinstitutionUPV/EHUes
local.contributor.otherinstitutionTecnaliaes
local.source.detailsVol 21. N. 4. N. artículo 1031, 2021en


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Except where otherwise noted, this item's license is described as Attribution 4.0 International