Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/114735
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dc.contributor.authorOrtega Zamorano, Franciscoen_US
dc.contributor.authorJerez, José M.en_US
dc.contributor.authorSubirats, José L.en_US
dc.contributor.authorMolina, Ignacioen_US
dc.contributor.authorFranco, Leonardoen_US
dc.date.accessioned2022-05-16T15:13:22Z-
dc.date.available2022-05-16T15:13:22Z-
dc.date.issued2014en_US
dc.identifier.issn0952-1976en_US
dc.identifier.urihttp://hdl.handle.net/10553/114735-
dc.description.abstractThe techniques currently developed for updating software in sensor nodes located in changing environments require usually the use of reprogramming procedures, which clearly increments the costs in terms of time and energy consumption. This work presents an alternative to the traditional reprogramming approach based on an on-chip learning scheme in order to adapt the node behaviour to the environment conditions. The proposed learning scheme is based on C-Mantec, a novel constructive neural network algorithm especially suitable for microcontroller implementations as it generates very compact size architectures. The Arduino UNO board was selected to implement this learning algorithm as it is a popular, economic and efficient open source single-board microcontroller. C-Mantec has been successfully implemented in a microcontroller board by adapting it in order to overcome the limitations imposed by the limited resources of memory and computing speed of the hardware device. Also, this work brings an in-depth analysis of the solutions adopted to overcome hardware resource limitations in the learning algorithm implementation (e.g.; data type), together with an efficiency assessment of this approach when the algorithm is tested on a set of circuit design benchmark functions. Finally, the utility, efficiency and versatility of the system is tested in three different-nature case studies in which the environmental conditions change its behaviour over time.en_US
dc.languageengen_US
dc.relation.ispartofEngineering Applications of Artificial Intelligenceen_US
dc.sourceEngineering Applications of Artificial Intelligence [ISSN 0952-1976], v. 30, p. 179-188en_US
dc.subject3304 Tecnología de los ordenadoresen_US
dc.subject.otherConstructive neural networksen_US
dc.subject.otherMicrocontrolleren_US
dc.subject.otherArduinoen_US
dc.titleSmart sensor/actuator node reprogramming in changing environments using a neural network modelen_US
dc.typeinfo:eu-repo/semantics/Articleen_US
dc.typearticleen_US
dc.identifier.doi10.1016/j.engappai.2014.01.006en_US
dc.identifier.scopus2-s2.0-84896394949-
dc.identifier.isiWOS:000334139600017-
dc.contributor.orcid#NODATA#-
dc.contributor.orcid#NODATA#-
dc.contributor.orcid#NODATA#-
dc.contributor.orcid#NODATA#-
dc.contributor.orcid#NODATA#-
dc.description.lastpage188en_US
dc.description.firstpage179en_US
dc.relation.volume30en_US
dc.investigacionIngeniería y Arquitecturaen_US
dc.type2Artículoen_US
dc.utils.revisionen_US
dc.identifier.ulpgcNoen_US
dc.contributor.buulpgcBU-INFen_US
dc.description.sjr1,201
dc.description.jcr2,207
dc.description.sjrqQ1
dc.description.jcrqQ1
dc.description.scieSCIE
item.fulltextSin texto completo-
item.grantfulltextnone-
crisitem.author.deptGIR SIANI: Inteligencia Artificial, Robótica y Oceanografía Computacional-
crisitem.author.deptIU Sistemas Inteligentes y Aplicaciones Numéricas-
crisitem.author.orcid0000-0002-4397-2905-
crisitem.author.parentorgIU Sistemas Inteligentes y Aplicaciones Numéricas-
crisitem.author.fullNameOrtega Zamorano,Francisco-
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