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dc.contributor.authorMadrena, Jordi-
dc.contributor.authorZapata, Mireya-
dc.contributor.authorFernández, Daniel-
dc.contributor.authorSánchez-Chiva, Josep María-
dc.contributor.authorValle, Juan-
dc.contributor.authorMata-Hernández, Diana-
dc.contributor.authorOltra, Josep-
dc.contributor.authorCosp-Vilella, Jordi-
dc.contributor.authorSato, Shigeo-
dc.date.accessioned2022-06-28T14:48:18Z-
dc.date.available2022-06-28T14:48:18Z-
dc.date.issued2020-
dc.identifier.urihttps://ieeexplore.ieee.org/document/9294982-
dc.identifier.urihttp://repositorio.uti.edu.ec//handle/123456789/3322-
dc.description.abstractThis work introduces multi-sensor integration combined with an efficient and adaptive Spiking Neural Network (SNN) emulation architecture for local intelligent processing. For this purpose, we propose CMOS-MEMS with on-chip conditioning electronics together with spike processing by means of a real-time bioinspired and model-programmable SIMD multiprocessor. System integration considerations and results in the MEMS and processor developments are provided. © 2020 IEEE.es
dc.language.isoenges
dc.publisherICECS 2020 - 27th IEEE International Conference on Electronics, Circuits and Systems, Proceedings. 23 November 2020. 27th IEEE International Conference on Electronics, Circuits and Systems, ICECS 2020. Glasgow. 23 November 2020 through 25 November 2020es
dc.rightsopenAccesses
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/es
dc.titleTowards efficient and adaptive cyber physical spiking neural integrated systemses
dc.typearticlees
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