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Evaluación de desempeño de algoritmos de inteligencia artificial embebida en microcontroladores

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datacite.rightshttp://purl.org/coar/access_right/c_f1cf
dc.contributor.advisorMéndez Torrenegra, Fernando Miguel
dc.contributor.authorCorrea Polo, Alejandro
dc.contributor.authorDe La Hoz Salas, Xavier
dc.contributor.authorGil Guzmán, Jaider José
dc.contributor.authorRomero Domínguez, Vanessa Paola
dc.contributor.authorTovio Caballero, Justin
dc.date30/12/2050
dc.date.accessioned2024-12-03T20:49:53Z
dc.date.available2024-12-03T20:49:53Z
dc.date.issued2024
dc.description.abstractLa Integración de la Inteligencia Artificial con el Internet de las Cosas (AIoT) implica combinar la capacidad de procesamiento inteligente de la IA con la conectividad y los datos generados por los dispositivos IoT, lo que potencia la automatización, el análisis de datos en tiempo real y la toma de decisiones autónoma en diversas áreas como el hogar inteligente, la salud, la agricultura y la manufactura. En el ámbito de la salud, por ejemplo, la AIoT permite monitorear la salud de los pacientes y prever problemas médicos. En la agricultura, optimiza el riego y la fertilización de los cultivos para aumentar los rendimientos. La fabricación se beneficia con el mantenimiento predictivo de equipos y la optimización de la cadena de suministro. Este artículo aborda los desafíos y propone enfoques para la integración efectiva de redes neuronales (RNA) en microcontroladores, como el ESP32. A pesar de los desafíos por las limitaciones del ESP32, se superaron mediante la utilización de materiales y herramientas apropiadas, implementando algoritmos de RNA y estableciendo una metodología para medir los tiempos de ejecución en el dispositivospa
dc.description.abstractThe Integration of Artificial Intelligence with the Internet of Things (AIoT) involves combining the intelligent processing capacity of AI with the connectivity and data generated by IoT devices, which enhances automation, real-time data analysis and autonomous decision making in various areas such as smart home, healthcare, agriculture and manufacturing. In the field of health, for example, AIoT makes it possible to monitor the health of patients and predict medical problems. In agriculture, it optimizes irrigation and fertilization of crops to increase yields. Manufacturing benefits from predictive equipment maintenance and supply chain optimization. This article addresses the challenges and proposes approaches for effective integration of neural networks (ANNs) into microcontrollers, such as the ESP32. Despite the challenges due to the limitations of the ESP32, they were overcome by using appropriate materials and tools, implementing ANN algorithms, and establishing a methodology to measure execution times on the device.eng
dc.format.mimetypepdf
dc.identifier.urihttps://hdl.handle.net/20.500.12442/16033
dc.language.isospa
dc.publisherEdiciones Universidad Simón Bolívarspa
dc.publisherFacultad de Ingenieríasspa
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 United Stateseng
dc.rights.accessrightsinfo:eu-repo/semantics/embargoedAccess
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/us/
dc.subjectInteligencia Artificialspa
dc.subjectDispositivos IoTspa
dc.subjectRedes Neuronales Artificialesspa
dc.subjectPerceptrones multicapaspa
dc.subjectESP32spa
dc.subject.keywordsArtificial Intelligenceeng
dc.subject.keywordsIoT Deviceseng
dc.subject.keywordsArtificial Neural Networkseng
dc.subject.keywordsMultilayer Perceptronseng
dc.subject.keywordsClassificationeng
dc.titleEvaluación de desempeño de algoritmos de inteligencia artificial embebida en microcontroladoresspa
dc.type.driverinfo:eu-repo/semantics/other
dc.type.spaTrabajo de grado - pregrado
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oaire.versioninfo:eu-repo/semantics/acceptedVersion
sb.programaIngeniería de Sistemasspa
sb.programaIngeniería Industrialspa
sb.sedeSede Barranquillaspa

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