Self-Learning Embedded System for Object Identification in Intelligent Infrastructure Sensors

Villaverde San José, Mónica; Pérez Daza, David y Moreno González, Félix Antonio (2015). Self-Learning Embedded System for Object Identification in Intelligent Infrastructure Sensors. "Sensors", v. 15 (n. 11); pp. 29056-29078. ISSN 1424-8220. https://doi.org/10.3390/s151129056.

Descripción

Título: Self-Learning Embedded System for Object Identification in Intelligent Infrastructure Sensors
Autor/es:
  • Villaverde San José, Mónica
  • Pérez Daza, David
  • Moreno González, Félix Antonio
Tipo de Documento: Artículo
Título de Revista/Publicación: Sensors
Fecha: 17 Noviembre 2015
Volumen: 15
Materias:
Palabras Clave Informales: embedded intelligence; sensors; cooperative sensor networks; object identification; self-learning
Escuela: E.T.S.I. Industriales (UPM)
Departamento: Automática, Ingeniería Eléctrica y Electrónica e Informática Industrial
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

The emergence of new horizons in the field of travel assistant management leads to the development of cutting-edge systems focused on improving the existing ones. Moreover, new opportunities are being also presented since systems trend to be more reliable and autonomous. In this paper, a self-learning embedded system for object identification based on adaptive-cooperative dynamic approaches is presented for intelligent sensor’s infrastructures. The proposed system is able to detect and identify moving objects using a dynamic decision tree. Consequently, it combines machine learning algorithms and cooperative strategies in order to make the system more adaptive to changing environments. Therefore, the proposed system may be very useful for many applications like shadow tolls since several types of vehicles may be distinguished, parking optimization systems, improved traffic conditions systems, etc.

Más información

ID de Registro: 40764
Identificador DC: http://oa.upm.es/40764/
Identificador OAI: oai:oa.upm.es:40764
Identificador DOI: 10.3390/s151129056
URL Oficial: http://www.mdpi.com/1424-8220/15/11/29056
Depositado por: Memoria Investigacion
Depositado el: 10 Jun 2016 09:49
Ultima Modificación: 23 Feb 2017 17:29
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