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

Villaverde San José, Mónica and Pérez Daza, David and 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.

Description

Title: Self-Learning Embedded System for Object Identification in Intelligent Infrastructure Sensors
Author/s:
  • Villaverde San José, Mónica
  • Pérez Daza, David
  • Moreno González, Félix Antonio
Item Type: Article
Título de Revista/Publicación: Sensors
Date: 17 November 2015
ISSN: 1424-8220
Volume: 15
Subjects:
Freetext Keywords: embedded intelligence; sensors; cooperative sensor networks; object identification; self-learning
Faculty: E.T.S.I. Industriales (UPM)
Department: Automática, Ingeniería Eléctrica y Electrónica e Informática Industrial
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

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.

More information

Item ID: 40764
DC Identifier: http://oa.upm.es/40764/
OAI Identifier: oai:oa.upm.es:40764
DOI: 10.3390/s151129056
Official URL: http://www.mdpi.com/1424-8220/15/11/29056
Deposited by: Memoria Investigacion
Deposited on: 10 Jun 2016 09:49
Last Modified: 23 Feb 2017 17:29
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