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Open AccessArticle

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

Centre of Industrial Electronics (CEI), Technical University of Madrid; Jose Gutierrez Abascal, 6, 28006 Madrid, Spain
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Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editor: Felipe Jimenez
Sensors 2015, 15(11), 29056-29078; https://doi.org/10.3390/s151129056
Received: 30 September 2015 / Revised: 2 November 2015 / Accepted: 5 November 2015 / Published: 17 November 2015
(This article belongs to the Special Issue Sensors in New Road Vehicles)
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. View Full-Text
Keywords: embedded intelligence; sensors; cooperative sensor networks; object identification; self-learning embedded intelligence; sensors; cooperative sensor networks; object identification; self-learning
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Villaverde, M.; Perez, D.; Moreno, F. Self-Learning Embedded System for Object Identification in Intelligent Infrastructure Sensors. Sensors 2015, 15, 29056-29078.

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