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In this paper, a self-learning embedded system for object identification based on adaptive-cooperative dynamic approaches is presented for intelligent sensor\u2019s 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.<\/jats:p>","DOI":"10.3390\/s151129056","type":"journal-article","created":{"date-parts":[[2015,11,17]],"date-time":"2015-11-17T10:23:41Z","timestamp":1447755821000},"page":"29056-29078","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Self-Learning Embedded System for Object Identification in Intelligent Infrastructure Sensors"],"prefix":"10.3390","volume":"15","author":[{"given":"Monica","family":"Villaverde","sequence":"first","affiliation":[{"name":"Centre of Industrial Electronics (CEI), Technical University of Madrid; Jose Gutierrez Abascal, 6, 28006 Madrid, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"David","family":"Perez","sequence":"additional","affiliation":[{"name":"Centre of Industrial Electronics (CEI), Technical University of Madrid; Jose Gutierrez Abascal, 6, 28006 Madrid, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Felix","family":"Moreno","sequence":"additional","affiliation":[{"name":"Centre of Industrial Electronics (CEI), Technical University of Madrid; Jose Gutierrez Abascal, 6, 28006 Madrid, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2015,11,17]]},"reference":[{"key":"ref_1","unstructured":"Liu, C., Wang, Q., and Zhan, F. 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Learn., 1.","DOI":"10.1007\/BF00116251"},{"key":"ref_12","unstructured":"MacQueen, J. (July, January 21). Some methods for classification and analysis of multivariate observations. Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, Volume 1: Statistics, 281-297, University of California Press, Berkeley, CA, USA."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1016\/j.eswa.2012.07.021","article-title":"A comparative study of efficient initialization methods for the k-means clustering algorithm","volume":"40","author":"Celebi","year":"2013","journal-title":"Expert Syst. Appl."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.spl.2013.09.026","article-title":"On K-means algorithm with the use of Mahalanobis distances","volume":"84","author":"Melnykov","year":"2014","journal-title":"Statist. Probabil. Lett."},{"key":"ref_15","unstructured":"Villaverde, M., P\u00e9rez, D., and Moreno, F. 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Proceedings of the on the Work in Progress Session of the 18th EUROMICRO Conference on Digital Design (DSD 2015), Funchal, Madeira, Portugal."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/15\/11\/29056\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T20:52:13Z","timestamp":1760215933000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/15\/11\/29056"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015,11,17]]},"references-count":16,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2015,11]]}},"alternative-id":["s151129056"],"URL":"https:\/\/doi.org\/10.3390\/s151129056","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2015,11,17]]}}}