Vehicle classification in Sensor Networks using time-domain signal processing and Neural Networks

Mazarakis, GP; Avaritsiotis, JN

dc.contributor.author	Mazarakis, GP	en
dc.contributor.author	Avaritsiotis, JN	en
dc.date.accessioned	2014-03-01T01:27:32Z
dc.date.available	2014-03-01T01:27:32Z
dc.date.issued	2007	en
dc.identifier.issn	0141-9331	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/18500
dc.subject	Neural Networks	en
dc.subject	TESPAR	en
dc.subject	Time domain	en
dc.subject	Vehicle classification	en
dc.subject	Wireless Sensor Networks	en
dc.subject.classification	Computer Science, Hardware & Architecture	en
dc.subject.classification	Computer Science, Theory & Methods	en
dc.subject.classification	Engineering, Electrical & Electronic	en
dc.subject.other	Low cost sensor nodes	en
dc.subject.other	Seismic signatures	en
dc.subject.other	TESPAR	en
dc.subject.other	Vehicle classification	en
dc.subject.other	Feature extraction	en
dc.subject.other	Microcontrollers	en
dc.subject.other	Neural networks	en
dc.subject.other	Signal processing	en
dc.subject.other	Time domain analysis	en
dc.subject.other	Wireless sensor networks	en
dc.title	Vehicle classification in Sensor Networks using time-domain signal processing and Neural Networks	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/j.micpro.2007.02.005	en
heal.identifier.secondary	http://dx.doi.org/10.1016/j.micpro.2007.02.005	en
heal.language	English	en
heal.publicationDate	2007	en
heal.abstract	Vehicle classification is a demanding application of Wireless Sensor Networks. In many cases, sensor nodes detect and classify vehicles from their acoustic and/or seismic signature using spectral or wavelet based feature extraction methods. Such methods, while providing good results are quite demanding in computational power and energy and are difficult to implement on low-cost sensor nodes with limited resources. In this work, we investigate the use of a time-domain encoding and feature extraction method, to produce simple, fixed-size matrices from complex acoustic and seismic signatures of vehicles for classification purposes. Classification is accomplished using an Artificial Neural Network and a basic, L1 distance, archetype classifier. Hardware implementation issues on a prototype sensor node, based on an 8-bit microcontroller, are also discussed. For evaluation purposes we use real data from DARPA's SensIt project, which contains various acoustic and seismic signatures from two different vehicle types, a tracked vehicle and a heavy truck. (c) 2007 Elsevier B.V. All rights reserved.	en
heal.publisher	ELSEVIER SCIENCE BV	en
heal.journalName	Microprocessors and Microsystems	en
dc.identifier.doi	10.1016/j.micpro.2007.02.005	en
dc.identifier.isi	ISI:000248782900003	en
dc.identifier.volume	31	en
dc.identifier.issue	6	en
dc.identifier.spage	381	en
dc.identifier.epage	392	en