Lightweight time encoded signal processing for vehicle recognition in sensor networks

Mazarakis, G; Avaritsiotis, J

dc.contributor.author	Mazarakis, G	en
dc.contributor.author	Avaritsiotis, J	en
dc.date.accessioned	2014-03-01T02:44:05Z
dc.date.available	2014-03-01T02:44:05Z
dc.date.issued	2006	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/31665
dc.subject	Artificial Neural Network	en
dc.subject	Feature Extraction	en
dc.subject	Feature Vector	en
dc.subject	Sensor Network	en
dc.subject	Signal Processing	en
dc.subject	Time Domain	en
dc.subject	Wireless Sensor Network	en
dc.subject.other	Classification (of information)	en
dc.subject.other	Spectrum analysis	en
dc.subject.other	Time domain analysis	en
dc.subject.other	Tracking (position)	en
dc.subject.other	Wavelet analysis	en
dc.subject.other	Wireless sensor networks	en
dc.subject.other	Encoded signal processing	en
dc.subject.other	Vehicle classification	en
dc.subject.other	Vehicle recognition	en
dc.subject.other	Signal encoding	en
dc.title	Lightweight time encoded signal processing for vehicle recognition in sensor networks	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/RME.2006.1690002	en
heal.identifier.secondary	http://dx.doi.org/10.1109/RME.2006.1690002	en
heal.identifier.secondary	1690002	en
heal.publicationDate	2006	en
heal.abstract	In this paper we investigate a different approach to the vehicle classification task in Wireless Sensor Networks. Instead of using traditional spectral or wavelet techniques to extract a feature vector, representative of each vehicle, we use a time-domain feature extraction method. The output of this coding procedure is a histogram-like matrix of fixed dimensions. These matrices can be used to train an Artificial Neural Network (ANN) to classify different types of vehicles. The method is evaluated using data from a real world experiment, which contains acoustic and seismic recordings from two vehicles, a heavy wheeled truck (Dragon Wagon) and a tracked vehicle (Assault Amphibian Vehicle). The dataset is available with the name Sitex02. © 2006 IEEE.	en
heal.journalName	PRIME 2006: 2nd Conference on Ph.D. Research in MicroElectronics and Electronics - Proceedings	en
dc.identifier.doi	10.1109/RME.2006.1690002	en
dc.identifier.spage	497	en
dc.identifier.epage	500	en