Lightweight Time Encoded Signal Processing for Vehicle Recognition in Sensor Networks

Author

Mazarakis, Georgios ; Avaritsiotis, John

Author_Institution

Dept. of Electr. & Comput. Eng., Nat. Tech. Univ. of Athens

fYear

0

fDate

0-0 0

Firstpage

497

Lastpage

500

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

Keywords

feature extraction; neural nets; pattern classification; road vehicles; signal processing; time-domain analysis; wireless sensor networks; SitexO2; artificial neural network; assault amphibian vehicle; dragon wagon; feature vector; time encoded signal processing; time-domain feature extraction; vehicle classification; vehicle recognition; wireless sensor networks; Acoustic sensors; Artificial neural networks; Feature extraction; Intelligent networks; Marine vehicles; Sensor phenomena and characterization; Signal processing; Time domain analysis; Vehicle detection; Wireless sensor networks;

fLanguage

English

Publisher

ieee

Conference_Titel

Research in Microelectronics and Electronics 2006, Ph. D.

Conference_Location

Otranto

Print_ISBN

1-4244-0157-7

Type

conf

DOI

10.1109/RME.2006.1690002

Filename

1690002