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Mathematical Problems in Engineering
Volume 2014, Article ID 142304, 8 pages
http://dx.doi.org/10.1155/2014/142304
Research Article

Vehicle Type Recognition in Sensor Networks Using Improved Time Encoded Signal Processing Algorithm

1College of Electronics and Information Engineering, Sichuan University, Chengdu 610065, China
2College of Computer Science, Chengdu University of Information Technology, Chengdu 610065, China
3College of Computer Science, Sichuan University, Chengdu 610065, China

Received 23 September 2013; Revised 15 January 2014; Accepted 30 January 2014; Published 6 March 2014

Academic Editor: Ilse C. Cervantes

Copyright © 2014 Yan Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Vehicle type recognition is a demanding application of wireless sensor networks (WSN). In many cases, sensor nodes detect and recognize vehicles from their acoustic or seismic signals using wavelet based or spectral feature extraction methods. Such methods, while providing convincing results, are quite demanding in computational power and energy and are difficult to implement on low-cost sensor nodes with limitation resources. In this paper, we investigate the use of time encoded signal processing (TESP) algorithm for vehicle type recognition. The conventional TESP algorithm, which is effective for the speech signal feature extraction, however, is not suitable for the vehicle sound signal which is more complex. To solve this problem, an improved time encoded signal processing (ITESP) is proposed as the feature extraction method according to the characteristics of the vehicle sound signal. Recognition procedure is accomplished using the support vector machine (SVM) and the -nearest neighbor (KNN) classifier. The experimental results indicate that the vehicle type recognition system with ITESP features give much better performance compared with the conventional TESP based features.