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Doctoral Dissertation Announcement
Candidate: Ahmad Aljaafreh
Doctor of Philosophy
Department: Electrical and Computer Engineering
Title: Collaborative Classification of Multiple Ground Vehicles in Wireless Sensor Networks Based on Acoustic Signals
Dr. Liang Dong, Chair
Dr. Janos L. Grantner
Dr. Ala Al-Fuqaha
Date: Thursday, April 15, 2010 11:30 a.m. - 1:30 p.m.
College of Engineering and Applied Sciences, Room D210
Classification of ground vehicles based on acoustic signals can be employed effectively in battlefield surveillance, traffic control, border monitoring, and many other applications. Classification of multiple dynamic targets based on time varying continuous signals in wireless sensor networks (WSN) is a big challenge. This project tackles the problem of estimation of the number and types of multiple moving ground vehicles that are passing through a region monitored by a WSN. This work is divided into three parts; the first is the feature extraction from the vehicle sounds where various feature extraction techniques for vehicle acoustic signal are evaluated based on different criteria. In the second part, Hidden Markov Model (HMM) is utilized as a framework for classification based on multiple hypotheses testing with maximum likelihood approach. The states in the HMM represent various combinations of vehicles of different types. With a sequence of observations, Viterbi algorithm is used at each sensor node to estimate the most likely sequence of states. This enables efficient local estimation of the number of vehicles for each vehicle type. In the third part, a collaborative fuzzy dynamic weighted majority voting (CFDWMV) algorithm is developed to fuse all of the local decisions and make a final decision on number of vehicles for each type. The weight of each local decision will be calculated by a fuzzy inference system based on the acoustic observation signal-to-noise ratio (SNR) as well as wireless communication SNR. Thus, the CFDWMV algorithm utilizes the spatial correlation between the observations of the sensor nodes, while HMM utilizes the temporal correlation and reduces the complexity of the optimal classification algorithm.