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The task of classifying the types of moving vehicles in a distributed, wireless sensor network is investigated. Specifically, based on an extensive real world experiment, we have compiled a dataset that consists of 820 MByte raw time series data, 70 MByte of pre-processed, extracted spectral feature vectors, and baseline classification results using the(More)
A novel sensor network source localization method based on acoustic energy measurements is presented. This method makes use of the characteristics that the acoustic energy decays exponentially with respect to distance from the source. By comparing energy readings measured at surrounding acoustic sensors within the same time interval, the source location(More)
N etworks of small, densely distributed wireless sensor nodes are being envisioned and developed for a variety of applications involving monitoring and manipulation of the physical world in a tetherless fashion [1], [16], [17], [22], [23]. Typically, each individual node can sense in multiple modalities but has limited communication and computation(More)
—A maximum likelihood (ML) acoustic source location estimation method is presented for the application in a wireless ad hoc sensor network. This method uses acoustic signal energy measurements taken at individual sensors of an ad hoc wireless sensor network to estimate the locations of multiple acoustic sources. Compared to the existing acoustic energy(More)
Target classification fusion problem in a distributed, wireless sensor network is investigated. We propose a distance-based decision fusion scheme exploiting the relationship between sensor to target distance, signal to noise ratio and classification rate, which requires less communication while achieving higher region classification rate when compared to(More)
Two novel distributed particle filters with Gaussian Mixer approximation are proposed to localize and track multiple moving targets in a wireless sensor network. The distributed particle filters run on a set of uncorrelated sensor cliques that are dynamically organized based on moving target trajectories. These two algorithms differ in how the distributive(More)
Recent study shows that the existing first order canonical timing model is not sufficient to represent the dependency of the gate delay on the variation sources when processing and operational variations become more and more significant. Due to the nonlinearity of the mapping from variation sources to the gate/wire delay, the distribution of the delay is no(More)
An efficient and accurate statistical static timing analysis (SSTA) algorithm is reported in this work which features (a) a conditional linear approximation method of the MAX/MIN timing operator, (b) an extended canonical representation of correlated timing variables, and (c) a variation pruning method that facilitates intelligent trade-off between(More)