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We describe a low-power VLSI wake-up detector for use in an acoustic surveillance sensor network. The detection criterion is based on the degree of low-frequency periodicity in the acoustic signal. To this end, we have developed a periodicity estimation algorithm that maps particularly well to a low-power VLSI implementation. The time-domain algorithm is(More)
—Sound localization using energy-aware hardware for sensor networks nodes is a problem with many applications in surveillance and security. In this paper, we evaluate four algorithms for sound localization using signals recorded in a natural environment with an array of commercial off-the-shelf microelectromechanical systems microphones and a specially(More)
—We propose a programmable architecture for a single instruction multiple data image processor that has its foundation on the mathematical framework of a simplicial cellular neural networks. We develop instruction primitives for basic image processing operations and show examples of processing binary and gray scale images. Fabricated in deep submicron CMOS(More)
— This paper describes experimental results of low power sensor nodes designed to perform bearing estimation. The nodes are intended to form a wireless sensor network able to locate an audio source. Two different nodes are tested: one is based on a Cross-correlation Derivative integrated circuit (IC), and the other on a Gradient Flow IC. Implementation(More)
This paper introduces a novel neural architecture which is capable of similar performance to any of the "classic" neural paradigms while having a very simple and efficient mixed-signal implementation which makes it a valuable candidate for intelligent signal processing in portable multimedia applications. The architecture and its realization circuit are(More)