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We propose a model for deterministic distributed function computation by a network of identical and anonymous nodes, with bounded computation and storage capabilities that do not scale with the network size. Our goal is to characterize the class of functions that can be computed within this model. In our main result, we exhibit a class of non-computable functions, and prove that every function outside...
We present a framework for the joint processing of multimodal data such as audio-video data streams. We first consider the problem of estimating the joint distribution of statistically dependent multimodal random variables. We discuss the issues involved and provide a copula based solution. Application of this approach to solve a multisensor fusion problem for the detection of a random event is also...
We derive the Neyman-Pearson error exponent for the detection of Gauss-Markov signals using randomly spaced sensors. We assume that the sensor spacings, d1,d2,..., are drawn independently from a common density fd(.), and we treat both stationary and nonstationary Markov models. Error exponents are evaluated using specialized forms of the strong law of large numbers, and are seen to take on algebraically...
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