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Internet traffic monitoring is a crucial task for network security. Self-similarity, a key property for a relevant description of internet traffic statistics, has already been massively and successfully involved in anomaly detection. Self-similar analysis was however so far applied either to byte or Packet count time series independently, while both signals are jointly collected and technically deeply related. The present contribution elaborates on a recently proposed multivariate self-similar model, Operator fractional Brownian Motion (OfBm), to

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This work is a part of our research on scalable and/or distributed fusion and sensor calibration. We address parameter estimation in multi-sensor state space models which underpins surveillance applications with sensor networks. The parameter likelihood of the problem involves centralised Bayesian filtering of multi-sensor data, which lacks scalability with the number of sensors and induces a large communication load. We propose separable likelihoods which approximate the centralised likelihood with single sensor filtering terms.

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A novel scheme for infrared small target detection in compressive domain is presented. First, the original image is separated into two components, i.e., the target and the background. Next, we compress them individually. Finally, the compressed target image is utilized to construct the corresponding compressive detector to perform detection in compressive domain.

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There has been much recent interest in damped sinusoidal models, probably as a result of their relevance to magnetic resonance imaging. In \cite{about2010}, a model which allowed the sinusoid to decay to $0$ was examined, and a Fourier coefficient estimation procedure was proposed.

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We consider stochastic motion planning in single-source single-destination robotic relay networks, under a cooperative beamforming framework. Assuming that the communication medium constitutes a spatiotemporal stochastic field, we propose a

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