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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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The short-time Fourier transform (STFT) is widely used to analyze the spectra of temporal signals that vary through time. Signals defined over graphs, due to their intrinsic complexity, exhibit large variations in their patterns. In this work we propose a new formulation for an STFT for signals defined over graphs. This formulation draws on recent ideas from spectral graph theory, using personalized PageRank vectors as its fundamental building block.

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We present an extension of recent semidefinite programming formulations for atomic decomposition over continuous dictionaries, with applications to continuous or 'gridless' compressed sensing. The dictionary considered in this paper is defined in terms of a general matrix pencil and is parameterized by a complex variable that varies over a segment of a line or circle in the complex plane. The main result of the paper is the formulation as a convex semidefinite optimization problem, and a simple constructive proof of the equivalence.

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