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This paper introduces a hierarchical Bayesian model for the reconstruction of hyperspectral images using compressed sensing measurements. This model exploits known properties of natural images, promoting the recovered image to be sparse on a selected basis and smooth in the image domain. The posterior distribution of this model is too complex to derive closed form expressions for the estimators of its parameters. Therefore, an MCMC method is investigated to sample this posterior distribution.

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In this presentation, the topic of robust beamforming is studied. We devise the minimum dispersion criterion which extends the minimum variance criterion from l2‐norm to lp‐norm. Formulations with different linear and nonlinear constraints are examined. The proposed framework generalizes existing approaches including the Capon and linearly constrained minimum variance beamformers as well as the method based on worst-case performance optimization. Computationally attractive algorithm realizations are also developed.

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In this paper, we present Discriminant Correlation Analysis (DCA), a feature level fusion technique that incorporates the class associations in correlation analysis of the feature sets. DCA performs an effective feature fusion by maximizing the pair-wise correlations across the two feature sets, and at the same time, eliminating the between-class correlations and restricting the correlations to be within classes.

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This paper presents a new Bayesian model and associated algorithm
for depth and intensity profiling using full waveforms from timecorrelated
single-photon counting (TCSPC) measurements when the
photon count in very low. The model represents each Lidar waveform
as an unknown constant background level, which is combined
in the presence of a target, to a known impulse response weighted
by the target intensity and finally corrupted by Poisson noise. The
joint target detection and depth imaging problem is expressed as a

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Time delay estimation refers to finding the time-differences-of-arrival between signals received at an array of sensors. In this presentation, representative applications of time delay estimation are first described. Algorithms for accurately estimating the time difference between two sensor outputs using random and deterministic signals are then presented and analyzed.

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