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A new method of kernel density estimation with a varying adaptive window size is proposed. It is based on the so-called intersection of confidence intervals (ICI) rule. Several examples of the proposed method are given for different types of densities and the quality of the adaptive density estimate is assessed by means of numerical simulations. [Copyright &y& Elsevier]

The weighted local polynomial approximation (LPA) of time-varying direction-of-arrival is exploited in order to derive a new form of beamforming for localization of moving sources. The weights are defined by the sample correlation matrix of observations. This new adaptive LPA beamformer demonstrates an improved performance as compared with its non-adaptive counterpart. The proposed technique can be viewed as a non-stationary extension of the popular Capon minimum variance beamformer. In this way it belongs to the class of high-resolution adaptive methods. [Copyright &y& Elsevier]

• New universal and highly efficient denoising algorithm, named CCF , for Hyperspectral complex-domain data is developed. • CCF is able to produce complex-domain noise suppressing for wrapped phases, without unwrapping procedures. • Hyperspectral phase imaging based on CCF is able to reconstruct objects' data even from highly noised spectral components. A novel algorithm for reconstruction of hyperspectral 3D complex domain images (phase/amplitude) from noisy complex domain observations has been developed and studied. This algorithm starts from the SVD (singular value decomposition) analysis of the observed complex-valued data and looks for the optimal low dimension eigenspace. These eigenspace images are processed based on special non-local block-matching complex domain filters. The accuracy and quantitative advantage of the new algorithm for phase and amplitude imaging are demonstrated in simulation tests and in processing of the experimental data. It is shown that the algorithm is effective and provides reliable results even for highly noisy data. [ABSTRACT FROM AUTHOR]