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Novel human gesture recognition and classification technique is suggested and experimentally studied. Suggested strategy is based on exploiting the interactions of human gestures with high-frequency electromagnetic field. Extracting of classification features contained in the wireless radio signal modulated by human gestures is proposed by utilizing bispectrum-based processing of the signal envelope...
The paper explores the shadows created by moving humans in two types of Forward Scatter (FS) systems, which use GPS signals and sound signals. The comparative analysis of results is based on the correlation analysis and focused on establishing of relationships between different types of shadows. The results show whether it is possible to use sound barriers for detection of moving objects.
Most of real-life signals are nonGaussian, therefore the linear parametrization/modeling methods, suitable for Gaussian signals are neither adequate nor optimal. Using a nonlinear approach, we can significantly improve filtering results. However, the price that has to be paid is the complexity of the nonlinear treatment. In this paper we consider the nonlinear Schurtype orthogonal transformations...
We present algorithms for orthogonal Schur parametrization, innovations transformation and stochastic modeling of second-order non-stationary time-series, showing statistical equivalence (in a weak second-order sense) of the parametrized and modeled time-series.
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