A novel adaptive beamforming algorithm against large direction-of-arrival (DOA) mismatch without using optimization toolboxes is proposed. In contrast to previous works, this new beamformer employs two reconstructed matrices, the interference-plus-noise covariance matrix and the desired signal-plus-noise covariance matrix, instead of their real sample covariance matrix, respectively. These reco...

This article studies the limiting behavior of a robust M-estimator of population covariance matrices as both the number of available samples and the population size are large. Using tools from random matrix theory, we prove that the difference between the sample covariance matrix and (a scaled version of) the robust M-estimator tends to zero in spectral norm, almost surely. This result is appli...

An efficient new method is presented for the characterization of motional correlations derived from a set of protein structures without requiring the separation of overall and internal motion. In this method, termed isotropically distributed ensemble (IDE) analysis, each structure is represented by an ensemble of isotropically distributed replicas corresponding to the situation found in an isot...

Mean value and cross-covariance function of chaotic binary sequences are evaluated for chaotic maps with specific properties. We also take into account the effect of fixedand floating-point representations on statistical properties of chaotic generators. Thus, one is able to obtain possible candidates for pseudo-random binary sequences generation. Results of statistical tests applied to chaotic...

Introduction Conclusions References Tables Figures

In this paper, we handle the problem of human action recognition by combining covariance matrices as local spatio-temporal (ST) descriptors and local ST features extracted densely from action video. Unlike traditional methods that separately utilizing gradient-based feature and optical flow-based feature, we use covariance matrix to fuse the two types of feature. Since covariance matrices are S...