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The problem of the representation of uniformly sampled signals in the Hermite transform basis is revisited. Namely, due to the application of Gauss-Hermite quadrature in the calculation of transformation coefficients, the discrete Hermite transform assumes that the analyzed signal is sampled at the points proportional to the roots of the Hermite polynomial of the corresponding order. Since the most...
Compressive sensing (CS) of signals that exhibit sparsity in the domain of 2D Hermite transform (HT) is considered. The aim is to provide a successful reconstruction of randomly positioned missing samples. Gradient algorithm originally developed for the case of 1D HT as the domain of sparsity is applied as the reconstruction method, generalized and adapted for the case of signals sparse in 2D HT domain...
The influence of missing samples in signals which exhibit sparsity in the domain of Hermite transform is analyzed. The study provides theoretical concepts for the efficient reconstruction of the signals with missing samples. Single component signals are analyzed, and the main results guarantee further generalization of the presented concepts to the case of multicomponent signals. The theoretical contributions...
Following a long and rich tradition (since 1990), the 12th NEUREL 2014 will take place in Belgrade, from November 25 to 27, 2014. Belgrade is the capital of Republic of Serbia, and represents its educational, scientific, historical, cultural, commercial and industrial center. Located at the place where the Sava River joins the blue Danube on its way from Germany to the Black Sea, Belgrade is also...
This paper defines criteria for assessing the imbalance of datasets for training predictive learning models. The most important criterion for evaluating the imbalance is the distribution of the error signal over the space of local measure of distances between the points of the training set. In this paper is presented the analysis of this indicator for the sets of various distributions, and it has...
A time-frequency approach for improved instantaneous frequency estimation of a noisy signal has been proposed. This approach is based on a time-frequency distribution obtained by averaging the L-spectrograms. The spectrogram with lower values of parameter L produces large bias and low variance, while higher L decreases bias and increases the variance of instantaneous frequency estimation. In order...
In this paper an analysis is given of the application of Bayesian Gaussian process statistical learning algorithms to the problem of text categorization. It is demonstrated that the informative vector machine method, as a sparse Bayesian compression scheme, provides results better than those obtained so far with the support vector machine method, with much less computational cost
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