AIMS The endocardial vs. epicardial origin of ventricular arrhythmia (VA) can be inferred from detailed electrocardiogram (ECG) analysis. However, despite its clinical usefulness, ECG has limitations. Alternatively, scarred tissue sustaining VAs can be identified by contrast-enhanced cardiac magnetic resonance (ce-CMR). The objective of this study was to determine the clinical value of analysin...

In this paper we present applications of methods from wavelet analysis to polynomial approximations for a number of accelerator physics problems. According to a variational approach in the general case we have the solution as a multiresolution (multiscales) expansion on the base of compactly supported wavelet basis. We give an extension of our results to the cases of periodic orbital particle m...

The aim of the present paper is to give a new kind of point of view in the theory of variational inequalities. Our approach makes possible the study of both scalar and vector variational inequalities under a great variety of assumptions. One can include here the variational inequalities defined on reflexive or nonreflexive Banach spaces, as well as the vector variational inequalities defined on...

In this paper, some properties of  pseudoinvex functions, defined by means of  limiting subdifferential, are discussed. Furthermore, the Minty vector variational-like inequality,  the Stampacchia vector variational-like inequality, and the  weak formulations of these two inequalities  defined by means of limiting subdifferential are studied. Moreover, some relationships  between the vector vari...

This work discuses a novel algorithm for joint sparse estimation of superimposed signals and their parameters. The proposed method is based on two concepts: a variational Bayesian version of the incremental sparse Bayesian learning (SBL)– fast variational SBL – and a variational Bayesian approach for parameter estimation of superimposed signal models. Both schemes estimate the unknown parameter...

Variational approximation methods have proven to be useful for scaling Bayesian computations to large data sets and highly parametrized models. Applying variational methods involves solving an optimization problem, and recent research in this area has focused on stochastic gradient ascent methods as a general approach to implementation. Here variational approximation is considered for a posteri...