0000000000000850
AUTHOR
Francomano E.
An adaptive algorithm for determining the optimal degree of regression in constrained mock-Chebyshev least squares quadrature
In this paper we develop an adaptive algorithm for determining the optimal degree of regression in the constrained mock-Chebyshev least-squares interpolation of an analytic function to obtain quadrature formulas with high degree of exactness and accuracy from equispaced nodes. We numerically prove the effectiveness of the proposed algorithm by several examples.
Multivariate approximation: Theory and applications 2020
A summary of the main facts about the Conference ‘Multivariate Approximation: Theory and Applications’ (MATA 2020) held at the Department of Mathematics and Computer Science of the University of Perugia (Italy), on January 16-18, 2020 and the contents of the associated Special Issue appearing on Dolomites Research Notes on Approximation, are here reported.
Electric scalar potential estimations for non-invasive brain activity detection through multinode Shepard method
Electric scalar potential estimation is a key step for non-invasive investigations of brain activity with high time resolutions. The neural sources can be reconstructed by solving a typical inverse problem based on a forward problem formulated as a set of boundary value problems coupled by interface conditions. In this paper, we propose a Shepard multinode method to numerically estimate electric scalar potentials via collocation. The method is based on a special kind of inverse distance weighting partition of unity method to increase polynomial precision, approximation order, and accuracy of the classical Shepard approximation. The barycentric form, through the use of cardinal basis functio…