Volume 2, Issue 5
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This issue features a single important research paper about improving digital information processing. The study explores using advanced math tools called spectral methods, based on classical orthogonal polynomials, to solve two key problems: making digital signals clearer and removing unwanted noise. The researchers used Jacobi polynomials for better signal approximation and Chebyshev-Laguerre polynomials for improved noise filtering. This work could lead to significant improvements in many digital devices, potentially resulting in clearer sound, sharper images, and more accurate data across various technologies.
Front Cover
Publication Month: May 2023, Page(s): i – iÂ
Editorial Board
Publication Month: May 2023, Page(s): ii – ii
Editorial
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Table of Contents
Publication Month: May 2023, Page(s): iv – iv
Orthogonal Polynomials in the Problems of Digital Information Processing
Yaroslav Pyanylo, Valentyna Sobko, Halyna Pyanylo, Oksana Pyanylo
J. Engg. Res. & Sci. 2(5), 1-9 (2023);
The paper examines spectral methods based on classical orthogonal polynomials for solving problems of digital information processing. Based on Jacobi polynomials, signal approximation methods are built to identify objects in the natural environment. Based on Chebyshev-Laguerre polynomials, methods of filtering multiplicative signal noises in linear filter models are proposed. Numerical experiments on model problems were conducted.
