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Approximation Theory: From Taylor Polynomials to Wavelets

TLDR
In this paper, a concise introduction to approximation theory is presented, enriched by illustrative examples, which help to develop intuition and give the reader an understanding of the fundamental concepts, sketch the history of wavelets and present their application in signal processing.
Abstract
The book is a concisely written introduction to approximation theory. The presentation of results demonstrates the dynamic nature of mathematics and is enriched by illustrative examples, which help to develop intuition. The book consists of five chapters, each followed by exercises. The first is devoted to function approximation with polynomials. It begins with a general introduction to the idea of approximation, then deals with the Weierstrass and Taylor theorems. In the second chapter the authors focus on the approxima- tion properties related to infinite series of functions. The basic idea of signal transmission is also presented. The next chapter indicates how Fourier series and Fourier transform can be used as tools to represent functions and how pro- perties of a function are reflected in the expansion coefficients. The purpose of the last two chapters is to introduce wavelets as natural continuation of the previous material. They give the reader an understanding of the fundamental concepts, sketch the history of wavelets and present their application in signal processing. The explanation for how wavelet expansions reflect the local beha- viour of a function and how wavelets can be used to detect jumps in a signal is also provided. The book ends with an outline for the frames and Gabor systems, which are frequently used as alternatives to wavelet systems. The authors focus on ideas rather than on technical details, so that proofs are generally omitted, and just a few are included in the appendices. Minimal prerequisites (elementary calculus) make the book comprehensible to undergra- duate students of mathematics, mathematical physics and engineering. Readers are, at the same time, led towards the advanced literature in approximation theory.

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Journal ArticleDOI

Influence of the Discretization Methods on the Distribution of Relaxation Times Deconvolution: Implementing Radial Basis Functions with DRTtools

TL;DR: A MATLAB GUI toolbox is developed, which can be used to solve DRT regularization problems, and it is shown that applying RBF discretization for deconvolving the DRT problem can lead to faster numerical convergence rate as compared with that of PWL discretized only at error free situation.

Application of Wavelet Transform and its Advantages Compared to Fourier Transform

M. Sifuzzaman
TL;DR: This paper aims to find out the advantages of wavelet transform compared to Fourier transform, the improved version of Fourier Transform for signal transmission.
Journal ArticleDOI

Optimal Regularization in Distribution of Relaxation Times applied to Electrochemical Impedance Spectroscopy: Ridge and Lasso Regression Methods - A Theoretical and Experimental Study

TL;DR: In this article, the authors investigated the optimal choice of the regularization parameter in the case of ridge regression and proposed two novel sets of test functions known as real and imaginary discrepancy test function, and real and real cross-validation test functions.
Journal ArticleDOI

Multi-objective optimization of heat exchanger in an automotive exhaust thermoelectric generator

TL;DR: In this article, the authors proposed an L 16 (4 5 ) orthogonal array computational fluid dynamics simulation to evaluate the thermal properties and pressure losses of a heat exchanger.
Journal ArticleDOI

Spectral likelihood expansions for Bayesian inference

TL;DR: A spectral approach to Bayesian inference is presented, which pursues the emulation of the posterior probability density through a series expansion of the likelihood function in terms of orthogonal polynomials.
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Book

Approximation theory