Harmonic Analysis from Fourier to Wavelets
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The Convolution Theorem
Module 2 : Signals in Frequency Domain Lecture 18 : The Convolution Theorem Objectives In this lecture you will learn the following We shall prove the most important theorem regarding the Fourier Transform- the Convolution Theorem We are going to learn about filters. Proof of 'the Convolution theorem for the Fourier Transform'. The Dual version of the Convolution Theorem Parseval's theorem The Convolution Theorem We shall in this lecture prove the most important theorem regarding the Fourier Transform- the Convolution Theorem. It is this theorem that links the Fourier Transform to LSI systems, and opens up a wide range of applications for the Fourier Transform. We shall inspire its importance with an application example. Modulation Modulation refers to the process of embedding an information-bearing signal into a second carrier signal. Extracting the information- bearing signal is called demodulation. Modulation allows us to transmit information signals efficiently. It also makes possible the simultaneous transmission of more than one signal with overlapping spectra over the same channel. That is why we can have so many channels being broadcast on radio at the same time which would have been impossible without modulation There are several ways in which modulation is done. One technique is amplitude modulation or AM in which the information signal is used to modulate the amplitude of the carrier signal. Another important technique is frequency modulation or FM, in which the information signal is used to vary the frequency of the carrier signal. Let us consider a very simple example of AM. Consider the signal x(t) which has the spectrum X(f) as shown : Why such a spectrum ? Because it's the simplest possible multi-valued function. -
HARMONIC ANALYSIS AS the EXPLOITATION of SYMMETRY-A HISTORICAL SURVEY Preface 1. Introduction 2. the Characters of Finite Groups
BULLETIN (New Series) OF THE AMERICAN MATHEMATICAL SOCIETY Volume 3, Number 1, July 1980 HARMONIC ANALYSIS AS THE EXPLOITATION OF SYMMETRY-A HISTORICAL SURVEY BY GEORGE W. MACKEY CONTENTS Preface 1. Introduction 2. The Characters of Finite Groups and the Connection with Fourier Analysis 3. Probability Theory Before the Twentieth Century 4. The Method of Generating Functions in Probability Theory 5. Number Theory Before 1801 6. The Work of Gauss and Dirichlet and the Introduction of Characters and Harmonic Analysis into Number Theory 7. Mathematical Physics Before 1807 8. The Work of Fourier, Poisson, and Cauchy, and Early Applications of Harmonic Analysis to Physics 9. Harmonic Analysis, Solutions by Definite Integrals, and the Theory of Functions of a Complex Variable 10. Elliptic Functions and Early Applications of the Theory of Functions of a Complex Variable to Number Theory 11. The Emergence of the Group Concept 12. Introduction to Sections 13-16 13. Thermodynamics, Atoms, Statistical Mechanics, and the Old Quantum Theory 14. The Lebesgue Integral, Integral Equations, and the Development of Real and Abstract Analysis 15. Group Representations and Their Characters 16. Group Representations in Hilbert Space and the Discovery of Quantum Mechanics 17. The Development of the Theory of Unitary Group Representations Be tween 1930 and 1945 Reprinted from Rice University Studies (Volume 64, Numbers 2 and 3, Spring- Summer 1978, pages 73 to 228), with the permission of the publisher. 1980 Mathematics Subject Classification. Primary 01,10,12, 20, 22, 26, 28, 30, 35, 40, 42, 43, 45,46, 47, 60, 62, 70, 76, 78, 80, 81, 82. -
Lecture 7: Fourier Series and Complex Power Series 1 Fourier
Math 1d Instructor: Padraic Bartlett Lecture 7: Fourier Series and Complex Power Series Week 7 Caltech 2013 1 Fourier Series 1.1 Definitions and Motivation Definition 1.1. A Fourier series is a series of functions of the form 1 C X + (a sin(nx) + b cos(nx)) ; 2 n n n=1 where C; an; bn are some collection of real numbers. The first, immediate use of Fourier series is the following theorem, which tells us that they (in a sense) can approximate far more functions than power series can: Theorem 1.2. Suppose that f(x) is a real-valued function such that • f(x) is continuous with continuous derivative, except for at most finitely many points in [−π; π]. • f(x) is periodic with period 2π: i.e. f(x) = f(x ± 2π), for any x 2 R. C P1 Then there is a Fourier series 2 + n=1 (an sin(nx) + bn cos(nx)) such that 1 C X f(x) = + (a sin(nx) + b cos(nx)) : 2 n n n=1 In other words, where power series can only converge to functions that are continu- ous and infinitely differentiable everywhere the power series is defined, Fourier series can converge to far more functions! This makes them, in practice, a quite useful concept, as in science we'll often want to study functions that aren't always continuous, or infinitely differentiable. A very specific application of Fourier series is to sound and music! Specifically, re- call/observe that a musical note with frequency f is caused by the propogation of the lon- gitudinal wave sin(2πft) through some medium. -
Fourier Series
Academic Press Encyclopedia of Physical Science and Technology Fourier Series James S. Walker Department of Mathematics University of Wisconsin–Eau Claire Eau Claire, WI 54702–4004 Phone: 715–836–3301 Fax: 715–836–2924 e-mail: [email protected] 1 2 Encyclopedia of Physical Science and Technology I. Introduction II. Historical background III. Definition of Fourier series IV. Convergence of Fourier series V. Convergence in norm VI. Summability of Fourier series VII. Generalized Fourier series VIII. Discrete Fourier series IX. Conclusion GLOSSARY ¢¤£¦¥¨§ Bounded variation: A function has bounded variation on a closed interval ¡ ¢ if there exists a positive constant © such that, for all finite sets of points "! "! $#&% (' #*) © ¥ , the inequality is satisfied. Jordan proved that a function has bounded variation if and only if it can be expressed as the difference of two non-decreasing functions. Countably infinite set: A set is countably infinite if it can be put into one-to-one £0/"£ correspondence with the set of natural numbers ( +,£¦-.£ ). Examples: The integers and the rational numbers are countably infinite sets. "! "!;: # # 123547698 Continuous function: If , then the function is continuous at the point : . Such a point is called a continuity point for . A function which is continuous at all points is simply referred to as continuous. Lebesgue measure zero: A set < of real numbers is said to have Lebesgue measure ! $#¨CED B ¢ £¦¥ zero if, for each =?>A@ , there exists a collection of open intervals such ! ! D D J# K% $#L) ¢ £¦¥ ¥ ¢ = that <GFIH and . Examples: All finite sets, and all countably infinite sets, have Lebesgue measure zero. "! "! % # % # Odd and even functions: A function is odd if for all in its "! "! % # # domain. -
The Summation of Power Series and Fourier Series
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Elsevier - Publisher Connector Journal of Computational and Applied Mathematics 12&13 (1985) 447-457 447 North-Holland The summation of power series and Fourier . series I.M. LONGMAN Department of Geophysics and Planetary Sciences, Tel Aviv University, Ramat Aviv, Israel Received 27 July 1984 Abstract: The well-known correspondence of a power series with a certain Stieltjes integral is exploited for summation of the series by numerical integration. The emphasis in this paper is thus on actual summation of series. rather than mere acceleration of convergence. It is assumed that the coefficients of the series are given analytically, and then the numerator of the integrand is determined by the aid of the inverse of the two-sided Laplace transform, while the denominator is standard (and known) for all power series. Since Fourier series can be expressed in terms of power series, the method is applicable also to them. The treatment is extended to divergent series, and a fair number of numerical examples are given, in order to illustrate various techniques for the numerical evaluation of the resulting integrals. Keywork Summation of series. 1. Introduction We start by considering a power series, which it is convenient to write in the form s(x)=/.Q-~2x+&x2..., (1) for reasons which will become apparent presently. Here there is no intention to limit ourselves to alternating series since, even if the pLkare all of one sign, x may take negative and even complex values. It will be assumed in this paper that the pLk are real. -
Convolution.Pdf
Convolution March 22, 2020 0.0.1 Setting up [23]: from bokeh.plotting import figure from bokeh.io import output_notebook, show,curdoc from bokeh.themes import Theme import numpy as np output_notebook() theme=Theme(json={}) curdoc().theme=theme 0.1 Convolution Convolution is a fundamental operation that appears in many different contexts in both pure and applied settings in mathematics. Polynomials: a simple case Let’s look at a simple case first. Consider the space of real valued polynomial functions P. If f 2 P, we have X1 i f(z) = aiz i=0 where ai = 0 for i sufficiently large. There is an obvious linear map a : P! c00(R) where c00(R) is the space of sequences that are eventually zero. i For each integer i, we have a projection map ai : c00(R) ! R so that ai(f) is the coefficient of z for f 2 P. Since P is a ring under addition and multiplication of functions, these operations must be reflected on the other side of the map a. 1 Clearly we can add sequences in c00(R) componentwise, and since addition of polynomials is also componentwise we have: a(f + g) = a(f) + a(g) What about multiplication? We know that X Xn X1 an(fg) = ar(f)as(g) = ar(f)an−r(g) = ar(f)an−r(g) r+s=n r=0 r=0 where we adopt the convention that ai(f) = 0 if i < 0. Given (a0; a1;:::) and (b0; b1;:::) in c00(R), define a ∗ b = (c0; c1;:::) where X1 cn = ajbn−j j=0 with the same convention that ai = bi = 0 if i < 0. -
Lecture Notes: Harmonic Analysis
Lecture notes: harmonic analysis Russell Brown Department of mathematics University of Kentucky Lexington, KY 40506-0027 August 14, 2009 ii Contents Preface vii 1 The Fourier transform on L1 1 1.1 Definition and symmetry properties . 1 1.2 The Fourier inversion theorem . 9 2 Tempered distributions 11 2.1 Test functions . 11 2.2 Tempered distributions . 15 2.3 Operations on tempered distributions . 17 2.4 The Fourier transform . 20 2.5 More distributions . 22 3 The Fourier transform on L2. 25 3.1 Plancherel's theorem . 25 3.2 Multiplier operators . 27 3.3 Sobolev spaces . 28 4 Interpolation of operators 31 4.1 The Riesz-Thorin theorem . 31 4.2 Interpolation for analytic families of operators . 36 4.3 Real methods . 37 5 The Hardy-Littlewood maximal function 41 5.1 The Lp-inequalities . 41 5.2 Differentiation theorems . 45 iii iv CONTENTS 6 Singular integrals 49 6.1 Calder´on-Zygmund kernels . 49 6.2 Some multiplier operators . 55 7 Littlewood-Paley theory 61 7.1 A square function that characterizes Lp ................... 61 7.2 Variations . 63 8 Fractional integration 65 8.1 The Hardy-Littlewood-Sobolev theorem . 66 8.2 A Sobolev inequality . 72 9 Singular multipliers 77 9.1 Estimates for an operator with a singular symbol . 77 9.2 A trace theorem. 87 10 The Dirichlet problem for elliptic equations. 91 10.1 Domains in Rn ................................ 91 10.2 The weak Dirichlet problem . 99 11 Inverse Problems: Boundary identifiability 103 11.1 The Dirichlet to Neumann map . 103 11.2 Identifiability . 107 12 Inverse problem: Global uniqueness 117 12.1 A Schr¨odingerequation . -
Fourier Series of Functions of A-Bounded Variation
PROCEEDINGS OF THE AMERICAN MATHEMATICAL SOCIETY Volume 74, Number 1, April 1979 FOURIER SERIES OF FUNCTIONS OF A-BOUNDED VARIATION DANIEL WATERMAN1 Abstract. It is shown that the Fourier coefficients of functions of A- bounded variation, A = {A„}, are 0(Kn/n). This was known for \, = n&+l, — 1 < ß < 0. The classes L and HBV are shown to be complementary, but L and ABV are not complementary if ABV is not contained in HBV. The partial sums of the Fourier series of a function of harmonic bounded variation are shown to be uniformly bounded and a theorem analogous to that of Dirichlet is shown for this class of functions without recourse to the Lebesgue test. We have shown elsewhere that functions of harmonic bounded variation (HBV) satisfy the Lebesgue test for convergence of their Fourier series, but if a class of functions of A-bounded variation (ABV) is not properly contained in HBV, it contains functions whose Fourier series diverge [1]. We have also shown that Fourier series of functions of class {nB+x} — BV, -1 < ß < 0, are (C, ß) bounded, implying that the Fourier coefficients are 0(nB) [2]. Here we shall estimate the Fourier coefficients of functions in ABV. Without recourse to the Lebesgue test, we shall prove a theorem for functions of HBV analogous to that of Dirichlet and also show that the partial sums of the Fourier series of an HBV function are uniformly bounded. From this one can conclude that L and HBV are complementary classes, i.e., Parseval's formula holds (with ordinary convergence) for/ G L and g G HBV. -
Convergence of Fourier Series
CONVERGENCE OF FOURIER SERIES KEVIN STEPHEN STOTTER CUDDY Abstract. This paper sets out to explore and explain some of the basic con- cepts of Fourier analysis and its applications. Convolution and questions of convergence will be central. An application to the isoperimetric inequality will conclude the paper. Contents 1. Introduction to Fourier Series 1 2. Convolution and Kernels 2 3. Criteria for Convergence 8 4. Isoperimetric Inequality 18 Acknowledgments 19 References 19 1. Introduction to Fourier Series It will be important for the reader to recall Euler's Formula: ix (1.1) e = cos(x) + i sin(x); 8x 2 R Throughout this paper, an \integrable" function should be interpreted as integrable in the Riemann sense as well as bounded. Definition 1.2. For an integrable, bounded function f :[a; b] ! C we can define, for any integer n, the nth Fourier coefficient 1 Z b f^(n) = f(x)e−2πinx=(b−a)dx: b − a a When there is no question about the function we are referring to we may write ^ P1 ^ 2πinx f(n) = an. From these coefficients we get the Fourier series S(x) = n=−∞ f(n)e (note that this sum may or may not converge). To denote that a Fourier series is associated to a function f we write 1 X f ∼ f^(n)e2πinx n=−∞ Definition 1.3. The N th partial sum of the Fourier series for f, where N is a PN ^ 2πinx=L positive integer, is given by SN (f)(x) = n=−N f(n)e . Date: DEADLINE AUGUST 26, 2012. -
On Evaluation of Riemann Zeta Function Ζ(S)
On Evaluation of Riemann Zeta function ζ(s) This is the Published version of the following publication Luo, Qiu-Ming, Guo, Bai-Ni and Qi, Feng (2002) On Evaluation of Riemann Zeta function ζ(s). RGMIA research report collection, 6 (1). The publisher’s official version can be found at Note that access to this version may require subscription. Downloaded from VU Research Repository https://vuir.vu.edu.au/17803/ ON EVALUATION OF RIEMANN ZETA FUNCTION ζ(s) QIU-MING LUO, BAI-NI GUO, AND FENG QI Abstract. In this paper, by using Fourier series theory, several summing formulae for Riemann Zeta function ζ(s) and Dirichlet series are deduced. 1. Introduction It is well-known that the Riemann Zeta function defined by ∞ X 1 ζ(s) = , <(s) > 1 (1) ns n=1 and Dirichlet series ∞ X (−1)n−1 D(s) = , <(s) > 1 (2) ns n=1 are related to the gamma functions and have important applications in mathemat- ics, especially in Analytic Number Theory. In 1734, Euler gave some remarkably elementary proofs of the following Bernoulli series ∞ X 1 π2 ζ(2) = = . (3) n2 6 n=1 The formula (3) has been studied by many mathematicians and many proofs have been provided, for example, see [2]. In 1748, Euler further gave the following general formula ∞ X 1 (−1)k−122k−1π2k ζ(2k) = = B , (4) n2k (2k)! 2k n=1 where B2k denotes Bernoulli numbers for k ∈ N. 2000 Mathematics Subject Classification. 11R40, 42A16. Key words and phrases. Riemann Zeta function, Fourier series, recursion formula. The authors were supported in part by NNSF (#10001016) of China, SF for the Prominent Youth of Henan Province (#0112000200), SF of Henan Innovation Talents at Universities, NSF of Henan Province (#004051800), Doctor Fund of Jiaozuo Institute of Technology, CHINA. -
Harmonic Analysis
אוניברסיטת תל אביב University Aviv Tel הפקולטה להנדסה Engineering of Faculty בית הספר להנדסת חשמל Engineering Electrical of School Harmonic Analysis Fall Semester LECTURER Nir Sharon Office: 03-640-7729 Location: Room 14M, Schreiber Bldg E-mail: [email protected] COURSE DESCRIPTION The aim of this course is to introduce the fundamentals of Harmonic analysis. In particular, we focus on three main subjects: the theory of Fourier series, approximation in Hilbert spaces by a general orthogonal system, and the basics of the theory of Fourier transform. Harmonic analysis is the study of objects (functions, measures, etc.), defined on different mathematical spaces. Specifically, we study the question of finding the "elementary components" of functions, and how to analyse a given function based on its elementary components. The trigonometric system of cosine and sine functions plays a major role in our presentation of the theory. The course is intended for undergraduate students of engineering, mathematics and physics, although we deal almost exclusively with aspects of Fourier analysis that are usefull in physics and engeneering rather than those of pure mathematics. We presume knowledge in: linear algebra, calculus, elementary theory of ordinary differential equations, and some acquaintance with the system of complex numbers. COURSE TOPICS Week 1: Fourier Series of piecewise continuous functions on a symmetrical segment. Complex and real representations of the Fourier Series. Week 2: Bessel Inequality, the Riemann-Lebesgue Lemma, partial -
Fourier Analysis Notes, Spring 2020
Fourier Analysis Notes, Spring 2020 Peter Woit Department of Mathematics, Columbia University [email protected] September 3, 2020 2 Contents 1 Introduction 5 1.1 Fourier series . .5 1.2 The Fourier transform . .7 2 The Poisson Summation Formula, Theta Functions, and the Zeta Function 9 2.1 The Poisson summation formula and some applications . .9 2.1.1 The heat kernel . 10 2.1.2 The Poisson kernel . 11 2.2 Theta functions . 12 2.2.1 The Jacobi theta function . 13 2.3 The Riemann zeta function . 15 2.3.1 The Mellin transform . 16 2.3.2 The zeta function and the Mellin transform of the theta function . 18 2.3.3 The functional equation for the zeta function . 19 3 Distributions 23 3.1 Introduction . 23 3.2 Distributions: definition . 24 3.3 Distributions: examples . 25 3.4 The derivative of a distribution . 28 3.4.1 The transpose of a linear transformation . 29 3.4.2 Translations . 29 3.4.3 The derivative . 30 3.5 The Fourier transform of a distribution . 33 3.6 Convolution of a function and a distribution . 38 3.7 Distributional solutions of differential equations . 41 4 Higher dimensions 43 4.1 Introduction . 43 4.2 Fourier series and the Fourier transform for d > 1 . 43 4.2.1 Fourier series for d > 1.................... 43 4.2.2 The Fourier transform for d > 1............... 44 3 4.3 Rotations and the Fourier transform . 45 4.3.1 Two dimensions . 47 4.3.2 Three dimensions . 48 5 Wave Equations 53 5.1 Introduction .