A Trip from Classical to Abstract Fourier Analysis
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B1. Fourier Analysis of Discrete Time Signals
B1. Fourier Analysis of Discrete Time Signals Objectives • Introduce discrete time periodic signals • Define the Discrete Fourier Series (DFS) expansion of periodic signals • Define the Discrete Fourier Transform (DFT) of signals with finite length • Determine the Discrete Fourier Transform of a complex exponential 1. Introduction In the previous chapter we defined the concept of a signal both in continuous time (analog) and discrete time (digital). Although the time domain is the most natural, since everything (including our own lives) evolves in time, it is not the only possible representation. In this chapter we introduce the concept of expanding a generic signal in terms of elementary signals, such as complex exponentials and sinusoids. This leads to the frequency domain representation of a signal in terms of its Fourier Transform and the concept of frequency spectrum so that we characterize a signal in terms of its frequency components. First we begin with the introduction of periodic signals, which keep repeating in time. For these signals it is fairly easy to determine an expansion in terms of sinusoids and complex exponentials, since these are just particular cases of periodic signals. This is extended to signals of a finite duration which becomes the Discrete Fourier Transform (DFT), one of the most widely used algorithms in Signal Processing. The concepts introduced in this chapter are at the basis of spectral estimation of signals addressed in the next two chapters. 2. Periodic Signals VIDEO: Periodic Signals (19:45) http://faculty.nps.edu/rcristi/eo3404/b-discrete-fourier-transform/videos/chapter1-seg1_media/chapter1-seg1- 0.wmv http://faculty.nps.edu/rcristi/eo3404/b-discrete-fourier-transform/videos/b1_02_periodicSignals.mp4 In this section we define a class of discrete time signals called Periodic Signals. -
An Introduction to Fourier Analysis Fourier Series, Partial Differential Equations and Fourier Transforms
An Introduction to Fourier Analysis Fourier Series, Partial Differential Equations and Fourier Transforms Notes prepared for MA3139 Arthur L. Schoenstadt Department of Applied Mathematics Naval Postgraduate School Code MA/Zh Monterey, California 93943 August 18, 2005 c 1992 - Professor Arthur L. Schoenstadt 1 Contents 1 Infinite Sequences, Infinite Series and Improper Integrals 1 1.1Introduction.................................... 1 1.2FunctionsandSequences............................. 2 1.3Limits....................................... 5 1.4TheOrderNotation................................ 8 1.5 Infinite Series . ................................ 11 1.6ConvergenceTests................................ 13 1.7ErrorEstimates.................................. 15 1.8SequencesofFunctions.............................. 18 2 Fourier Series 25 2.1Introduction.................................... 25 2.2DerivationoftheFourierSeriesCoefficients.................. 26 2.3OddandEvenFunctions............................. 35 2.4ConvergencePropertiesofFourierSeries.................... 40 2.5InterpretationoftheFourierCoefficients.................... 48 2.6TheComplexFormoftheFourierSeries.................... 53 2.7FourierSeriesandOrdinaryDifferentialEquations............... 56 2.8FourierSeriesandDigitalDataTransmission.................. 60 3 The One-Dimensional Wave Equation 70 3.1Introduction.................................... 70 3.2TheOne-DimensionalWaveEquation...................... 70 3.3 Boundary Conditions ............................... 76 3.4InitialConditions................................ -
Projective Fourier Duality and Weyl Quantization
BR9736398 Instituto de Fisica Teorica IFT Universidade Estadual Paulista Aiigust/9f) IFT -P.025/96 Projective Fourier duality and Weyl quantization R. Aldrovandi and L. A. Saeger Instituto de Fisica Teorica Universidade Estadual Paulista Rua Pamplona 145 01405-900 - Sao Paulo, S.P. Brazil "Snlmutf'l to Internal.J.Tiieor.I'hvs. Instituto de Fisica Teorica Universidade Estadual Paulista Rua Pamplona, 145 01405-900 - Sao Paulo, S.P. Brazil Telephone: 55 (11) 251.5155 Telefax: 55(11) 288.8224 Telex: 55 (11) 31870 UJMFBR Electronic Address: [email protected] 47857::LIBRARY Projective Fourier Duality and Weyl Quantization1" R. Aldrovandi* and L.A. Saeger* Instituto de Fisica Teorica - UNESP Rua Pamplona 145 01405-900 - Sao Paulo, SP Brazil The Weyl-Wigner correspondence prescription, which makes large use of Fourier duality, is reexamined from the point of view of Kac algebras, the most general background for non- commutative Fourier analysis allowing for that property. It is shown how the standard Kac structure has to be extended in order to accommodate the physical requirements. An abelian and a symmetric projective Kac algebras are shown to provide, in close parallel to the standard case, a new dual framework and a well-defined notion of projective Fourier duality for the group of translations on the plane. The Weyl formula arises naturally as an irreducible component of the duality mapping between these projective algebras. Keywords: Weyl Quantization, Weyl-Wigner Correspondence, Projective Group Duality, Projective Kac Algebras, -
Fourier Analysis
Chapter 1 Fourier analysis In this chapter we review some basic results from signal analysis and processing. We shall not go into detail and assume the reader has some basic background in signal analysis and processing. As basis for signal analysis, we use the Fourier transform. We start with the continuous Fourier transformation. But in applications on the computer we deal with a discrete Fourier transformation, which introduces the special effect known as aliasing. We use the Fourier transformation for processes such as convolution, correlation and filtering. Some special attention is given to deconvolution, the inverse process of convolution, since it is needed in later chapters of these lecture notes. 1.1 Continuous Fourier Transform. The Fourier transformation is a special case of an integral transformation: the transforma- tion decomposes the signal in weigthed basis functions. In our case these basis functions are the cosine and sine (remember exp(iφ) = cos(φ) + i sin(φ)). The result will be the weight functions of each basis function. When we have a function which is a function of the independent variable t, then we can transform this independent variable to the independent variable frequency f via: +1 A(f) = a(t) exp( 2πift)dt (1.1) −∞ − Z In order to go back to the independent variable t, we define the inverse transform as: +1 a(t) = A(f) exp(2πift)df (1.2) Z−∞ Notice that for the function in the time domain, we use lower-case letters, while for the frequency-domain expression the corresponding uppercase letters are used. A(f) is called the spectrum of a(t). -
Extended Fourier Analysis of Signals
Dr.sc.comp. Vilnis Liepiņš Email: [email protected] Extended Fourier analysis of signals Abstract. This summary of the doctoral thesis [8] is created to emphasize the close connection of the proposed spectral analysis method with the Discrete Fourier Transform (DFT), the most extensively studied and frequently used approach in the history of signal processing. It is shown that in a typical application case, where uniform data readings are transformed to the same number of uniformly spaced frequencies, the results of the classical DFT and proposed approach coincide. The difference in performance appears when the length of the DFT is selected greater than the length of the data. The DFT solves the unknown data problem by padding readings with zeros up to the length of the DFT, while the proposed Extended DFT (EDFT) deals with this situation in a different way, it uses the Fourier integral transform as a target and optimizes the transform basis in the extended frequency set without putting such restrictions on the time domain. Consequently, the Inverse DFT (IDFT) applied to the result of EDFT returns not only known readings, but also the extrapolated data, where classical DFT is able to give back just zeros, and higher resolution are achieved at frequencies where the data has been extrapolated successfully. It has been demonstrated that EDFT able to process data with missing readings or gaps inside or even nonuniformly distributed data. Thus, EDFT significantly extends the usability of the DFT based methods, where previously these approaches have been considered as not applicable [10-45]. The EDFT founds the solution in an iterative way and requires repeated calculations to get the adaptive basis, and this makes it numerical complexity much higher compared to DFT. -
Fourier Analysis of Discrete-Time Signals
Fourier analysis of discrete-time signals (Lathi Chapt. 10 and these slides) Towards the discrete-time Fourier transform • How we will get there? • Periodic discrete-time signal representation by Discrete-time Fourier series • Extension to non-periodic DT signals using the “periodization trick” • Derivation of the Discrete Time Fourier Transform (DTFT) • Discrete Fourier Transform Discrete-time periodic signals • A periodic DT signal of period N0 is called N0-periodic signal f[n + kN0]=f[n] f[n] n N0 • For the frequency it is customary to use a different notation: the frequency of a DT sinusoid with period N0 is 2⇡ ⌦0 = N0 Fourier series representation of DT periodic signals • DT N0-periodic signals can be represented by DTFS with 2⇡ fundamental frequency ⌦ 0 = and its multiples N0 • The exponential DT exponential basis functions are 0k j⌦ k j2⌦ k jn⌦ k e ,e± 0 ,e± 0 ,...,e± 0 Discrete time 0k j! t j2! t jn! t e ,e± 0 ,e± 0 ,...,e± 0 Continuous time • Important difference with respect to the continuous case: only a finite number of exponentials are different! • This is because the DT exponential series is periodic of period 2⇡ j(⌦ 2⇡)k j⌦k e± ± = e± Increasing the frequency: continuous time • Consider a continuous time sinusoid with increasing frequency: the number of oscillations per unit time increases with frequency Increasing the frequency: discrete time • Discrete-time sinusoid s[n]=sin(⌦0n) • Changing the frequency by 2pi leaves the signal unchanged s[n]=sin((⌦0 +2⇡)n)=sin(⌦0n +2⇡n)=sin(⌦0n) • Thus when the frequency increases from zero, the number of oscillations per unit time increase until the frequency reaches pi, then decreases again towards the value that it had in zero. -
Amenability for the Fourier Algebra
Amenability for the Fourier Algebra by Aaron P. Tikuiˇsis A thesis presented to the University of Waterloo in fulfillment of the thesis requirement for the degree of Master of Mathematics in Pure Mathematics Waterloo, Ontario, Canada, 2007 c Aaron P. Tikuiˇsis 2007 Author’s Declaration I hereby declare that I am the sole author of this thesis. This is a true copy of the thesis, including any required final revisions, as accepted by my examiners. I understand that my thesis may be made electronically available to the public. ii Abstract The Fourier algebra A(G) can be viewed as a dual object for the group G and, in turn, for the group algebra L1(G). It is a commutative Banach algebra constructed using the representation theory of the group, and from which the group G may be recovered as its spectrum. When G is abelian, A(G) coincides with L1(Gˆ); for non-abelian groups, it is viewed as a generalization of this object. B. Johnson has shown that G is amenable as a group if and only if L1(G) is amenable as a Banach algebra. Hence, it is natural to expect that the cohomology of A(G) will reflect the amenability of G. The initial hypothesis to this effect is that G is amenable if and only if A(G) is amenable as a Banach algebra. Interestingly, it turns out that A(G) is amenable only when G has an abelian group of finite index, leaving a large class of amenable groups with non-amenable Fourier algebras. The dual of A(G) is a von Neumann algebra (denoted VN(G)); as such, A(G) inherits a natural operator space structure. -
Fourier Analysis
Fourier Analysis Hilary Weller <[email protected]> 19th October 2015 This is brief introduction to Fourier analysis and how it is used in atmospheric and oceanic science, for: Analysing data (eg climate data) • Numerical methods • Numerical analysis of methods • 1 1 Fourier Series Any periodic, integrable function, f (x) (defined on [ π,π]), can be expressed as a Fourier − series; an infinite sum of sines and cosines: ∞ ∞ a0 f (x) = + ∑ ak coskx + ∑ bk sinkx (1) 2 k=1 k=1 The a and b are the Fourier coefficients. • k k The sines and cosines are the Fourier modes. • k is the wavenumber - number of complete waves that fit in the interval [ π,π] • − sinkx for different values of k 1.0 k =1 k =2 k =4 0.5 0.0 0.5 1.0 π π/2 0 π/2 π − − x The wavelength is λ = 2π/k • The more Fourier modes that are included, the closer their sum will get to the function. • 2 Sum of First 4 Fourier Modes of a Periodic Function 1.0 Fourier Modes Original function 4 Sum of first 4 Fourier modes 0.5 2 0.0 0 2 0.5 4 1.0 π π/2 0 π/2 π π π/2 0 π/2 π − − − − x x 3 The first four Fourier modes of a square wave. The additional oscillations are “spectral ringing” Each mode can be represented by motion around a circle. ↓ The motion around each circle has a speed and a radius. These represent the wavenumber and the Fourier coefficients. -
An Order Theoretical Characterization of the Fourier Transform
Math. Ann. 272, 331-347 (1985) O Springer-Verlag1985 An Order Theoretical Characterization of the Fourier Transform Wolfgang Arendt Mathematisches Institut der Universit~it, Auf der Morgenstelle 10, D-7400 Tiibingen 1, Federal Republic of Germany Dedicated to Prof. H.H. Schaefer on the occasion of his 60 th birthday Most spaces of functions or measures on a locally compact group G carry two different orderings: a pointwise ordering and a positive-definite orderino. For example, on Lt(G) the pointwise ordering is defined by the cone LI(G)+ = {f~ LI(G) :f(t) > 0 for almost all t ~ G} and a positive-definite ordering by the cone LI(G)p=Ur{f **f:f~Ll(G)}. In [1] and [2] we investigated these two 0rderings for the Fourier algebra A (G), the Fourier-Stieltjes algebra B(G) and for LI(G). The principal result was that each of these biordered spaces determines the group up to isomorphism. In the present article we take up an idea of H.H. Schaefer's and discuss order properties of the Fourier transform ~-. If G is abelian, then ~ is a biorder antiisomorphism from L I(G) onto A(G) (where (~ denotes the dual group of G), i.e. maps positive-definite functions onto pointwise positive functions and vice versa. So the following question arises: Given an arbitrary locally compact group G, can it happen that one finds a locally compact group d and a biorder anti, isomorphism from LI(G) onto A(G)? By the results mentioned above, the group Gwould be uniquely determined by G, and one could consider G as the dual group of G. -
On the Imaginary Part of the Characteristic Function
On the imaginary part of the characteristic function Saulius Norvidas Institute of Data Science and Digital Technologies, Vilnius University, Akademijos str. 4, Vilnius LT-04812, Lithuania (e-mail: [email protected]) Abstract Suppose that f is the characteristic function of a probability measure on the real line R. In this paper, we deal with the following problem posed by N.G. Ushakov: Is it true that f is never determined by its imaginary part ℑ f ? In other words, is it true that for any characteristic function f there exists a characteristic function g such that ℑ f ≡ ℑg but f 6≡ g? We study this question in the more general case of the characteristic function defined on an arbitrary locally compact abelian group. A characterization of what characteristic functions are uniquely determined by their imaginary parts are given. As a consequence of this characterization, we obtain that several frequently used characteristic functions on the classical locally compact abelian groups are uniquely determined by their imaginary parts. Keywords: Locally compact Abelian group; Fourier transform; characteristic function; Fourier algebra. Mathematics Subject Classification: Primary 43A35; Secondary 42A82; 60E10. 1 Introduction Let B(R) denote the usual σ-algebra of all Borel subsets of the real line R, and let M(R) be the Banach algebra of bounded regular complex-valued Borel measures µ on R. M(R) is equipped with the usual total variation norm kµk. We define the Fourier (Fourier-Stieltjes) transform of µ ∈ M(R) by µˆ (ξ ) = e−iξxdµ(x), ξ ∈ R. R Z The family of these functions µˆ forms the Fourier-Stieltjes algebra B R . -
FOURIER ANALYSIS 1. the Best Approximation Onto Trigonometric
FOURIER ANALYSIS ERIK LØW AND RAGNAR WINTHER 1. The best approximation onto trigonometric polynomials Before we start the discussion of Fourier series we will review some basic results on inner–product spaces and orthogonal projections mostly presented in Section 4.6 of [1]. 1.1. Inner–product spaces. Let V be an inner–product space. As usual we let u, v denote the inner–product of u and v. The corre- sponding normh isi given by v = v, v . k k h i A basic relation between the inner–productp and the norm in an inner– product space is the Cauchy–Scwarz inequality. It simply states that the absolute value of the inner–product of u and v is bounded by the product of the corresponding norms, i.e. (1.1) u, v u v . |h i|≤k kk k An outline of a proof of this fundamental inequality, when V = Rn and is the standard Eucledian norm, is given in Exercise 24 of Section 2.7k·k of [1]. We will give a proof in the general case at the end of this section. Let W be an n dimensional subspace of V and let P : V W be the corresponding projection operator, i.e. if v V then w∗ =7→P v W is the element in W which is closest to v. In other∈ words, ∈ v w∗ v w for all w W. k − k≤k − k ∈ It follows from Theorem 12 of Chapter 4 of [1] that w∗ is characterized by the conditions (1.2) v P v, w = v w∗, w =0 forall w W. -
Groups Meet Analysis: the Fourier Algebra
Groups meet Analysis: the Fourier Algebra Matthew Daws Leeds York, June 2013 Matthew Daws (Leeds) The Fourier Algebra York, June 2013 1 / 24 Colloquium talk So I believe this is a talk to a general audience of Mathematicians. Some old advice for giving talks: the first 10 minutes should be aimed at the janitor; then at undergrads; then at graduates; then at researchers; then at specialists; and finish by talking to yourself. The janitor won’t understand me; and I’ll try not to talk to myself. I’m going to try just to give a survey talk about a particular area at the interface between algebra and analysis. Please ask questions! Matthew Daws (Leeds) The Fourier Algebra York, June 2013 2 / 24 Colloquium talk So I believe this is a talk to a general audience of Mathematicians. Some old advice for giving talks: the first 10 minutes should be aimed at the janitor; then at undergrads; then at graduates; then at researchers; then at specialists; and finish by talking to yourself. The janitor won’t understand me; and I’ll try not to talk to myself. I’m going to try just to give a survey talk about a particular area at the interface between algebra and analysis. Please ask questions! Matthew Daws (Leeds) The Fourier Algebra York, June 2013 2 / 24 Colloquium talk So I believe this is a talk to a general audience of Mathematicians. Some old advice for giving talks: the first 10 minutes should be aimed at the janitor; then at undergrads; then at graduates; then at researchers; then at specialists; and finish by talking to yourself.