Working out Fourier Transforms Pairs

Total Page:16

File Type:pdf, Size:1020Kb

Working out Fourier Transforms Pairs Fourier Transform Pairs The Fourier transform transforms a function of time, f (t), into a function of frequency, F(s): ∞ π F { f (t)}(s)= F(s)= f (t)e− j2 stdt. Z−∞ The inverse Fourier transform transforms a func- tion of frequency, F(s), into a function of time, f (t): ∞ π F −1{F(s)}(t)= f (t)= F(s)e j2 stds. Z−∞ The inverse Fourier transform of the Fourier trans- form is the identity transform: ∞ ∞ π τ π f (t)= f (τ)e− j2 s dτ e j2 stds. Z−∞ Z−∞ Fourier Transform Pairs (contd). Because the Fourier transform and the inverse Fourier transform differ only in the sign of the exponential’s argument, the following recipro- cal relation holds between f (t) and F(s): F f (t) −→ F(s) is equivalent to F F(t) −→ f (−s). This relationship is often written more econom- ically as follows: F f (t) ←→ F(s) where f (t) and F(s) are said to be a Fourier transform pair. Fourier Transform of Gaussian Let f (t) be a Gaussian: π 2 f (t)= e− t . By the definition of Fourier transform we see that: ∞ π 2 π F(s) = e− t e− j2 stdt Z ∞ −∞ π 2 = e− (t + j2st)dt. Z−∞ Now we can multiply the right hand side by 2 2 e−πs eπs = 1: ∞ π 2 π 2 π 2 F(s) = e− s e− (t + j2st)+ s dt Z ∞ −∞ π 2 π 2 2 = e− s e− (t + j2st−s )dt Z ∞ −∞ π 2 π = e− s e− (t+ js)(t+ js)dt Z ∞ −∞ π 2 π 2 = e− s e− (t+ js) dt Z−∞ Fourier Transform of Gaussian (contd.) ∞ π 2 π 2 F(s) = e− s e− (t+ js) dt Z−∞ After substituting u for t + js and du for dt we see that: ∞ π 2 π 2 F(s)= e− s e− u du. Z−∞ 1 It follows that the Gaussian| {z is its} own Fourier transform: π 2 F π 2 e− t ←→ e− s . Fourier Transform of Dirac Delta Function To compute the Fourier transform of an impulse we apply the definition of Fourier transform: ∞ − j2πst F {δ(t −t0)}(s)= F(s)= δ(t −t0)e dt Z−∞ which, by the sifting property of the impulse, is just: π e− j2 st0. It follows that: F − j2π st0 δ(t −t0) −→ e . Fourier Transform of Harmonic Signal What is the inverse Fourier transform of an im- pulse located at s0? Applying the definition of inverse Fourier transform yields: ∞ −1 j2πst F {δ(s−s0)}(t)= f (t)= δ(s−s0)e ds Z−∞ which, by the sifting property of the impulse, is just: π e j2 s0 t. It follows that: F j2π s0 t e −→ δ(s − s0). Fourier Transform of Sine and Cosine We can compute the Fourier transforms of the sine and cosine by exploiting the sifting prop- erty of the impulse: ∞ f (x)δ(x − x0)dx = f (x0). Z−∞ • Question What is the inverse Fourier trans- form of a pair of impulses spaced symmetri- cally about the origin? −1 F {δ(s + s0)+ δ(s − s0)} • Answer By definition of inverse Fourier trans- form: ∞ j2πst f (t)= [δ(s + s0)+ δ(s − s0)]e ds. Z−∞ Fourier Transform of Sine and Cosine (contd.) Expanding the above yields the following ex- pression for f (t): ∞ ∞ j2πst j2πst δ(s + s0)e ds + δ(s − s0)e ds Z−∞ Z−∞ Which by the sifting property is just: π π f (t) = e j2 s0 t + e− j2 s0 t = 2cos(2π s0 t). Fourier Transform of Sine and Cosine (contd.) It follows that F 1 cos(2π s t) ←→ [δ(s + s )+ δ(s − s )]. 0 2 0 0 A similar argument can be used to show: F j sin(2π s t) ←→ [δ(s + s ) − δ(s − s )]. 0 2 0 0 Fourier Transform of the Pulse To compute the Fourier transform of a pulse we apply the definition of Fourier transform: ∞ π F(s) = Π(t)e− j2 stdt Z−∞ 1 2 π = e− j2 stdt Z 1 −2 1 1 π 2 = e− j2 st − j2πs 1 −2 1 π π = e− j s − e j s − j2πs π π 1 e j s − e− j s = πs 2 j (e jx−e− jx) Using the fact that sin(x) = 2 j we see that: sin(πs) F(s) = . πs Fourier Transform of the Shah Function Recall the Fourier series for the Shah function: ∞ 1 t 1 jω t. πIII π = π ∑ e 2 2 2 ω=−∞ By the sifting property, t ∞ ∞ III = ∑ δ(s − ω)e jstds. π Z ∞ 2 ω=−∞ − Changing the order of the summation and the integral yields t ∞ ∞ III = ∑ δ(s − ω)e jstds. π Z ∞ 2 − ω=−∞ Factoring out e jst from the summation t ∞ ∞ III = e jst ∑ δ(s − ω)ds π Z ∞ 2 − ω=−∞ ∞ = e jstIII(s)ds. Z−∞ Fourier Transform of the Shah Function Substituting 2πτ for t yields ∞ π τ III(τ) = III(s)e j2 s ds Z−∞ = F −1{III(s)}(τ). Consequently we see that F {III} = III..
Recommended publications
  • F(P,Q) = Ffeic(R+TQ)
    674 MATHEMA TICS: N. H. McCOY PR.OC. N. A. S. ON THE FUNCTION IN QUANTUM MECHANICS WHICH CORRESPONDS TO A GIVEN FUNCTION IN CLASSICAL MECHANICS By NEAL H. MCCoy DEPARTMENT OF MATHEMATICS, SMrTH COLLEGE Communicated October 11, 1932 Let f(p,q) denote a function of the canonical variables p,q of classical mechanics. In making the transition to quantum mechanics, the variables p,q are represented by Hermitian operators P,Q which, satisfy the com- mutation rule PQ- Qp = 'y1 (1) where Y = h/2ri and 1 indicates the unit operator. From group theoretic considerations, Weyll has obtained the following general rule for carrying a function over from classical to quantum mechanics. Express f(p,q) as a Fourier integral, f(p,q) = f feiC(r+TQ) r(,r)d(rdT (2) (or in any other way as a linear combination of the functions eW(ffP+T)). Then the function F(P,Q) in quantum mechanics which corresponds to f(p,q) is given by F(P,Q)- ff ei(0P+7Q) t(cr,r)dodr. (3) It is the purpose of this note to obtain an explicit expression for F(P,Q), and although we confine our statements to the case in which f(p,q) is a polynomial, the results remain formally correct for infinite series. Any polynomial G(P,Q) may, by means of relation (1), be written in a form in which all of the Q-factors occur on the left in each term. This form of the function G(P,Q) will be denoted by GQ(P,Q).
    [Show full text]
  • FOURIER TRANSFORM Very Broadly Speaking, the Fourier Transform Is a Systematic Way to Decompose “Generic” Functions Into
    FOURIER TRANSFORM TERENCE TAO Very broadly speaking, the Fourier transform is a systematic way to decompose “generic” functions into a superposition of “symmetric” functions. These symmetric functions are usually quite explicit (such as a trigonometric function sin(nx) or cos(nx)), and are often associated with physical concepts such as frequency or energy. What “symmetric” means here will be left vague, but it will usually be associated with some sort of group G, which is usually (though not always) abelian. Indeed, the Fourier transform is a fundamental tool in the study of groups (and more precisely in the representation theory of groups, which roughly speaking describes how a group can define a notion of symmetry). The Fourier transform is also related to topics in linear algebra, such as the representation of a vector as linear combinations of an orthonormal basis, or as linear combinations of eigenvectors of a matrix (or a linear operator). To give a very simple prototype of the Fourier transform, consider a real-valued function f : R → R. Recall that such a function f(x) is even if f(−x) = f(x) for all x ∈ R, and is odd if f(−x) = −f(x) for all x ∈ R. A typical function f, such as f(x) = x3 + 3x2 + 3x + 1, will be neither even nor odd. However, one can always write f as the superposition f = fe + fo of an even function fe and an odd function fo by the formulae f(x) + f(−x) f(x) − f(−x) f (x) := ; f (x) := . e 2 o 2 3 2 2 3 For instance, if f(x) = x + 3x + 3x + 1, then fe(x) = 3x + 1 and fo(x) = x + 3x.
    [Show full text]
  • 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................................
    [Show full text]
  • THE ZEROS of QUASI-ANALYTIC FUNCTIONS1 (3) H(V) = — F U-2 Log
    THE ZEROS OF QUASI-ANALYTICFUNCTIONS1 ARTHUR O. GARDER, JR. 1. Statement of the theorem. Definition 1. C(Mn, k) is the class of functions f(x) ior which there exist positive constants A and k and a sequence (Af„)"_0 such that (1) |/(n)(x)| ^AknM„, - oo < x< oo,» = 0,l,2, • • • . Definition 2. C(Mn, k) is a quasi-analytic class if 0 is the only element / of C(Mn, k) ior which there exists a point x0 at which /<">(*„)=0 for « = 0, 1, 2, ••• . We introduce the functions (2) T(u) = max u"(M„)_l, 0 ^ m < oo, and (3) H(v) = — f u-2 log T(u)du. T J l The following property of H(v) characterizes quasi-analytic classes. Theorem 3 [5, 78 ].2 Let lim,,.,*, M„/n= °o.A necessary and sufficient condition that C(Mn, k) be a quasi-analytic class is that (4) lim H(v) = oo, where H(v) is defined by (2) and (3). Definition 4. Let v(x) be a function satisfying (i) v(x) is continuously differentiable for 0^x< oo, (ii) v(0) = l, v'(x)^0, 0^x<oo, (iii) xv'(x)/v(x)=0 (log x)-1, x—>oo. We shall say that/(x)GC* if and only if f(x)EC(Ml, k), where M* = n\[v(n)]n and v(n) satisfies the above conditions. Definition 5. Let Z(u) be a real-valued function which is less Received by the editors November 29, 1954 and, in revised form, February 24, 1955. 1 Presented to the Graduate School of Washington University in partial fulfill- ment of the requirements for the degree of Doctor of Philosophy.
    [Show full text]
  • Fourier Transforms & the Convolution Theorem
    Convolution, Correlation, & Fourier Transforms James R. Graham 11/25/2009 Introduction • A large class of signal processing techniques fall under the category of Fourier transform methods – These methods fall into two broad categories • Efficient method for accomplishing common data manipulations • Problems related to the Fourier transform or the power spectrum Time & Frequency Domains • A physical process can be described in two ways – In the time domain, by h as a function of time t, that is h(t), -∞ < t < ∞ – In the frequency domain, by H that gives its amplitude and phase as a function of frequency f, that is H(f), with -∞ < f < ∞ • In general h and H are complex numbers • It is useful to think of h(t) and H(f) as two different representations of the same function – One goes back and forth between these two representations by Fourier transforms Fourier Transforms ∞ H( f )= ∫ h(t)e−2πift dt −∞ ∞ h(t)= ∫ H ( f )e2πift df −∞ • If t is measured in seconds, then f is in cycles per second or Hz • Other units – E.g, if h=h(x) and x is in meters, then H is a function of spatial frequency measured in cycles per meter Fourier Transforms • The Fourier transform is a linear operator – The transform of the sum of two functions is the sum of the transforms h12 = h1 + h2 ∞ H ( f ) h e−2πift dt 12 = ∫ 12 −∞ ∞ ∞ ∞ h h e−2πift dt h e−2πift dt h e−2πift dt = ∫ ( 1 + 2 ) = ∫ 1 + ∫ 2 −∞ −∞ −∞ = H1 + H 2 Fourier Transforms • h(t) may have some special properties – Real, imaginary – Even: h(t) = h(-t) – Odd: h(t) = -h(-t) • In the frequency domain these
    [Show full text]
  • STATISTICAL FOURIER ANALYSIS: CLARIFICATIONS and INTERPRETATIONS by DSG Pollock
    STATISTICAL FOURIER ANALYSIS: CLARIFICATIONS AND INTERPRETATIONS by D.S.G. Pollock (University of Leicester) Email: stephen [email protected] This paper expounds some of the results of Fourier theory that are es- sential to the statistical analysis of time series. It employs the algebra of circulant matrices to expose the structure of the discrete Fourier transform and to elucidate the filtering operations that may be applied to finite data sequences. An ideal filter with a gain of unity throughout the pass band and a gain of zero throughout the stop band is commonly regarded as incapable of being realised in finite samples. It is shown here that, to the contrary, such a filter can be realised both in the time domain and in the frequency domain. The algebra of circulant matrices is also helpful in revealing the nature of statistical processes that are band limited in the frequency domain. In order to apply the conventional techniques of autoregressive moving-average modelling, the data generated by such processes must be subjected to anti- aliasing filtering and sub sampling. These techniques are also described. It is argued that band-limited processes are more prevalent in statis- tical and econometric time series than is commonly recognised. 1 D.S.G. POLLOCK: Statistical Fourier Analysis 1. Introduction Statistical Fourier analysis is an important part of modern time-series analysis, yet it frequently poses an impediment that prevents a full understanding of temporal stochastic processes and of the manipulations to which their data are amenable. This paper provides a survey of the theory that is not overburdened by inessential complications, and it addresses some enduring misapprehensions.
    [Show full text]
  • Fourier Transform, Convolution Theorem, and Linear Dynamical Systems April 28, 2016
    Mathematical Tools for Neuroscience (NEU 314) Princeton University, Spring 2016 Jonathan Pillow Lecture 23: Fourier Transform, Convolution Theorem, and Linear Dynamical Systems April 28, 2016. Discrete Fourier Transform (DFT) We will focus on the discrete Fourier transform, which applies to discretely sampled signals (i.e., vectors). Linear algebra provides a simple way to think about the Fourier transform: it is simply a change of basis, specifically a mapping from the time domain to a representation in terms of a weighted combination of sinusoids of different frequencies. The discrete Fourier transform is therefore equiv- alent to multiplying by an orthogonal (or \unitary", which is the same concept when the entries are complex-valued) matrix1. For a vector of length N, the matrix that performs the DFT (i.e., that maps it to a basis of sinusoids) is an N × N matrix. The k'th row of this matrix is given by exp(−2πikt), for k 2 [0; :::; N − 1] (where we assume indexing starts at 0 instead of 1), and t is a row vector t=0:N-1;. Recall that exp(iθ) = cos(θ) + i sin(θ), so this gives us a compact way to represent the signal with a linear superposition of sines and cosines. The first row of the DFT matrix is all ones (since exp(0) = 1), and so the first element of the DFT corresponds to the sum of the elements of the signal. It is often known as the \DC component". The next row is a complex sinusoid that completes one cycle over the length of the signal, and each subsequent row has a frequency that is an integer multiple of this \fundamental" frequency.
    [Show full text]
  • CONDITIONAL EXPECTATION Definition 1. Let (Ω,F,P)
    CONDITIONAL EXPECTATION 1. CONDITIONAL EXPECTATION: L2 THEORY ¡ Definition 1. Let (­,F ,P) be a probability space and let G be a σ algebra contained in F . For ¡ any real random variable X L2(­,F ,P), define E(X G ) to be the orthogonal projection of X 2 j onto the closed subspace L2(­,G ,P). This definition may seem a bit strange at first, as it seems not to have any connection with the naive definition of conditional probability that you may have learned in elementary prob- ability. However, there is a compelling rationale for Definition 1: the orthogonal projection E(X G ) minimizes the expected squared difference E(X Y )2 among all random variables Y j ¡ 2 L2(­,G ,P), so in a sense it is the best predictor of X based on the information in G . It may be helpful to consider the special case where the σ algebra G is generated by a single random ¡ variable Y , i.e., G σ(Y ). In this case, every G measurable random variable is a Borel function Æ ¡ of Y (exercise!), so E(X G ) is the unique Borel function h(Y ) (up to sets of probability zero) that j minimizes E(X h(Y ))2. The following exercise indicates that the special case where G σ(Y ) ¡ Æ for some real-valued random variable Y is in fact very general. Exercise 1. Show that if G is countably generated (that is, there is some countable collection of set B G such that G is the smallest σ algebra containing all of the sets B ) then there is a j 2 ¡ j G measurable real random variable Y such that G σ(Y ).
    [Show full text]
  • A Hilbert Space Theory of Generalized Graph Signal Processing
    1 A Hilbert Space Theory of Generalized Graph Signal Processing Feng Ji and Wee Peng Tay, Senior Member, IEEE Abstract Graph signal processing (GSP) has become an important tool in many areas such as image pro- cessing, networking learning and analysis of social network data. In this paper, we propose a broader framework that not only encompasses traditional GSP as a special case, but also includes a hybrid framework of graph and classical signal processing over a continuous domain. Our framework relies extensively on concepts and tools from functional analysis to generalize traditional GSP to graph signals in a separable Hilbert space with infinite dimensions. We develop a concept analogous to Fourier transform for generalized GSP and the theory of filtering and sampling such signals. Index Terms Graph signal proceesing, Hilbert space, generalized graph signals, F-transform, filtering, sampling I. INTRODUCTION Since its emergence, the theory and applications of graph signal processing (GSP) have rapidly developed (see for example, [1]–[10]). Traditional GSP theory is essentially based on a change of orthonormal basis in a finite dimensional vector space. Suppose G = (V; E) is a weighted, arXiv:1904.11655v2 [eess.SP] 16 Sep 2019 undirected graph with V the vertex set of size n and E the set of edges. Recall that a graph signal f assigns a complex number to each vertex, and hence f can be regarded as an element of n C , where C is the set of complex numbers. The heart of the theory is a shift operator AG that is usually defined using a property of the graph.
    [Show full text]
  • 20. the Fourier Transform in Optics, II Parseval’S Theorem
    20. The Fourier Transform in optics, II Parseval’s Theorem The Shift theorem Convolutions and the Convolution Theorem Autocorrelations and the Autocorrelation Theorem The Shah Function in optics The Fourier Transform of a train of pulses The spectrum of a light wave The spectrum of a light wave is defined as: 2 SFEt {()} where F{E(t)} denotes E(), the Fourier transform of E(t). The Fourier transform of E(t) contains the same information as the original function E(t). The Fourier transform is just a different way of representing a signal (in the frequency domain rather than in the time domain). But the spectrum contains less information, because we take the magnitude of E(), therefore losing the phase information. Parseval’s Theorem Parseval’s Theorem* says that the 221 energy in a function is the same, whether f ()tdt F ( ) d 2 you integrate over time or frequency: Proof: f ()tdt2 f ()t f *()tdt 11 F( exp(j td ) F *( exp(j td ) dt 22 11 FF() *(') exp([j '])tdtd ' d 22 11 FF( ) * ( ') [2 ')] dd ' 22 112 FF() *() d F () d * also known as 22Rayleigh’s Identity. The Fourier Transform of a sum of two f(t) F() functions t g(t) G() Faft() bgt () aF ft() bFgt () t The FT of a sum is the F() + sum of the FT’s. f(t)+g(t) G() Also, constants factor out. t This property reflects the fact that the Fourier transform is a linear operation. Shift Theorem The Fourier transform of a shifted function, f ():ta Ffta ( ) exp( jaF ) ( ) Proof : This theorem is F ft a ft( a )exp( jtdt ) important in optics, because we often encounter functions Change variables : uta that are shifting (continuously) along fu( )exp( j [ u a ]) du the time axis – they are called waves! exp(ja ) fu ( )exp( judu ) exp(jaF ) ( ) QED An example of the Shift Theorem in optics Suppose that we’re measuring the spectrum of a light wave, E(t), but a small fraction of the irradiance of this light, say , takes a different path that also leads to the spectrometer.
    [Show full text]
  • The Fourier Transform
    The Fourier Transform CS/CME/BIOPHYS/BMI 279 Fall 2015 Ron Dror The Fourier transform is a mathematical method that expresses a function as the sum of sinusoidal functions (sine waves). Fourier transforms are widely used in many fields of sciences and engineering, including image processing, quantum mechanics, crystallography, geoscience, etc. Here we will use, as examples, functions with finite, discrete domains (i.e., functions defined at a finite number of regularly spaced points), as typically encountered in computational problems. However the concept of Fourier transform can be readily applied to functions with infinite or continuous domains. (We won’t differentiate between “Fourier series” and “Fourier transform.”) A graphical example ! ! Suppose we have a function � � defined in the range − < � < on 2� + 1 discrete points such that ! ! ! � = �. By a Fourier transform we aim to express it as: ! !!!! � �! = �! + �! cos 2��!� + �! + �! cos 2��!� + �! + ⋯ (1) We first demonstrate graphically how a function may be described by its Fourier components. Fig. 1 shows a function defined on 101 discrete data points integer � = −50, −49, … , 49, 50. In fig. 2, the first Fig. 1. The function to be Fourier transformed few Fourier components are plotted separately (left) and added together (right) to form an approximation to the original function. Finally, fig. 3 demonstrates that by including the components up to � = 50, a faithful representation of the original function can be obtained. � ��, �� & �� Single components Summing over components up to m 0 �! = 0.6 �! = 0 �! = 0 1 �! = 1.9 �! = 0.01 �! = 2.2 2 �! = 0.27 �! = 0.02 �! = −1.3 3 �! = 0.39 �! = 0.03 �! = 0.4 Fig.2.
    [Show full text]
  • Fourier Series, Haar Wavelets and Fast Fourier Transform
    FOURIER SERIES, HAAR WAVELETS AND FAST FOURIER TRANSFORM VESA KAARNIOJA, JESSE RAILO AND SAMULI SILTANEN Abstract. This handout is for the course Applications of matrix computations at the University of Helsinki in Spring 2018. We recall basic algebra of complex numbers, define the Fourier series, the Haar wavelets and discrete Fourier transform, and describe the famous Fast Fourier Transform (FFT) algorithm. The discrete convolution is also considered. Contents 1. Revision on complex numbers 1 2. Fourier series 3 2.1. Fourier series: complex formulation 5 3. *Haar wavelets 5 3.1. *Theoretical approach as an orthonormal basis of L2([0, 1]) 6 4. Discrete Fourier transform 7 4.1. *Discrete convolutions 10 5. Fast Fourier transform (FFT) 10 1. Revision on complex numbers A complex number is a pair x + iy := (x, y) ∈ C of x, y ∈ R. Let z = x + iy, w = a + ib ∈ C be two complex numbers. We define the sum as z + w := (x + a) + i(y + b) • Version 1. Suggestions and corrections could be send to jesse.railo@helsinki.fi. • Things labaled with * symbol are supposed to be extra material and might be more advanced. • There are some exercises in the text that are supposed to be relatively easy, even trivial, and support understanding of the main concepts. These are not part of the official course work. 1 2 VESA KAARNIOJA, JESSE RAILO AND SAMULI SILTANEN and the product as zw := (xa − yb) + i(ya + xb). These satisfy all the same algebraic rules as the real numbers. Recall and verify that i2 = −1. However, C is not an ordered field, i.e.
    [Show full text]