The Dirac Delta Function Kurt Bryan
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J-DSP Lab 2: the Z-Transform and Frequency Responses
J-DSP Lab 2: The Z-Transform and Frequency Responses Introduction This lab exercise will cover the Z transform and the frequency response of digital filters. The goal of this exercise is to familiarize you with the utility of the Z transform in digital signal processing. The Z transform has a similar role in DSP as the Laplace transform has in circuit analysis: a) It provides intuition in certain cases, e.g., pole location and filter stability, b) It facilitates compact signal representations, e.g., certain deterministic infinite-length sequences can be represented by compact rational z-domain functions, c) It allows us to compute signal outputs in source-system configurations in closed form, e.g., using partial functions to compute transient and steady state responses. d) It associates intuitively with frequency domain representations and the Fourier transform In this lab we use the Filter block of J-DSP to invert the Z transform of various signals. As we have seen in the previous lab, the Filter block in J-DSP can implement a filter transfer function of the following form 10 −i ∑bi z i=0 H (z) = 10 − j 1+ ∑ a j z j=1 This is essentially realized as an I/P-O/P difference equation of the form L M y(n) = ∑∑bi x(n − i) − ai y(n − i) i==01i The transfer function is associated with the impulse response and hence the output can also be written as y(n) = x(n) * h(n) Here, * denotes convolution; x(n) and y(n) are the input signal and output signal respectively. -
The Laplace Transform of the Psi Function
PROCEEDINGS OF THE AMERICAN MATHEMATICAL SOCIETY Volume 138, Number 2, February 2010, Pages 593–603 S 0002-9939(09)10157-0 Article electronically published on September 25, 2009 THE LAPLACE TRANSFORM OF THE PSI FUNCTION ATUL DIXIT (Communicated by Peter A. Clarkson) Abstract. An expression for the Laplace transform of the psi function ∞ L(a):= e−atψ(t +1)dt 0 is derived using two different methods. It is then applied to evaluate the definite integral 4 ∞ x2 dx M(a)= , 2 2 −a π 0 x +ln (2e cos x) for a>ln 2 and to resolve a conjecture posed by Olivier Oloa. 1. Introduction Let ψ(x) denote the logarithmic derivative of the gamma function Γ(x), i.e., Γ (x) (1.1) ψ(x)= . Γ(x) The psi function has been studied extensively and still continues to receive attention from many mathematicians. Many of its properties are listed in [6, pp. 952–955]. Surprisingly, an explicit formula for the Laplace transform of the psi function, i.e., ∞ (1.2) L(a):= e−atψ(t +1)dt, 0 is absent from the literature. Recently in [5], the nature of the Laplace trans- form was studied by demonstrating the relationship between L(a) and the Glasser- Manna-Oloa integral 4 ∞ x2 dx (1.3) M(a):= , 2 2 −a π 0 x +ln (2e cos x) namely, that, for a>ln 2, γ (1.4) M(a)=L(a)+ , a where γ is the Euler’s constant. In [1], T. Amdeberhan, O. Espinosa and V. H. Moll obtained certain analytic expressions for M(a) in the complementary range 0 <a≤ ln 2. -
7 Laplace Transform
7 Laplace transform The Laplace transform is a generalised Fourier transform that can handle a larger class of signals. Instead of a real-valued frequency variable ω indexing the exponential component ejωt it uses a complex-valued variable s and the generalised exponential est. If all signals of interest are right-sided (zero for negative t) then a unilateral variant can be defined that is simple to use in practice. 7.1 Development The Fourier transform of a signal x(t) exists if it is absolutely integrable: ∞ x(t) dt < . | | ∞ Z−∞ While it’s possible that the transform might exist even if this condition isn’t satisfied, there are a whole class of signals of interest that do not have a Fourier transform. We still need to be able to work with them. Consider the signal x(t)= e2tu(t). For positive values of t this signal grows exponentially without bound, and the Fourier integral does not converge. However, we observe that the modified signal σt φ(t)= x(t)e− does have a Fourier transform if we choose σ > 2. Thus φ(t) can be expressed in terms of frequency components ejωt for <ω< . −∞ ∞ The bilateral Laplace transform of a signal x(t) is defined to be ∞ st X(s)= x(t)e− dt, Z−∞ where s is a complex variable. The set of values of s for which this transform exists is called the region of convergence, or ROC. Suppose the imaginary axis s = jω lies in the ROC. The values of the Laplace transform along this line are ∞ jωt X(jω)= x(t)e− dt, Z−∞ which are precisely the values of the Fourier transform. -
Subtleties of the Laplace Transform
Troubles at the Origin: Consistent Usage and Properties of the Unilateral Laplace Transform Kent H. Lundberg, Haynes R. Miller, and David L. Trumper Massachusetts Institute of Technology The Laplace transform is a standard tool associated with the analysis of signals, models, and control systems, and is consequently taught in some form to almost all engineering students. The bilateral and unilateral forms of the Laplace transform are closely related, but have somewhat different domains of application. The bilateral transform is most frequently seen in the context of signal processing, whereas the unilateral transform is most often associated with the study of dynamic system response where the role of initial conditions takes on greater significance. In our teaching we have found some significant pitfalls associated with teaching our students to understand and apply the Laplace transform. These confusions extend to the presentation of this material in many of the available mathematics and engineering textbooks as well. The most significant confusion in much of the textbook literature is how to deal with the origin in the application of the unilateral Laplace transform. That is, many texts present the transform of a time function f(t) as Z ∞ L{f(t)} = f(t)e−st dt (1) 0 without properly specifiying the meaning of the lower limit of integration. Said informally, does the integral include the origin fully, partially, or not at all? This issue becomes significant as soon as singularity functions such as the unit impulse are introduced. While it is not possible to devote full attention to this issue within the context of a typical undergraduate course, this “skeleton in the closet” as Kailath [8] called it needs to be brought out fully into the light. -
A Note on Laplace Transforms of Some Particular Function Types
A Note on Laplace Transforms of Some Particular Function Types Henrik Stenlund∗ Visilab Signal Technologies Oy, Finland 9th February, 2014 Abstract This article handles in a short manner a few Laplace transform pairs and some extensions to the basic equations are developed. They can be applied to a wide variety of functions in order to find the Laplace transform or its inverse when there is a specific type of an implicit function involved.1 0.1 Keywords Laplace transform, inverse Laplace transform 0.2 Mathematical Classification MSC: 44A10 1 Introduction 1.1 General The following two Laplace transforms have appeared in numerous editions and prints of handbooks and textbooks, for decades. arXiv:1402.2876v1 [math.GM] 9 Feb 2014 1 ∞ 3 s2 2 2 4u Lt[F (t )],s = u− e− f(u)du (F ALSE) (1) 2√π Z0 u 1 f(ln(s)) ∞ t f(u)du L− [ ],t = (F ALSE) (2) s s ln(s) Z Γ(u + 1) · 0 They have mostly been removed from the latest editions and omitted from other new handbooks. However, one fresh edition still carries them [1]. Obviously, many people have tried to apply them. No wonder that they are not accepted ∗The author is obliged to Visilab Signal Technologies for supporting this work. 1Visilab Report #2014-02 1 anymore since they are false. The actual reasons for the errors are not known to the author; possibly it is a misprint inherited from one print to another and then transported to other books, believed to be true. The author tried to apply these transforms, stumbling to a serious conflict. -
Control Theory
Control theory S. Simrock DESY, Hamburg, Germany Abstract In engineering and mathematics, control theory deals with the behaviour of dynamical systems. The desired output of a system is called the reference. When one or more output variables of a system need to follow a certain ref- erence over time, a controller manipulates the inputs to a system to obtain the desired effect on the output of the system. Rapid advances in digital system technology have radically altered the control design options. It has become routinely practicable to design very complicated digital controllers and to carry out the extensive calculations required for their design. These advances in im- plementation and design capability can be obtained at low cost because of the widespread availability of inexpensive and powerful digital processing plat- forms and high-speed analog IO devices. 1 Introduction The emphasis of this tutorial on control theory is on the design of digital controls to achieve good dy- namic response and small errors while using signals that are sampled in time and quantized in amplitude. Both transform (classical control) and state-space (modern control) methods are described and applied to illustrative examples. The transform methods emphasized are the root-locus method of Evans and fre- quency response. The state-space methods developed are the technique of pole assignment augmented by an estimator (observer) and optimal quadratic-loss control. The optimal control problems use the steady-state constant gain solution. Other topics covered are system identification and non-linear control. System identification is a general term to describe mathematical tools and algorithms that build dynamical models from measured data. -
Laplace Transform
Chapter 7 Laplace Transform The Laplace transform can be used to solve differential equations. Be- sides being a different and efficient alternative to variation of parame- ters and undetermined coefficients, the Laplace method is particularly advantageous for input terms that are piecewise-defined, periodic or im- pulsive. The direct Laplace transform or the Laplace integral of a function f(t) defined for 0 ≤ t< 1 is the ordinary calculus integration problem 1 f(t)e−stdt; Z0 succinctly denoted L(f(t)) in science and engineering literature. The L{notation recognizes that integration always proceeds over t = 0 to t = 1 and that the integral involves an integrator e−stdt instead of the usual dt. These minor differences distinguish Laplace integrals from the ordinary integrals found on the inside covers of calculus texts. 7.1 Introduction to the Laplace Method The foundation of Laplace theory is Lerch's cancellation law 1 −st 1 −st 0 y(t)e dt = 0 f(t)e dt implies y(t)= f(t); (1) R R or L(y(t)= L(f(t)) implies y(t)= f(t): In differential equation applications, y(t) is the sought-after unknown while f(t) is an explicit expression taken from integral tables. Below, we illustrate Laplace's method by solving the initial value prob- lem y0 = −1; y(0) = 0: The method obtains a relation L(y(t)) = L(−t), whence Lerch's cancel- lation law implies the solution is y(t)= −t. The Laplace method is advertised as a table lookup method, in which the solution y(t) to a differential equation is found by looking up the answer in a special integral table. -
Laplace Transforms: Theory, Problems, and Solutions
Laplace Transforms: Theory, Problems, and Solutions Marcel B. Finan Arkansas Tech University c All Rights Reserved 1 Contents 43 The Laplace Transform: Basic Definitions and Results 3 44 Further Studies of Laplace Transform 15 45 The Laplace Transform and the Method of Partial Fractions 28 46 Laplace Transforms of Periodic Functions 35 47 Convolution Integrals 45 48 The Dirac Delta Function and Impulse Response 53 49 Solving Systems of Differential Equations Using Laplace Trans- form 61 50 Solutions to Problems 68 2 43 The Laplace Transform: Basic Definitions and Results Laplace transform is yet another operational tool for solving constant coeffi- cients linear differential equations. The process of solution consists of three main steps: • The given \hard" problem is transformed into a \simple" equation. • This simple equation is solved by purely algebraic manipulations. • The solution of the simple equation is transformed back to obtain the so- lution of the given problem. In this way the Laplace transformation reduces the problem of solving a dif- ferential equation to an algebraic problem. The third step is made easier by tables, whose role is similar to that of integral tables in integration. The above procedure can be summarized by Figure 43.1 Figure 43.1 In this section we introduce the concept of Laplace transform and discuss some of its properties. The Laplace transform is defined in the following way. Let f(t) be defined for t ≥ 0: Then the Laplace transform of f; which is denoted by L[f(t)] or by F (s), is defined by the following equation Z T Z 1 L[f(t)] = F (s) = lim f(t)e−stdt = f(t)e−stdt T !1 0 0 The integral which defined a Laplace transform is an improper integral. -
Lectures on the Local Semicircle Law for Wigner Matrices
Lectures on the local semicircle law for Wigner matrices Florent Benaych-Georges∗ Antti Knowlesy September 11, 2018 These notes provide an introduction to the local semicircle law from random matrix theory, as well as some of its applications. We focus on Wigner matrices, Hermitian random matrices with independent upper-triangular entries with zero expectation and constant variance. We state and prove the local semicircle law, which says that the eigenvalue distribution of a Wigner matrix is close to Wigner's semicircle distribution, down to spectral scales containing slightly more than one eigenvalue. This local semicircle law is formulated using the Green function, whose individual entries are controlled by large deviation bounds. We then discuss three applications of the local semicircle law: first, complete delocalization of the eigenvectors, stating that with high probability the eigenvectors are approximately flat; second, rigidity of the eigenvalues, giving large deviation bounds on the locations of the individual eigenvalues; third, a comparison argument for the local eigenvalue statistics in the bulk spectrum, showing that the local eigenvalue statistics of two Wigner matrices coincide provided the first four moments of their entries coincide. We also sketch further applications to eigenvalues near the spectral edge, and to the distribution of eigenvectors. arXiv:1601.04055v4 [math.PR] 10 Sep 2018 ∗Universit´eParis Descartes, MAP5. Email: [email protected]. yETH Z¨urich, Departement Mathematik. Email: [email protected]. -
The Scientist and Engineer's Guide to Digital Signal Processing Properties of Convolution
CHAPTER 7 Properties of Convolution A linear system's characteristics are completely specified by the system's impulse response, as governed by the mathematics of convolution. This is the basis of many signal processing techniques. For example: Digital filters are created by designing an appropriate impulse response. Enemy aircraft are detected with radar by analyzing a measured impulse response. Echo suppression in long distance telephone calls is accomplished by creating an impulse response that counteracts the impulse response of the reverberation. The list goes on and on. This chapter expands on the properties and usage of convolution in several areas. First, several common impulse responses are discussed. Second, methods are presented for dealing with cascade and parallel combinations of linear systems. Third, the technique of correlation is introduced. Fourth, a nasty problem with convolution is examined, the computation time can be unacceptably long using conventional algorithms and computers. Common Impulse Responses Delta Function The simplest impulse response is nothing more that a delta function, as shown in Fig. 7-1a. That is, an impulse on the input produces an identical impulse on the output. This means that all signals are passed through the system without change. Convolving any signal with a delta function results in exactly the same signal. Mathematically, this is written: EQUATION 7-1 The delta function is the identity for ( ' convolution. Any signal convolved with x[n] *[n] x[n] a delta function is left unchanged. This property makes the delta function the identity for convolution. This is analogous to zero being the identity for addition (a%0 ' a), and one being the identity for multiplication (a×1 ' a). -
Semi-Parametric Likelihood Functions for Bivariate Survival Data
Old Dominion University ODU Digital Commons Mathematics & Statistics Theses & Dissertations Mathematics & Statistics Summer 2010 Semi-Parametric Likelihood Functions for Bivariate Survival Data S. H. Sathish Indika Old Dominion University Follow this and additional works at: https://digitalcommons.odu.edu/mathstat_etds Part of the Mathematics Commons, and the Statistics and Probability Commons Recommended Citation Indika, S. H. S.. "Semi-Parametric Likelihood Functions for Bivariate Survival Data" (2010). Doctor of Philosophy (PhD), Dissertation, Mathematics & Statistics, Old Dominion University, DOI: 10.25777/ jgbf-4g75 https://digitalcommons.odu.edu/mathstat_etds/30 This Dissertation is brought to you for free and open access by the Mathematics & Statistics at ODU Digital Commons. It has been accepted for inclusion in Mathematics & Statistics Theses & Dissertations by an authorized administrator of ODU Digital Commons. For more information, please contact [email protected]. SEMI-PARAMETRIC LIKELIHOOD FUNCTIONS FOR BIVARIATE SURVIVAL DATA by S. H. Sathish Indika BS Mathematics, 1997, University of Colombo MS Mathematics-Statistics, 2002, New Mexico Institute of Mining and Technology MS Mathematical Sciences-Operations Research, 2004, Clemson University MS Computer Science, 2006, College of William and Mary A Dissertation Submitted to the Faculty of Old Dominion University in Partial Fulfillment of the Requirement for the Degree of DOCTOR OF PHILOSOPHY DEPARTMENT OF MATHEMATICS AND STATISTICS OLD DOMINION UNIVERSITY August 2010 Approved by: DayanandiN; Naik Larry D. Le ia M. Jones ABSTRACT SEMI-PARAMETRIC LIKELIHOOD FUNCTIONS FOR BIVARIATE SURVIVAL DATA S. H. Sathish Indika Old Dominion University, 2010 Director: Dr. Norou Diawara Because of the numerous applications, characterization of multivariate survival dis tributions is still a growing area of research. -
2913 Public Disclosure Authorized
WPS A 13 POLICY RESEARCH WORKING PAPER 2913 Public Disclosure Authorized Financial Development and Dynamic Investment Behavior Public Disclosure Authorized Evidence from Panel Vector Autoregression Inessa Love Lea Zicchino Public Disclosure Authorized The World Bank Public Disclosure Authorized Development Research Group Finance October 2002 POLIcy RESEARCH WORKING PAPER 2913 Abstract Love and Zicchino apply vector autoregression to firm- availability of internal finance) that influence the level of level panel data from 36 countries to study the dynamic investment. The authors find that the impact of the relationship between firms' financial conditions and financial factors on investment, which they interpret as investment. They argue that by using orthogonalized evidence of financing constraints, is significantly larger in impulse-response functions they are able to separate the countries with less developed financial systems. The "fundamental factors" (such as marginal profitability of finding emphasizes the role of financial development in investment) from the "financial factors" (such as improving capital allocation and growth. This paper-a product of Finance, Development Research Group-is part of a larger effort in the group to study access to finance. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Kari Labrie, room MC3-456, telephone 202-473-1001, fax 202-522-1155, email address [email protected]. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at [email protected] or [email protected]. October 2002. (32 pages) The Policy Research Working Paper Series disseminates the findmygs of work mn progress to encouirage the excbange of ideas about development issues.