A. Carbery's Notes on Integration

Total Page:16

File Type:pdf, Size:1020Kb

A. Carbery's Notes on Integration Mathematics 3 Analysis & Integration 2004 Notes on Integration 1. Null sets A subset E of R is null if, given ε > 0, we can find a countable collection of open ∞ ∞ S P intervals {Ij} such that E ⊆ Ij and |Ij| < ε. (It’s easy to see the word ‘open’ can be j=1 j=1 omitted.) Clearly singletons are null. So is any countable set. Subsets of null sets are null. Notation: For an interval I of the form I = [a, b], (a, b), (a, b] or [a, b), |I| := b − a denotes its length. If E is a finite union of disjoint bounded intervals, E = I1 ∪ .... ∪ IN , then N P |E| := |Ij| . It is tedious but routine to show that this is well-defined, and, as a consequence, j=1 if E1 and E2 are such sets with E1 ⊆ E2, then |E1| ≤ |E2|. ∞ S Theorem 1.1 If Ej (j = 1, 2, ....∞) is null, so is Ej. j=1 j+1 Proof Let ε > 0; cover Ej by intervals of total length < ε/2 . The union of all such ∞ P −j−1 intervals has length ≤ ε 2 = ε/2 < ε. j=1 However not every null set is countable: Example: The Cantor middle third set 1 2 Let E0 = [0, 1]; let E1 = 0, 3 ∪ 3 , 1 , the set obtained by removing the middle third of 1 2 1 2 7 8 E0; let E2 = 0, 9 ∪ 9 , 3 ∪ 3 , 9 ∪ 9 , 1 , the set obtained by removing the middle third of each of the two intervals comprising E1. Continuing in this way we obtain Ej, a union of j −j 2 intervals of length 3 , and Ej+1 is obtained by removing the middle third of each of these ∞ T j intervals. Let E = Ej. As the intervals comprising Ej have total length (2/3) which tends j=0 to 0 as j → ∞,E is null. E is uncountable by the usual Cantor argument. Example: Generalised Cantor sets 1 Consider the unit interval I = [0, 1]. Let F1 be the “middle” open subinterval of length 5 . Let 1 F2 be the union of the 2 “middle” open subintervals of I \F1, each of length 52 . Having defined j F1,F2, ...., Fj such that I \ (F1 ∪ .... ∪ Fj) consists of 2 closed intervals, we define Fj+1 as j 1 the union of the 2 “middle” open subintervals of I \ (F1 ∪ .... ∪ Fj), each of length 5j+1 . An ∞ S example of a generalised Cantor set is given by E = I \ Fj . Notice that E contains no j=1 j−1 j nontrivial interval, and that |Fj| = 2 /5 , so that ∞ ∞ j X 1 X 2 1 2/5 1 |F | = = = . j 2 5 2 (1 − 2/5) 3 j=1 j=1 We shall soon see that this shows that E is not null. (One can play games by varying the ratio 1/5.) 1 ∞ ∞ P Theorem 1.2 If [a, b] is covered by open intervals {Ij}j=1, then |Ij| ≥ b−a. In particular, j=1 [a, b] is not null, if a < b. ∞ P Proof Assume that |Ij| < b − a. By the Heine-Borel Theorem, we may find a finite j=1 N S subcollection {I1, ....IN } with [a, b] ⊆ Ij. Then, by the remark preceding Theorem 1.1, j=1 N N S P b − a ≤ | Ij| ≤ |Ij| , which is a contradiction. (The final inequality while ”obvious”, can j=1 j=1 easily be proved by induction on N.) Theorem 1.3 Let I be a bounded interval. Suppose that Fj (j = 1, 2, ....∞) is a finite ∞ S union of disjoint intervals, Fj ∩ Fk = ∅ when j 6= k, each Fj ⊆ I and that I \ Fj is null. j=1 ∞ P Then |Fj| = |I| . j=1 N N S P Proof Since Fj ⊆ I for all N we have |Fj| ≤ |I| for all N (using disjointness of the j=1 j=1 ∞ ∞ P P {Fj}) and so |Fj| ≤ |I| . Thus we have to show |Fj| ≥ |I| . Without loss of generality j=1 j=1 we may assume that I is closed and that each Fj consists of open intervals as this affects ∞ S neither |Fj|, |I| nor the statement that I \ Fj is null. (We have modified matters only on a j=1 ∞ ∞ P S countable, hence null set.) Suppose for a contradiction that |Fj| < |I| . Cover I \ Fj by j=1 j=1 ∞ ∞ ∞ P P ∞ ∞ open intervals {Ji}i=1 with |Ji| < |I| − |Fj| . Then {Fj}j=1 ∪ {Ji}i=1 gives a cover of i=1 j=1 I by open intervals of total length less than |I| , in contradiction to Theorem 1.2. Corollary The generalised Cantor set constructed above is not null – if it were we’d have ∞ ∞ P P 1 to have |Fj| = 1, and we showed that |Fj| = . j=1 j=1 3 Null sets will be systematically regarded as ‘negligible’ in integration theory. A property of the real numbers holds almost everywhere or holds for almost all x if it holds for all real numbers except those in some null set. Thus, for example, χ = 0 almost everywhere. Q 1 x ∈ E Notation: if E ⊆ , χ (x) = R E 0 x∈ / E. 2. Integration – what are we aiming for? We wish to develop an integral with the following features: R (i) if f : R → R is “nice”, then f should represent the “area under the graph of f” 2 (ii) if f ≥ 0, R f ≥ 0 (iii) the integral should be linear ∞ P (iv) if Ij (j = 1....∞) is a sequence of pairwise disjoint intervals with |Ij| < ∞, then j=1 R ∞ ∞ ∞ P χ S should be integrable and χ S should equal |Ij| . Ij Ij j=1 j=1 j=1 Of course we also wish the integral to be calculable by the standard techniques of integral calculus (antiderivatves, parts, substitution ....) for sufficiently nice integrands. n P If φ = cjχIj is a step function (i.e. a finite linear combination of characteristic functions j=1 of bounded intervals) then this wish list prescribes that we must have n Z X φ = cj |Ij| . j=1 We will then use analysis to extend the definition of the integral to a wider class of functions. Convention All functions f have domain R, and usually have codomain R, (but we will in occasion allow f to take the values ±∞). If g :[a, b] → R, we extend g to be zero outside [a, b] to obtain a function with domain R. (We make an exception to this convention in Sections 7-9.) 3. Integration of Step functions Definition A step function φ : R → R is a finite linear combination of characteristic functions of bounded intervals, i.e. n X φ = cjχIj j=1 where |Ij| < ∞. Evidently, φ is a step function if and only if ∃x0 < x1 < ... < xN such that (i) φ(x) = 0 for x < x0 and x > xN (ii) φ is constant on (xj−1, xj) 1 ≤ j ≤ N. We say that such a φ is a step function with respect to {x0, ..., xN }. We define the integral of such a φ by N Z X φ := φj(xj − xj−1) j=1 where φj is the constant value of φ on (xj−1, xj). Note that if φ is a step function with respect to {x0, ..., xN }, and xj−1 < c < xj, φ is also a step function with respect to R {x0, ..., xj−1, c, xj, ..., xN } and the two definitions of φ agree. Thus if φ is a step function with respect to {x0, ..., xN } and also with respect to {y0, ..., yM }, upon ordering {x0, ..., xN } ∪ 3 R {y0, ..., yM } as z0 < .... < zK (K ≤ M + N) we see that the definitions of φ with respect to {x0, ..., xN }, {z0, ..., zK } and {y0, ..., yM } all agree. Thus R φ is well-defined for any step function φ. It is clear that if φ and ψ are step functions and α, β ∈ R, then αφ + βψ is a step function; moreover Z Z Z (αφ + βψ) = α φ + β ψ (*) as if we list all the “jump points” of either φ or ψ together as {x0 < .... < xN }, the left hand N N N P P P R R side is (αφj + βψj)(xj − xj−1) = α φj(xj − xj−1) + β ψj(xj − xj−1) = α φ + β ψ. j=1 j=1 j=1 Similarly if φ ≥ ψ, with φ, ψ step functions, then R φ ≥ R ψ. R Since χI = |I| , linearity of the integral (*) implies that n n Z X X cjχIj = cj |Ij| . j=1 j=1 So points (i)-(iii) of our “wish list” are adequately resolved for step functions. How about point (iv)? As so far we’ve only defined the integral for step functions, (iv) is tantamount to asking: ∞ S If {Ij}j=1 is a disjoint sequence of intervals with union Ij which is also an interval j=1 ∞ P I, does |I| = |Ij|? This is guaranteed by Theorem 1.3. In fact, Theorem 1.3 can be j=1 re-phrased as follows: Theorem 1.30 Let φ be the characteristic function of a finite union of bounded intervals. n R Suppose that φn+1(x) ≤ φn(x) for all x ∈ R, and φn(x) → 0 a.e. then φn → 0. Proof Let φn = χEn , thus En+1 ⊆ En. We may assume without loss of generality that E1 is an interval. Set Fn = En \ En+1 and I = E1. Then φn(x) → 0 a.e. is the same as saying ∞ ∞ ∞ ∞ S P P P I \ Fj is null.
Recommended publications
  • An Analytic Exact Form of the Unit Step Function
    Mathematics and Statistics 2(7): 235-237, 2014 http://www.hrpub.org DOI: 10.13189/ms.2014.020702 An Analytic Exact Form of the Unit Step Function J. Venetis Section of Mechanics, Faculty of Applied Mathematics and Physical Sciences, National Technical University of Athens *Corresponding Author: [email protected] Copyright © 2014 Horizon Research Publishing All rights reserved. Abstract In this paper, the author obtains an analytic Meanwhile, there are many smooth analytic exact form of the unit step function, which is also known as approximations to the unit step function as it can be seen in Heaviside function and constitutes a fundamental concept of the literature [4,5,6]. Besides, Sullivan et al [7] obtained a the Operational Calculus. Particularly, this function is linear algebraic approximation to this function by means of a equivalently expressed in a closed form as the summation of linear combination of exponential functions. two inverse trigonometric functions. The novelty of this However, the majority of all these approaches lead to work is that the exact representation which is proposed here closed – form representations consisting of non - elementary is not performed in terms of non – elementary special special functions, e.g. Logistic function, Hyperfunction, or functions, e.g. Dirac delta function or Error function and Error function and also most of its algebraic exact forms are also is neither the limit of a function, nor the limit of a expressed in terms generalized integrals or infinitesimal sequence of functions with point wise or uniform terms, something that complicates the related computational convergence. Therefore it may be much more appropriate in procedures.
    [Show full text]
  • Appendix I Integrating Step Functions
    Appendix I Integrating Step Functions In Chapter I, section 1, we were considering step functions of the form (1) where II>"" In were brick functions and AI> ... ,An were real coeffi­ cients. The integral of I was defined by the formula (2) In case of a single real variable, it is almost obvious that the value JI does not depend on the representation (2). In Chapter I, we admitted formula (2) for several variables, relying on analogy only. There are various methods to prove that formula precisely. In this Appendix we are going to give a proof which uses the concept of Heaviside functions. Since the geometrical intuition, above all in multidimensional case, might be decep­ tive, all the given arguments will be purely algebraic. 1. Heaviside functions The Heaviside function H(x) on the real line Rl admits the value 1 for x;;;o °and vanishes elsewhere; it thus can be defined on writing H(x) = {I for x ;;;0O, ° for x<O. By a Heaviside function H(x) in the plane R2, where x actually denotes points x = (~I> ~2) of R2, we understand the function which admits the value 1 for x ;;;0 0, i.e., for ~l ~ 0, ~2;;;o 0, and vanishes elsewhere. In symbols, H(x) = {I for x ;;;0O, (1.1) ° for x to. Note that we cannot use here the notation x < °(which would mean ~l <0, ~2<0) instead of x~O, because then H(x) would not be defined in the whole plane R 2 • Similarly, in Euclidean q-dimensional space R'l, the Heaviside function H(x), where x = (~I>' .
    [Show full text]
  • On the Approximation of the Cut and Step Functions by Logistic and Gompertz Functions
    www.biomathforum.org/biomath/index.php/biomath ORIGINAL ARTICLE On the Approximation of the Cut and Step Functions by Logistic and Gompertz Functions Anton Iliev∗y, Nikolay Kyurkchievy, Svetoslav Markovy ∗Faculty of Mathematics and Informatics Paisii Hilendarski University of Plovdiv, Plovdiv, Bulgaria Email: [email protected] yInstitute of Mathematics and Informatics Bulgarian Academy of Sciences, Sofia, Bulgaria Emails: [email protected], [email protected] Received: , accepted: , published: will be added later Abstract—We study the uniform approximation practically important classes of sigmoid functions. of the sigmoid cut function by smooth sigmoid Two of them are the cut (or ramp) functions and functions such as the logistic and the Gompertz the step functions. Cut functions are continuous functions. The limiting case of the interval-valued but they are not smooth (differentiable) at the two step function is discussed using Hausdorff metric. endpoints of the interval where they increase. Step Various expressions for the error estimates of the corresponding uniform and Hausdorff approxima- functions can be viewed as limiting case of cut tions are obtained. Numerical examples are pre- functions; they are not continuous but they are sented using CAS MATHEMATICA. Hausdorff continuous (H-continuous) [4], [43]. In some applications smooth sigmoid functions are Keywords-cut function; step function; sigmoid preferred, some authors even require smoothness function; logistic function; Gompertz function; squashing function; Hausdorff approximation. in the definition of sigmoid functions. Two famil- iar classes of smooth sigmoid functions are the logistic and the Gompertz functions. There are I. INTRODUCTION situations when one needs to pass from nonsmooth In this paper we discuss some computational, sigmoid functions (e.
    [Show full text]
  • Lecture 2 ELE 301: Signals and Systems
    Lecture 2 ELE 301: Signals and Systems Prof. Paul Cuff Princeton University Fall 2011-12 Cuff (Lecture 2) ELE 301: Signals and Systems Fall 2011-12 1 / 70 Models of Continuous Time Signals Today's topics: Signals I Sinuoidal signals I Exponential signals I Complex exponential signals I Unit step and unit ramp I Impulse functions Systems I Memory I Invertibility I Causality I Stability I Time invariance I Linearity Cuff (Lecture 2) ELE 301: Signals and Systems Fall 2011-12 2 / 70 Sinusoidal Signals A sinusoidal signal is of the form x(t) = cos(!t + θ): where the radian frequency is !, which has the units of radians/s. Also very commonly written as x(t) = A cos(2πft + θ): where f is the frequency in Hertz. We will often refer to ! as the frequency, but it must be kept in mind that it is really the radian frequency, and the frequency is actually f . Cuff (Lecture 2) ELE 301: Signals and Systems Fall 2011-12 3 / 70 The period of the sinuoid is 1 2π T = = f ! with the units of seconds. The phase or phase angle of the signal is θ, given in radians. cos(ωt) cos(ωt θ) − 0 0 -2T -T T 2T t -2T -T T 2T t Cuff (Lecture 2) ELE 301: Signals and Systems Fall 2011-12 4 / 70 Complex Sinusoids The Euler relation defines ejφ = cos φ + j sin φ. A complex sinusoid is Aej(!t+θ) = A cos(!t + θ) + jA sin(!t + θ): e jωt -2T -T 0 T 2T Real sinusoid can be represented as the real part of a complex sinusoid Aej(!t+θ) = A cos(!t + θ) <f g Cuff (Lecture 2) ELE 301: Signals and Systems Fall 2011-12 5 / 70 Exponential Signals An exponential signal is given by x(t) = eσt If σ < 0 this is exponential decay.
    [Show full text]
  • Step and Delta Functions 18.031 Haynes Miller and Jeremy Orloff 1
    Step and Delta Functions 18.031 Haynes Miller and Jeremy Orloff 1 The unit step function 1.1 Definition Let's start with the definition of the unit step function, u(t): ( 0 for t < 0 u(t) = 1 for t > 0 We do not define u(t) at t = 0. Rather, at t = 0 we think of it as in transition between 0 and 1. It is called the unit step function because it takes a unit step at t = 0. It is sometimes called the Heaviside function. The graph of u(t) is simple. u(t) 1 t We will use u(t) as an idealized model of a natural system that goes from 0 to 1 very quickly. In reality it will make a smooth transition, such as the following. 1 t Figure 1. u(t) is an idealized version of this curve But, if the transition happens on a time scale much smaller than the time scale of the phenomenon we care about then the function u(t) is a good approximation. It is also much easier to deal with mathematically. One of our main uses for u(t) will be as a switch. It is clear that multiplying a function f(t) by u(t) gives ( 0 for t < 0 u(t)f(t) = f(t) for t > 0: We say the effect of multiplying by u(t) is that for t < 0 the function f(t) is switched off and for t > 0 it is switched on. 1 18.031 Step and Delta Functions 2 1.2 Integrals of u0(t) From calculus we know that Z Z b u0(t) dt = u(t) + c and u0(t) dt = u(b) − u(a): a For example: Z 5 u0(t) dt = u(5) − u(−2) = 1; −2 Z 3 u0(t) dt = u(3) − u(1) = 0; 1 Z −3 u0(t) dt = u(−3) − u(−5) = 0: −5 In fact, the following rule for the integral of u0(t) over any interval is obvious ( Z b 1 if 0 is inside the interval (a; b) u0(t) = (1) a 0 if 0 is outside the interval [a; b].
    [Show full text]
  • Twisting Algorithm for Controlling F-16 Aircraft
    TWISTING ALGORITHM FOR CONTROLLING F-16 AIRCRAFT WITH PERFORMANCE MARGINS IDENTIFICATION by AKSHAY KULKARNI A THESIS Submitted in partial fulfillment of the requirements For the degree of Master of Science in Engineering In The Department of Electrical and Computer Engineering To The School of Graduate Studies Of The University of Alabama in Huntsville HUNTSVILLE, ALABAMA 2014 In presenting this thesis in partial fulfillment of the requirements for a master's degree from The University of Alabama in Huntsville, I agree that the Library of this University shall make it freely available for inspection. I further agree that permission for extensive copying for scholarly purposes may be granted by my advisor or, in his absence, by the Chair of the Department or the Dean of the School of Graduate Studies. It is also understood that due recognition shall be given to me and to The University of Alabama in Huntsville in any scholarly use which may be made of any material in this thesis. ____________________________ ___________ (Student signature) (Date) ii THESIS APPROVAL FORM Submitted by Akshay Kulkarni in partial fulfillment of the requirements for the degree of Master of Science in Engineering and accepted on behalf of the Faculty of the School of Graduate Studies by the thesis committee. We, the undersigned members of the Graduate Faculty of The University of Alabama in Huntsville, certify that we have advised and/or supervised the candidate on the work described in this thesis. We further certify that we have reviewed the thesis manuscript and approve it in partial fulfillment of the requirements for the degree of Master of Science in Engineering.
    [Show full text]
  • Piecewise-Defined Functions and Periodic Functions
    28 Piecewise-Defined Functions and Periodic Functions At the start of our study of the Laplace transform, it was claimed that the Laplace transform is “particularly useful when dealing with nonhomogeneous equations in which the forcing func- tions are not continuous”. Thus far, however, we’ve done precious little with any discontinuous functions other than step functions. Let us now rectify the situation by looking at the sort of discontinuous functions (and, more generally, “piecewise-defined” functions) that often arise in applications, and develop tools and skills for dealing with these functions. We willalsotakea brieflookat transformsof periodicfunctions other than sines and cosines. As you will see, many of these functions are, themselves, piecewise defined. And finally, we will use some of the material we’ve recently developed to re-examine the issue of resonance in mass/spring systems. 28.1 Piecewise-Defined Functions Piecewise-Defined Functions, Defined When we talk about a “discontinuous function f ” in the context of Laplace transforms, we usually mean f is a piecewise continuous function that is not continuous on the interval (0, ) . ∞ Such a functionwill have jump discontinuitiesat isolatedpoints in this interval. Computationally, however, the real issue is often not so much whether there is a nonzero jump in the graph of f at a point t0 , but whether the formula for computing f (t) is the same on either side of t0 . So we really should be looking at the more general class of “piecewise-defined” functions that, at worst, have jump discontinuities. Just what is a piecewise-defined function? It is any function given by different formulas on different intervals.
    [Show full text]
  • Chapter 9 Integration on Rn
    Integration on Rn 1 Chapter 9 Integration on Rn This chapter represents quite a shift in our thinking. It will appear for a while that we have completely strayed from calculus. In fact, we will not be able to \calculate" anything at all until we get the fundamental theorem of calculus in Section E and the Fubini theorem in Section F. At that time we'll have at our disposal the tremendous tools of di®erentiation and integration and we shall be able to perform prodigious feats. In fact, the rest of the book has this interplay of \di®erential calculus" and \integral calculus" as the underlying theme. The climax will come in Chapter 12 when it all comes together with our tremendous knowledge of manifolds. Exciting things are ahead, and it won't take long! In the preceding chapter we have gained some familiarity with the concept of n-dimensional volume. All our examples, however, were restricted to the simple shapes of n-dimensional parallelograms. We did not discuss volume in anything like a systematic way; instead we chose the elementary \de¯nition" of the volume of a parallelogram as base times altitude. What we need to do now is provide a systematic way to think about volume, so that our \de¯nition" of Chapter 8 actually becomes a theorem. We shall in fact accomplish much more than a good discussion of volume. We shall de¯ne the concept of integration on Rn. Our goal is to analyze a function f D ¡! R; where D is a bounded subset of Rn and f is bounded.
    [Show full text]
  • Derivative and Integral of the Heaviside Step Function
    Derivative and Integral of the Heaviside Step Function Originally appeared at: http://behindtheguesses.blogspot.com/2009/06/derivative-and-integral-of-heaviside.html Eli Lansey | [email protected] June 30, 2009 The Setup HHxL HHxL 1.0 1.0 0.8 0.8 0.6 0.6 0.4 0.4 0.2 0.2 - - - - x -4 -2 2 4 x -1. ´ 10 100 -5. ´ 10 101 5. ´ 10 101 1. ´ 10 100 (a) Large horizontal scale (b) \Zoomed in" Figure 1: The Heaviside step function. Note how it doesn't matter how close we get to x = 0 the function looks exactly the same. The Heaviside step function H(x), sometimes called the Heaviside theta function, appears in many places in physics, see [1] for a brief discussion. Simply put, it is a function whose value is zero for x < 0 and one for x > 0. Explicitly, ( 0 x < 0; H(x) = : (1) 1 x > 0 We won't worry about precisely what its value is at zero for now, since it won't e®ect our discussion, see [2] for a lengthier discussion. Fig. 1 plots H(x). The key point is that crossing zero flips the function from 0 to 1. Derivative { The Dirac Delta Function Say we wanted to take the derivative of H. Recall that a derivative is the slope of the curve at at point. One way of formulating this is dH ¢H = lim : (2) dx ¢x!0 ¢x Now, for any points x < 0 or x > 0, graphically, the derivative is very clear: H is a flat line in those regions, and the slope of a flat line is zero.
    [Show full text]
  • 0.1 Step Functions. Integration of Step Functions
    MA244 Analysis III Assignment 1. 15% of the credit for this module will come from your work on four assignments submitted by a 3pm deadline on the Monday in weeks 4,6,8,10. Each assignment will be marked out of 25 for answers to three randomly chosen 'B' and one 'A' questions. Working through all questions is vital for understanding lecture material and success at the exam. 'A' questions will constitute a base for the first exam problem worth 40% of the final mark, the rest of the problems will be based on 'B' questions. The answers to ALL questions are to be submitted by the deadline of 3pm on Monday 26st Octo- ber 2015. Your work should be stapled together, and you should state legibly at the top your name, your department and the name of your supervisor or your teaching assistant. Your work should be deposited in your supervisor's slot in the pigeonloft if you are a Maths student, or in the dropbox labelled with the course's code, opposite the Maths Undergraduate Office, if you are a non-Maths or a visiting student. 0.1 Step functions. Integration of step functions. 1. A. Construct a function f : [0; 1] ! R such that f is not a step function, but for any 2 (0; 1) the restriction of f to [, 1] is a step function. 2. A. Show that, given any two step functions on the same interval, there is a partition compatible with both of them. 3. A. Prove that an arbitrary linear combination of step functions φ1; φ2; : : : φn 2 S[a; b] is a step function.
    [Show full text]
  • Signals & System
    mywbut.com Module 1: Signals & System Lecture 6: Basic Signals in Detail Basic Signals in detail We now introduce formally some of the basic signals namely 1) The Unit Impulse function. 2) The Unit Step function These signals are of considerable importance in signals and systems analysis. Later in the course we will see these signals as the building blocks for representation of other signals. We will cover both signals in continuous and discrete time. However, these concepts are easily comprehended in Discrete Time domain, so we begin with Discrete Time Unit Impulse and Unit Step function. The Discrete Time Unit Impulse Function: This is the simplest discrete time signal and is defined as The Discrete Time Unit Step Function u[n]: It is defined as Unit step in terms of unit impulse function Having studied the basic signal operations namely Time Shifting, Time Scaling and Time Inversion it is easy to see that 1 mywbut.com Similarly, Summing over we get Looking directly at the Unit Step Function we observe that it can be constructed as a sum of shifted Unit Impulse Functions The unit function can also be expressed as a running sum of the Unit Impulse Function We see that the running sum is 0 for n < 0 and equal to 1 for n >= 0 thus defining the Unit Step Function u[n]. Sifting property Consider the product . The delta function is non zero only at the origin so it follows the signal is the same as . 2 mywbut.com More generally It is important to understand the above expression.
    [Show full text]
  • Delta Functions and All That1 We Can Define the Step Function
    Delta functions and all that1 We can de¯ne the step function (sometimes called the Heaviside step function), θ(x), by θ(x) = 0 for x < 0 (1) = 1 for x > 0 : (2) It is strictly not de¯ned for x = 0 but physicists like to de¯ne it to be symmetric, θ(0) = 1=2. One can think of this discontinuous function as a limit: 1 + tanh(x=a) θ(x) = Lim + ; a!0 2 this is easy to check if one remembers that the hyperbolic tangent assumes the values §1 as x ! §1 respectively. a goes to zero from the positive direction. This limiting procedure is consistent with θ(0) = 1=2. The step function is useful when something is switched on abruptly, say a voltage which is 0 for t < 0 and a constant above. In physical situations the step function is a convenient mathematical approximation. The (Dirac) delta function is strictly not a function but is a so-called distribution or a generalized function. The theory behind this was worked out by Laurent Schwartz who received the Fields Medal, the mathematics equivalent of the Nobel Prize. We will use the delta function carefully, con¯dent that the rigorous aspects have been checked. The delta function, ±(x ¡ a), is de¯ned by the property Z c2 dx f(x) ±(x ¡ a) = f(a) if a 2 (c1; c2) (3) c1 = 0 otherwise (4) for any suitably continuous function.2 Note that if a does not lie in the range of integration then the argument of the delta function does not vanish and therefore 1For a rigorous yet clear exposition see M.
    [Show full text]