Suggested Reading. Signals and Systems 4-2



From this document you will learn the answers to the following questions:

What do we do in his lecure?

What is the decomposiion of signals as linear combinaions of complex exponenials?

Arbirary sequences can be expressed as linear combinaions of delayed impulses?

Similar documents
4 Convolution. Recommended Problems. x2[n] 1 2[n]

Differential Equations. Solving for Impulse Response. Linear systems are often described using differential equations.

AP Calculus BC 2010 Scoring Guidelines

17 Laplace transform. Solving linear ODE with piecewise continuous right hand sides

Equation for a line. Synthetic Impulse Response Time (sec) x(t) m

3 Signals and Systems: Part II

Signal Processing and Linear Systems I

The Transport Equation

Differential Equations and Linear Superposition

AP Calculus AB 2013 Scoring Guidelines

Vector Autoregressions (VARs): Operational Perspectives

4. International Parity Conditions

Fourier Series & The Fourier Transform

Analogue and Digital Signal Processing. First Term Third Year CS Engineering By Dr Mukhtiar Ali Unar

CHARGE AND DISCHARGE OF A CAPACITOR

9. Capacitor and Resistor Circuits

AP Calculus AB 2010 Scoring Guidelines

Cointegration: The Engle and Granger approach

Signal Rectification

Chapter 7. Response of First-Order RL and RC Circuits

Voltage level shifting

10 Discrete-Time Fourier Series

2.5 Life tables, force of mortality and standard life insurance products

Duration and Convexity ( ) 20 = Bond B has a maturity of 5 years and also has a required rate of return of 10%. Its price is $613.

Fourier Series and Fourier Transform

Multiprocessor Systems-on-Chips

Chapter 4: Exponential and Logarithmic Functions

Module 4. Single-phase AC circuits. Version 2 EE IIT, Kharagpur

AP Calculus AB 2007 Scoring Guidelines

Why Did the Demand for Cash Decrease Recently in Korea?

Appendix A: Area. 1 Find the radius of a circle that has circumference 12 inches.

11/6/2013. Chapter 14: Dynamic AD-AS. Introduction. Introduction. Keeping track of time. The model s elements

Newton s Laws of Motion

DATA SHEET. 1N4148; 1N4446; 1N4448 High-speed diodes DISCRETE SEMICONDUCTORS Sep 03

Keldysh Formalism: Non-equilibrium Green s Function

Statistical Analysis with Little s Law. Supplementary Material: More on the Call Center Data. by Song-Hee Kim and Ward Whitt

E0 370 Statistical Learning Theory Lecture 20 (Nov 17, 2011)

B-Splines and NURBS Week 5, Lecture 9

USE OF EDUCATION TECHNOLOGY IN ENGLISH CLASSES

9 Fourier Transform Properties

Chapter 8: Regression with Lagged Explanatory Variables

Capacitors and inductors

The Fourier Transform

Morningstar Investor Return

Communication Networks II Contents

Risk Modelling of Collateralised Lending

The Greek financial crisis: growing imbalances and sovereign spreads. Heather D. Gibson, Stephan G. Hall and George S. Tavlas

Pulse-Width Modulation Inverters

cooking trajectory boiling water B (t) microwave time t (mins)

Mathematics in Pharmacokinetics What and Why (A second attempt to make it clearer)

Making Use of Gate Charge Information in MOSFET and IGBT Data Sheets

I. Basic Concepts (Ch. 1-4)

GoRA. For more information on genetics and on Rheumatoid Arthritis: Genetics of Rheumatoid Arthritis. Published work referred to in the results:

Direc Manipulaion Inerface and EGN algorithms

The Torsion of Thin, Open Sections

µ r of the ferrite amounts to It should be noted that the magnetic length of the + δ

PATHWISE PROPERTIES AND PERFORMANCE BOUNDS FOR A PERISHABLE INVENTORY SYSTEM

Forecasting and Information Sharing in Supply Chains Under Quasi-ARMA Demand

Random Walk in 1-D. 3 possible paths x vs n. -5 For our random walk, we assume the probabilities p,q do not depend on time (n) - stationary

Working Paper On the timing option in a futures contract. SSE/EFI Working Paper Series in Economics and Finance, No. 619

Astable multivibrator using the 555 IC.(10)

Example What is the minimum bandwidth for transmitting data at a rate of 33.6 kbps without ISI?

ARCH Proceedings

Name: Algebra II Review for Quiz #13 Exponential and Logarithmic Functions including Modeling

A Re-examination of the Joint Mortality Functions

Mechanical Fasteners Tensile and Shear Stress Areas

On the degrees of irreducible factors of higher order Bernoulli polynomials

Introduction to Option Pricing with Fourier Transform: Option Pricing with Exponential Lévy Models

T = 10-' s. p(t)= ( (t-nt), T= 3. n=-oo. Figure P16.2

Lecture 2: Telegrapher Equations For Transmission Lines. Power Flow.

6 Systems Represented by Differential and Difference Equations

1 A B C D E F G H I J K L M N O P Q R S { U V W X Y Z 1 A B C D E F G H I J K L M N O P Q R S { U V W X Y Z

4 Convolution. Solutions to Recommended Problems

Single-machine Scheduling with Periodic Maintenance and both Preemptive and. Non-preemptive jobs in Remanufacturing System 1

MTH6121 Introduction to Mathematical Finance Lesson 5

Forecasting, Ordering and Stock- Holding for Erratic Demand

Fixed Income Analysis: Securities, Pricing, and Risk Management

Automatic measurement and detection of GSM interferences

The naive method discussed in Lecture 1 uses the most recent observations to forecast future values. That is, Y ˆ t + 1

Table of contents Chapter 1 Interest rates and factors Chapter 2 Level annuities Chapter 3 Varying annuities

PRECISE positioning/tracking control is being studied

Full-wave rectification, bulk capacitor calculations Chris Basso January 2009

CHAPTER FIVE. Solutions for Section 5.1

DETERMINISTIC INVENTORY MODEL FOR ITEMS WITH TIME VARYING DEMAND, WEIBULL DISTRIBUTION DETERIORATION AND SHORTAGES KUN-SHAN WU

Module 3. R-L & R-C Transients. Version 2 EE IIT, Kharagpur

RC (Resistor-Capacitor) Circuits. AP Physics C

Information Theoretic Evaluation of Change Prediction Models for Large-Scale Software

Making a Faster Cryptanalytic Time-Memory Trade-Off

SELF-EVALUATION FOR VIDEO TRACKING SYSTEMS

Usefulness of the Forward Curve in Forecasting Oil Prices

Transient Analysis of First Order RC and RL circuits

Improper Integrals. Dr. Philippe B. laval Kennesaw State University. September 19, f (x) dx over a finite interval [a, b].

Performance Center Overview. Performance Center Overview 1

TEMPORAL PATTERN IDENTIFICATION OF TIME SERIES DATA USING PATTERN WAVELETS AND GENETIC ALGORITHMS

Mortality Variance of the Present Value (PV) of Future Annuity Payments

Chapter 2 Kinematics in One Dimension

A Conceptual Framework for Commercial Property Price Indexes

IR Receiver Module for Light Barrier Systems

Optimal Investment and Consumption Decision of Family with Life Insurance

Transcription:

4 Convoluion In Lecure 3 we inroduced and defined a variey of sysem properies o which we will make frequen reference hroughou he course. Of paricular imporance are he properies of lineariy and ime invariance, boh because sysems wih hese properies represen a very broad and useful class and because wih jus hese wo properies i is possible o develop some exremely powerful ools for sysem analysis and design. A linear sysem has he propery ha he response o a linear combinaion of inpus is he same linear combinaion of he individual responses. The propery of ime invariance saes ha, in effec, he sysem is no sensiive o he ime origin. More specifically, if he inpu is shifed in ime by some amoun, hen he oupu is simply shifed by he same amoun. The imporance of lineariy derives from he basic noion ha for a linear sysem if he sysem inpus can be decomposed as a linear combinaion of some basic inpus and he sysem response is known for each of he basic inpus, hen he response can be consruced as he same linear combinaion of he responses o each of he basic inpus. Signals (or funcions) can be decomposed as a linear combinaion of basic signals in a wide variey of ways. For example, we migh consider a Taylor series expansion ha expresses a funcion in polynomial form. However, in he conex of our reamen of signals and sysems, i is paricularly imporan o choose he basic signals in he expansion so ha in some sense he response is easy o compue. For sysems ha are boh linear and ime-invarian, here are wo paricularly useful choices for hese basic signals: delayed impulses and complex exponenials. In his lecure we develop in deail he represenaion of boh coninuousime and discree-ime signals as a linear combinaion of delayed impulses and he consequences for represening linear, ime-invarian sysems. The resuling represenaion is referred o as convoluion. Laer in his series of lecures we develop in deail he decomposiion of signals as linear combinaions of complex exponenials (referred o as Fourier analysis) and he consequence of ha represenaion for linear, ime-invarian sysems. In developing convoluion in his lecure we begin wih he represenaion of discree-ime signals and linear combinaions of delayed impulses. As we discuss, since arbirary sequences can be expressed as linear combinaions of delayed impulses, he oupu for linear, ime-invarian sysems can be

Signals and Sysems 4-2 expressed as he same linear combinaion of he sysem response o a delayed impulse. Specifically, because of ime invariance, once he response o one impulse a any ime posiion is known, hen he response o an impulse a any oher arbirary ime posiion is also known. In developing convoluion for coninuous ime, he procedure is much he same as in discree ime alhough in he coninuous-ime case he signal is represened firs as a linear combinaion of narrow recangles (basically a saircase approximaion o he ime funcion). As he widh of hese recangles becomes infiniesimally small, hey behave like impulses. The superposiion of hese recangles o form he original ime funcion in is limiing form becomes an inegral, and he represenaion of he oupu of a linear, ime-invarian sysem as a linear combinaion of delayed impulse responses also becomes an inegral. The resuling inegral is referred o as he convoluion inegral and is similar in is properies o he convoluion sum for discree-ime signals and sysems. A number of he imporan properies of convoluion ha have inerpreaions and consequences for linear, ime-invarian sysems are developed in Lecure 5. In he curren lecure, we focus on some examples of he evaluaion of he convoluion sum and he convoluion inegral. Suggesed Reading Secion 3.0, Inroducion, pages 69-70 Secion 3.1, The Represenaion of Signals in Terms of Impulses, pages 70-75 Secion 3.2, Discree-Time LTI Sysems: The Convoluion Sum, pages 75-84 Secion 3.3, Coninuous-Time LTI Sysems: The Convoluion Inegral, pages 88 o mid-90

Convoluion MARKERBOARD 4.1 - InAvr - causal '4s~w rkv"o' "TVNVQI;Q,,c.I, c-7t: X1I Tkiv% \ r - L,Y%3 /9 va STM recy: % clecorpose p 5 pwl invo G Lineer come'ncl4zo1. o C 0'sM basic Sina V -ha. respolase eqs o 6.~i LT I SWs ens- Co, e g Convo + I x[-i] x[0] 1 x[2] -l IOJ fr 2 x[0] x[o]8a[n] -.- e--.-0- n -1 0 I2 X[1] -1 0 I 2 x[1]8[n-1] 0-0 -- *- n x[-i]8[n+1] x[o]8[n]+x(i] 8[n -1] + x [-I]8[n+ ]+.-- +X kr =2 x[k]8[n-k] k= -c 4.1 A general discreeime signal expressed as a superposiion of weighed, delayed uni impulses. -1 0 I 2 X[-] x [-2]8[n +2] 0--0-0 n -1 0 1 2

Signals and Sysems 4.2 The convoluion sum for linear, imeinvarian discree-ime sysems expressing he sysem oupu as a weighed sum of delayed uni impulse responses. jj )x[ x[2] -1 0 I 2 X 10{ x[0]8[n] -I 0 1 2 X x[1]8[n+1] n -0-0-0-0-- m- x [0] h [n] 0 0 x [ 1] h [n-i] x[ 2 91T ] x[-2]8[n+2] -1 0 i 2 n 0 0 x [-I] h [n+1. x [-2] h [n.2] x[n] =E k = -o x[k] S[n-k] 4.3 One inerpreaion of he convoluion sum for an LTI sysem. Each individual sequence value can be viewed as riggering a response; all he responses are added o form he oal oupu. Linear Sysem: If Time-invarian: +0n y [n] =E x[k] hk [n] k = - 010 5 [n - k] - h [n] hk [n] = h + [n-k] o0 LTI: y[n] x[k] h[n - k]i =E Convoluion Sum

Convoluion 4-5 x(-2a)&a(+2a)a x(-2a) -2A -A -A 0 4.4 Approximaion of a coninuous-ime signal as a linear combinaion of weighed, delayed, recangular pulses. [The ampliude of he fourh graph has been correced o read x(o).] I X (0) X (0) BA A 0 A - xa) x (A) 86 (-A) A 2A x() X() x() x(o) 6A() A + x(a) 6 A( + x(- A) 6A( + A)A+... x(k A) 6,( - k A) A = lim 1 x(k A) S( - k A) A A+O k=-oc +W f x(,r) 6( -,r) d-r --00 4.5 As he recangular pulses in Transparency 4.4 become increasingly narrow, he represenaion approaches an inegral, ofen referred o as he sifing inegral.

Signals and Sysems 4-6 x() = Eim ( x(k A) 'L+0 k=-o 56( - ka) A 4.6 Derivaion of he convoluion inegral represenaion for coninuous-ime LTI sysems. Linear Sysem: +o y() = 0 x(ka) +O k=- o +00 =f xt) ht() dr hk() A If Time-Invarian: hkj ) = ho( - ka) LTI: h,() = he ( - r) +01 v() f x(r) h(-7) dr- 1-0 Convoluion Inegral x() 0 i 4.7 Inerpreaion of he convoluion inegral as a superposiion of he responses from each of he recangular pulses in he represenaion of he inpu. x (0) x(a) x (0) h M x(ka) oa AA y() ka 0 x() y() 0 oa

Convoluion 4-7 Convoluion Sum: +0o x[n] =E x[k] k= -0ok y [n] = x [k] k= -o00 S[n-k] h[n-k] =x[n] * h[n] 4.8 Comparison of he convoluion sum for discree-ime LTI sysems and he convoluion inegral for coninuous-ime LTI sysems. Convoluion Inegral: x() y() +00 =f x(-) 6(-r) dr +fd = X(r) h(-,r) dr- = x() -* h() y [n] Z x[k]h [n-k] x [n]= u [n] h[n]=an u[n] 0 n x [n] h [n] 4.9 Evaluaion of he convoluion sum for an inpu ha is a uni sep and a sysem impulse response ha is a decaying exponenial for n > 0. x [k] k h [n-k] n k

Signals and Sysems y()f x(r)h(-r)dr x() u () 4.10 Evaluaion of he convoluion inegral for an inpu ha is a uni sep and a sysem impulse response ha is a decaying exponenial for > 0. h ( )=e~43 u ) O x () h () 0 r x (r) h (-r) T MARKERBOARD 4.2

Convoluion MARKERBOARD 4.3 v4egva.z: ). Te Lk (- -C jt C k L-T)aT 1 U -e 3 (0 o- eoverkp ~3e ee ov0 C- L h*o,<0o E J r)

MIT OpenCourseWare hp://ocw.mi.edu Resource: Signals and Sysems Professor Alan V. Oppenheim The following may no correspond o a paricular course on MIT OpenCourseWare, bu has been provided by he auhor as an individual learning resource. For informaion abou ciing hese maerials or our Terms of Use, visi: hp://ocw.mi.edu/erms.