EE (082) Ch. II: Intro. to Signals Lecture 2 Dr. Wajih Abu-Al-Saud

Similar documents
EE1 and ISE1 Communications I

Absolute Convergence and the Ratio Test

Solutions to Homework 2

Logarithmic Functions

MAHALAKSHMI ENGINEERING COLLEGE-TRICHY

MATH 251 Examination II November 5, 2018 FORM A. Name: Student Number: Section:

MATH115. Sequences and Infinite Series. Paolo Lorenzo Bautista. June 29, De La Salle University. PLBautista (DLSU) MATH115 June 29, / 16

Linear Variable coefficient equations (Sect. 1.2) Review: Linear constant coefficient equations

Introduction to Vector Functions

Absolute Convergence and the Ratio Test

ECE 301 Fall 2011 Division 1. Homework 1 Solutions.

Math 21B - Homework Set 8

Introduction to Biomedical Engineering

University Question Paper Solution

EE5713 : Advanced Digital Communications

Solution of ECE 315 Test #1 Su03

Midterm 1 Solutions. Monday, 10/24/2011

Fourier Series and Transform KEEE343 Communication Theory Lecture #7, March 24, Prof. Young-Chai Ko

ENGIN 211, Engineering Math. Fourier Series and Transform

ESS Dirac Comb and Flavors of Fourier Transforms

Linear Variable coefficient equations (Sect. 2.1) Review: Linear constant coefficient equations

Lecture 32: Taylor Series and McLaurin series We saw last day that some functions are equal to a power series on part of their domain.

Lecture 8: Discrete-Time Signals and Systems Dr.-Ing. Sudchai Boonto

ENSC327 Communications Systems 2: Fourier Representations. Jie Liang School of Engineering Science Simon Fraser University

Module 6: Deadbeat Response Design Lecture Note 1

LOPE3202: Communication Systems 10/18/2017 2

MATH section 3.1 Maximum and Minimum Values Page 1 of 7

Math 180A. Lecture 16 Friday May 7 th. Expectation. Recall the three main probability density functions so far (1) Uniform (2) Exponential.

Math 308 Exam II Practice Problems

Notes on uniform convergence

Classification of Phase Portraits at Equilibria for u (t) = f( u(t))

Lecture 2. Introduction to Systems (Lathi )

Discrete-Time Signals: Time-Domain Representation

Solutions to Math 53 Math 53 Practice Final

Continuous Random Variables

Student s Printed Name: _Key

Fourier transform representation of CT aperiodic signals Section 4.1

CHAPTER 4 FOURIER SERIES S A B A R I N A I S M A I L

Review of Power Series

ELEG 3143 Probability & Stochastic Process Ch. 6 Stochastic Process

MATH1120 Calculus II Solution to Supplementary Exercises on Improper Integrals Alex Fok November 2, 2013

AP Calculus BC Fall Final Part IIa

2.1 The Tangent and Velocity Problems

Student s Printed Name:

MAT137 Calculus! Lecture 9

f(s)ds, i.e. to write down the general solution in

Figure 1.1 (a) Model of a communication system, and (b) signal processing functions.


Limits: An Intuitive Approach

Chapter 2. Signals. Static and Dynamic Characteristics of Signals. Signals classified as

MATH20411 PDEs and Vector Calculus B

Math 54. Selected Solutions for Week 5

In this Lecture. Frequency domain analysis

Signals and Systems Lecture Notes


MATH115. Indeterminate Forms and Improper Integrals. Paolo Lorenzo Bautista. June 24, De La Salle University

2 nd ORDER O.D.E.s SUBSTITUTIONS

EE 574 Detection and Estimation Theory Lecture Presentation 8

VANDERBILT UNIVERSITY. MATH 2610 ORDINARY DIFFERENTIAL EQUATIONS Practice for test 1 solutions

Study guide for Exam 1. by William H. Meeks III October 26, 2012

Discrete Mathematics. Spring 2017

MA26600 FINAL EXAM INSTRUCTIONS December 13, You must use a #2 pencil on the mark sense sheet (answer sheet).

Discrete-Time Signals: Time-Domain Representation

Signals & Systems. Lecture 5 Continuous-Time Fourier Transform. Alp Ertürk

CHAPTER 72 AREAS UNDER AND BETWEEN CURVES

G.PULLAIAH COLLEGE OF ENGINEERING & TECHNOLOGY DEPARTMENT OF ELECTRONICS & COMMUNICATION ENGINEERING PROBABILITY THEORY & STOCHASTIC PROCESSES

MATH115. Infinite Series. Paolo Lorenzo Bautista. July 17, De La Salle University. PLBautista (DLSU) MATH115 July 17, / 43

JUST THE MATHS UNIT NUMBER LAPLACE TRANSFORMS 3 (Differential equations) A.J.Hobson

MATH 1190 Exam 4 (Version 2) Solutions December 1, 2006 S. F. Ellermeyer Name

Discrete-time Fourier transform (DTFT) representation of DT aperiodic signals Section The (DT) Fourier transform (or spectrum) of x[n] is

Lecture 4: Fourier Transforms.

Review Guideline for Final

Basic Differentiation - A Refresher

Let s Get Series(ous)

Laplace Transforms and use in Automatic Control

Some suggested repetition for the course MAA508

Honors Differential Equations

Dynamical Systems and Chaos Part I: Theoretical Techniques. Lecture 4: Discrete systems + Chaos. Ilya Potapov Mathematics Department, TUT Room TD325

Assignment #09 - Solution Manual

The First Derivative and Second Derivative Test

Discrete-Time Signals and Systems. Efficient Computation of the DFT: FFT Algorithms. Analog-to-Digital Conversion. Sampling Process.

The First Derivative and Second Derivative Test

Math 266 Midterm Exam 2

MATH 251 Examination II April 3, 2017 FORM A. Name: Student Number: Section:

EE Homework 13 - Solutions

Chapter The function f and its graph are shown below: + < x. lim f ( x) (a) Calculate. (b) Which value is greater

MA FINAL EXAM Green December 16, You must use a #2 pencil on the mark sense sheet (answer sheet).

UNIT 1. SIGNALS AND SYSTEM

Ex. 1. Find the general solution for each of the following differential equations:

MA 123 Calculus I Midterm II Practice Exam Answer Key

Name Date Period. MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question.

Continuous-time Signals. (AKA analog signals)

Chapter 2: Functions, Limits and Continuity

Fourier Analysis Fourier Series C H A P T E R 1 1

The Laplace Transform

Notes 07 largely plagiarized by %khc

Chapter 1: Review of Real Numbers

Last Update: March 1 2, 201 0

Mathematical Models. MATH 365 Ordinary Differential Equations. J. Robert Buchanan. Spring Department of Mathematics

TAYLOR AND MACLAURIN SERIES

Transcription:

Classification of Signals Some important classifications of signals Analog vs. Digital signals: as stated in the previous lecture, a signal with a magnitude that may take any real value in a specific range is called an analog signal while a signal with amplitude that takes only a finite number of values is called a digital signal. Continuous-time vs. discrete-time signals: continuous-time signals may be analog or digital signals such that their magnitudes are defined for all values of t, while discrete-time signal are analog or digital signals with magnitudes that are defined at specific instants of time only and are undefined for other time instants. Periodic vs. aperiodic signals: periodic signals are those that are constructed from a specific shape that repeats regularly after a specific amount of time, [i.e., a periodic signal f(t) with period satisfies f(t) = f(t+n ) for all integer values of n], while aperiodic signals do not repeat regularly. Deterministic vs. probabilistic signals: deterministic signals are those that can be computed beforehand at any instant of time while a probabilistic signal is one that is random and cannot be determined beforehand. Energy vs. Power signals: as described below. Energy and Power Signals he total energy contained in and average power provided by a signal f(t) (which is a function of time) are defined as and Ef f ( t) dt =, respectively. / Pf lim f ( t) dt / =, For periodic signals, the power P can be computed using a simpler form based on the periodicity of the signal as + t P Periodic f = f ( t) dt, t where here is the period of the signal and t is an arbitrary time instant that is chosen to simply the computation of the integration (to reduce the functions you have to integrate over one period).

Classification of Signals into Power and Energy Signals Most signals can be classified into Energy signals or Power signals. A signal is classified into an energy or a power signal according to the following criteria Comments: a) Energy Signals: an energy signal is a signal with finite energy and zero average power ( E <, P = ), b) Power Signals: a power signal is a signal with infinite energy but finite average power ( < P <, E ).. he square root of the average power P of a power signal is what is usually defined as the RMS value of that signal.. Your book says that if a signal approaches zero as t approaches then the signal is an energy signal. his is in most cases true but not always as you can verify in part (d) in the following example. 3. All periodic signals are power signals (but not all non periodic signals are energy signals). 4. Any signal f that has limited amplitude ( f < ) and is time limited (f = for t > t for some t > ) is an energy signal as in part (g) in the following example. Exercise : determine if the following signals are Energy signals, Power signals, or neither, and evaluate E and P for each signal (see examples. and. on pages 7 and 8 of your textbook for help). a) at () = 3sin( πt), < t<, his is a periodic signal, so it must be a power signal. Let us prove it. E a = at ( ) dt= 3sin( πt) dt [ cos(4 πt) ] dt dt 9 cos(4 πt ) dt = J Notice that the evaluation of the last line in the above equation is infinite because of the first term. he second term has a value between to so it has no effect in the overall value of the energy. Since a(t) is periodic with period = π/π = second, we get

Pa = a ( t ) dt = 3sin( πt ) dt [ cos(4 πt) ] dt dt 9 cos(4 πt ) dt 9 9 = sin(4 πt ) 4π 9 = W So, the energy of that signal is infinite and its average power is finite (9/). his means that it is a power signal as expected. Notice that the average power of this signal is as expected (square of the amplitude divided by ) b) t bt () 5 e, t = < <, Let us first find the total energy of the signal. t E = bt ( ) dt= 5e dt b 4t 4t e dt e dt = 5 + 5 5 4t 5 4t = 4 e + e 4 5 5 5 = + = J 4 4 4 he average power of the signal is / / t Pb = lim b( t) dt lim 5e dt = / / / 4t 4t = 5 lim e dt 5 lim e dt + / 5 4t 5 4t / = lim e lim e 4 + / 4 5 5 = lim e lim e 4 + 4 = + =

So, the signal b(t) is definitely an energy signal. So, the energy of that signal is infinite and its average power is finite (9/). his means that it is a power signal as expected. Notice that the average power of this signal is as expected (the square of the amplitude divided by ) c) d) ct () = + 3t 4 e, t 5,, t > 5, t > dt () = t,, t Let us first find the total energy of the signal. E d = d ( t ) dt = dt t [] t = ln = = J So, this signal is NO an energy signal. However, it is also NO a power signal since its average power as shown below is zero. he average power of the signal is / / / Pd = lim d( t) dt = lim dt t / = lim ln [] t lim ln ln [] = ln = lim ln lim = Using Le hopital s rule, we see that the power of the signal is zero. hat is P d ln lim lim = = =

So, not all signals that approach zero as time approaches positive and negative infinite is an energy signal. hey may not be power signals either. e) f) g) et () 7 t, t = < <, f () t cos( πt), t = < <. cos ( πt), 8 < t < 3 gt () =., elsewhere