Solutions - Homework # 3
|
|
- Mitchell Waters
- 5 years ago
- Views:
Transcription
1 ECE-34: Signals and Systems Summer 23 PROBLEM One period of the DTFS coefficients is given by: X[] = (/3) 2, 8. Solutions - Homewor # 3 a) What is the fundamental period 'N' of the time-domain signal x[n]? b) Using MATLAB, plot X[] for three periods. Plot the magnitude and the phase spectra. c) Find the time-domain signal x[n] (provide x[n] as a function of 'n'). Plot x[n] for three periods. a) The period of is the same as that of. N = 9. b) X[] = (/3) 2, 8. Note that this equation only wors for one period. We have to generate infinite replicas on both sides. clear all; close all; clc = :8; X = (/3).^(2*); % The function as given only wors from to 8 X_3p = [X X X]; % Here, we generate replicas _3p = :26; n = :26; % 3 periods x = 8./(9 - exp(i*2*n*pi/9)); % This function is periodic (N=9), so it % wors for all 'n' figure; subplot (2,,), stem(_3p, abs(x_3p),'.b'); axis ([ 28.2]); set(gca, 'Fontsize',8); xlabel(''); title (' X[], 3 periods'); subplot (2,,2), stem(_3p, angle(x_3p),'.r'); set(gca, 'Fontsize',8); xlabel(''); title ('arg(x[]), 3 periods'); figure; subplot (2,,), stem(n,real(x),'.b'); set(gca, 'Fontsize',8); xlabel('n'); title ('Re(x[n]), 3 periods'); subplot (2,,2), stem(n,imag(x),'.r'); set(gca, 'Fontsize',8); xlabel('n'); title ('Im(x[n]), 3 periods'); X[], 3 periods arg(x[]), 3 periods
2 ECE-34: Signals and Systems Summer 23 c) Re(x[n]), 3 periods n. Im(x[n]), 3 periods n PROBLEM 2 Identify the appropriate Fourier representation (FT, DTFT, FS, DTFS) for each of the following signals. If the signals are periodic, provide the fundamental period and the fundamental angular frequency a) x[n] = cos((6 /3)n + /3) b) x[n] = exp(j( /4)n) c) x(t) = cos(t/6) d) x(t) = e -t u(-t + 2) e) x(t) = sin(( /5)t) f) x(t) = cos(( /3)t + /5) g) x[n] = [n+2] + [n-4] h) x[n] = (3/8) n u[n-3] Once you identified the appropriate Fourier representation, use the defining equation to obtain the DTFS coefficients, the FS coefficients, the DTFT, or the FT. a) Signal is periodic with DTFS Since the signal is a cosine, we can use the inspection method: Thus, we have the DTFS coefficients for one period ( ):
3 ECE-34: Signals and Systems Summer 23 b) Signal is periodic with DTFS Since the signal is a complex exponential, we can use the inspection method: Thus, we have the DTFS coefficients for one period ( ): c) Signal is periodic with FS Since the signal is a cosine, we can use the inspection method: Thus, we have the FS coefficients for all : d) Non-periodic FT u(-t+2) The integral diverges, thus the FT representation does not exist. 2 t e) Signal is periodic with FS. Signal is a sine, thus we can use the inspection method: Thus, we have the FS coefficients for all : f) Signal is periodic with FS Since the signal is a cosine, we can use the inspection method: Thus, we have the FS coefficients for all :
4 ECE-34: Signals and Systems Summer 23 g). Non-periodic signal DTFT h). Non-periodic signal DTFT PROBLEM 3 Use the defining equation for the DTFT to evaluate the frequency-domain representations of the following signals. You must show the procedure. a) x[n] = (3/5) n (u(n-4) - u(n+4)) b) x[n] = b n, b < c) x[n] = 2 [5-3n] d) x[n] = (/4)( [n] + 3 [n-] + 2 [n] + [n-3]) e) x[n] = 2 + e -3n a) u[n-4] - u[n+4] n - b) c) [n+5] = v[n] n [-3n+5] = v[-3n] n
5 ECE-34: Signals and Systems Summer 23 d) e) tends to infinity. Thus, is undefined PROBLEM 4 Determine the time-domain signals corresponding to the following DTFTs. You must show the procedure. a) X(e j ) = sin(2 ) + jcos(2 ) b) X(e j ) = 3sin(4 ) c) X(e j -j /2 ) = (/2)e d) X(e j ) = cos( ) + sin( /2) a) Thus: b) We use the time-shift property of the DTFT along with the fact that the DTFT of an impulse is. And we determine that: In exercise 4(a), we demonstrate that the DTFT of is Finally: c)
6 ECE-34: Signals and Systems Summer 23 d) Finally: PROBLEM 5 The following LTI system has an input described by: x[n] h[n] y[n] = x[n]*h[n] x[n] = sin((5 /7)n + /8) The Fourier representation of the impulse response h[n] is given by: H[] = e -, on N-. a) Determine the period 'N' of the signal x[n]. b) Determine the DTFS coefficients X[]. c) Obtain the frequency domain representation Y[] of the output signal y[n]. d) Using MATLAB, plot X[], H[], and Y[] for three periods. Plot the magnitude and the phase spectra. a) b) Since the signal is a sine, we can use the inspection method: Thus, we have the DTFS coefficients for one period ( ):
7 ECE-34: Signals and Systems Summer 23 c), on 3 is defined for a different range. We can either redefine from -4 to 8, or from to 3. Let's redefine from to 3: One period X[] One period Now, using the convolution property, we get the DTFS for one period: d) MATLAB: clear all; close all; clc = :3; _3p = :4; % In the period to 3: H =.*exp(-); X(:4) = ; X(6) = (/2i)*exp(i*pi/8); X() = -(/2i)*exp(-i*pi/8); Y(:4) = ; Y(6) = (/2i)*exp(i*pi/8)*4*5*exp(-5); Y() = -(/2i)*exp(-i*pi/8)*4*9*exp(-9); X_3p = [X X X]; % Here, we generate replicas H_3p = [H H H]; % Here, we generate replicas Y_3p = [Y Y Y]; % Here, we generate replicas figure; subplot (2,,), stem(_3p, abs(x_3p),'.b'); axis ([ 45.8]); set(gca, 'Fontsize',8); xlabel(''); title (' X[], 3 periods'); subplot (2,,2), stem(_3p, angle(x_3p),'.r'); set(gca, 'Fontsize',8); xlabel(''); title ('arg(x[]), 3 periods'); figure; subplot (2,,), stem(_3p, abs(h_3p),'.b'); set(gca, 'Fontsize',8); xlabel(''); title (' H[], 3 periods'); subplot (2,,2), stem(_3p, angle(h_3p),'.r'); set(gca, 'Fontsize',8); xlabel(''); title ('arg(h[]), 3 periods');
8 ECE-34: Signals and Systems Summer 23 figure; subplot (2,,), stem(_3p, abs(y_3p),'.b'); set(gca, 'Fontsize',8); xlabel(''); title (' Y[], 3 periods'); subplot (2,,2), stem(_3p, angle(y_3p),'.r'); axis ([ ]); set(gca, 'Fontsize',8); xlabel(''); title ('arg(y[]), 3 periods');.8 X[], 3 periods arg(x[]), 3 periods H[], 3 periods arg(h[]), 3 periods
9 ECE-34: Signals and Systems Summer Y[], 3 periods arg(y[]), 3 periods PROBLEM 6 Use the properties of Fourier representation (e.g., time-differentiation, convolution, time-shift, frequencyshift) to find the FT of: Note: '*' denotes convolution. Hint: It might help you that the FT of e -at u(t) is /(a+j ) Differentiation Property of FT: If Then:, then: Convolution Property of FT: Then: Knowledge of a common FT pair: Then: where:, and Also: where:, and Time Shift Property of FT: If Then:, then:
10 ECE-34: Signals and Systems Summer 23 Finally: PROBLEM 7 Given the following DTFS pair ( = /): Evaluate the time-domain signal y[n] for the following DTFS coefficients Y[]. These DTFS coefficients Y[] happen to have a relationship with the DTFS coefficients X[]. You can use properties of the DTFS. a) Y[] = (/2)(X[-4] + X[+4]) b) Y[] = 3X[] c) Y[] = X[] (*)X[], where (*) denotes periodic convolution. a) We use the frequency shift property: b) Here, we also use the frequency shift property: c) Here, se use the multiplication property:
ECE-314 Fall 2012 Review Questions for Midterm Examination II
ECE-314 Fall 2012 Review Questions for Midterm Examination II First, make sure you study all the problems and their solutions from homework sets 4-7. Then work on the following additional problems. Problem
More information3.2 Complex Sinusoids and Frequency Response of LTI Systems
3. Introduction. A signal can be represented as a weighted superposition of complex sinusoids. x(t) or x[n]. LTI system: LTI System Output = A weighted superposition of the system response to each complex
More informationSolution 7 August 2015 ECE301 Signals and Systems: Final Exam. Cover Sheet
Solution 7 August 2015 ECE301 Signals and Systems: Final Exam Cover Sheet Test Duration: 120 minutes Coverage: Chap. 1, 2, 3, 4, 5, 7 One 8.5" x 11" crib sheet is allowed. Calculators, textbooks, notes
More informationNew Mexico State University Klipsch School of Electrical Engineering EE312 - Signals and Systems I Fall 2015 Final Exam
New Mexico State University Klipsch School of Electrical Engineering EE312 - Signals and Systems I Fall 2015 Name: Solve problems 1 3 and two from problems 4 7. Circle below which two of problems 4 7 you
More informationFinal Exam ECE301 Signals and Systems Friday, May 3, Cover Sheet
Name: Final Exam ECE3 Signals and Systems Friday, May 3, 3 Cover Sheet Write your name on this page and every page to be safe. Test Duration: minutes. Coverage: Comprehensive Open Book but Closed Notes.
More informationEEL3135: Homework #4
EEL335: Homework #4 Problem : For each of the systems below, determine whether or not the system is () linear, () time-invariant, and (3) causal: (a) (b) (c) xn [ ] cos( 04πn) (d) xn [ ] xn [ ] xn [ 5]
More informationECE 301 Fall 2011 Division 1 Homework 5 Solutions
ECE 301 Fall 2011 ivision 1 Homework 5 Solutions Reading: Sections 2.4, 3.1, and 3.2 in the textbook. Problem 1. Suppose system S is initially at rest and satisfies the following input-output difference
More informationUniversity Question Paper Solution
Unit 1: Introduction University Question Paper Solution 1. Determine whether the following systems are: i) Memoryless, ii) Stable iii) Causal iv) Linear and v) Time-invariant. i) y(n)= nx(n) ii) y(t)=
More informationOverview of Discrete-Time Fourier Transform Topics Handy Equations Handy Limits Orthogonality Defined orthogonal
Overview of Discrete-Time Fourier Transform Topics Handy equations and its Definition Low- and high- discrete-time frequencies Convergence issues DTFT of complex and real sinusoids Relationship to LTI
More informationQuestion Paper Code : AEC11T02
Hall Ticket No Question Paper Code : AEC11T02 VARDHAMAN COLLEGE OF ENGINEERING (AUTONOMOUS) Affiliated to JNTUH, Hyderabad Four Year B. Tech III Semester Tutorial Question Bank 2013-14 (Regulations: VCE-R11)
More informationDiscrete-time Signals and Systems in
Discrete-time Signals and Systems in the Frequency Domain Chapter 3, Sections 3.1-39 3.9 Chapter 4, Sections 4.8-4.9 Dr. Iyad Jafar Outline Introduction The Continuous-Time FourierTransform (CTFT) The
More informationHow to manipulate Frequencies in Discrete-time Domain? Two Main Approaches
How to manipulate Frequencies in Discrete-time Domain? Two Main Approaches Difference Equations (an LTI system) x[n]: input, y[n]: output That is, building a system that maes use of the current and previous
More informationx(t) = t[u(t 1) u(t 2)] + 1[u(t 2) u(t 3)]
ECE30 Summer II, 2006 Exam, Blue Version July 2, 2006 Name: Solution Score: 00/00 You must show all of your work for full credit. Calculators may NOT be used.. (5 points) x(t) = tu(t ) + ( t)u(t 2) u(t
More informationDSP Laboratory (EELE 4110) Lab#5 DTFS & DTFT
Islamic University of Gaza Faculty of Engineering Electrical Engineering Department EG.MOHAMMED ELASMER Spring-22 DSP Laboratory (EELE 4) Lab#5 DTFS & DTFT Discrete-Time Fourier Series (DTFS) The discrete-time
More informationQUESTION BANK SIGNALS AND SYSTEMS (4 th SEM ECE)
QUESTION BANK SIGNALS AND SYSTEMS (4 th SEM ECE) 1. For the signal shown in Fig. 1, find x(2t + 3). i. Fig. 1 2. What is the classification of the systems? 3. What are the Dirichlet s conditions of Fourier
More informationLAB 2: DTFT, DFT, and DFT Spectral Analysis Summer 2011
University of Illinois at Urbana-Champaign Department of Electrical and Computer Engineering ECE 311: Digital Signal Processing Lab Chandra Radhakrishnan Peter Kairouz LAB 2: DTFT, DFT, and DFT Spectral
More informationSignals and Systems Profs. Byron Yu and Pulkit Grover Fall Midterm 2 Solutions
8-90 Signals and Systems Profs. Byron Yu and Pulkit Grover Fall 08 Midterm Solutions Name: Andrew ID: Problem Score Max 8 5 3 6 4 7 5 8 6 7 6 8 6 9 0 0 Total 00 Midterm Solutions. (8 points) Indicate whether
More informationECE 301 Fall 2010 Division 2 Homework 10 Solutions. { 1, if 2n t < 2n + 1, for any integer n, x(t) = 0, if 2n 1 t < 2n, for any integer n.
ECE 3 Fall Division Homework Solutions Problem. Reconstruction of a continuous-time signal from its samples. Consider the following periodic signal, depicted below: {, if n t < n +, for any integer n,
More informationEE 3054: Signals, Systems, and Transforms Spring A causal discrete-time LTI system is described by the equation. y(n) = 1 4.
EE : Signals, Systems, and Transforms Spring 7. A causal discrete-time LTI system is described by the equation Test y(n) = X x(n k) k= No notes, closed book. Show your work. Simplify your answers.. A discrete-time
More informationFast Fourier Transform Discrete-time windowing Discrete Fourier Transform Relationship to DTFT Relationship to DTFS Zero padding
Fast Fourier Transform Discrete-time windowing Discrete Fourier Transform Relationship to DTFT Relationship to DTFS Zero padding Fourier Series & Transform Summary x[n] = X[k] = 1 N k= n= X[k]e jkω
More informationSIGNALS AND SYSTEMS. Unit IV. Analysis of DT signals
SIGNALS AND SYSTEMS Unit IV Analysis of DT signals Contents: 4.1 Discrete Time Fourier Transform 4.2 Discrete Fourier Transform 4.3 Z Transform 4.4 Properties of Z Transform 4.5 Relationship between Z
More information/ (2π) X(e jω ) dω. 4. An 8 point sequence is given by x(n) = {2,2,2,2,1,1,1,1}. Compute 8 point DFT of x(n) by
Code No: RR320402 Set No. 1 III B.Tech II Semester Regular Examinations, Apr/May 2006 DIGITAL SIGNAL PROCESSING ( Common to Electronics & Communication Engineering, Electronics & Instrumentation Engineering,
More informationFast Fourier Transform Discrete-time windowing Discrete Fourier Transform Relationship to DTFT Relationship to DTFS Zero padding
Fast Fourier Transform Discrete-time windowing Discrete Fourier Transform Relationship to DTFT Relationship to DTFS Zero padding J. McNames Portland State University ECE 223 FFT Ver. 1.03 1 Fourier Series
More informationDigital Signal Processing, Homework 1, Spring 2013, Prof. C.D. Chung
Digital Signal Processing, Homework, Spring 203, Prof. C.D. Chung. (0.5%) Page 99, Problem 2.2 (a) The impulse response h [n] of an LTI system is known to be zero, except in the interval N 0 n N. The input
More informationSolution 10 July 2015 ECE301 Signals and Systems: Midterm. Cover Sheet
Solution 10 July 2015 ECE301 Signals and Systems: Midterm Cover Sheet Test Duration: 60 minutes Coverage: Chap. 1,2,3,4 One 8.5" x 11" crib sheet is allowed. Calculators, textbooks, notes are not allowed.
More informationEC Signals and Systems
UNIT I CLASSIFICATION OF SIGNALS AND SYSTEMS Continuous time signals (CT signals), discrete time signals (DT signals) Step, Ramp, Pulse, Impulse, Exponential 1. Define Unit Impulse Signal [M/J 1], [M/J
More informationDiscrete-time signals and systems
Discrete-time signals and systems 1 DISCRETE-TIME DYNAMICAL SYSTEMS x(t) G y(t) Linear system: Output y(n) is a linear function of the inputs sequence: y(n) = k= h(k)x(n k) h(k): impulse response of the
More informationComplex symmetry Signals and Systems Fall 2015
18-90 Signals and Systems Fall 015 Complex symmetry 1. Complex symmetry This section deals with the complex symmetry property. As an example I will use the DTFT for a aperiodic discrete-time signal. The
More informationLet H(z) = P(z)/Q(z) be the system function of a rational form. Let us represent both P(z) and Q(z) as polynomials of z (not z -1 )
Review: Poles and Zeros of Fractional Form Let H() = P()/Q() be the system function of a rational form. Let us represent both P() and Q() as polynomials of (not - ) Then Poles: the roots of Q()=0 Zeros:
More informationDigital Signal Processing. Midterm 1 Solution
EE 123 University of California, Berkeley Anant Sahai February 15, 27 Digital Signal Processing Instructions Midterm 1 Solution Total time allowed for the exam is 8 minutes Some useful formulas: Discrete
More information3 rd class Mech. Eng. Dept. hamdiahmed.weebly.com Fourier Series
Definition 1 Fourier Series A function f is said to be piecewise continuous on [a, b] if there exists finitely many points a = x 1 < x 2
More informationEE538 Final Exam Fall :20 pm -5:20 pm PHYS 223 Dec. 17, Cover Sheet
EE538 Final Exam Fall 005 3:0 pm -5:0 pm PHYS 3 Dec. 17, 005 Cover Sheet Test Duration: 10 minutes. Open Book but Closed Notes. Calculators ARE allowed!! This test contains five problems. Each of the five
More informationAspects of Continuous- and Discrete-Time Signals and Systems
Aspects of Continuous- and Discrete-Time Signals and Systems C.S. Ramalingam Department of Electrical Engineering IIT Madras C.S. Ramalingam (EE Dept., IIT Madras) Networks and Systems 1 / 45 Scaling the
More informationUNIVERSITI MALAYSIA PERLIS
UNIVERSITI MALAYSIA PERLIS SCHOOL OF COMPUTER & COMMUNICATIONS ENGINEERING EKT 230 SIGNALS AND SYSTEMS LABORATORY MODULE LAB 5 : LAPLACE TRANSFORM & Z-TRANSFORM 1 LABORATORY OUTCOME Ability to describe
More informationRepresenting a Signal
The Fourier Series Representing a Signal The convolution method for finding the response of a system to an excitation takes advantage of the linearity and timeinvariance of the system and represents the
More information(i) Understanding the characteristics and properties of DTFT
Discrete-Time Fourier Transform (DTFT) Chapter Intended Learning Outcomes: (i) Understanding the characteristics and properties of DTFT (ii) Ability to perform discrete-time signal conversion between the
More informationSignals and Systems. Problem Set: The z-transform and DT Fourier Transform
Signals and Systems Problem Set: The z-transform and DT Fourier Transform Updated: October 9, 7 Problem Set Problem - Transfer functions in MATLAB A discrete-time, causal LTI system is described by the
More informationFourier series for continuous and discrete time signals
8-9 Signals and Systems Fall 5 Fourier series for continuous and discrete time signals The road to Fourier : Two weeks ago you saw that if we give a complex exponential as an input to a system, the output
More informationChap 2. Discrete-Time Signals and Systems
Digital Signal Processing Chap 2. Discrete-Time Signals and Systems Chang-Su Kim Discrete-Time Signals CT Signal DT Signal Representation 0 4 1 1 1 2 3 Functional representation 1, n 1,3 x[ n] 4, n 2 0,
More informationDigital Signal Processing: Signal Transforms
Digital Signal Processing: Signal Transforms Aishy Amer, Mohammed Ghazal January 19, 1 Instructions: 1. This tutorial introduces frequency analysis in Matlab using the Fourier and z transforms.. More Matlab
More informationChapter 3 Convolution Representation
Chapter 3 Convolution Representation DT Unit-Impulse Response Consider the DT SISO system: xn [ ] System yn [ ] xn [ ] = δ[ n] If the input signal is and the system has no energy at n = 0, the output yn
More informationNew Mexico State University Klipsch School of Electrical Engineering. EE312 - Signals and Systems I Spring 2018 Exam #1
New Mexico State University Klipsch School of Electrical Engineering EE312 - Signals and Systems I Spring 2018 Exam #1 Name: Prob. 1 Prob. 2 Prob. 3 Prob. 4 Total / 30 points / 20 points / 25 points /
More informationIntroduction to DFT. Deployment of Telecommunication Infrastructures. Azadeh Faridi DTIC UPF, Spring 2009
Introduction to DFT Deployment of Telecommunication Infrastructures Azadeh Faridi DTIC UPF, Spring 2009 1 Review of Fourier Transform Many signals can be represented by a fourier integral of the following
More informationECE 308 Discrete-Time Signals and Systems
ECE 38-6 ECE 38 Discrete-Time Signals and Systems Z. Aliyazicioglu Electrical and Computer Engineering Department Cal Poly Pomona ECE 38-6 1 Intoduction Two basic methods for analyzing the response of
More informationFlash File. Module 3 : Sampling and Reconstruction Lecture 28 : Discrete time Fourier transform and its Properties. Objectives: Scope of this Lecture:
Module 3 : Sampling and Reconstruction Lecture 28 : Discrete time Fourier transform and its Properties Objectives: Scope of this Lecture: In the previous lecture we defined digital signal processing and
More information(t ) a 1. (t ).x 1..y 1
Introduction to the convolution Experiment # 4 LTI S ystems & Convolution Amongst the concepts that cause the most confusion to electrical engineering students, the Convolution Integral stands as a repeat
More informationUsing MATLAB with the Convolution Method
ECE 350 Linear Systems I MATLAB Tutorial #5 Using MATLAB with the Convolution Method A linear system with input, x(t), and output, y(t), can be described in terms of its impulse response, h(t). x(t) h(t)
More informationDiscrete-time Fourier transform (DTFT) representation of DT aperiodic signals Section The (DT) Fourier transform (or spectrum) of x[n] is
Discrete-time Fourier transform (DTFT) representation of DT aperiodic signals Section 5. 3 The (DT) Fourier transform (or spectrum) of x[n] is X ( e jω) = n= x[n]e jωn x[n] can be reconstructed from its
More informationThe Discrete-Time Fourier
Chapter 3 The Discrete-Time Fourier Transform 清大電機系林嘉文 cwlin@ee.nthu.edu.tw 03-5731152 Original PowerPoint slides prepared by S. K. Mitra 3-1-1 Continuous-Time Fourier Transform Definition The CTFT of
More informationDiscrete Time Systems
Discrete Time Systems Valentina Hubeika, Jan Černocký DCGM FIT BUT Brno, {ihubeika,cernocky}@fit.vutbr.cz 1 LTI systems In this course, we work only with linear and time-invariant systems. We talked about
More informationDiscrete-Time Fourier Transform
Discrete-Time Fourier Transform Chapter Intended Learning Outcomes: (i) (ii) (iii) Represent discrete-time signals using discrete-time Fourier transform Understand the properties of discrete-time Fourier
More informationModule 4. Related web links and videos. 1. FT and ZT
Module 4 Laplace transforms, ROC, rational systems, Z transform, properties of LT and ZT, rational functions, system properties from ROC, inverse transforms Related web links and videos Sl no Web link
More informationChapter 7: The z-transform
Chapter 7: The -Transform ECE352 1 The -Transform - definition Continuous-time systems: e st H(s) y(t) = e st H(s) e st is an eigenfunction of the LTI system h(t), and H(s) is the corresponding eigenvalue.
More informationEE482: Digital Signal Processing Applications
Professor Brendan Morris, SEB 3216, brendan.morris@unlv.edu EE482: Digital Signal Processing Applications Spring 2014 TTh 14:30-15:45 CBC C222 Lecture 02 DSP Fundamentals 14/01/21 http://www.ee.unlv.edu/~b1morris/ee482/
More informationGATE EE Topic wise Questions SIGNALS & SYSTEMS
www.gatehelp.com GATE EE Topic wise Questions YEAR 010 ONE MARK Question. 1 For the system /( s + 1), the approximate time taken for a step response to reach 98% of the final value is (A) 1 s (B) s (C)
More informationChapter 3 Fourier Representations of Signals and Linear Time-Invariant Systems
Chapter 3 Fourier Representations of Signals and Linear Time-Invariant Systems Introduction Complex Sinusoids and Frequency Response of LTI Systems. Fourier Representations for Four Classes of Signals
More informationReview of Discrete-Time System
Review of Discrete-Time System Electrical & Computer Engineering University of Maryland, College Park Acknowledgment: ENEE630 slides were based on class notes developed by Profs. K.J. Ray Liu and Min Wu.
More informationFourier Transform 4: z-transform (part 2) & Introduction to 2D Fourier Analysis
052600 VU Signal and Image Processing Fourier Transform 4: z-transform (part 2) & Introduction to 2D Fourier Analysis Torsten Möller + Hrvoje Bogunović + Raphael Sahann torsten.moeller@univie.ac.at hrvoje.bogunovic@meduniwien.ac.at
More informationDiscrete-Time Fourier Transform
C H A P T E R 7 Discrete-Time Fourier Transform In Chapter 3 and Appendix C, we showed that interesting continuous-time waveforms x(t) can be synthesized by summing sinusoids, or complex exponential signals,
More informationHomework 6 Solutions
8-290 Signals and Systems Profs. Byron Yu and Pulkit Grover Fall 208 Homework 6 Solutions. Part One. (2 points) Consider an LTI system with impulse response h(t) e αt u(t), (a) Compute the frequency response
More informationDiscrete Time Fourier Transform (DTFT) Digital Signal Processing, 2011 Robi Polikar, Rowan University
Discrete Time Fourier Transform (DTFT) Digital Signal Processing, 2 Robi Polikar, Rowan University Sinusoids & Exponentials Signals Phasors Frequency Impulse, step, rectangular Characterization Power /
More informationCh.11 The Discrete-Time Fourier Transform (DTFT)
EE2S11 Signals and Systems, part 2 Ch.11 The Discrete-Time Fourier Transform (DTFT Contents definition of the DTFT relation to the -transform, region of convergence, stability frequency plots convolution
More informationSession 1 : Fundamental concepts
BRUFACE Vibrations and Acoustics MA1 Academic year 17-18 Cédric Dumoulin (cedumoul@ulb.ac.be) Arnaud Deraemaeker (aderaema@ulb.ac.be) Exercise 1 Session 1 : Fundamental concepts Consider the following
More information(i) Represent continuous-time periodic signals using Fourier series
Fourier Series Chapter Intended Learning Outcomes: (i) Represent continuous-time periodic signals using Fourier series (ii) (iii) Understand the properties of Fourier series Understand the relationship
More informationThe Johns Hopkins University Department of Electrical and Computer Engineering Introduction to Linear Systems Fall 2002.
The Johns Hopkins University Department of Electrical and Computer Engineering 505.460 Introduction to Linear Systems Fall 2002 Final exam Name: You are allowed to use: 1. Table 3.1 (page 206) & Table
More informationEEL3135: Homework #3 Solutions
EEL335: Homework #3 Solutions Problem : (a) Compute the CTFT for the following signal: xt () cos( πt) cos( 3t) + cos( 4πt). First, we use the trigonometric identity (easy to show by using the inverse Euler
More informationEE 224 Signals and Systems I Review 1/10
EE 224 Signals and Systems I Review 1/10 Class Contents Signals and Systems Continuous-Time and Discrete-Time Time-Domain and Frequency Domain (all these dimensions are tightly coupled) SIGNALS SYSTEMS
More informationDigital Signal Processing Module 1 Analysis of Discrete time Linear Time - Invariant Systems
Digital Signal Processing Module 1 Analysis of Discrete time Linear Time - Invariant Systems Objective: 1. To understand the representation of Discrete time signals 2. To analyze the causality and stability
More informationFourier Analysis Overview (0A)
CTFS: Fourier Series CTFT: Fourier Transform DTFS: Fourier Series DTFT: Fourier Transform DFT: Discrete Fourier Transform Copyright (c) 2011-2016 Young W. Lim. Permission is granted to copy, distribute
More informationLinear Convolution Using FFT
Linear Convolution Using FFT Another useful property is that we can perform circular convolution and see how many points remain the same as those of linear convolution. When P < L and an L-point circular
More informationChapter Intended Learning Outcomes: (i) Understanding the relationship between transform and the Fourier transform for discrete-time signals
z Transform Chapter Intended Learning Outcomes: (i) Understanding the relationship between transform and the Fourier transform for discrete-time signals (ii) Understanding the characteristics and properties
More informationLaplace Transforms and use in Automatic Control
Laplace Transforms and use in Automatic Control P.S. Gandhi Mechanical Engineering IIT Bombay Acknowledgements: P.Santosh Krishna, SYSCON Recap Fourier series Fourier transform: aperiodic Convolution integral
More informationCore Concepts Review. Orthogonality of Complex Sinusoids Consider two (possibly non-harmonic) complex sinusoids
Overview of Continuous-Time Fourier Transform Topics Definition Compare & contrast with Laplace transform Conditions for existence Relationship to LTI systems Examples Ideal lowpass filters Relationship
More informationLecture 3 January 23
EE 123: Digital Signal Processing Spring 2007 Lecture 3 January 23 Lecturer: Prof. Anant Sahai Scribe: Dominic Antonelli 3.1 Outline These notes cover the following topics: Eigenvectors and Eigenvalues
More informationProblem Value
GEORGIA INSTITUTE OF TECHNOLOGY SCHOOL of ELECTRICAL & COMPUTER ENGINEERING FINAL EXAM DATE: 30-Apr-04 COURSE: ECE-2025 NAME: GT #: LAST, FIRST Recitation Section: Circle the date & time when your Recitation
More informationESE 531: Digital Signal Processing
ESE 531: Digital Signal Processing Lec 6: January 30, 2018 Inverse z-transform Lecture Outline! z-transform " Tie up loose ends " Regions of convergence properties! Inverse z-transform " Inspection " Partial
More informationENT 315 Medical Signal Processing CHAPTER 2 DISCRETE FOURIER TRANSFORM. Dr. Lim Chee Chin
ENT 315 Medical Signal Processing CHAPTER 2 DISCRETE FOURIER TRANSFORM Dr. Lim Chee Chin Outline Introduction Discrete Fourier Series Properties of Discrete Fourier Series Time domain aliasing due to frequency
More informationFourier Series Summary (From Salivahanan et al, 2002)
Fourier Series Suary (Fro Salivahanan et al, ) A periodic continuous signal f(t), - < t
More informationThe Z-Transform. Fall 2012, EE123 Digital Signal Processing. Eigen Functions of LTI System. Eigen Functions of LTI System
The Z-Transform Fall 202, EE2 Digital Signal Processing Lecture 4 September 4, 202 Used for: Analysis of LTI systems Solving di erence equations Determining system stability Finding frequency response
More informationIntroduction to DSP Time Domain Representation of Signals and Systems
Introduction to DSP Time Domain Representation of Signals and Systems Dr. Waleed Al-Hanafy waleed alhanafy@yahoo.com Faculty of Electronic Engineering, Menoufia Univ., Egypt Digital Signal Processing (ECE407)
More informationAnalog vs. discrete signals
Analog vs. discrete signals Continuous-time signals are also known as analog signals because their amplitude is analogous (i.e., proportional) to the physical quantity they represent. Discrete-time signals
More informationProblem 1. Suppose we calculate the response of an LTI system to an input signal x(n), using the convolution sum:
EE 438 Homework 4. Corrections in Problems 2(a)(iii) and (iv) and Problem 3(c): Sunday, 9/9, 10pm. EW DUE DATE: Monday, Sept 17 at 5pm (you see, that suggestion box does work!) Problem 1. Suppose we calculate
More informationFinal Exam of ECE301, Prof. Wang s section 1 3pm Tuesday, December 11, 2012, Lily 1105.
Final Exam of ECE301, Prof. Wang s section 1 3pm Tuesday, December 11, 2012, Lily 1105. 1. Please make sure that it is your name printed on the exam booklet. Enter your student ID number, e-mail address,
More informationEE 210. Signals and Systems Solutions of homework 2
EE 2. Signals and Systems Solutions of homework 2 Spring 2 Exercise Due Date Week of 22 nd Feb. Problems Q Compute and sketch the output y[n] of each discrete-time LTI system below with impulse response
More informationFourier Analysis Overview (0B)
CTFS: Continuous Time Fourier Series CTFT: Continuous Time Fourier Transform DTFS: Fourier Series DTFT: Fourier Transform DFT: Discrete Fourier Transform Copyright (c) 2009-2016 Young W. Lim. Permission
More informationLABORATORY 1 DISCRETE-TIME SIGNALS
LABORATORY DISCRETE-TIME SIGNALS.. Introduction A discrete-time signal is represented as a sequence of numbers, called samples. A sample value of a typical discrete-time signal or sequence is denoted as:
More informationE : Lecture 1 Introduction
E85.2607: Lecture 1 Introduction 1 Administrivia 2 DSP review 3 Fun with Matlab E85.2607: Lecture 1 Introduction 2010-01-21 1 / 24 Course overview Advanced Digital Signal Theory Design, analysis, and implementation
More informationEE 3054: Signals, Systems, and Transforms Summer It is observed of some continuous-time LTI system that the input signal.
EE 34: Signals, Systems, and Transforms Summer 7 Test No notes, closed book. Show your work. Simplify your answers. 3. It is observed of some continuous-time LTI system that the input signal = 3 u(t) produces
More informationThe Z transform (2) 1
The Z transform (2) 1 Today Properties of the region of convergence (3.2) Read examples 3.7, 3.8 Announcements: ELEC 310 FINAL EXAM: April 14 2010, 14:00 pm ECS 123 Assignment 2 due tomorrow by 4:00 pm
More informationEE301 Signals and Systems In-Class Exam Exam 3 Thursday, Apr. 19, Cover Sheet
EE301 Signals and Systems In-Class Exam Exam 3 Thursday, Apr. 19, 2012 Cover Sheet Test Duration: 75 minutes. Coverage: Chaps. 5,7 Open Book but Closed Notes. One 8.5 in. x 11 in. crib sheet Calculators
More informationLecture 13: Discrete Time Fourier Transform (DTFT)
Lecture 13: Discrete Time Fourier Transform (DTFT) ECE 401: Signal and Image Analysis University of Illinois 3/9/2017 1 Sampled Systems Review 2 DTFT and Convolution 3 Inverse DTFT 4 Ideal Lowpass Filter
More informationDepartment of Electrical and Computer Engineering Digital Speech Processing Homework No. 6 Solutions
Problem 1 Department of Electrical and Computer Engineering Digital Speech Processing Homework No. 6 Solutions The complex cepstrum, ˆx[n], of a sequence x[n] is the inverse Fourier transform of the complex
More informationUniversity of Kentucky Department of Electrical and Computer Engineering. EE421G: Signals and Systems I Fall 2007
University of Kentucky Department of Electrical an Computer Engineering EE4G: Signals an Systems I Fall 7 Issue: October 4, 7 Problem Set 6 Due: October, 7 (In class) Reaing Assignments: Rea Chapter 3.7,
More informationELEN 4810 Midterm Exam
ELEN 4810 Midterm Exam Wednesday, October 26, 2016, 10:10-11:25 AM. One sheet of handwritten notes is allowed. No electronics of any kind are allowed. Please record your answers in the exam booklet. Raise
More informationFinal Exam of ECE301, Section 1 (Prof. Chih-Chun Wang) 1 3pm, Friday, December 13, 2016, EE 129.
Final Exam of ECE301, Section 1 (Prof. Chih-Chun Wang) 1 3pm, Friday, December 13, 2016, EE 129. 1. Please make sure that it is your name printed on the exam booklet. Enter your student ID number, and
More informationDigital Signal Processing Lecture 3 - Discrete-Time Systems
Digital Signal Processing - Discrete-Time Systems Electrical Engineering and Computer Science University of Tennessee, Knoxville August 25, 2015 Overview 1 2 3 4 5 6 7 8 Introduction Three components of
More informationCh 2: Linear Time-Invariant System
Ch 2: Linear Time-Invariant System A system is said to be Linear Time-Invariant (LTI) if it possesses the basic system properties of linearity and time-invariance. Consider a system with an output signal
More information7.17. Determine the z-transform and ROC for the following time signals: Sketch the ROC, poles, and zeros in the z-plane. X(z) = x[n]z n.
Solutions to Additional Problems 7.7. Determine the -transform and ROC for the following time signals: Sketch the ROC, poles, and eros in the -plane. (a) x[n] δ[n k], k > 0 X() x[n] n n k, 0 Im k multiple
More informationFourier analysis of discrete-time signals. (Lathi Chapt. 10 and these slides)
Fourier analysis of discrete-time signals (Lathi Chapt. 10 and these slides) Towards the discrete-time Fourier transform How we will get there? Periodic discrete-time signal representation by Discrete-time
More informationESE 531: Digital Signal Processing
ESE 531: Digital Signal Processing Lec 6: January 31, 2017 Inverse z-transform Lecture Outline! z-transform " Tie up loose ends " Regions of convergence properties! Inverse z-transform " Inspection " Partial
More information