EE 341 A Few Review Problems Fall 2018

Size: px
Start display at page:

Download "EE 341 A Few Review Problems Fall 2018"

Transcription

1 EE 341 A Few Review Problems Fall A linear, time-invariant system with input, xx(tt), and output, yy(tt), has the unit-step response, cc(tt), shown below. a. Find, and accurately sketch, the system impulse response, h(tt). b. Find, and accurately sketch, yy(tt) = h(tt) [uu(tt) uu(tt 2)]. 2. A discrete-time, linear, time-invariant system with input xx(nn) and output yy(nn) has the impulse response h(nn) = [2, 2, 1, 1], where the underline denotes the nn = 0 sample, and all signal values outside the range specified are zero. a. (2 points) Is the system causal? (You must provide correct justification to receive credit.) b. (3 points) Is the system BIBO stable? (You must provide correct justification to receive credit.) c. The system input is xx(nn) = [ 1, 1, 0, 1, 1], where the underline denotes the nn = 0 sample, and all signal values outside the range specified are zero. i. (2 points) yy(nn) begins at nn =? ii. (3 points) yy(nn) ends at nn =? iii. (10 points) Find yy(nn) for all nn. 1

2 3. A discrete-time system with input, xx(nn), and output, yy(nn), is described by yy(nn) = 2xx(nn) 1. a. (5 points) Find the system response to the (Kronecker) impulse input, δδ(nn). b. (5 points) Determine if the system is BIBO stable. Proper justification must be provided to receive credit. 4. A continuous-time system with input, xx(tt), and output, yy(tt), has the inputoutput description yy(tt) = 2 cos(tt) xx(tt). Provide yes/no answers to the following by circling the correct answer. Determine if the system is a. Memoryless? b. Causal? c. BIBO stable? d. Linear? e. Time-invariant? 2

3 5. A linear, time-invariant system with input, xx(tt), and output, yy(tt), has the impulse response, h(tt), and the unit-step response, cc(tt), shown below. Find, and accurately sketch, the response to the input xx(tt) = 2δδ(tt 1) uu(tt 3). 3

4 6. Consider the composite system below, constructed from distinct linear, time- invariant systems. a. (10 points) Determine the overall impulse response, gg(tt), in terms of h(tt) and rr(tt). b. (10 points) Let h(tt) = uu(tt) uu(tt 1) and rr(tt) = δδ(tt 1). Find yy(tt) if the system input is xx(tt) = uu(tt) 7. A discrete-time, linear, time-invariant system with input, xx(nn), and output, yy(nn), has impulse response h(nn). a. If h(nn) = (1, 2, 1), use convolution to find yy(nn) = h(nn) xx(nn), for xx(nn) = [uu(nn) uu(nn 6)]. b. If h(nn) = 1 2 nn uu(nn), use convolution to find yy(nn) = h(nn) xx(nn), for xx(nn) = [uu(nn) uu(nn 6)]. 4

5 5. A periodic signal, xx(tt), has period TT = ππ sec, fundamental frequency 5 ωω 0 = 2ππ = 10 rad/s, and exponential Fourier series coefficients TT 5, kk = 0 10, kk = 1, 1 7jj, kk = 2 cc kk = 7jj, kk = 2 3, kk = 3, 3 0, otherwise d. Find the power in the signal xx(tt). e. A truncated Fourier series is of the form NN xx NN (tt) = cc kk ee jj2ππππππ/tt. kk= NN Find the normalized mean-square truncation error for i. NN = 1. ii. NN = 5. f. Accurately sketch the magnitude spectrum for xx(tt), labeling all relevant amplitudes. g. The signal, xx(tt) is applied as input to a linear, time-invariant system with output, yy(tt). The system has impulse response h(tt) with Fourier transform (the system frequency response) HH(ωω), where 20, 5 ωω 25 HH(ωω) =. Find the Fourier series for yy(tt). Express yy(tt) in simplest form (e.g., 0, otherwise sines and cosines). 5

6 6. A linear, time-invariant (LTI) system with overall impulse response h(tt) is constructed from three simpler LTI subsystems, as shown below, with respective impulse responses gg 1 (tt), gg 2 (tt), and gg 3 (tt). a. ) Find h(tt) in terms of gg 1 (tt), gg 2 (tt), and gg 3 (tt). b. If gg 1 (tt) = δδ(tt 2), gg 2 (tt) = 6δδ(tt 3), and gg 3 (tt) = 2δδ(tt 1), find h(tt). c. Suppose that h(tt) = δδ(tt + 1) + 2δδ(tt 3). Find, and accurately sketch yy(tt) = xx(tt) h(tt) for xx(tt) = [uu(tt + 2) uu(tt 2)]. d. Suppose that h(tt) = 2ee 2tt uu(tt). Find, and sketch, yy(tt) = xx(tt) h(tt) for the same xx(tt) as in part c), xx(tt) = [uu(tt + 2) uu(tt 2)]. 6

7 7. A periodic signal, xx(tt), is shown below. i. Find the exponential Fourier series, xx(tt) = kk= cc kk ee jj2ππππππ/tt i. Period TT = ii. cc 0 = iii. cc kk = ii. Let zz(tt) = 3xx(tt) + 6. Find the exponential Fourier series for zz(tt) in terms of the Fourier series coefficients for xx(tt). iii. The signal xx(tt) in part a) is applied as the input to a linear, time-invariant system with 0.2 ππ output yy(tt) and transfer function HH(ss) =. ss+0.4 ππ i. Find the Fourier series for yy(tt). ii. Find the ratio of the amplitude of the 1 st harmonic of the Fourier series for yy(tt) to the amplitude of the 1 st harmonic of the Fourier series for xx(tt). 7

8 8. The square wave signal, xx(tt), shown below, has period T sec and exponential Fourier series xx(tt) = cc kk ee jj2ππππππ/tt, kk= where the Fourier series coefficients are given by 1, kk = 0 cc kk = sinc kk, kk 0 2 sin (ππππ) with sinc(tt) =, and the fundamental frequency ωω 0 = 2ππ rad/s. TT ππππ iv. Find the Fourier transform of xx(tt), and plot the magnitude spectrum, XX(ωω), carefully labeling important amplitudes and frequency axis intercepts. v. Let TT = sec (so ωω 0 = 2ππ = 32ππ rad/s). The signal xx(tt) is applied as input to a TT linear, time-invariant system with output yy(tt). The system frequency response is shown below. Find an explicit expression for yy(tt). 8

9 9. Analog signal xx(tt) = 2 cos(32ππππ) 4 sin (7ππππ). vi. Find XX(ωω) and accurately sketch XX(ωω), carefully labeling important amplitudes and frequency axis intercepts. vii. The signal xx(tt) is sampled at rate FF ss = 1 samples/sec, and the analog signal yy(tt) is formed TT as shown in the block diagram below, yy(tt) = nn= xx(nnnn)h(tt nnnn), where h(tt) FF HH(ωω) and HH(ωω) = TT PPππ(ωω) = TT, ωω ππ TT TT 0, otherwise. i. From the sampling theorem, what is the smallest rate at which xx(tt) can be sampled and the reconstructed signal is yy(tt) = xx(tt). ii. Let FF ss = 1 = 25 samples/sec. Sketch the spectrum at the output of the sampler. Find an TT explicit (time-domain) expression for yy(tt). 10. viii. ix. Use Fourier transform pairs and properties to find the Fourier transform of the signal xx(tt) = 10ee 3 tt cos (30ππππ), where uu(tt) is the unit step function. Sketch XX(ωω), carefully indicating important amplitudes and frequencies. 9

10 11. A discrete source is modeled as a discrete random variable, XX, with 6 outcomes in the sample space SS = {0,1,2,3,4,5}. a. If PP(xx) = 1, for xx = 0,,5, determine the entropy, HH(XX). (Note that, log 6 22 = 1.0, log 2 3 = , and log 2 6 = ) b. Let the symbol probabilities be PP(xx) = (0.35, 0.25, 0.12, 0.12, 0.08, 0.08) for xx = 0,, 5, with the source entropy, HH(XX) = 6 xx=0 PP(xx)log 2 PP(xx) = bits. Design a Huffman code for the source. List all codewords, determine the average codeword length, and compare the average codeword length to the entropy. Codeword X P(x)

EE120 Fall 2016 HOMEWORK 3 Solutions. Problem 1 Solution. 1.a. GSI: Phillip Sandborn

EE120 Fall 2016 HOMEWORK 3 Solutions. Problem 1 Solution. 1.a. GSI: Phillip Sandborn EE Fall 6 HOMEWORK 3 Solutions GSI: Phillip Sandborn Problem Solution For each solution, draw xx(ττ) and flip around ττ =, then slide the result across h(ττ)..a. .b Use the same x as part a. .c .d Analytical

More information

EECE 2150 Circuits and Signals Final Exam Fall 2016 Dec 9

EECE 2150 Circuits and Signals Final Exam Fall 2016 Dec 9 EECE 2150 Circuits and Signals Final Exam Fall 2016 Dec 9 Name: Instructions: Write your name and section number on all pages Closed book, closed notes; Computers and cell phones are not allowed You can

More information

EECE 2150 Circuits and Signals Final Exam Fall 2016 Dec 12

EECE 2150 Circuits and Signals Final Exam Fall 2016 Dec 12 EECE 2150 Circuits and Signals Final Exam Fall 2016 Dec 12 Instructions: Write your name and section number on all pages Closed book, closed notes; Computers and cell phones are not allowed You can use

More information

Math 171 Spring 2017 Final Exam. Problem Worth

Math 171 Spring 2017 Final Exam. Problem Worth Math 171 Spring 2017 Final Exam Problem 1 2 3 4 5 6 7 8 9 10 11 Worth 9 6 6 5 9 8 5 8 8 8 10 12 13 14 15 16 17 18 19 20 21 22 Total 8 5 5 6 6 8 6 6 6 6 6 150 Last Name: First Name: Student ID: Section:

More information

ECE 6540, Lecture 06 Sufficient Statistics & Complete Statistics Variations

ECE 6540, Lecture 06 Sufficient Statistics & Complete Statistics Variations ECE 6540, Lecture 06 Sufficient Statistics & Complete Statistics Variations Last Time Minimum Variance Unbiased Estimators Sufficient Statistics Proving t = T(x) is sufficient Neyman-Fischer Factorization

More information

Time Domain Analysis of Linear Systems Ch2. University of Central Oklahoma Dr. Mohamed Bingabr

Time Domain Analysis of Linear Systems Ch2. University of Central Oklahoma Dr. Mohamed Bingabr Time Domain Analysis of Linear Systems Ch2 University of Central Oklahoma Dr. Mohamed Bingabr Outline Zero-input Response Impulse Response h(t) Convolution Zero-State Response System Stability System Response

More information

Solution 10 July 2015 ECE301 Signals and Systems: Midterm. Cover Sheet

Solution 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 information

New 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 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 information

New Mexico State University Klipsch School of Electrical Engineering. EE312 - Signals and Systems I Fall 2017 Exam #1

New Mexico State University Klipsch School of Electrical Engineering. EE312 - Signals and Systems I Fall 2017 Exam #1 New Mexico State University Klipsch School of Electrical Engineering EE312 - Signals and Systems I Fall 2017 Exam #1 Name: Prob. 1 Prob. 2 Prob. 3 Prob. 4 Total / 30 points / 20 points / 25 points / 25

More information

Problem Value

Problem 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 information

Math 191G Final Review Last Edited: Spring 2019

Math 191G Final Review Last Edited: Spring 2019 Math 191G Final Review Last Edited: Spring 2019 1. Evaluate the following its using the method of your choice. Show work to justify your answer. If the it does not exist, state it clearly. a. x -1 2x 2

More information

Problem Value Score No/Wrong Rec

Problem Value Score No/Wrong Rec GEORGIA INSTITUTE OF TECHNOLOGY SCHOOL of ELECTRICAL & COMPUTER ENGINEERING QUIZ #2 DATE: 14-Oct-11 COURSE: ECE-225 NAME: GT username: LAST, FIRST (ex: gpburdell3) 3 points 3 points 3 points Recitation

More information

The domain and range of lines is always R Graphed Examples:

The domain and range of lines is always R Graphed Examples: Graphs/relations in R 2 that should be familiar at the beginning of your University career in order to do well (The goal here is to be ridiculously complete, hence I have started with lines). 1. Lines

More information

Q. 1 Q. 25 carry one mark each.

Q. 1 Q. 25 carry one mark each. Q. 1 Q. 25 carry one mark each. Q.1 Given ff(zz) = gg(zz) + h(zz), where ff, gg, h are complex valued functions of a complex variable zz. Which one of the following statements is TUE? (A) If ff(zz) is

More information

Problem Value

Problem 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 information

Now, suppose that the signal is of finite duration (length) NN. Specifically, the signal is zero outside the range 0 nn < NN. Then

Now, suppose that the signal is of finite duration (length) NN. Specifically, the signal is zero outside the range 0 nn < NN. Then EE 464 Discrete Fourier Transform Fall 2018 Read Text, Chapter 4. Recall that for a complex-valued discrete-time signal, xx(nn), we can compute the Z-transform, XX(zz) = nn= xx(nn)zz nn. Evaluating on

More information

Radial Basis Function (RBF) Networks

Radial Basis Function (RBF) Networks CSE 5526: Introduction to Neural Networks Radial Basis Function (RBF) Networks 1 Function approximation We have been using MLPs as pattern classifiers But in general, they are function approximators Depending

More information

ECE 3084 QUIZ 2 SCHOOL OF ELECTRICAL AND COMPUTER ENGINEERING GEORGIA INSTITUTE OF TECHNOLOGY APRIL 2, Name:

ECE 3084 QUIZ 2 SCHOOL OF ELECTRICAL AND COMPUTER ENGINEERING GEORGIA INSTITUTE OF TECHNOLOGY APRIL 2, Name: ECE 3084 QUIZ 2 SCHOOL OF ELECTRICAL AND COMPUTER ENGINEERING GEORGIA INSTITUTE OF TECHNOLOGY APRIL 2, 205 Name:. The quiz is closed book, except for one 2-sided sheet of handwritten notes. 2. Turn off

More information

10.4 Controller synthesis using discrete-time model Example: comparison of various controllers

10.4 Controller synthesis using discrete-time model Example: comparison of various controllers 10. Digital 10.1 Basic principle of digital control 10.2 Digital PID controllers 10.2.1 A 2DOF continuous-time PID controller 10.2.2 Discretisation of PID controllers 10.2.3 Implementation and tuning 10.3

More information

BIOE 198MI Biomedical Data Analysis. Spring Semester Lab 4: Introduction to Probability and Random Data

BIOE 198MI Biomedical Data Analysis. Spring Semester Lab 4: Introduction to Probability and Random Data BIOE 98MI Biomedical Data Analysis. Spring Semester 209. Lab 4: Introduction to Probability and Random Data A. Random Variables Randomness is a component of all measurement data, either from acquisition

More information

Digital Signal Processing, Homework 1, Spring 2013, Prof. C.D. Chung

Digital 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 information

Question Paper Code : AEC11T02

Question 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 information

Mathematics Ext 2. HSC 2014 Solutions. Suite 403, 410 Elizabeth St, Surry Hills NSW 2010 keystoneeducation.com.

Mathematics Ext 2. HSC 2014 Solutions. Suite 403, 410 Elizabeth St, Surry Hills NSW 2010 keystoneeducation.com. Mathematics Ext HSC 4 Solutions Suite 43, 4 Elizabeth St, Surry Hills NSW info@keystoneeducation.com.au keystoneeducation.com.au Mathematics Extension : HSC 4 Solutions Contents Multiple Choice... 3 Question...

More information

Review of Analog Signal Analysis

Review of Analog Signal Analysis Review of Analog Signal Analysis Chapter Intended Learning Outcomes: (i) Review of Fourier series which is used to analyze continuous-time periodic signals (ii) Review of Fourier transform which is used

More information

CDS 101/110: Lecture 3.1 Linear Systems

CDS 101/110: Lecture 3.1 Linear Systems CDS /: Lecture 3. Linear Systems Goals for Today: Describe and motivate linear system models: Summarize properties, examples, and tools Joel Burdick (substituting for Richard Murray) jwb@robotics.caltech.edu,

More information

TEXT AND OTHER MATERIALS:

TEXT AND OTHER MATERIALS: 1. TEXT AND OTHER MATERIALS: Check Learning Resources in shared class files Calculus Wiki-book: https://en.wikibooks.org/wiki/calculus (Main Reference e-book) Paul s Online Math Notes: http://tutorial.math.lamar.edu

More information

Worksheets for GCSE Mathematics. Quadratics. mr-mathematics.com Maths Resources for Teachers. Algebra

Worksheets for GCSE Mathematics. Quadratics. mr-mathematics.com Maths Resources for Teachers. Algebra Worksheets for GCSE Mathematics Quadratics mr-mathematics.com Maths Resources for Teachers Algebra Quadratics Worksheets Contents Differentiated Independent Learning Worksheets Solving x + bx + c by factorisation

More information

Review for MA Final Exam

Review for MA Final Exam Review for MA 00 Final Eam Date Time Room Student ID Number (This will be needed on the scantron form for the eam.) Instructions for Students to get ID# a. Go to the IPFW website at www.@ ipfw.edu b. Log

More information

Specialist Mathematics 2019 v1.2

Specialist Mathematics 2019 v1.2 181314 Mensuration circumference of a circle area of a parallelogram CC = ππππ area of a circle AA = ππrr AA = h area of a trapezium AA = 1 ( + )h area of a triangle AA = 1 h total surface area of a cone

More information

Classical RSA algorithm

Classical RSA algorithm Classical RSA algorithm We need to discuss some mathematics (number theory) first Modulo-NN arithmetic (modular arithmetic, clock arithmetic) 9 (mod 7) 4 3 5 (mod 7) congruent (I will also use = instead

More information

ME5286 Robotics Spring 2017 Quiz 2

ME5286 Robotics Spring 2017 Quiz 2 Page 1 of 5 ME5286 Robotics Spring 2017 Quiz 2 Total Points: 30 You are responsible for following these instructions. Please take a minute and read them completely. 1. Put your name on this page, any other

More information

Print Name : ID : ECE Test #1 9/22/2016

Print Name :  ID : ECE Test #1 9/22/2016 Print Name : Email ID : ECE 2660 Test #1 9/22/2016 All answers must be recorded on the answer page (page 2). You must do all questions on the exam. For Part 4 you must show all your work and write your

More information

3.2 A2 - Just Like Derivatives but Backwards

3.2 A2 - Just Like Derivatives but Backwards 3. A - Just Like Derivatives but Backwards The Definite Integral In the previous lesson, you saw that as the number of rectangles got larger and larger, the values of Ln, Mn, and Rn all grew closer and

More information

Therefore the new Fourier coefficients are. Module 2 : Signals in Frequency Domain Problem Set 2. Problem 1

Therefore the new Fourier coefficients are. Module 2 : Signals in Frequency Domain Problem Set 2. Problem 1 Module 2 : Signals in Frequency Domain Problem Set 2 Problem 1 Let be a periodic signal with fundamental period T and Fourier series coefficients. Derive the Fourier series coefficients of each of the

More information

ECE 421 Introduction to Signal Processing

ECE 421 Introduction to Signal Processing ECE 421 Introduction to Signal Processing Dror Baron Assistant Professor Dept. of Electrical and Computer Engr. North Carolina State University, NC, USA Denoising and Project 4 Where does denoising appear?

More information

FINAL EXAM. Problem Value Score

FINAL EXAM. Problem Value Score GEORGIA INSTITUTE OF TECHNOLOGY SCHOOL of ELECTRICAL & COMPUTER ENGINEERING FINAL EXAM DATE: 27-Apr-09 COURSE: ECE-2025 NAME: GT username: LAST, FIRST (ex: gpburdell3) 3 points 3 points 3 points Recitation

More information

EEL3135: Homework #4

EEL3135: 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 information

Lesson 1: Successive Differences in Polynomials

Lesson 1: Successive Differences in Polynomials Lesson 1 Lesson 1: Successive Differences in Polynomials Classwork Opening Exercise John noticed patterns in the arrangement of numbers in the table below. 2.4 3.4 4.4 5.4 6.4 5.76 11.56 19.36 29.16 40.96

More information

(i) Represent discrete-time signals using transform. (ii) Understand the relationship between transform and discrete-time Fourier transform

(i) Represent discrete-time signals using transform. (ii) Understand the relationship between transform and discrete-time Fourier transform z Transform Chapter Intended Learning Outcomes: (i) Represent discrete-time signals using transform (ii) Understand the relationship between transform and discrete-time Fourier transform (iii) Understand

More information

Lecture No. 1 Introduction to Method of Weighted Residuals. Solve the differential equation L (u) = p(x) in V where L is a differential operator

Lecture No. 1 Introduction to Method of Weighted Residuals. Solve the differential equation L (u) = p(x) in V where L is a differential operator Lecture No. 1 Introduction to Method of Weighted Residuals Solve the differential equation L (u) = p(x) in V where L is a differential operator with boundary conditions S(u) = g(x) on Γ where S is a differential

More information

(1) Correspondence of the density matrix to traditional method

(1) Correspondence of the density matrix to traditional method (1) Correspondence of the density matrix to traditional method New method (with the density matrix) Traditional method (from thermal physics courses) ZZ = TTTT ρρ = EE ρρ EE = dddd xx ρρ xx ii FF = UU

More information

EE 3054: Signals, Systems, and Transforms Spring A causal discrete-time LTI system is described by the equation. y(n) = 1 4.

EE 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 information

1. The graph of a function f is given above. Answer the question: a. Find the value(s) of x where f is not differentiable. Ans: x = 4, x = 3, x = 2,

1. The graph of a function f is given above. Answer the question: a. Find the value(s) of x where f is not differentiable. Ans: x = 4, x = 3, x = 2, 1. The graph of a function f is given above. Answer the question: a. Find the value(s) of x where f is not differentiable. x = 4, x = 3, x = 2, x = 1, x = 1, x = 2, x = 3, x = 4, x = 5 b. Find the value(s)

More information

ENGIN 211, Engineering Math. Fourier Series and Transform

ENGIN 211, Engineering Math. Fourier Series and Transform ENGIN 11, Engineering Math Fourier Series and ransform 1 Periodic Functions and Harmonics f(t) Period: a a+ t Frequency: f = 1 Angular velocity (or angular frequency): ω = ππ = π Such a periodic function

More information

On one Application of Newton s Method to Stability Problem

On one Application of Newton s Method to Stability Problem Journal of Multidciplinary Engineering Science Technology (JMEST) ISSN: 359-0040 Vol. Issue 5, December - 204 On one Application of Newton s Method to Stability Problem Şerife Yılmaz Department of Mathematics,

More information

Logarithmic Functions

Logarithmic Functions Name Student ID Number Group Name Group Members Logarithmic Functions 1. Solve the equations below. xx = xx = 5. Were you able solve both equations above? If so, was one of the equations easier to solve

More information

CHAPTER 5 Wave Properties of Matter and Quantum Mechanics I

CHAPTER 5 Wave Properties of Matter and Quantum Mechanics I CHAPTER 5 Wave Properties of Matter and Quantum Mechanics I 1 5.1 X-Ray Scattering 5.2 De Broglie Waves 5.3 Electron Scattering 5.4 Wave Motion 5.5 Waves or Particles 5.6 Uncertainty Principle Topics 5.7

More information

10.4 The Cross Product

10.4 The Cross Product Math 172 Chapter 10B notes Page 1 of 9 10.4 The Cross Product The cross product, or vector product, is defined in 3 dimensions only. Let aa = aa 1, aa 2, aa 3 bb = bb 1, bb 2, bb 3 then aa bb = aa 2 bb

More information

Q1 Q2 Q3 Q4 Q5 Total

Q1 Q2 Q3 Q4 Q5 Total EE 120: Signals & Systems Department of Electrical Engineering and Computer Sciences University of California, Berkeley Midterm Exam #1 February 29, 2016, 2:10-4:00pm Instructions: There are five questions

More information

Lesson 7: Linear Transformations Applied to Cubes

Lesson 7: Linear Transformations Applied to Cubes Classwork Opening Exercise Consider the following matrices: AA = 1 2 0 2, BB = 2, and CC = 2 2 4 0 0 2 2 a. Compute the following determinants. i. det(aa) ii. det(bb) iii. det(cc) b. Sketch the image of

More information

(i) Represent continuous-time periodic signals using Fourier series

(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 information

The Role of Unbound Wavefunctions in Energy Quantization and Quantum Bifurcation

The Role of Unbound Wavefunctions in Energy Quantization and Quantum Bifurcation The Role of Unbound Wavefunctions in Energy Quantization and Quantum Bifurcation Ciann-Dong Yang 1 Department of Aeronautics and Astronautics National Cheng Kung University, Tainan, Taiwan cdyang@mail.ncku.edu.tw

More information

NAME: ht () 1 2π. Hj0 ( ) dω Find the value of BW for the system having the following impulse response.

NAME: ht () 1 2π. Hj0 ( ) dω Find the value of BW for the system having the following impulse response. University of California at Berkeley Department of Electrical Engineering and Computer Sciences Professor J. M. Kahn, EECS 120, Fall 1998 Final Examination, Wednesday, December 16, 1998, 5-8 pm NAME: 1.

More information

Roll No. :... Invigilator s Signature :.. CS/B.Tech (EE-N)/SEM-6/EC-611/ DIGITAL SIGNAL PROCESSING. Time Allotted : 3 Hours Full Marks : 70

Roll No. :... Invigilator s Signature :.. CS/B.Tech (EE-N)/SEM-6/EC-611/ DIGITAL SIGNAL PROCESSING. Time Allotted : 3 Hours Full Marks : 70 Name : Roll No. :.... Invigilator s Signature :.. CS/B.Tech (EE-N)/SEM-6/EC-611/2011 2011 DIGITAL SIGNAL PROCESSING Time Allotted : 3 Hours Full Marks : 70 The figures in the margin indicate full marks.

More information

Secondary 3H Unit = 1 = 7. Lesson 3.3 Worksheet. Simplify: Lesson 3.6 Worksheet

Secondary 3H Unit = 1 = 7. Lesson 3.3 Worksheet. Simplify: Lesson 3.6 Worksheet Secondary H Unit Lesson Worksheet Simplify: mm + 2 mm 2 4 mm+6 mm + 2 mm 2 mm 20 mm+4 5 2 9+20 2 0+25 4 +2 2 + 2 8 2 6 5. 2 yy 2 + yy 6. +2 + 5 2 2 2 0 Lesson 6 Worksheet List all asymptotes, holes and

More information

Chapter Intended Learning Outcomes: (i) Understanding the relationship between transform and the Fourier transform for discrete-time signals

Chapter 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 information

Chapter 5: Spectral Domain From: The Handbook of Spatial Statistics. Dr. Montserrat Fuentes and Dr. Brian Reich Prepared by: Amanda Bell

Chapter 5: Spectral Domain From: The Handbook of Spatial Statistics. Dr. Montserrat Fuentes and Dr. Brian Reich Prepared by: Amanda Bell Chapter 5: Spectral Domain From: The Handbook of Spatial Statistics Dr. Montserrat Fuentes and Dr. Brian Reich Prepared by: Amanda Bell Background Benefits of Spectral Analysis Type of data Basic Idea

More information

OSIsoft Data Compression Analysis

OSIsoft Data Compression Analysis OSIsoft Data Compression Analysis Raymond de Callafon Charles H. Wells University of California, San Diego (UCSD) OSIsoft email: callafon@ucsd.edu NASPI Work Group Meeting, Knoxville, TN, March 11-12,

More information

EE538 Digital Signal Processing I Session 13 Exam 1 Live: Wed., Sept. 18, Cover Sheet

EE538 Digital Signal Processing I Session 13 Exam 1 Live: Wed., Sept. 18, Cover Sheet EE538 Digital Signal Processing I Session 3 Exam Live: Wed., Sept. 8, 00 Cover Sheet Test Duration: 50 minutes. Coverage: Sessions -0. Open Book but Closed Notes. Calculators not allowed. This test contains

More information

CDS 101/110: Lecture 6.2 Transfer Functions

CDS 101/110: Lecture 6.2 Transfer Functions CDS 11/11: Lecture 6.2 Transfer Functions November 2, 216 Goals: Continued study of Transfer functions Review Laplace Transform Block Diagram Algebra Bode Plot Intro Reading: Åström and Murray, Feedback

More information

(1) Introduction: a new basis set

(1) Introduction: a new basis set () Introduction: a new basis set In scattering, we are solving the S eq. for arbitrary VV in integral form We look for solutions to unbound states: certain boundary conditions (EE > 0, plane and spherical

More information

Grover s algorithm. We want to find aa. Search in an unordered database. QC oracle (as usual) Usual trick

Grover s algorithm. We want to find aa. Search in an unordered database. QC oracle (as usual) Usual trick Grover s algorithm Search in an unordered database Example: phonebook, need to find a person from a phone number Actually, something else, like hard (e.g., NP-complete) problem 0, xx aa Black box ff xx

More information

EE 16B Final, December 13, Name: SID #:

EE 16B Final, December 13, Name: SID #: EE 16B Final, December 13, 2016 Name: SID #: Important Instructions: Show your work. An answer without explanation is not acceptable and does not guarantee any credit. Only the front pages will be scanned

More information

Grades will be determined by the correctness of your answers (explanations are not required).

Grades will be determined by the correctness of your answers (explanations are not required). 6.00 (Fall 2011) Final Examination December 19, 2011 Name: Kerberos Username: Please circle your section number: Section Time 2 11 am 1 pm 4 2 pm Grades will be determined by the correctness of your answers

More information

Examples of the Fourier Theorem (Sect. 10.3). The Fourier Theorem: Continuous case.

Examples of the Fourier Theorem (Sect. 10.3). The Fourier Theorem: Continuous case. s of the Fourier Theorem (Sect. 1.3. The Fourier Theorem: Continuous case. : Using the Fourier Theorem. The Fourier Theorem: Piecewise continuous case. : Using the Fourier Theorem. The Fourier Theorem:

More information

信號與系統 Signals and Systems

信號與系統 Signals and Systems Spring 2010 信號與系統 Signals and Systems Chapter SS-2 Linear Time-Invariant Systems Feng-Li Lian NTU-EE Feb10 Jun10 Figures and images used in these lecture notes are adopted from Signals & Systems by Alan

More information

Chap 2. Discrete-Time Signals and Systems

Chap 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 information

Review of Last Class 1

Review of Last Class 1 Review of Last Class 1 X-Ray diffraction of crystals: the Bragg formulation Condition of diffraction peak: 2dd sin θθ = nnλλ Review of Last Class 2 X-Ray diffraction of crystals: the Von Laue formulation

More information

BHASVIC MαTHS. Skills 1

BHASVIC MαTHS. Skills 1 PART A: Integrate the following functions with respect to x: (a) cos 2 2xx (b) tan 2 xx (c) (d) 2 PART B: Find: (a) (b) (c) xx 1 2 cosec 2 2xx 2 cot 2xx (d) 2cccccccccc2 2xx 2 ccccccccc 5 dddd Skills 1

More information

Signals and Systems Chapter 2

Signals and Systems Chapter 2 Signals and Systems Chapter 2 Continuous-Time Systems Prof. Yasser Mostafa Kadah Overview of Chapter 2 Systems and their classification Linear time-invariant systems System Concept Mathematical transformation

More information

GATE EE Topic wise Questions SIGNALS & SYSTEMS

GATE 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 information

Problem Value

Problem Value GEORGIA INSTITUTE OF TECHNOLOGY SCHOOL of ELECTRICAL & COMPUTER ENGINEERING FINAL EXAM DATE: 2-May-05 COURSE: ECE-2025 NAME: GT #: LAST, FIRST (ex: gtz123a) Recitation Section: Circle the date & time when

More information

Chapter 10. Simple Harmonic Motion and Elasticity

Chapter 10. Simple Harmonic Motion and Elasticity Chapter 10 Simple Harmonic Motion and Elasticity 10.1 The Ideal Spring and Simple Harmonic Motion F Applied x = k x spring constant Units: N/m FF SSSSSSSSSSSS = kkkk 10.1 The Ideal Spring and Simple Harmonic

More information

Variations. ECE 6540, Lecture 02 Multivariate Random Variables & Linear Algebra

Variations. ECE 6540, Lecture 02 Multivariate Random Variables & Linear Algebra Variations ECE 6540, Lecture 02 Multivariate Random Variables & Linear Algebra Last Time Probability Density Functions Normal Distribution Expectation / Expectation of a function Independence Uncorrelated

More information

Frequency-Domain C/S of LTI Systems

Frequency-Domain C/S of LTI Systems Frequency-Domain C/S of LTI Systems x(n) LTI y(n) LTI: Linear Time-Invariant system h(n), the impulse response of an LTI systems describes the time domain c/s. H(ω), the frequency response describes the

More information

SAMPLE COURSE OUTLINE MATHEMATICS METHODS ATAR YEAR 11

SAMPLE COURSE OUTLINE MATHEMATICS METHODS ATAR YEAR 11 SAMPLE COURSE OUTLINE MATHEMATICS METHODS ATAR YEAR 11 Copyright School Curriculum and Standards Authority, 2017 This document apart from any third party copyright material contained in it may be freely

More information

Control of Mobile Robots

Control of Mobile Robots Control of Mobile Robots Regulation and trajectory tracking Prof. Luca Bascetta (luca.bascetta@polimi.it) Politecnico di Milano Dipartimento di Elettronica, Informazione e Bioingegneria Organization and

More information

Modeling and Analysis of Systems Lecture #8 - Transfer Function. Guillaume Drion Academic year

Modeling and Analysis of Systems Lecture #8 - Transfer Function. Guillaume Drion Academic year Modeling and Analysis of Systems Lecture #8 - Transfer Function Guillaume Drion Academic year 2015-2016 1 Input-output representation of LTI systems Can we mathematically describe a LTI system using the

More information

FINAL EXAM. Problem Value Score No/Wrong Rec 3

FINAL EXAM. Problem Value Score No/Wrong Rec 3 GEORGIA INSTITUTE OF TECHNOLOGY SCHOOL of ELECTRICAL & COMPUTER ENGINEERING FINAL EXAM DATE: 1-May-08 COURSE: ECE-2025 NAME: GT username: LAST, FIRST (ex: gpburdell3) 3 points 3 points 3 points Recitation

More information

信號與系統 Signals and Systems

信號與系統 Signals and Systems Spring 2015 信號與系統 Signals and Systems Chapter SS-2 Linear Time-Invariant Systems Feng-Li Lian NTU-EE Feb15 Jun15 Figures and images used in these lecture notes are adopted from Signals & Systems by Alan

More information

Signals and Systems Profs. Byron Yu and Pulkit Grover Fall Midterm 2 Solutions

Signals 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 information

P.3 Division of Polynomials

P.3 Division of Polynomials 00 section P3 P.3 Division of Polynomials In this section we will discuss dividing polynomials. The result of division of polynomials is not always a polynomial. For example, xx + 1 divided by xx becomes

More information

COMM901 Source Coding and Compression. Quiz 1

COMM901 Source Coding and Compression. Quiz 1 German University in Cairo - GUC Faculty of Information Engineering & Technology - IET Department of Communication Engineering Winter Semester 2013/2014 Students Name: Students ID: COMM901 Source Coding

More information

Ch.11 The Discrete-Time Fourier Transform (DTFT)

Ch.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 information

9. Switched Capacitor Filters. Electronic Circuits. Prof. Dr. Qiuting Huang Integrated Systems Laboratory

9. Switched Capacitor Filters. Electronic Circuits. Prof. Dr. Qiuting Huang Integrated Systems Laboratory 9. Switched Capacitor Filters Electronic Circuits Prof. Dr. Qiuting Huang Integrated Systems Laboratory Motivation Transmission of voice signals requires an active RC low-pass filter with very low ff cutoff

More information

EE 3054: Signals, Systems, and Transforms Summer It is observed of some continuous-time LTI system that the input signal.

EE 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 information

Grades will be determined by the correctness of your answers (explanations are not required).

Grades will be determined by the correctness of your answers (explanations are not required). 6.00 (Fall 20) Final Examination December 9, 20 Name: Kerberos Username: Please circle your section number: Section Time 2 am pm 4 2 pm Grades will be determined by the correctness of your answers (explanations

More information

10.1 Three Dimensional Space

10.1 Three Dimensional Space Math 172 Chapter 10A notes Page 1 of 12 10.1 Three Dimensional Space 2D space 0 xx.. xx-, 0 yy yy-, PP(xx, yy) [Fig. 1] Point PP represented by (xx, yy), an ordered pair of real nos. Set of all ordered

More information

Prof. Dr.-Ing. Armin Dekorsy Department of Communications Engineering. Stochastic Processes and Linear Algebra Recap Slides

Prof. Dr.-Ing. Armin Dekorsy Department of Communications Engineering. Stochastic Processes and Linear Algebra Recap Slides Prof. Dr.-Ing. Armin Dekorsy Department of Communications Engineering Stochastic Processes and Linear Algebra Recap Slides Stochastic processes and variables XX tt 0 = XX xx nn (tt) xx 2 (tt) XX tt XX

More information

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

ENSC327 Communications Systems 2: Fourier Representations. School of Engineering Science Simon Fraser University ENSC37 Communications Systems : Fourier Representations School o Engineering Science Simon Fraser University Outline Chap..5: Signal Classiications Fourier Transorm Dirac Delta Function Unit Impulse Fourier

More information

Cosc 3451 Signals and Systems. What is a system? Systems Terminology and Properties of Systems

Cosc 3451 Signals and Systems. What is a system? Systems Terminology and Properties of Systems Cosc 3451 Signals and Systems Systems Terminology and Properties of Systems What is a system? an entity that manipulates one or more signals to yield new signals (often to accomplish a function) can be

More information

Diffusion modeling for Dip-pen Nanolithography Apoorv Kulkarni Graduate student, Michigan Technological University

Diffusion modeling for Dip-pen Nanolithography Apoorv Kulkarni Graduate student, Michigan Technological University Diffusion modeling for Dip-pen Nanolithography Apoorv Kulkarni Graduate student, Michigan Technological University Abstract The diffusion model for the dip pen nanolithography is similar to spreading an

More information

FOURIER SERIES. Chapter Introduction

FOURIER SERIES. Chapter Introduction Chapter 1 FOURIER SERIES 1.1 Introduction Fourier series introduced by a French physicist Joseph Fourier (1768-1830), is a mathematical tool that converts some specific periodic signals into everlasting

More information

GEORGIA INSTITUTE OF TECHNOLOGY SCHOOL OF ELECTRICAL AND COMPUTER ENGINEERING

GEORGIA INSTITUTE OF TECHNOLOGY SCHOOL OF ELECTRICAL AND COMPUTER ENGINEERING GEORGIA INSTITUTE OF TECHNOLOGY SCHOOL OF ELECTRICAL AND COMPUTER ENGINEERING ECE 06 Summer 08 Final Exam July 7, 08 NAME: Important Notes: Do not unstaple the test. Closed book and notes, except for three

More information

G.6 Function Notation and Evaluating Functions

G.6 Function Notation and Evaluating Functions G.6 Function Notation and Evaluating Functions ff ff() A function is a correspondence that assigns a single value of the range to each value of the domain. Thus, a function can be seen as an input-output

More information

Campus Academic Resource Program Differentiation Rules

Campus Academic Resource Program Differentiation Rules This handout will: Outline the definition of the derivative and introduce different notations for the derivative Introduce Differentiation rules and provide concise explanation of them Provide examples

More information

Digital Signal Processing Lecture 3 - Discrete-Time Systems

Digital 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 information

Linear Convolution Using FFT

Linear 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 information

Lesson 11 Inverse Trig Functions

Lesson 11 Inverse Trig Functions Unit : Trig Equations & Graphs Student ID #: Lesson 11 Inverse Trig Functions Goal: IX. use inverse trig to calculate an angle measure given a (special) ratio of sides Opener: Determine which values of

More information

EE301 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, 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 information