3 Signals and Systems: Part II
|
|
|
- Darcy Anthony
- 9 years ago
- Views:
Transcription
1 3 Signals and Systems: Part II Recommended Problems P3.1 Sketch each of the following signals. (a) x[n] = b[n] + 3[n - 3] (b) x[n] = u[n] - u[n - 5] (c) x[n] = 6[n] + 1n + (i)2 [n - 2] + (i)ag[n - 3] (d) x(t) = u(t + 3) - u(t - 3) (e) x(t) = 6(t + 2) (f) x(t) = e-'u(t) P3.2 Below are two columns of signals expressed analytically. For each signal in column A, find the signal or signals in column B that are identical. A (1) 6[n + 1] (a) t[k] 6 (2) (I)"U [n] k=-0 (3) 6(t) (b) du(t) (4) u(t) (5) u[n] (C) 6[k] (6) b[n + 1]u[n] k=o B (d) L (I)k[n - k] k=o (e) J t6(r) dr (f) u[n] (g) E (I)kb[n - k] k= -oo (h) b[n + 1] (i) $ P3.3 (a) Express the following as sums of weighted delayed impulses, i.e., in the form x[n] = ( ako[n - k] k= -o P3-1
2 Signals and Systems P3-2 x [n] ? Figure P3.3-1 (b) Express the following sequence as a sum of step functions, i.e., in the form s[n] = ( aku[n - k] k= -00 s[n] Figure P n P3.4 For x(t) indicated in Figure P3.4, sketch the following: (a) x(1 - t)[u(t + 1) - u(t - 2)] (b) x(1 - t)[u(t + 1) - u(2-3t)] x(t) 0 12 Figure P3.4
3 Signals and Systems: Part II / Problems P3-3 P3.5 Consider the three systems H, G, and F defined in Figure P x [n] H y[n]=x 2 [nj x[n] G. y[n]=x[n]-x[n-1] x [n F y[n]=2x[n]+x[n-1] Figure P3.5-1 (a) The systems in Figures P3.5-2 to P3.5-7 are formed by parallel and cascade combination of H, G, and F. By expressing the output y[n] in terms of the input x[n], determine which of the systems are equivalent. (b) H G x + y x + y H Figure P3.5-2 Figure P3.5-3 (c) (d) x-- G H Y X- H -- + G -- WY Figure P3.5-4 Figure P3.5-5 (e) (f) x G F Y x -- F G Y Figure P3.5-6 Figure P3.5-7 P3.6 Table P3.6 contains the input-output relations for several continuous-time and discrete-time systems, where x(t) or x[n] is the input. Indicate whether the property along the top row applies to each system by answering yes or no in the appropriate boxes. Do not mark the shaded boxes.
4 Signals and Systems P3-4 Properties y(t), y[n] Memoryless Linear Time-Invariant Causal Invertible Stable Table P3.6 P3.7 Consider the following systems H: y(t) = x(r) dr (an integrator), G: y(t) = x(2t), where the input is x(t) and the output is y(t). (a) What is H-', the inverse of H? What is G-? (b) Consider the system in Figure P3.7. Find the inverse F-1 and draw it in block diagram form in terms of H-1 and G-1. x(t) --- H G w(t) x(t) F --- o w(t) Figure P3.7
5 Signals and Systems: Part II / Problems P3-5 Optional Problems P3.8 In this problem we illustrate one of the most important consequences of the properties of linearity and time invariance. Specifically, once we know the response of a linear system or of a linear, time-invariant (LTI) system to a single input or the responses to several inputs, we can directly compute the responses to many other input signals. (a) Consider an LTI system whose response to the signal x 1 (t) in Figure P3.8-1(a) is the signal y 1 (t) illustrated in Figure P8-1(b). Determine and sketch carefully the response of the system to the input x 2 (t) depicted in Figure P3.8-1(c). x 1 (t) Y 1 (t) t (a) (b) x 2 (t) x 3 (t) i 7 1 t (c) (d) x(t (e) Figure P3.8-1 (b) Determine and sketch the response of the system considered in part (a) to the input x 3 (t) shown in Figure P3.8-1(d).
6 Signals and Systems P3-6 (c) Suppose that a second LTI system has the following output y(t) when the input is the unit step x(t) = u(t): y(t) = e-'u(t) + u(-1 - t) Determine and sketch the response of this system to the input x(t) shown in Figure P3.8-1(e). (d) Suppose that a particular discrete-time linear (but possibly not time-invariant) system has the responses yi[n], y2[n], and yan] to the input signals xi[n], x2[n], and x[n], respectively, as illustrated in Figure P3.8-2(a). If the input to this system is x[n] as illustrated in Figure P3.8-2(b), what is the output y[n]? x, [n] y, [n] 1 n -. a 0 n X2 [n] Y 2 [n] x 3 [n] y 3 [n] p 0pop0 o n -..- n (a) x[n] n -2 (b) Figure P3.8-2 (e) If an LTI system has the response y1[n] to the input xi[n] as in Figure P3.8-2(a), what would its responses be to x 2 [n] and x 3 [n]? (f) A particular linear system has the property that the response to t' is cos kt. What is the response of this system to the inputs x 1 (t) =,r + 6t 2-47t 5 + \/et6 1+ t0 x 2 (t) = 1 +t 2
7 Signals and Systems: Part II / Problems P3-7 P3.9 (a) Consider a system with input x(t) and with output y(t) given by +0o y(t) = ( x(t)b(t - nt) (i) (ii) Is this system linear? Is this system time-invariant? For each part, if your answer is yes, show why this is so. If your answer is no, produce a counterexample. (b) Suppose that the input to this system is x(t) = cos 27rt. Sketch and label carefully the output y(t) for each of the following values of T: T = 1, 1, 1, 1, -. All of your sketches should have the same horizontal and vertical scales. (c) Repeat part (b) for x(t) = e' cos 27rt. P3.10 (a) Is the following statement true or false? The series interconnection of two linear, time-invariant systems is itself a lin ear, time-invariant system. Justify your answer. (b) Is the following statement true or false? The series connection of two nonlinear systems is itself nonlinear. Justify your answer. (c) Consider three systems with the following input-output relations: x[n/2], n even System 1: y[n] = 1o, n odd System 2: y[n] = x[n] + 1x[n - 1] + ix[n - 2] System 3: y[n] = x[2n] Suppose that these systems are connected in series as depicted in Figure P3.10. Find the input-output relation for the overall interconnected system. Is this system linear? Is it time-invariant? x[n] System 1 - System 2 System 3 y[n] Figure P3.10 (d) Consider a second series interconnection of the form of Figure P3.10 where the three systems are specified by the following equations, with a, b, and c real numbers: System 1: y[n] = x[-n] System 2: y[n] = ax[n - 1] + bx[n] + cx[n + 1] System 3: y[n] = x[-n]
8 Signals and Systems P3-8 Find the input-output relation for the overall interconnected system. Under what conditions on the numbers a, b, and c does the overall system have each of the following properties? (i) (ii) (iii) The overall system is linear and time-invariant. The input-output relation of the overall system is identical to that of system 2. The overall system is causal. P3.11 Determine whether each of the following systems is linear and/or time-invariant. In each case, x[n] denotes the input and y[n] denotes the output. Assume that x[] > 0. (a) y[n] = x[n] + x[n - 1] (b) y[n] = x[n] + x[n - 1] + x[o] P3.12 (a) Show that causality for a continuous-time linear system implies the following statement: For any time to and any input x(t) such that x(t) = 0 for t < to, the correspond ing output y(t) must also be zero for t < to. The analogous statement can be made for discrete-time linear systems. (b) Find a nonlinear system that satisfies this condition but is not causal. (c) Find a nonlinear system that is causal but does not satisfy this condition. (d) Show that invertibility for a discrete-time linear system is equivalent to the following statement: The only input that produces the output y[n] = 0 for all n is x[n] = 0 for all n. The analogous statement is also true for continuous-time linear systems. (e) Find a nonlinear system that satisfies the condition of part (d) but is not invertible.
9 MIT OpenCourseWare Resource: Signals and Systems Professor Alan V. Oppenheim The following may not correspond to a particular course on MIT OpenCourseWare, but has been provided by the author as an individual learning resource. For information about citing these materials or our Terms of Use, visit:
UNIVERSITY OF CALIFORNIA, SAN DIEGO Electrical & Computer Engineering Department ECE 101 - Fall 2010 Linear Systems Fundamentals
UNIVERSITY OF CALIFORNIA, SAN DIEGO Electrical & Computer Engineering Department ECE 101 - Fall 2010 Linear Systems Fundamentals FINAL EXAM WITH SOLUTIONS (YOURS!) You are allowed one 2-sided sheet of
4 Convolution. Solutions to Recommended Problems
4 Convolution Solutions to Recommended Problems S4.1 The given input in Figure S4.1-1 can be expressed as linear combinations of xi[n], x 2 [n], X 3 [n]. x,[ n] 0 2 Figure S4.1-1 (a) x 4[n] = 2x 1 [n]
UNIVERSITY OF CALIFORNIA, SAN DIEGO Electrical & Computer Engineering Department ECE 101 - Fall 2009 Linear Systems Fundamentals
UNIVERSITY OF CALIFORNIA, SAN DIEGO Electrical & Computer Engineering Department ECE 101 - Fall 2009 Linear Systems Fundamentals MIDTERM EXAM You are allowed one 2-sided sheet of notes. No books, no other
6 Systems Represented by Differential and Difference Equations
6 Systems Represented by ifferential and ifference Equations An important class of linear, time-invariant systems consists of systems represented by linear constant-coefficient differential equations in
T = 10-' s. p(t)= ( (t-nt), T= 3. n=-oo. Figure P16.2
16 Sampling Recommended Problems P16.1 The sequence x[n] = (-1)' is obtained by sampling the continuous-time sinusoidal signal x(t) = cos oot at 1-ms intervals, i.e., cos(oont) = (-1)", Determine three
10 Discrete-Time Fourier Series
10 Discrete-Time Fourier Series In this and the next lecture we parallel for discrete time the discussion of the last three lectures for continuous time. Specifically, we consider the representation of
9 Fourier Transform Properties
9 Fourier Transform Properties The Fourier transform is a major cornerstone in the analysis and representation of signals and linear, time-invariant systems, and its elegance and importance cannot be overemphasized.
Discrete-Time Signals and Systems
2 Discrete-Time Signals and Systems 2.0 INTRODUCTION The term signal is generally applied to something that conveys information. Signals may, for example, convey information about the state or behavior
TTT4120 Digital Signal Processing Suggested Solution to Exam Fall 2008
Norwegian University of Science and Technology Department of Electronics and Telecommunications TTT40 Digital Signal Processing Suggested Solution to Exam Fall 008 Problem (a) The input and the input-output
2 Signals and Systems: Part I
2 Signals and Systems: Part I In this lecture, we consider a number of basic signals that will be important building blocks later in the course. Specifically, we discuss both continuoustime and discrete-time
1 2 3 1 1 2 x = + x 2 + x 4 1 0 1
(d) If the vector b is the sum of the four columns of A, write down the complete solution to Ax = b. 1 2 3 1 1 2 x = + x 2 + x 4 1 0 0 1 0 1 2. (11 points) This problem finds the curve y = C + D 2 t which
NETWORK STRUCTURES FOR IIR SYSTEMS. Solution 12.1
NETWORK STRUCTURES FOR IIR SYSTEMS Solution 12.1 (a) Direct Form I (text figure 6.10) corresponds to first implementing the right-hand side of the difference equation (i.e. the eros) followed by the left-hand
1.1 Discrete-Time Fourier Transform
1.1 Discrete-Time Fourier Transform The discrete-time Fourier transform has essentially the same properties as the continuous-time Fourier transform, and these properties play parallel roles in continuous
Frequency Response of FIR Filters
Frequency Response of FIR Filters Chapter 6 This chapter continues the study of FIR filters from Chapter 5, but the emphasis is frequency response, which relates to how the filter responds to an input
Signal Detection C H A P T E R 14 14.1 SIGNAL DETECTION AS HYPOTHESIS TESTING
C H A P T E R 4 Signal Detection 4. SIGNAL DETECTION AS HYPOTHESIS TESTING In Chapter 3 we considered hypothesis testing in the context of random variables. The detector resulting in the minimum probability
min ǫ = E{e 2 [n]}. (11.2)
C H A P T E R 11 Wiener Filtering INTRODUCTION In this chapter we will consider the use of LTI systems in order to perform minimum mean-square-error (MMSE) estimation of a WSS random process of interest,
4 Convolution. Recommended Problems. x2[n] 1 2[n]
4 Convoluion Recommended Problems P4.1 This problem is a simple example of he use of superposiion. Suppose ha a discree-ime linear sysem has oupus y[n] for he given inpus x[n] as shown in Figure P4.1-1.
3.1 State Space Models
31 State Space Models In this section we study state space models of continuous-time linear systems The corresponding results for discrete-time systems, obtained via duality with the continuous-time models,
SGN-1158 Introduction to Signal Processing Test. Solutions
SGN-1158 Introduction to Signal Processing Test. Solutions 1. Convolve the function ( ) with itself and show that the Fourier transform of the result is the square of the Fourier transform of ( ). (Hints:
Class Note for Signals and Systems. Stanley Chan University of California, San Diego
Class Note for Signals and Systems Stanley Chan University of California, San Diego 2 Acknowledgement This class note is prepared for ECE 101: Linear Systems Fundamentals at the University of California,
(Refer Slide Time: 01:11-01:27)
Digital Signal Processing Prof. S. C. Dutta Roy Department of Electrical Engineering Indian Institute of Technology, Delhi Lecture - 6 Digital systems (contd.); inverse systems, stability, FIR and IIR,
Lecture 13 Linear quadratic Lyapunov theory
EE363 Winter 28-9 Lecture 13 Linear quadratic Lyapunov theory the Lyapunov equation Lyapunov stability conditions the Lyapunov operator and integral evaluating quadratic integrals analysis of ARE discrete-time
Probability and Random Variables. Generation of random variables (r.v.)
Probability and Random Variables Method for generating random variables with a specified probability distribution function. Gaussian And Markov Processes Characterization of Stationary Random Process Linearly
24 Butterworth Filters
24 Butterworth Filters To illustrate some of the ideas developed in Lecture 23, we introduce in this lecture a simple and particularly useful class of filters referred to as Butterworthfilters. Filters
Lecture 7: Finding Lyapunov Functions 1
Massachusetts Institute of Technology Department of Electrical Engineering and Computer Science 6.243j (Fall 2003): DYNAMICS OF NONLINEAR SYSTEMS by A. Megretski Lecture 7: Finding Lyapunov Functions 1
Digital Signal Processing IIR Filter Design via Impulse Invariance
Digital Signal Processing IIR Filter Design via Impulse Invariance D. Richard Brown III D. Richard Brown III 1 / 11 Basic Procedure We assume here that we ve already decided to use an IIR filter. The basic
I. Pointwise convergence
MATH 40 - NOTES Sequences of functions Pointwise and Uniform Convergence Fall 2005 Previously, we have studied sequences of real numbers. Now we discuss the topic of sequences of real valued functions.
PYKC Jan-7-10. Lecture 1 Slide 1
Aims and Objectives E 2.5 Signals & Linear Systems Peter Cheung Department of Electrical & Electronic Engineering Imperial College London! By the end of the course, you would have understood: Basic signal
Chapter 8 - Power Density Spectrum
EE385 Class Notes 8/8/03 John Stensby Chapter 8 - Power Density Spectrum Let X(t) be a WSS random process. X(t) has an average power, given in watts, of E[X(t) ], a constant. his total average power is
Chapter 12 Modal Decomposition of State-Space Models 12.1 Introduction The solutions obtained in previous chapters, whether in time domain or transfor
Lectures on Dynamic Systems and Control Mohammed Dahleh Munther A. Dahleh George Verghese Department of Electrical Engineering and Computer Science Massachuasetts Institute of Technology 1 1 c Chapter
List the elements of the given set that are natural numbers, integers, rational numbers, and irrational numbers. (Enter your answers as commaseparated
MATH 142 Review #1 (4717995) Question 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 Description This is the review for Exam #1. Please work as many problems as possible
Solutions to old Exam 1 problems
Solutions to old Exam 1 problems Hi students! I am putting this old version of my review for the first midterm review, place and time to be announced. Check for updates on the web site as to which sections
G(s) = Y (s)/u(s) In this representation, the output is always the Transfer function times the input. Y (s) = G(s)U(s).
Transfer Functions The transfer function of a linear system is the ratio of the Laplace Transform of the output to the Laplace Transform of the input, i.e., Y (s)/u(s). Denoting this ratio by G(s), i.e.,
Part IB Paper 6: Information Engineering LINEAR SYSTEMS AND CONTROL Dr Glenn Vinnicombe HANDOUT 3. Stability and pole locations.
Part IB Paper 6: Information Engineering LINEAR SYSTEMS AND CONTROL Dr Glenn Vinnicombe HANDOUT 3 Stability and pole locations asymptotically stable marginally stable unstable Imag(s) repeated poles +
α = u v. In other words, Orthogonal Projection
Orthogonal Projection Given any nonzero vector v, it is possible to decompose an arbitrary vector u into a component that points in the direction of v and one that points in a direction orthogonal to v
Lab 7: Operational Amplifiers Part I
Lab 7: Operational Amplifiers Part I Objectives The objective of this lab is to study operational amplifier (op amp) and its applications. We will be simulating and building some basic op amp circuits,
MATH 140 Lab 4: Probability and the Standard Normal Distribution
MATH 140 Lab 4: Probability and the Standard Normal Distribution Problem 1. Flipping a Coin Problem In this problem, we want to simualte the process of flipping a fair coin 1000 times. Note that the outcomes
Nonlinear Systems and Control Lecture # 15 Positive Real Transfer Functions & Connection with Lyapunov Stability. p. 1/?
Nonlinear Systems and Control Lecture # 15 Positive Real Transfer Functions & Connection with Lyapunov Stability p. 1/? p. 2/? Definition: A p p proper rational transfer function matrix G(s) is positive
Lecture L3 - Vectors, Matrices and Coordinate Transformations
S. Widnall 16.07 Dynamics Fall 2009 Lecture notes based on J. Peraire Version 2.0 Lecture L3 - Vectors, Matrices and Coordinate Transformations By using vectors and defining appropriate operations between
8. Linear least-squares
8. Linear least-squares EE13 (Fall 211-12) definition examples and applications solution of a least-squares problem, normal equations 8-1 Definition overdetermined linear equations if b range(a), cannot
Question 2: How do you solve a matrix equation using the matrix inverse?
Question : How do you solve a matrix equation using the matrix inverse? In the previous question, we wrote systems of equations as a matrix equation AX B. In this format, the matrix A contains the coefficients
LS.6 Solution Matrices
LS.6 Solution Matrices In the literature, solutions to linear systems often are expressed using square matrices rather than vectors. You need to get used to the terminology. As before, we state the definitions
The Z transform (3) 1
The Z transform (3) 1 Today Analysis of stability and causality of LTI systems in the Z domain The inverse Z Transform Section 3.3 (read class notes first) Examples 3.9, 3.11 Properties of the Z Transform
ALGEBRA. sequence, term, nth term, consecutive, rule, relationship, generate, predict, continue increase, decrease finite, infinite
ALGEBRA Pupils should be taught to: Generate and describe sequences As outcomes, Year 7 pupils should, for example: Use, read and write, spelling correctly: sequence, term, nth term, consecutive, rule,
Similar matrices and Jordan form
Similar matrices and Jordan form We ve nearly covered the entire heart of linear algebra once we ve finished singular value decompositions we ll have seen all the most central topics. A T A is positive
EQUATIONS and INEQUALITIES
EQUATIONS and INEQUALITIES Linear Equations and Slope 1. Slope a. Calculate the slope of a line given two points b. Calculate the slope of a line parallel to a given line. c. Calculate the slope of a line
Key Concept. Density Curve
MAT 155 Statistical Analysis Dr. Claude Moore Cape Fear Community College Chapter 6 Normal Probability Distributions 6 1 Review and Preview 6 2 The Standard Normal Distribution 6 3 Applications of Normal
Chapter 1. Vector autoregressions. 1.1 VARs and the identi cation problem
Chapter Vector autoregressions We begin by taking a look at the data of macroeconomics. A way to summarize the dynamics of macroeconomic data is to make use of vector autoregressions. VAR models have become
Structural Axial, Shear and Bending Moments
Structural Axial, Shear and Bending Moments Positive Internal Forces Acting Recall from mechanics of materials that the internal forces P (generic axial), V (shear) and M (moment) represent resultants
1.4 Fast Fourier Transform (FFT) Algorithm
74 CHAPTER AALYSIS OF DISCRETE-TIME LIEAR TIME-IVARIAT SYSTEMS 4 Fast Fourier Transform (FFT Algorithm Fast Fourier Transform, or FFT, is any algorithm for computing the -point DFT with a computational
Chapter 4 State Machines
Chapter 4 State Machines 6.01 Spring 2011 April 25, 2011 117 Chapter 4 State Machines State machines are a method of modeling systems whose output depends on the entire history of their inputs, and not
M/M/1 and M/M/m Queueing Systems
M/M/ and M/M/m Queueing Systems M. Veeraraghavan; March 20, 2004. Preliminaries. Kendall s notation: G/G/n/k queue G: General - can be any distribution. First letter: Arrival process; M: memoryless - exponential
Some Lecture Notes and In-Class Examples for Pre-Calculus:
Some Lecture Notes and In-Class Examples for Pre-Calculus: Section.7 Definition of a Quadratic Inequality A quadratic inequality is any inequality that can be put in one of the forms ax + bx + c < 0 ax
Review of Fundamental Mathematics
Review of Fundamental Mathematics As explained in the Preface and in Chapter 1 of your textbook, managerial economics applies microeconomic theory to business decision making. The decision-making tools
Notes on Determinant
ENGG2012B Advanced Engineering Mathematics Notes on Determinant Lecturer: Kenneth Shum Lecture 9-18/02/2013 The determinant of a system of linear equations determines whether the solution is unique, without
Introduction. Appendix D Mathematical Induction D1
Appendix D Mathematical Induction D D Mathematical Induction Use mathematical induction to prove a formula. Find a sum of powers of integers. Find a formula for a finite sum. Use finite differences to
Similarity and Diagonalization. Similar Matrices
MATH022 Linear Algebra Brief lecture notes 48 Similarity and Diagonalization Similar Matrices Let A and B be n n matrices. We say that A is similar to B if there is an invertible n n matrix P such that
MAT 200, Midterm Exam Solution. a. (5 points) Compute the determinant of the matrix A =
MAT 200, Midterm Exam Solution. (0 points total) a. (5 points) Compute the determinant of the matrix 2 2 0 A = 0 3 0 3 0 Answer: det A = 3. The most efficient way is to develop the determinant along the
TTT4110 Information and Signal Theory Solution to exam
Norwegian University of Science and Technology Department of Electronics and Telecommunications TTT4 Information and Signal Theory Solution to exam Problem I (a The frequency response is found by taking
Lecture 5 Least-squares
EE263 Autumn 2007-08 Stephen Boyd Lecture 5 Least-squares least-squares (approximate) solution of overdetermined equations projection and orthogonality principle least-squares estimation BLUE property
Lecture 2. Marginal Functions, Average Functions, Elasticity, the Marginal Principle, and Constrained Optimization
Lecture 2. Marginal Functions, Average Functions, Elasticity, the Marginal Principle, and Constrained Optimization 2.1. Introduction Suppose that an economic relationship can be described by a real-valued
Kevin James. MTHSC 102 Section 1.5 Exponential Functions and Models
MTHSC 102 Section 1.5 Exponential Functions and Models Exponential Functions and Models Definition Algebraically An exponential function has an equation of the form f (x) = ab x. The constant a is called
Mathematics (Project Maths Phase 3)
2014. M329 Coimisiún na Scrúduithe Stáit State Examinations Commission Leaving Certificate Examination 2014 Mathematics (Project Maths Phase 3) Paper 1 Higher Level Friday 6 June Afternoon 2:00 4:30 300
MATRIX ALGEBRA AND SYSTEMS OF EQUATIONS. + + x 2. x n. a 11 a 12 a 1n b 1 a 21 a 22 a 2n b 2 a 31 a 32 a 3n b 3. a m1 a m2 a mn b m
MATRIX ALGEBRA AND SYSTEMS OF EQUATIONS 1. SYSTEMS OF EQUATIONS AND MATRICES 1.1. Representation of a linear system. The general system of m equations in n unknowns can be written a 11 x 1 + a 12 x 2 +
Answer Key for California State Standards: Algebra I
Algebra I: Symbolic reasoning and calculations with symbols are central in algebra. Through the study of algebra, a student develops an understanding of the symbolic language of mathematics and the sciences.
Chapter 6. Linear Transformation. 6.1 Intro. to Linear Transformation
Chapter 6 Linear Transformation 6 Intro to Linear Transformation Homework: Textbook, 6 Ex, 5, 9,, 5,, 7, 9,5, 55, 57, 6(a,b), 6; page 7- In this section, we discuss linear transformations 89 9 CHAPTER
MATH 304 Linear Algebra Lecture 9: Subspaces of vector spaces (continued). Span. Spanning set.
MATH 304 Linear Algebra Lecture 9: Subspaces of vector spaces (continued). Span. Spanning set. Vector space A vector space is a set V equipped with two operations, addition V V (x,y) x + y V and scalar
Solving Linear Programs in Excel
Notes for AGEC 622 Bruce McCarl Regents Professor of Agricultural Economics Texas A&M University Thanks to Michael Lau for his efforts to prepare the earlier copies of this. 1 http://ageco.tamu.edu/faculty/mccarl/622class/
SECTION 10-2 Mathematical Induction
73 0 Sequences and Series 6. Approximate e 0. using the first five terms of the series. Compare this approximation with your calculator evaluation of e 0.. 6. Approximate e 0.5 using the first five terms
T ( a i x i ) = a i T (x i ).
Chapter 2 Defn 1. (p. 65) Let V and W be vector spaces (over F ). We call a function T : V W a linear transformation form V to W if, for all x, y V and c F, we have (a) T (x + y) = T (x) + T (y) and (b)
San José State University Department of Electrical Engineering EE 112, Linear Systems, Spring 2010
San José State University Department of Electrical Engineering EE 112, Linear Systems, Spring 2010 Instructor: Robert H. Morelos-Zaragoza Office Location: ENGR 373 Telephone: (408) 924-3879 Email: [email protected]
MATRIX ALGEBRA AND SYSTEMS OF EQUATIONS
MATRIX ALGEBRA AND SYSTEMS OF EQUATIONS Systems of Equations and Matrices Representation of a linear system The general system of m equations in n unknowns can be written a x + a 2 x 2 + + a n x n b a
Lecture 1: Systems of Linear Equations
MTH Elementary Matrix Algebra Professor Chao Huang Department of Mathematics and Statistics Wright State University Lecture 1 Systems of Linear Equations ² Systems of two linear equations with two variables
Overview of Violations of the Basic Assumptions in the Classical Normal Linear Regression Model
Overview of Violations of the Basic Assumptions in the Classical Normal Linear Regression Model 1 September 004 A. Introduction and assumptions The classical normal linear regression model can be written
Worksheet 1. What You Need to Know About Motion Along the x-axis (Part 1)
Worksheet 1. What You Need to Know About Motion Along the x-axis (Part 1) In discussing motion, there are three closely related concepts that you need to keep straight. These are: If x(t) represents the
ECE 516: System Control Engineering
ECE 516: System Control Engineering This course focuses on the analysis and design of systems control. This course will introduce time-domain systems dynamic control fundamentals and their design issues
Design of Efficient Digital Interpolation Filters for Integer Upsampling. Daniel B. Turek
Design of Efficient Digital Interpolation Filters for Integer Upsampling by Daniel B. Turek Submitted to the Department of Electrical Engineering and Computer Science in partial fulfillment of the requirements
Matrix Algebra. Some Basic Matrix Laws. Before reading the text or the following notes glance at the following list of basic matrix algebra laws.
Matrix Algebra A. Doerr Before reading the text or the following notes glance at the following list of basic matrix algebra laws. Some Basic Matrix Laws Assume the orders of the matrices are such that
CHAPTER 6 Frequency Response, Bode Plots, and Resonance
ELECTRICAL CHAPTER 6 Frequency Response, Bode Plots, and Resonance 1. State the fundamental concepts of Fourier analysis. 2. Determine the output of a filter for a given input consisting of sinusoidal
Lab #4 - Linear Impulse and Momentum
Purpose: Lab #4 - Linear Impulse and Momentum The objective of this lab is to understand the linear and angular impulse/momentum relationship. Upon completion of this lab you will: Understand and know
AP Calculus AB 2006 Scoring Guidelines
AP Calculus AB 006 Scoring Guidelines The College Board: Connecting Students to College Success The College Board is a not-for-profit membership association whose mission is to connect students to college
Orthogonal Projections
Orthogonal Projections and Reflections (with exercises) by D. Klain Version.. Corrections and comments are welcome! Orthogonal Projections Let X,..., X k be a family of linearly independent (column) vectors
DATA ANALYSIS II. Matrix Algorithms
DATA ANALYSIS II Matrix Algorithms Similarity Matrix Given a dataset D = {x i }, i=1,..,n consisting of n points in R d, let A denote the n n symmetric similarity matrix between the points, given as where
Reliability Guarantees in Automata Based Scheduling for Embedded Control Software
1 Reliability Guarantees in Automata Based Scheduling for Embedded Control Software Santhosh Prabhu, Aritra Hazra, Pallab Dasgupta Department of CSE, IIT Kharagpur West Bengal, India - 721302. Email: {santhosh.prabhu,
2.161 Signal Processing: Continuous and Discrete Fall 2008
MT OpenCourseWare http://ocw.mit.edu.6 Signal Processing: Continuous and Discrete Fall 00 For information about citing these materials or our Terms of Use, visit: http://ocw.mit.edu/terms. MASSACHUSETTS
Sampling and Interpolation. Yao Wang Polytechnic University, Brooklyn, NY11201
Sampling and Interpolation Yao Wang Polytechnic University, Brooklyn, NY1121 http://eeweb.poly.edu/~yao Outline Basics of sampling and quantization A/D and D/A converters Sampling Nyquist sampling theorem
Em bedded DSP : I ntroduction to Digital Filters
Embedded DSP : Introduction to Digital Filters 1 Em bedded DSP : I ntroduction to Digital Filters Digital filters are a important part of DSP. In fact their extraordinary performance is one of the keys
Introduction to the Z-transform
ECE 3640 Lecture 2 Z Transforms Objective: Z-transforms are to difference equations what Laplace transforms are to differential equations. In this lecture we are introduced to Z-transforms, their inverses,
Solutions to Math 51 First Exam January 29, 2015
Solutions to Math 5 First Exam January 29, 25. ( points) (a) Complete the following sentence: A set of vectors {v,..., v k } is defined to be linearly dependent if (2 points) there exist c,... c k R, not
EECE 460 : Control System Design
EECE 460 : Control System Design PID Controller Design and Tuning Guy A. Dumont UBC EECE January 2012 Guy A. Dumont (UBC EECE) EECE 460 PID Tuning January 2012 1 / 37 Contents 1 Introduction 2 Control
MATH 60 NOTEBOOK CERTIFICATIONS
MATH 60 NOTEBOOK CERTIFICATIONS Chapter #1: Integers and Real Numbers 1.1a 1.1b 1.2 1.3 1.4 1.8 Chapter #2: Algebraic Expressions, Linear Equations, and Applications 2.1a 2.1b 2.1c 2.2 2.3a 2.3b 2.4 2.5
PYTHAGOREAN TRIPLES KEITH CONRAD
PYTHAGOREAN TRIPLES KEITH CONRAD 1. Introduction A Pythagorean triple is a triple of positive integers (a, b, c) where a + b = c. Examples include (3, 4, 5), (5, 1, 13), and (8, 15, 17). Below is an ancient
CONTROLLABILITY. Chapter 2. 2.1 Reachable Set and Controllability. Suppose we have a linear system described by the state equation
Chapter 2 CONTROLLABILITY 2 Reachable Set and Controllability Suppose we have a linear system described by the state equation ẋ Ax + Bu (2) x() x Consider the following problem For a given vector x in
MATH 4330/5330, Fourier Analysis Section 11, The Discrete Fourier Transform
MATH 433/533, Fourier Analysis Section 11, The Discrete Fourier Transform Now, instead of considering functions defined on a continuous domain, like the interval [, 1) or the whole real line R, we wish
Bond Price Arithmetic
1 Bond Price Arithmetic The purpose of this chapter is: To review the basics of the time value of money. This involves reviewing discounting guaranteed future cash flows at annual, semiannual and continuously
Binary Adders: Half Adders and Full Adders
Binary Adders: Half Adders and Full Adders In this set of slides, we present the two basic types of adders: 1. Half adders, and 2. Full adders. Each type of adder functions to add two binary bits. In order
