EE313 Linear Systems and Signals Fall 2010 Initial conversion of content to PowerPoint by Dr. Wade C. Schwartzkopf Prof. Brian L. Evans Dept. of Electrical.

Slides:



Advertisements
Similar presentations
EE313 Linear Systems and Signals Fall 2010 Initial conversion of content to PowerPoint by Dr. Wade C. Schwartzkopf Prof. Brian L. Evans Dept. of Electrical.
Advertisements

Signal and System I Causality ROC for n < 0 causal All z -n terms, not include any z terms If and only if ROC is exterior of a circle and include.
ELEN 5346/4304 DSP and Filter Design Fall Lecture 7: Z-transform Instructor: Dr. Gleb V. Tcheslavski Contact:
AMI 4622 Digital Signal Processing
Z Transform (2) Hany Ferdinando Dept. of Electrical Eng. Petra Christian University.
EECS 20 Chapter 12 Part 11 Stability and Z Transforms Last time we Explored sampling and reconstruction of signals Saw examples of the phenomenon known.
Lecture 19: Discrete-Time Transfer Functions
DT systems and Difference Equations Monday March 22, 2010
EE-2027 SaS, L11 1/13 Lecture 11: Discrete Fourier Transform 4 Sampling Discrete-time systems (2 lectures): Sampling theorem, discrete Fourier transform.
EE-2027 SaS, L18 1/12 Lecture 18: Discrete-Time Transfer Functions 7 Transfer Function of a Discrete-Time Systems (2 lectures): Impulse sampler, Laplace.
LTI system stability Time domain analysis
Frequency Response of Discrete-time LTI Systems Prof. Siripong Potisuk.
Z-Transform Fourier Transform z-transform. Z-transform operator: The z-transform operator is seen to transform the sequence x[n] into the function X{z},
Continuous-Time Convolution EE 313 Linear Systems and Signals Fall 2005 Initial conversion of content to PowerPoint by Dr. Wade C. Schwartzkopf Prof. Brian.
Image (and Video) Coding and Processing Lecture 2: Basic Filtering Wade Trappe.
Difference Equations and Stability Linear Systems and Signals Lecture 10 Spring 2008.
Difference Equations Linear Systems and Signals Lecture 9 Spring 2008.
EE313 Linear Systems and Signals Fall 2010 Initial conversion of content to PowerPoint by Dr. Wade C. Schwartzkopf Prof. Brian L. Evans Dept. of Electrical.
Differential Equations EE 313 Linear Systems and Signals Fall 2010 Initial conversion of content to PowerPoint by Dr. Wade C. Schwartzkopf Prof. Brian.
EE313 Linear Systems and Signals Fall 2010 Initial conversion of content to PowerPoint by Dr. Wade C. Schwartzkopf Prof. Brian L. Evans Dept. of Electrical.
EC 2314 Digital Signal Processing By Dr. K. Udhayakumar.
Discrete-time Systems Prof. Siripong Potisuk. Input-output Description A DT system transforms DT inputs into DT outputs.
Analysis of Discrete Linear Time Invariant Systems
Lecture 9 FIR and IIR Filter design using Matlab
EE513 Audio Signals and Systems Digital Signal Processing (Systems) Kevin D. Donohue Electrical and Computer Engineering University of Kentucky.
ECE 352 Systems II Manish K. Gupta, PhD Office: Caldwell Lab ece. osu. ece. osu. edu Home Page:
EE313 Linear Systems and Signals Fall 2010 Initial conversion of content to PowerPoint by Dr. Wade C. Schwartzkopf Prof. Brian L. Evans Dept. of Electrical.
Properties of the z-Transform
The z-Transform Prof. Siripong Potisuk. LTI System description Previous basis function: unit sample or DT impulse  The input sequence is represented.
CE Digital Signal Processing Fall 1992 Z Transform
Prof. Brian L. Evans Dept. of Electrical and Computer Engineering The University of Texas at Austin EE445S Real-Time Digital Signal Processing Lab Fall.
Prof. Brian L. Evans Dept. of Electrical and Computer Engineering The University of Texas at Austin EE 313 Linear Systems and Signals Spring 2013 Continuous-Time.
Ch.7 The z-Transform and Discrete-Time Systems. 7.1 The z-Transform Definition: –Consider the DTFT: X(Ω) = Σ all n x[n]e -jΩn (7.1) –Now consider a real.
EE313 Linear Systems and Signals Fall 2010 Initial conversion of content to PowerPoint by Dr. Wade C. Schwartzkopf Prof. Brian L. Evans Dept. of Electrical.
Discrete-time Systems Prof. Siripong Potisuk. Input-output Description A DT system transforms DT inputs into DT outputs.
1 1 Chapter 3 The z-Transform 2 2  Consider a sequence x[n] = u[n]. Its Fourier transform does not converge.  Consider that, instead of e j , we use.
System Function of discrete-time systems
EE313 Linear Systems and Signals Fall 2010 Initial conversion of content to PowerPoint by Dr. Wade C. Schwartzkopf Prof. Brian L. Evans Dept. of Electrical.
EE313 Linear Systems and Signals Fall 2005 Initial conversion of content to PowerPoint by Dr. Wade C. Schwartzkopf Prof. Brian L. Evans Dept. of Electrical.
1 Z-Transform. CHAPTER 5 School of Electrical System Engineering, UniMAP School of Electrical System Engineering, UniMAP NORSHAFINASH BT SAUDIN
Infinite Impulse Response Filters
1 Lecture 1: February 20, 2007 Topic: 1. Discrete-Time Signals and Systems.
EE313 Linear Systems and Signals Spring 2013 Initial conversion of content to PowerPoint by Dr. Wade C. Schwartzkopf Prof. Brian L. Evans Dept. of Electrical.
EE313 Linear Systems and Signals Fall 2010 Initial conversion of content to PowerPoint by Dr. Wade C. Schwartzkopf Prof. Brian L. Evans Dept. of Electrical.
ES97H Biomedical Signal Processing
Homework 3.
1 Today's lecture −Cascade Systems −Frequency Response −Zeros of H(z) −Significance of zeros of H(z) −Poles of H(z) −Nulling Filters.
Digital Signal Processing
EE313 Linear Systems and Signals Fall 2010 Initial conversion of content to PowerPoint by Dr. Wade C. Schwartzkopf Prof. Brian L. Evans Dept. of Electrical.
Signal and Systems Prof. H. Sameti Chapter 10: Introduction to the z-Transform Properties of the ROC of the z-Transform Inverse z-Transform Examples Properties.
EE313 Linear Systems and Signals Fall 2010 Initial conversion of content to PowerPoint by Dr. Wade C. Schwartzkopf Prof. Brian L. Evans Dept. of Electrical.
Transform Analysis of LTI Systems Quote of the Day Any sufficiently advanced technology is indistinguishable from magic. Arthur C. Clarke Content and Figures.
Common Signals Prof. Brian L. Evans
Z Transform The z-transform of a digital signal x[n] is defined as:
Lecture 5 – 6 Z - Transform By Dileep Kumar.
Sampling and Reconstruction The impulse response of an continuous-time ideal low pass filter is the inverse continuous Fourier transform of its frequency.
EE421, Fall 1998 Michigan Technological University Timothy J. Schulz 08-Oct, 98EE421, Lecture 81 Lecture 8: Z-Transforms l ROC and Causality |z|
EE313 Linear Systems and Signals Fall 2010 Initial conversion of content to PowerPoint by Dr. Wade C. Schwartzkopf Prof. Brian L. Evans Dept. of Electrical.
Digital Signal Processing
Chapter 2 The z-transform and Fourier Transforms The Z Transform The Inverse of Z Transform The Prosperity of Z Transform System Function System Function.
Lecture 26 Outline: Z Transforms Announcements: Reading: “8: z-transforms” pp (no inverse or unilateral z transforms) HW 9 posted, due 6/3 midnight.
EE313 Linear Systems and Signals Fall 2010 Initial conversion of content to PowerPoint by Dr. Wade C. Schwartzkopf Prof. Brian L. Evans Dept. of Electrical.
Properties of the z-Transform
CHAPTER 5 Z-Transform. EKT 230.
Discrete-time Systems
Laplace and Z transforms
The Laplace Transform Prof. Brian L. Evans
Quick Review of LTI Systems
Chapter 5 DT System Analysis : Z Transform Basil Hamed
Discrete-Time Signal processing Chapter 3 the Z-transform
Presentation transcript:

EE313 Linear Systems and Signals Fall 2010 Initial conversion of content to PowerPoint by Dr. Wade C. Schwartzkopf Prof. Brian L. Evans Dept. of Electrical and Computer Engineering The University of Texas at Austin Difference Equations

Linear Difference Equations Discrete-time LTI systems can sometimes be characterized by difference equations y[n] = (1/2) y[n-1] + (1/8) y[n-2] + x[n] Taking z-transform of difference equation gives description of system in z-domain  x[n]x[n]y[n]y[n] Unit Delay 1/2 1/ y[n-1] y[n-2]

Advances and Delays Sometimes differential equations will be presented as unit advances rather than delays y[n+2] – 5 y[n+1] + 6 y[n] = 3 x[n+1] + 5 x[n] One can make a substitution that reindexes the equation so that it is in terms of delays Substitute n with n-2 to yield y[n] – 5 y[n-1] + 6 y[n-2] = 3 x[n-1] + 5 x[n-2] Before taking the z-transform, recognize that we work with time n  0 so u[n] is often implied y[n-1] → y[n-1] u[n]  y[n-1] u[n-1]

Example System described by a difference equation y[n] – 5 y[n-1] + 6 y[n-2] = 3 x[n-1] + 5 x[n-2] y[-1] = 11/6, y[-2] = 37/36 x[n] = 2 -n u[n]

Transfer Functions Previous example describes output in time domain for specific input and initial conditions It is not a general solution, which motivates us to look at system transfer functions. In order to derive the transfer function, one must separate “Zero state” response of the system to a given input with zero initial conditions “Zero input” response to initial conditions only

Transfer Functions Consider zero-state response LTI properties → all initial conditions are zero Causality → initial conditions are with respect to index 0 LTI + causality → y[-n] = 0 and x[-n] = 0 for all n > 0 Write general N th order difference equation

BIBO Stability Given H(z) and X(z), compute output Y(z) = H(z) X(z) Product is only valid for values of z in region of convergence for H(z) and region of convergence for X(z) Since H(z) is ratio of two polynomials, roots of denominator polynomial (called poles) control where H(z) may blow up H(z) can be represented as a series Series converges when poles lie inside (not on) unit circle Corresponds to magnitudes of all poles being less than 1 System is said to be bounded-input bounded-output stable H(z)H(z) Y(z)Y(z)X(z)X(z)

Relation between h[n] and H(z) Either can be used to describe an LTI system Having one is equivalent to having the other since they are a z-transform pair By definition, impulse response, h[n], is y[n] = h[n] when f[n] =  [n] Z{h[n]} = H(z) Z{  [n]}  H(z) = H(z) · 1 h[n]  H(z) Since discrete-time signals can be built up from unit impulses, knowing the impulse response completely characterizes the LTI system