EECE 301 Signals & Systems

Similar documents
EECE 301 Signals & Systems

CHAPTER 4 RADICAL EXPRESSIONS

d dt d d dt dt Also recall that by Taylor series, / 2 (enables use of sin instead of cos-see p.27 of A&F) dsin

Mu Sequences/Series Solutions National Convention 2014

Statistics: Unlocking the Power of Data Lock 5

Lecture 5: Interpolation. Polynomial interpolation Rational approximation

Polyphase Filters. Section 12.4 Porat

Transforms that are commonly used are separable

Functions of Random Variables

Assignment 5/MATH 247/Winter Due: Friday, February 19 in class (!) (answers will be posted right after class)

Descriptive Statistics

Lecture 07: Poles and Zeros

Lecture Notes Types of economic variables

Multiple Regression. More than 2 variables! Grade on Final. Multiple Regression 11/21/2012. Exam 2 Grades. Exam 2 Re-grades

Mean is only appropriate for interval or ratio scales, not ordinal or nominal.

means the first term, a2 means the term, etc. Infinite Sequences: follow the same pattern forever.

Chapter 9 Jordan Block Matrices

hp calculators HP 30S Statistics Averages and Standard Deviations Average and Standard Deviation Practice Finding Averages and Standard Deviations

Log1 Contest Round 2 Theta Complex Numbers. 4 points each. 5 points each

Signal,autocorrelation -0.6

Signals & Systems Chapter3

Discrete Mathematics and Probability Theory Fall 2016 Seshia and Walrand DIS 10b

STATISTICAL PROPERTIES OF LEAST SQUARES ESTIMATORS. x, where. = y - ˆ " 1

Econometric Methods. Review of Estimation

Lecture 8: Linear Regression

x y exp λ'. x exp λ 2. x exp 1.

The z-transform. LTI System description. Prof. Siripong Potisuk

X X X E[ ] E X E X. is the ()m n where the ( i,)th. j element is the mean of the ( i,)th., then

Simple Linear Regression

Summary of the lecture in Biostatistics

Laboratory I.10 It All Adds Up

5 Short Proofs of Simplified Stirling s Approximation

MA/CSSE 473 Day 27. Dynamic programming

Taylor s Series and Interpolation. Interpolation & Curve-fitting. CIS Interpolation. Basic Scenario. Taylor Series interpolates at a specific

PGE 310: Formulation and Solution in Geosystems Engineering. Dr. Balhoff. Interpolation

1 Onto functions and bijections Applications to Counting

ε. Therefore, the estimate

Lecture 2: Linear Least Squares Regression

Analysis of Lagrange Interpolation Formula

X ε ) = 0, or equivalently, lim

Beam Warming Second-Order Upwind Method

MA 524 Homework 6 Solutions

8.1 Hashing Algorithms

Chapter Business Statistics: A First Course Fifth Edition. Learning Objectives. Correlation vs. Regression. In this chapter, you learn:

2/20/2013. Topics. Power Flow Part 1 Text: Power Transmission. Power Transmission. Power Transmission. Power Transmission

Lecture 02: Bounding tail distributions of a random variable

Chapter 5 Properties of a Random Sample

2.28 The Wall Street Journal is probably referring to the average number of cubes used per glass measured for some population that they have chosen.

Lecture 3. Sampling, sampling distributions, and parameter estimation

Ordinary Least Squares Regression. Simple Regression. Algebra and Assumptions.

Chapter 4 Multiple Random Variables

Bounds on the expected entropy and KL-divergence of sampled multinomial distributions. Brandon C. Roy

The Selection Problem - Variable Size Decrease/Conquer (Practice with algorithm analysis)

Investigating Cellular Automata

ENGI 4421 Propagation of Error Page 8-01

arxiv:math/ v1 [math.gm] 8 Dec 2005

UNIT 2 SOLUTION OF ALGEBRAIC AND TRANSCENDENTAL EQUATIONS

EECE 301 Signals & Systems Prof. Mark Fowler

MATH 247/Winter Notes on the adjoint and on normal operators.

ENGI 3423 Simple Linear Regression Page 12-01

CLASS NOTES. for. PBAF 528: Quantitative Methods II SPRING Instructor: Jean Swanson. Daniel J. Evans School of Public Affairs

For combinatorial problems we might need to generate all permutations, combinations, or subsets of a set.

STA 108 Applied Linear Models: Regression Analysis Spring Solution for Homework #1

We have already referred to a certain reaction, which takes place at high temperature after rich combustion.

Unsupervised Learning and Other Neural Networks

12.2 Estimating Model parameters Assumptions: ox and y are related according to the simple linear regression model

Example: Multiple linear regression. Least squares regression. Repetition: Simple linear regression. Tron Anders Moger

Exercises for Square-Congruence Modulo n ver 11

Investigation of Partially Conditional RP Model with Response Error. Ed Stanek

Applied Fitting Theory VII. Building Virtual Particles

Random Variate Generation ENM 307 SIMULATION. Anadolu Üniversitesi, Endüstri Mühendisliği Bölümü. Yrd. Doç. Dr. Gürkan ÖZTÜRK.

Evaluating Polynomials

Spreadsheet Problem Solving

18.413: Error Correcting Codes Lab March 2, Lecture 8

CHAPTER VI Statistical Analysis of Experimental Data

Block-Based Compact Thermal Modeling of Semiconductor Integrated Circuits

ELEG3503 Introduction to Digital Signal Processing

Ideal multigrades with trigonometric coefficients

ECON 482 / WH Hong The Simple Regression Model 1. Definition of the Simple Regression Model

Algorithms Theory, Solution for Assignment 2

Third handout: On the Gini Index

Arithmetic Mean Suppose there is only a finite number N of items in the system of interest. Then the population arithmetic mean is

( q Modal Analysis. Eigenvectors = Mode Shapes? Eigenproblem (cont) = x x 2 u 2. u 1. x 1 (4.55) vector and M and K are matrices.

Johns Hopkins University Department of Biostatistics Math Review for Introductory Courses

The number of observed cases The number of parameters. ith case of the dichotomous dependent variable. the ith case of the jth parameter

Feature Selection: Part 2. 1 Greedy Algorithms (continued from the last lecture)

{ }{ ( )} (, ) = ( ) ( ) ( ) Chapter 14 Exercises in Sampling Theory. Exercise 1 (Simple random sampling): Solution:

MATH 371 Homework assignment 1 August 29, 2013

Uniform DFT Filter Banks 1/27

Numerical Simulations of the Complex Modied Korteweg-de Vries Equation. Thiab R. Taha. The University of Georgia. Abstract

Mechanics of Materials CIVL 3322 / MECH 3322

L5 Polynomial / Spline Curves

PTAS for Bin-Packing

Lecture 1 Review of Fundamental Statistical Concepts

QR Factorization and Singular Value Decomposition COS 323

4 Inner Product Spaces

Chapter 14 Logistic Regression Models

CHAPTER 3 POSTERIOR DISTRIBUTIONS

UNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS

Can we take the Mysticism Out of the Pearson Coefficient of Linear Correlation?

Transcription:

EECE 01 Sgals & Systems Prof. Mark Fowler Note Set #9 Computg D-T Covoluto Readg Assgmet: Secto. of Kame ad Heck 1/

Course Flow Dagram The arrows here show coceptual flow betwee deas. Note the parallel structure betwee the pk blocks (C-T Freq. Aalyss) ad the blue blocks (D-T Freq. Aalyss). New Sgal Models Ch. 1 Itro C-T Sgal Model Fuctos o Real Le System Propertes LTI Causal Etc Ch. : CT Fourer Sgal Models Fourer Seres Perodc Sgals Fourer Trasform (CTFT) No-Perodc Sgals Ch. Dff Eqs C-T System Model Dfferetal Equatos D-T Sgal Model Dfferece Equatos Zero-State Respose Ch. 5: CT Fourer System Models Frequecy Respose Based o Fourer Trasform New System Model Ch. Covoluto C-T System Model Covoluto Itegral Ch. 6 & 8: Laplace Models for CT Sgals & Systems Trasfer Fucto New System Model New System Model D-T Sgal Model Fuctos o Itegers New Sgal Model Powerful Aalyss Tool Zero-Iput Respose Characterstc Eq. Ch. 4: DT Fourer Sgal Models DTFT (for Had Aalyss) DFT & FFT (for Computer Aalyss) D-T System Model Covoluto Sum Ch. 5: DT Fourer System Models Freq. Respose for DT Based o DTFT New System Model Ch. 7: Z Tras. Models for DT Sgals & Systems Trasfer Fucto New System Model /

. Computg D-T covoluto -We kow about the mpulse respose h -We foud out that h teracts wth x through covoluto to gve the zero-state respose: y x h How do we work ths? Ths s eeded for uderstadg how: (1) To aalyze systems () To mplemet systems Two cases, depedg o form of x: 1. x s kow aalytcally. x s kow umercally or graphcally Do t forget The desg process cludes aalyss Aalytcal Covoluto (used for by-had aalyss): Pretty straghtforward coceptually: - put fuctos to covoluto summato - explot math propertes to evaluate/smplfy /

4/ Example: u a x u b h Recall ths form from 1 st -order dfferece equato example b u u a? y h x y u b u a ) ( a fucto of gets summed away < 0 0, 0 1, u 0 ) ( u b a Now use: > u 0, 1, b a b b a 0 0 ) ( Now use: You should be able to go here drectly

5/ + + b a b a b a b a y, 1 1 1, 1 Geometrc Sum b a b y 0 If a b you are addg ( + 1) 1 s ad that gves + 1 So ow we smplfy ths summato If a b, the a stadard math relatoshp gves: 1, 1 1 1 0 r r r r N N Kow Ths!!!

6/ Asde: Commutatvty Property of Covoluto A smple chage of varables shows that * * x h h x x h h x y So we ca use whch ever of these s easer

Graphcal Covoluto Steps Ca do covoluto ths way whe sgals are kow umercally or by equato - Covoluto volves the sum of a product of two sgals: xh - At each output dex, the product chages Commutatvty says we Step 1: Wrte both as fuctos of : x & h ca flp ether x or h ad get the same aswer Repeat for each Step : Flp h to get h- (The book calls ths fold ) Step : For each output dex value of terest, shft by to get h - (Note: postve gves rght shft!!!!) Step 4: Form product xh ad sum ts elemets to get the umber y 7/

Example of Graphcal Covoluto x 1 - -1 1 4 5 h Fd yx*h for all teger values of Soluto For ths problem I choose to flp x My persoal preferece s to flp the shorter sgal although I sometmes do t follow that rule oly through lots of practce ca you lear how to best choose whch oe to flp. 8/

Step 1: Wrte both as fuctos of : x & h h 1 x - -1 1 4 5 Step : Flp x to get x- h Commutatvty says we ca flp ether x or h ad get the same aswer Here I flpped x x- 1 9/

We wat a soluto for -, -1, 0, 1,, so must do Steps &4 for all. But let s frst do: Steps &4 for 0 ad the proceed from there. Step : For 0, shft by to get x- h For 0 case there s o shft! x0 - x- x- x0-1 Step 4: For 0, Form the product xh ad sum ts elemets to gve y hx0 - Sum over y0 6-10/

Steps &4 for all < 0 Step : For < 0, shft by to get x- h x- x-1-1 Negatve gves a left-shft Show here for -1 Step 4: For < 0, Form the product xh ad sum ts elemets to gve y hx-1-0 Sum over y 0 < 0-11/

So what we kow so far s that: y 0, 6, < 0 0 6 y y??? So ow we have to do Steps &4 for > 0 1/

Steps &4 for 1 Step : For 1, shft by to get x- h Postve gves a Rght-shft 1 x- x1 - shfted to the rght by oe Step 4: For 1, Form the product xh ad sum ts elemets to gve y x1 - h Sum over y1 6 + 6 1 1/

Steps &4 for Step : For, shft by to get x- h Postve gves a Rght-shft 1 x- x - shfted to the rght by two Step 4: For, Form the product xh ad sum ts elemets to gve y x1 - h 1 Sum over y + 6 + 6 15 14/

Steps &4 for Step : For, shft by to get x- h 1 x- x - Postve gves a Rght-shft shfted to the rght by three Step 4: For, Form the product xh ad sum ts elemets to gve y x1 - h 1 Sum over y + 6 + 6 15 15/

Steps &4 for 4 Step : For 4, shft by to get x- h 1 x- x - Postve gves a Rght-shft shfted to the.. rght. by four Step 4: For 4, Form the product xh ad sum ts elemets to gve y x1 - h 1 Sum over y4 + 6 + 6 15 16/

Steps &4 for 5 Step : For 5, shft by to get x- Postve gves a Rght-shft h 1 x- x - shfted to the rght by fve Step 4: For 5, Form the product xh ad sum ts elemets to gve y x1 - h 1 Sum over y5 + 6 9 17/

Steps &4 for 6 Step : For 6, shft by to get x- Postve gves a Rght-shft h 1 x- x - shfted to the rght by sx Step 4: For 6, Form the product xh ad sum ts elemets to gve y x1 - h 1 Sum over y6 18/

Steps &4 for all > 6 Step : For > 6, shft by to get x- h Postve gves a Rght-shft x- x - 1 shfted to the rght by seve Step 4: For > 6, Form the product xh ad sum ts elemets to gve y 1 x1 - h 0 Sum over y 0 > 6 19/

So ow we kow the values of y for all values of We just eed to put t all together as a fucto Here t s easest to just plot t you could also lst t as a table. y 15 1 9 6 Note that covolvg these kds of sgals gves a ramp-up at the begg ad a ramp-dow at the ed. Varous kds of trasets at the begg ad ed of a covoluto are commo. 0/

BIG PICTURE: So what we have just doe s foud the zero-state output of a system havg a mpulse respose gve by ths h whe the put s gve by ths x: x y x * h h x y 1 - -1 1 4 h - -1 1 4 5 6 15 1 9 6 Lk: Web Demos of Graphcal D-T Covoluto Ths s a good ste that provdes sght to how to vsualze D-T covoluto However, be sure you ca do graphcal covoluto by had wthout the ad of ths ste!! 1/

Implemetato Issues Cosder a D-T system wth mpulse respose h that has fte durato Could Buld a dgtal hardware system or a software program for D-T covoluto lke ths: x(t) Clock x1 x0 0 Shft Regsters or Memory Locatos 0 clock ADC h0 h1 h h-1 Storage Regsters Note that the ormal order of sampled sgals comg to the shft regsters follows the flpped verso of the sgal. y1, y0, etc. /

Covoluto Propertes These are thgs you ca explot to make t easer to solve problems 1.Commutatvty x h h x You ca choose whch sgal to flp. Assocatvty x ( v w ) ( x v ) w Ca chage order sometmes oe order s easer tha aother. Dstrbutvty x ( h + h ) x h + x h 1 1 may be easer to splt complcated system h to sum of smple oes OR.. we ca splt complcated put to sum of smple oes (othg more tha learty ) δ 4. Covoluto wth mpulses x q x q Ths oe s VERY easy to see usg the graphcal covoluto steps. TRY IT!! /

4/