Wavenumber-Frequency Space. Material drawn from Sec. 2.5

Size: px
Start display at page:

Download "Wavenumber-Frequency Space. Material drawn from Sec. 2.5"

Transcription

1 Where We Are in J&D Wavenumber-Frequency Space ECE 6279: Spatial Array Processing Spring 20 Lecture 3 Material drawn from Sec. 2.5 For now, we will skip Sec. 2.6 on random space-time fields (but we will come back to those ideas later Prof. Aaron D. Lanterman School of Electrical & Computer Engineering Georgia Institute of Technology AL: <lanterma@ece.gatech.edu> Different Definitions of FT Pairs S eng ( = s(texp( jtdt s(t = S eng (exp( jtd Mathematician s style: S math ( = s(texp(+ jtdt s(t = S math (exp( jtd Adapting an Engineer s FT Table S math ( = s(t = = s(texp(+ jtdt = s(texp[ j( t ]dt = S eng ( = F eng {S math }( t S math (exp( jtd S math (exp[ j( t ]d

2 Fourier Transforms of a Delta Function S( = δ(texp( jtdt = δ(texp( j0dt Mathematician s style: S( = δ(texp(+ jtdt = = δ(tdt = Inverse FT of a Delta Function Engineer s style (works for math style too: s(t = δ(exp( jtd = δ(exp( j0td = F δ( From previous slide: δ(t F Time Shift Property: Engineer s Style eng S timesh ( = exp( jtdt Time Shift Prop.: Mathematician s Style Mathematician s style: S math timesh ( = exp(+ jtdt substitute t = t t 0, t = t + t 0 substitute t = t t 0, t = t + t 0 eng S timesh ( = s( t exp[ j( t + t 0 ]dt = exp( jt 0 s( t exp( j t d t = exp( jt 0 S eng ( math ( = s( t exp[ + j( t + t 0 ]dt S timesh = exp(+ jt 0 s( t exp(+ j t d t = exp(+ jt 0 S math ( 2

3 Freq. Shift Property: Engineer s Style s freqsh (t = S eng ( 0 exp( jtd substitute = 0, = + 0 s freqsh (t = S eng ( exp[ j( + 0 t ]d = exp( j 0 t S eng ( exp( j td = exp( j 0 ts(t Freq. Shift Prop.: Mathematicians s Style Mathematicians s style: s freqsh (t = S math ( 0 exp( jtd substitute = 0, = + 0 s freqsh (t = S math ( exp[ j( + 0 t ]d = exp( j 0 t S math ( exp( j td = exp( j 0 ts(t Quick Proofs of Math-Style Shift Props. Time shift: Feng exp( jt 0 S eng ( Fmath exp(+ jt 0 S eng ( = exp(+ jt 0 S math ( Frequency shift: exp( j 0 ts(t Feng S eng ( + 0 exp( j 0 ts(t Fmath S eng ( + 0 = S math ( 0 Special Case: Deltas and Constants δ Feng exp( jt 0 S eng ( exp( j 0 ts(t Feng S eng ( 0 Mathematician s style: δ Fmath exp(+ jt 0 S math ( exp( j 0 ts(t Fmath S math ( 0 δ δ 3

4 Transforms of Delta Functions Engineers s style: δ(t t 0 Feng exp( jt 0 exp( j 0 t Feng δ( 0 Mathematicians style: δ(t t 0 Fmath exp(+ jt 0 exp( j 0 t Fmath δ( 0 Space-Time FT Pair A 4-D S-T Fourier transform S( k, = s( x,texp j t k x A 4-D S-T Inverse Fourier s( x,t = S( k transform,exp{ j( t k ( x }dk d 4 d d dk z { ( } dx dt dxdydz Engineer s style in time Mathematician s style in space Take Home Message Just like any -D function can be written as a weighted integral of complex exponentials exp jt any space-time signal - even nonpropagating ones! - can be written as a weighted integral of propagating plane waves ( exp j t k x ( Monochromatic Plane Wave What s the 4-D S-T FT of S( k, = { ( } s( x,t = exp j 0 t k 0 x { ( } s( x,texp j t k x dx dt = exp( j 0 texp( jtexp( jk 0 xexp( jk x dxdt = ( 4 δ( k k 0 δ( 0 δ( v where δ(v x δ(v y δ(v z A point in wavenumberfrequency space 4

5 General Plane Wave What s the 4-D S-T FT of ss( x,t = s(t α 0 x Axes for Showing S-T Fourier Support Notation borrowed from Chris Barnes Take Eng. ss( x FT in time domain, = S(exp( j first: α 0 x Then SS( k, Math. FT in spatial = S(( 3 δ( k domain: α 0 A line in wavenumber-frequency space Narrowband, Nonpropagating S-T Signal Wideband, Directional S-T Signal Not fixing a specific speed of propagation! = 0 k ζ 0 5

6 Wideband, Iso., Fixed-Speed S-T Signal Narrowband, Iso., Fixed-Speed S-T Signal Now fixing a specific speed of propagation Now fixing a specific speed of propagation = c k 0 = c k and fixing frequency Isotropic: propagating in all directions Isotropic: propagating in all directions Wideband, Dir., Fixed-Speed S-T Signal Narrowband, Dir., Fixed-Speed S-T Signal Now fixing a specific speed of propagation Now fixing a specific speed of propagation = c k 0 = c k and fixing direction and fixing frequency and fixing direction k ζ 0 6

7 Monochromatic Spherical Wave What s the 4-D S-T FT of s(r,t = exp{ j( 0 t k 0 r }/r With polar wavenumber coordinates: S(k, = 2 jk δ(k k 0 + 4π k 2 (k 0 2 δ( 0 (at least according to J&D, p. 44 exp( jt ss( x, Space-frequency Element space Post-Doppler exp( j k x Doug Williams Chart ss( x,t Space-Time exp( jt Element space Pre-Doppler exp( j k x exp( j exp( j k x k x Ss( k,t Wavenumber-time Beamspace Pre-Doppler SS( k exp( jt, exp( jt Wavenumber-frequency Beamspace Post-Doppler Filtering to Extract Information Filter data in wavenumber-frequency space: Y( k, = H( k,s( k, Ideal examples: Focus on one frequency H( k, = δ( 0 Focus in one direction H( k, = δ( k k 0 Spatiotemporal Convolution Multiplication in Fourier domain Y( k, = H( k,s( k, Corresponds to convolution in space-time domain: y( x,t = h( x ξ,t τs( ξ,τ d ξ dτ Hence ideal filters on previous slide aren t practical - have infinite extent in space-time 7

8 Spatiotemporal Filter Design Problem Challenge is to find a space-time impulse response h( x,t that gets close to the desired H( k, under some constraints: If we want real-time implementation, temporal support must be restricted to t>0 (causality Tricks from ECE4270 come into play More importantly, spatial support must be limited to where you can put sensors! New spin in ECE6279 8

Wavenumber-Frequency Space

Wavenumber-Frequency Space Wavenumber-Frequency Space ECE 6279: Spatial Array Processing Spring 2011 Lecture 3 Prof. Aaron D. Lanterman School of Electrical & Computer Engineering Georgia Institute of Technology AL: 404-385-2548

More information

Delay-and-Sum Beamforming for Plane Waves

Delay-and-Sum Beamforming for Plane Waves Delay-and-Sum Beamforming for Plane Waves ECE 6279: Spatial Array Processing Spring 2011 Lecture 6 Prof. Aaron D. Lanterman School of Electrical & Computer Engineering Georgia Institute of Technology AL:

More information

Wave Phenomena Physics 15c. Lecture 11 Dispersion

Wave Phenomena Physics 15c. Lecture 11 Dispersion Wave Phenomena Physics 15c Lecture 11 Dispersion What We Did Last Time Defined Fourier transform f (t) = F(ω)e iωt dω F(ω) = 1 2π f(t) and F(w) represent a function in time and frequency domains Analyzed

More information

6.003 Homework #10 Solutions

6.003 Homework #10 Solutions 6.3 Homework # Solutions Problems. DT Fourier Series Determine the Fourier Series coefficients for each of the following DT signals, which are periodic in N = 8. x [n] / n x [n] n x 3 [n] n x 4 [n] / n

More information

06/12/ rws/jMc- modif SuFY10 (MPF) - Textbook Section IX 1

06/12/ rws/jMc- modif SuFY10 (MPF) - Textbook Section IX 1 IV. Continuous-Time Signals & LTI Systems [p. 3] Analog signal definition [p. 4] Periodic signal [p. 5] One-sided signal [p. 6] Finite length signal [p. 7] Impulse function [p. 9] Sampling property [p.11]

More information

Microphone-Array Signal Processing

Microphone-Array Signal Processing Microphone-Array Signal Processing, c Apolinárioi & Campos p. 1/27 Microphone-Array Signal Processing José A. Apolinário Jr. and Marcello L. R. de Campos {apolin},{mcampos}@ieee.org IME Lab. Processamento

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

Continuous Time Signal Analysis: the Fourier Transform. Lathi Chapter 4

Continuous Time Signal Analysis: the Fourier Transform. Lathi Chapter 4 Continuous Time Signal Analysis: the Fourier Transform Lathi Chapter 4 Topics Aperiodic signal representation by the Fourier integral (CTFT) Continuous-time Fourier transform Transforms of some useful

More information

ECE Spring Prof. David R. Jackson ECE Dept. Notes 15

ECE Spring Prof. David R. Jackson ECE Dept. Notes 15 ECE 6341 Spring 216 Prof. David R. Jackson ECE Dept. Notes 15 1 Arbitrary Line Current TM : A (, ) Introduce Fourier Transform: I I + ( k ) jk = I e d x y 1 I = I ( k ) jk e dk 2π 2 Arbitrary Line Current

More information

The Continuous Time Fourier Transform

The Continuous Time Fourier Transform COMM 401: Signals & Systems Theory Lecture 8 The Continuous Time Fourier Transform Fourier Transform Continuous time CT signals Discrete time DT signals Aperiodic signals nonperiodic periodic signals Aperiodic

More information

SEISMIC WAVE PROPAGATION. Lecture 2: Fourier Analysis

SEISMIC WAVE PROPAGATION. Lecture 2: Fourier Analysis SEISMIC WAVE PROPAGATION Lecture 2: Fourier Analysis Fourier Series & Fourier Transforms Fourier Series Review of trigonometric identities Analysing the square wave Fourier Transform Transforms of some

More information

Practice Problems For Test 3

Practice Problems For Test 3 Practice Problems For Test 3 Power Series Preliminary Material. Find the interval of convergence of the following. Be sure to determine the convergence at the endpoints. (a) ( ) k (x ) k (x 3) k= k (b)

More information

GEORGIA INSTITUTE OF TECHNOLOGY SCHOOL of ELECTRICAL & COMPUTER ENGINEERING FINAL EXAM. COURSE: ECE 3084A (Prof. Michaels)

GEORGIA INSTITUTE OF TECHNOLOGY SCHOOL of ELECTRICAL & COMPUTER ENGINEERING FINAL EXAM. COURSE: ECE 3084A (Prof. Michaels) GEORGIA INSTITUTE OF TECHNOLOGY SCHOOL of ELECTRICAL & COMPUTER ENGINEERING FINAL EXAM DATE: 30-Apr-14 COURSE: ECE 3084A (Prof. Michaels) NAME: STUDENT #: LAST, FIRST Write your name on the front page

More information

EECE 3620: Linear Time-Invariant Systems: Chapter 2

EECE 3620: Linear Time-Invariant Systems: Chapter 2 EECE 3620: Linear Time-Invariant Systems: Chapter 2 Prof. K. Chandra ECE, UMASS Lowell September 7, 2016 1 Continuous Time Systems In the context of this course, a system can represent a simple or complex

More information

Chapter 6: The Laplace Transform 6.3 Step Functions and

Chapter 6: The Laplace Transform 6.3 Step Functions and Chapter 6: The Laplace Transform 6.3 Step Functions and Dirac δ 2 April 2018 Step Function Definition: Suppose c is a fixed real number. The unit step function u c is defined as follows: u c (t) = { 0

More information

LTI Systems (Continuous & Discrete) - Basics

LTI Systems (Continuous & Discrete) - Basics LTI Systems (Continuous & Discrete) - Basics 1. A system with an input x(t) and output y(t) is described by the relation: y(t) = t. x(t). This system is (a) linear and time-invariant (b) linear and time-varying

More information

Path Integral methods for solving stochastic problems. Carson C. Chow, NIH

Path Integral methods for solving stochastic problems. Carson C. Chow, NIH Path Integral methods for solving stochastic problems Carson C. Chow, NIH Why? Often in neuroscience we run into stochastic ODEs of the form dx dt = f(x)+g(x)η(t) η(t) =0 η(t)η(t ) = δ(t t ) Examples Integrate-and-fire

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

Nov : Lecture 18: The Fourier Transform and its Interpretations

Nov : Lecture 18: The Fourier Transform and its Interpretations 3 Nov. 04 2005: Lecture 8: The Fourier Transform and its Interpretations Reading: Kreyszig Sections: 0.5 (pp:547 49), 0.8 (pp:557 63), 0.9 (pp:564 68), 0.0 (pp:569 75) Fourier Transforms Expansion of a

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

ESS Finite Impulse Response Filters and the Z-transform

ESS Finite Impulse Response Filters and the Z-transform 9. Finite Impulse Response Filters and the Z-transform We are going to have two lectures on filters you can find much more material in Bob Crosson s notes. In the first lecture we will focus on some of

More information

Practice Problems For Test 3

Practice Problems For Test 3 Practice Problems For Test 3 Power Series Preliminary Material. Find the interval of convergence of the following. Be sure to determine the convergence at the endpoints. (a) ( ) k (x ) k (x 3) k= k (b)

More information

1 otherwise. Note that the area of the pulse is one. The Dirac delta function (a.k.a. the impulse) can be defined using the pulse as follows:

1 otherwise. Note that the area of the pulse is one. The Dirac delta function (a.k.a. the impulse) can be defined using the pulse as follows: The Dirac delta function There is a function called the pulse: { if t > Π(t) = 2 otherwise. Note that the area of the pulse is one. The Dirac delta function (a.k.a. the impulse) can be defined using the

More information

MAE143A Signals & Systems - Homework 5, Winter 2013 due by the end of class Tuesday February 12, 2013.

MAE143A Signals & Systems - Homework 5, Winter 2013 due by the end of class Tuesday February 12, 2013. MAE43A Signals & Systems - Homework 5, Winter 23 due by the end of class Tuesday February 2, 23. If left under my door, then straight to the recycling bin with it. This week s homework will be a refresher

More information

27. The Fourier Transform in optics, II

27. The Fourier Transform in optics, II 27. The Fourier Transform in optics, II Parseval s Theorem The Shift theorem Convolutions and the Convolution Theorem Autocorrelations and the Autocorrelation Theorem The Shah Function in optics The Fourier

More information

EECE 301 Signals & Systems Prof. Mark Fowler

EECE 301 Signals & Systems Prof. Mark Fowler EECE 301 Signals & Systems Prof. Mark Fowler Note Set #15 C-T Systems: CT Filters & Frequency Response 1/14 Ideal Filters Often we have a scenario where part of the input signal s spectrum comprises what

More information

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

ENSC327 Communications Systems 2: Fourier Representations. Jie Liang School of Engineering Science Simon Fraser University ENSC327 Communications Systems 2: Fourier Representations Jie Liang School of Engineering Science Simon Fraser University 1 Outline Chap 2.1 2.5: Signal Classifications Fourier Transform Dirac Delta Function

More information

GEORGIA INSTITUTE OF TECHNOLOGY SCHOOL of ELECTRICAL & COMPUTER ENGINEERING FINAL EXAM. COURSE: ECE 3084A (Prof. Michaels)

GEORGIA INSTITUTE OF TECHNOLOGY SCHOOL of ELECTRICAL & COMPUTER ENGINEERING FINAL EXAM. COURSE: ECE 3084A (Prof. Michaels) GEORGIA INSTITUTE OF TECHNOLOGY SCHOOL of ELECTRICAL & COMPUTER ENGINEERING FINAL EXAM DATE: 09-Dec-13 COURSE: ECE 3084A (Prof. Michaels) NAME: STUDENT #: LAST, FIRST Write your name on the front page

More information

ECE 301 Fall 2011 Division 1 Homework 5 Solutions

ECE 301 Fall 2011 Division 1 Homework 5 Solutions ECE 301 Fall 2011 ivision 1 Homework 5 Solutions Reading: Sections 2.4, 3.1, and 3.2 in the textbook. Problem 1. Suppose system S is initially at rest and satisfies the following input-output difference

More information

Math 3313: Differential Equations Laplace transforms

Math 3313: Differential Equations Laplace transforms Math 3313: Differential Equations Laplace transforms Thomas W. Carr Department of Mathematics Southern Methodist University Dallas, TX Outline Introduction Inverse Laplace transform Solving ODEs with Laplace

More information

2.161 Signal Processing: Continuous and Discrete Fall 2008

2.161 Signal Processing: Continuous and Discrete Fall 2008 MIT OpenCourseWare http://ocw.mit.edu.6 Signal Processing: Continuous and Discrete Fall 008 For information about citing these materials or our Terms of Use, visit: http://ocw.mit.edu/terms. MASSACHUSETTS

More information

ELEG 3124 SYSTEMS AND SIGNALS Ch. 5 Fourier Transform

ELEG 3124 SYSTEMS AND SIGNALS Ch. 5 Fourier Transform Department of Electrical Engineering University of Arkansas ELEG 3124 SYSTEMS AND SIGNALS Ch. 5 Fourier Transform Dr. Jingxian Wu wuj@uark.edu OUTLINE 2 Introduction Fourier Transform Properties of Fourier

More information

LINEAR RESPONSE THEORY

LINEAR RESPONSE THEORY MIT Department of Chemistry 5.74, Spring 5: Introductory Quantum Mechanics II Instructor: Professor Andrei Tokmakoff p. 8 LINEAR RESPONSE THEORY We have statistically described the time-dependent behavior

More information

2.161 Signal Processing: Continuous and Discrete Fall 2008

2.161 Signal Processing: Continuous and Discrete Fall 2008 MIT OpenCourseWare http://ocw.mit.edu 2.161 Signal Processing: Continuous and Discrete Fall 2008 For information about citing these materials or our Terms of Use, visit: http://ocw.mit.edu/terms. Massachusetts

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

CS-9645 Introduction to Computer Vision Techniques Winter 2018

CS-9645 Introduction to Computer Vision Techniques Winter 2018 Table of Contents Spectral Analysis...1 Special Functions... 1 Properties of Dirac-delta Functions...1 Derivatives of the Dirac-delta Function... 2 General Dirac-delta Functions...2 Harmonic Analysis...

More information

AMS 147 Computational Methods and Applications Lecture 13 Copyright by Hongyun Wang, UCSC

AMS 147 Computational Methods and Applications Lecture 13 Copyright by Hongyun Wang, UCSC Lecture 13 Copyright y Hongyun Wang, UCSC Recap: Fitting to exact data *) Data: ( x j, y j ), j = 1,,, N y j = f x j *) Polynomial fitting Gis phenomenon *) Cuic spline Convergence of cuic spline *) Application

More information

PART 1. Review of DSP. f (t)e iωt dt. F(ω) = f (t) = 1 2π. F(ω)e iωt dω. f (t) F (ω) The Fourier Transform. Fourier Transform.

PART 1. Review of DSP. f (t)e iωt dt. F(ω) = f (t) = 1 2π. F(ω)e iωt dω. f (t) F (ω) The Fourier Transform. Fourier Transform. PART 1 Review of DSP Mauricio Sacchi University of Alberta, Edmonton, AB, Canada The Fourier Transform F() = f (t) = 1 2π f (t)e it dt F()e it d Fourier Transform Inverse Transform f (t) F () Part 1 Review

More information

Fourier Series and Transform KEEE343 Communication Theory Lecture #7, March 24, Prof. Young-Chai Ko

Fourier Series and Transform KEEE343 Communication Theory Lecture #7, March 24, Prof. Young-Chai Ko Fourier Series and Transform KEEE343 Communication Theory Lecture #7, March 24, 20 Prof. Young-Chai Ko koyc@korea.ac.kr Summary Fourier transform Properties Fourier transform of special function Fourier

More information

EE 210. Signals and Systems Solutions of homework 2

EE 210. Signals and Systems Solutions of homework 2 EE 2. Signals and Systems Solutions of homework 2 Spring 2 Exercise Due Date Week of 22 nd Feb. Problems Q Compute and sketch the output y[n] of each discrete-time LTI system below with impulse response

More information

Review of Frequency Domain Fourier Series: Continuous periodic frequency components

Review of Frequency Domain Fourier Series: Continuous periodic frequency components Today we will review: Review of Frequency Domain Fourier series why we use it trig form & exponential form how to get coefficients for each form Eigenfunctions what they are how they relate to LTI systems

More information

Basic Theorems in Dynamic Elasticity

Basic Theorems in Dynamic Elasticity Basic Theorems in Dynamic Elasticity 1. Stress-Strain relationships 2. Equation of motion 3. Uniqueness and reciprocity theorems 4. Elastodynamic Green s function 5. Representation theorems Víctor M. CRUZ-ATIENZA

More information

Lecture 1 January 5, 2016

Lecture 1 January 5, 2016 MATH 262/CME 372: Applied Fourier Analysis and Winter 26 Elements of Modern Signal Processing Lecture January 5, 26 Prof. Emmanuel Candes Scribe: Carlos A. Sing-Long; Edited by E. Candes & E. Bates Outline

More information

Continuous-Time Fourier Transform

Continuous-Time Fourier Transform Signals and Systems Continuous-Time Fourier Transform Chang-Su Kim continuous time discrete time periodic (series) CTFS DTFS aperiodic (transform) CTFT DTFT Lowpass Filtering Blurring or Smoothing Original

More information

信號與系統 Signals and Systems

信號與系統 Signals and Systems Spring 2011 信號與系統 Signals and Systems Chapter SS-4 The Continuous-Time Fourier Transform Feng-Li Lian NTU-EE Feb11 Jun11 Figures and images used in these lecture notes are adopted from Signals & Systems

More information

e st f (t) dt = e st tf(t) dt = L {t f(t)} s

e st f (t) dt = e st tf(t) dt = L {t f(t)} s Additional operational properties How to find the Laplace transform of a function f (t) that is multiplied by a monomial t n, the transform of a special type of integral, and the transform of a periodic

More information

2. CONVOLUTION. Convolution sum. Response of d.t. LTI systems at a certain input signal

2. CONVOLUTION. Convolution sum. Response of d.t. LTI systems at a certain input signal 2. CONVOLUTION Convolution sum. Response of d.t. LTI systems at a certain input signal Any signal multiplied by the unit impulse = the unit impulse weighted by the value of the signal in 0: xn [ ] δ [

More information

sinc function T=1 sec T=2 sec angle(f(w)) angle(f(w))

sinc function T=1 sec T=2 sec angle(f(w)) angle(f(w)) T=1 sec sinc function 3 angle(f(w)) T=2 sec angle(f(w)) 1 A quick script to plot mag & phase in MATLAB w=0:0.2:50; Real exponential func b=5; Fourier transform (filter) F=1.0./(b+j*w); subplot(211), plot(w,

More information

Homework 4. May An LTI system has an input, x(t) and output y(t) related through the equation y(t) = t e (t t ) x(t 2)dt

Homework 4. May An LTI system has an input, x(t) and output y(t) related through the equation y(t) = t e (t t ) x(t 2)dt Homework 4 May 2017 1. An LTI system has an input, x(t) and output y(t) related through the equation y(t) = t e (t t ) x(t 2)dt Determine the impulse response of the system. Rewriting as y(t) = t e (t

More information

FOURIER TRANSFORM METHODS David Sandwell, January, 2013

FOURIER TRANSFORM METHODS David Sandwell, January, 2013 1 FOURIER TRANSFORM METHODS David Sandwell, January, 2013 1. Fourier Transforms Fourier analysis is a fundamental tool used in all areas of science and engineering. The fast fourier transform (FFT) algorithm

More information

EE Experiment 11 The Laplace Transform and Control System Characteristics

EE Experiment 11 The Laplace Transform and Control System Characteristics EE216:11 1 EE 216 - Experiment 11 The Laplace Transform and Control System Characteristics Objectives: To illustrate computer usage in determining inverse Laplace transforms. Also to determine useful signal

More information

Lecture 15. Theory of random processes Part III: Poisson random processes. Harrison H. Barrett University of Arizona

Lecture 15. Theory of random processes Part III: Poisson random processes. Harrison H. Barrett University of Arizona Lecture 15 Theory of random processes Part III: Poisson random processes Harrison H. Barrett University of Arizona 1 OUTLINE Poisson and independence Poisson and rarity; binomial selection Poisson point

More information

e iωt dt and explained why δ(ω) = 0 for ω 0 but δ(0) =. A crucial property of the delta function, however, is that

e iωt dt and explained why δ(ω) = 0 for ω 0 but δ(0) =. A crucial property of the delta function, however, is that Phys 531 Fourier Transforms In this handout, I will go through the derivations of some of the results I gave in class (Lecture 14, 1/11). I won t reintroduce the concepts though, so you ll want to refer

More information

A.1 THE SAMPLED TIME DOMAIN AND THE Z TRANSFORM. 0 δ(t)dt = 1, (A.1) δ(t)dt =

A.1 THE SAMPLED TIME DOMAIN AND THE Z TRANSFORM. 0 δ(t)dt = 1, (A.1) δ(t)dt = APPENDIX A THE Z TRANSFORM One of the most useful techniques in engineering or scientific analysis is transforming a problem from the time domain to the frequency domain ( 3). Using a Fourier or Laplace

More information

Lecture 8 ELE 301: Signals and Systems

Lecture 8 ELE 301: Signals and Systems Lecture 8 ELE 30: Signals and Systems Prof. Paul Cuff Princeton University Fall 20-2 Cuff (Lecture 7) ELE 30: Signals and Systems Fall 20-2 / 37 Properties of the Fourier Transform Properties of the Fourier

More information

MATH 312 Section 7.1: Definition of a Laplace Transform

MATH 312 Section 7.1: Definition of a Laplace Transform MATH 312 Section 7.1: Definition of a Laplace Transform Prof. Jonathan Duncan Walla Walla University Spring Quarter, 2008 Outline 1 The Laplace Transform 2 The Theory of Laplace Transforms 3 Conclusions

More information

Wave Phenomena Physics 15c

Wave Phenomena Physics 15c Wave Phenomena Physics 5c Lecture Fourier Analysis (H&L Sections 3. 4) (Georgi Chapter ) What We Did Last ime Studied reflection of mechanical waves Similar to reflection of electromagnetic waves Mechanical

More information

L2 gains and system approximation quality 1

L2 gains and system approximation quality 1 Massachusetts Institute of Technology Department of Electrical Engineering and Computer Science 6.242, Fall 24: MODEL REDUCTION L2 gains and system approximation quality 1 This lecture discusses the utility

More information

EA2.3 - Electronics 2 1

EA2.3 - Electronics 2 1 In the previous lecture, I talked about the idea of complex frequency s, where s = σ + jω. Using such concept of complex frequency allows us to analyse signals and systems with better generality. In this

More information

Chapter 2: Linear systems & sinusoids OVE EDFORS DEPT. OF EIT, LUND UNIVERSITY

Chapter 2: Linear systems & sinusoids OVE EDFORS DEPT. OF EIT, LUND UNIVERSITY Chapter 2: Linear systems & sinusoids OVE EDFORS DEPT. OF EIT, LUND UNIVERSITY Learning outcomes After this lecture, the student should understand what a linear system is, including linearity conditions,

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

An Internal Stability Example

An Internal Stability Example An Internal Stability Example Roy Smith 26 April 2015 To illustrate the concept of internal stability we will look at an example where there are several pole-zero cancellations between the controller and

More information

Advanced Analog Building Blocks. Prof. Dr. Peter Fischer, Dr. Wei Shen, Dr. Albert Comerma, Dr. Johannes Schemmel, etc

Advanced Analog Building Blocks. Prof. Dr. Peter Fischer, Dr. Wei Shen, Dr. Albert Comerma, Dr. Johannes Schemmel, etc Advanced Analog Building Blocks Prof. Dr. Peter Fischer, Dr. Wei Shen, Dr. Albert Comerma, Dr. Johannes Schemmel, etc 1 Topics 1. S domain and Laplace Transform Zeros and Poles 2. Basic and Advanced current

More information

ECE 350 Signals and Systems Spring 2011 Final Exam - Solutions. Three 8 ½ x 11 sheets of notes, and a calculator are allowed during the exam.

ECE 350 Signals and Systems Spring 2011 Final Exam - Solutions. Three 8 ½ x 11 sheets of notes, and a calculator are allowed during the exam. ECE 35 Spring - Final Exam 9 May ECE 35 Signals and Systems Spring Final Exam - Solutions Three 8 ½ x sheets of notes, and a calculator are allowed during the exam Write all answers neatly and show your

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

ENGIN 211, Engineering Math. Laplace Transforms

ENGIN 211, Engineering Math. Laplace Transforms ENGIN 211, Engineering Math Laplace Transforms 1 Why Laplace Transform? Laplace transform converts a function in the time domain to its frequency domain. It is a powerful, systematic method in solving

More information

The integrating factor method (Sect. 1.1)

The integrating factor method (Sect. 1.1) The integrating factor method (Sect. 1.1) Overview of differential equations. Linear Ordinary Differential Equations. The integrating factor method. Constant coefficients. The Initial Value Problem. Overview

More information

Correlation, discrete Fourier transforms and the power spectral density

Correlation, discrete Fourier transforms and the power spectral density Correlation, discrete Fourier transforms and the power spectral density visuals to accompany lectures, notes and m-files by Tak Igusa tigusa@jhu.edu Department of Civil Engineering Johns Hopkins University

More information

Signals & Systems. Lecture 5 Continuous-Time Fourier Transform. Alp Ertürk

Signals & Systems. Lecture 5 Continuous-Time Fourier Transform. Alp Ertürk Signals & Systems Lecture 5 Continuous-Time Fourier Transform Alp Ertürk alp.erturk@kocaeli.edu.tr Fourier Series Representation of Continuous-Time Periodic Signals Synthesis equation: x t = a k e jkω

More information

2 Classification of Continuous-Time Systems

2 Classification of Continuous-Time Systems Continuous-Time Signals and Systems 1 Preliminaries Notation for a continuous-time signal: x(t) Notation: If x is the input to a system T and y the corresponding output, then we use one of the following

More information

ESS Dirac Comb and Flavors of Fourier Transforms

ESS Dirac Comb and Flavors of Fourier Transforms 6. Dirac Comb and Flavors of Fourier ransforms Consider a periodic function that comprises pulses of amplitude A and duration τ spaced a time apart. We can define it over one period as y(t) = A, τ / 2

More information

26. The Fourier Transform in optics

26. The Fourier Transform in optics 26. The Fourier Transform in optics What is the Fourier Transform? Anharmonic waves The spectrum of a light wave Fourier transform of an exponential The Dirac delta function The Fourier transform of e

More information

The Laplace transform

The Laplace transform The Laplace transform Samy Tindel Purdue University Differential equations - MA 266 Taken from Elementary differential equations by Boyce and DiPrima Samy T. Laplace transform Differential equations 1

More information

MIT 2.71/2.710 Optics 10/31/05 wk9-a-1. The spatial frequency domain

MIT 2.71/2.710 Optics 10/31/05 wk9-a-1. The spatial frequency domain 10/31/05 wk9-a-1 The spatial frequency domain Recall: plane wave propagation x path delay increases linearly with x λ z=0 θ E 0 x exp i2π sinθ + λ z i2π cosθ λ z plane of observation 10/31/05 wk9-a-2 Spatial

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

Signals and Systems Spring 2004 Lecture #9

Signals and Systems Spring 2004 Lecture #9 Signals and Systems Spring 2004 Lecture #9 (3/4/04). The convolution Property of the CTFT 2. Frequency Response and LTI Systems Revisited 3. Multiplication Property and Parseval s Relation 4. The DT Fourier

More information

E2.5 Signals & Linear Systems. Tutorial Sheet 1 Introduction to Signals & Systems (Lectures 1 & 2)

E2.5 Signals & Linear Systems. Tutorial Sheet 1 Introduction to Signals & Systems (Lectures 1 & 2) E.5 Signals & Linear Systems Tutorial Sheet 1 Introduction to Signals & Systems (Lectures 1 & ) 1. Sketch each of the following continuous-time signals, specify if the signal is periodic/non-periodic,

More information

06EC44-Signals and System Chapter Fourier Representation for four Signal Classes

06EC44-Signals and System Chapter Fourier Representation for four Signal Classes Chapter 5.1 Fourier Representation for four Signal Classes 5.1.1Mathematical Development of Fourier Transform If the period is stretched without limit, the periodic signal no longer remains periodic but

More information

ECG782: Multidimensional Digital Signal Processing

ECG782: Multidimensional Digital Signal Processing Professor Brendan Morris, SEB 3216, brendan.morris@unlv.edu ECG782: Multidimensional Digital Signal Processing Spring 2014 TTh 14:30-15:45 CBC C313 Lecture 05 Image Processing Basics 13/02/04 http://www.ee.unlv.edu/~b1morris/ecg782/

More information

Lecture 8 - IIR Filters (II)

Lecture 8 - IIR Filters (II) Lecture 8 - IIR Filters (II) James Barnes (James.Barnes@colostate.edu) Spring 2009 Colorado State University Dept of Electrical and Computer Engineering ECE423 1 / 27 Lecture 8 Outline Introduction Digital

More information

Math 211. Lecture #6. Linear Equations. September 9, 2002

Math 211. Lecture #6. Linear Equations. September 9, 2002 1 Math 211 Lecture #6 Linear Equations September 9, 2002 2 Air Resistance 2 Air Resistance Acts in the direction opposite to the velocity. 2 Air Resistance Acts in the direction opposite to the velocity.

More information

19. The Fourier Transform in optics

19. The Fourier Transform in optics 19. The Fourier Transform in optics What is the Fourier Transform? Anharmonic waves The spectrum of a light wave Fourier transform of an exponential The Dirac delta function The Fourier transform of e

More information

Chapter 1 Fundamental Concepts

Chapter 1 Fundamental Concepts Chapter 1 Fundamental Concepts 1 Signals A signal is a pattern of variation of a physical quantity, often as a function of time (but also space, distance, position, etc). These quantities are usually the

More information

Communication Signals (Haykin Sec. 2.4 and Ziemer Sec Sec. 2.4) KECE321 Communication Systems I

Communication Signals (Haykin Sec. 2.4 and Ziemer Sec Sec. 2.4) KECE321 Communication Systems I Communication Signals (Haykin Sec..4 and iemer Sec...4-Sec..4) KECE3 Communication Systems I Lecture #3, March, 0 Prof. Young-Chai Ko 년 3 월 일일요일 Review Signal classification Phasor signal and spectra Representation

More information

Ch 4: The Continuous-Time Fourier Transform

Ch 4: The Continuous-Time Fourier Transform Ch 4: The Continuous-Time Fourier Transform Fourier Transform of x(t) Inverse Fourier Transform jt X ( j) x ( t ) e dt jt x ( t ) X ( j) e d 2 Ghulam Muhammad, King Saud University Continuous-time aperiodic

More information

Introduction to Seismology Spring 2008

Introduction to Seismology Spring 2008 MIT OpenCourseWare http://ocw.mit.edu 12.510 Introduction to Seismology Spring 2008 For information about citing these materials or our Terms of Use, visit: http://ocw.mit.edu/terms. 12.510 Introduction

More information

Fourier Transform for Continuous Functions

Fourier Transform for Continuous Functions Fourier Transform for Continuous Functions Central goal: representing a signal by a set of orthogonal bases that are corresponding to frequencies or spectrum. Fourier series allows to find the spectrum

More information

3.2 Complex Sinusoids and Frequency Response of LTI Systems

3.2 Complex Sinusoids and Frequency Response of LTI Systems 3. Introduction. A signal can be represented as a weighted superposition of complex sinusoids. x(t) or x[n]. LTI system: LTI System Output = A weighted superposition of the system response to each complex

More information

2.161 Signal Processing: Continuous and Discrete Fall 2008

2.161 Signal Processing: Continuous and Discrete Fall 2008 MIT OpenCourseWare http://ocw.mit.edu 2.6 Signal Processing: Continuous and Discrete Fall 2008 For information about citing these materials or our Terms of Use, visit: http://ocw.mit.edu/terms. MASSACHUSETTS

More information

Unit 2: Modeling in the Frequency Domain Part 2: The Laplace Transform. The Laplace Transform. The need for Laplace

Unit 2: Modeling in the Frequency Domain Part 2: The Laplace Transform. The Laplace Transform. The need for Laplace Unit : Modeling in the Frequency Domain Part : Engineering 81: Control Systems I Faculty of Engineering & Applied Science Memorial University of Newfoundland January 1, 010 1 Pair Table Unit, Part : Unit,

More information

Lecture 9. Systems of Two First Order Linear ODEs

Lecture 9. Systems of Two First Order Linear ODEs Math 245 - Mathematics of Physics and Engineering I Lecture 9. Systems of Two First Order Linear ODEs January 30, 2012 Konstantin Zuev (USC) Math 245, Lecture 9 January 30, 2012 1 / 15 Agenda General Form

More information

G52IVG, School of Computer Science, University of Nottingham

G52IVG, School of Computer Science, University of Nottingham Image Transforms Fourier Transform Basic idea 1 Image Transforms Fourier transform theory Let f(x) be a continuous function of a real variable x. The Fourier transform of f(x) is F ( u) f ( x)exp[ j2πux]

More information

THE UNIVERSITY OF WESTERN ONTARIO. Applied Mathematics 375a Instructor: Matt Davison. Final Examination December 14, :00 12:00 a.m.

THE UNIVERSITY OF WESTERN ONTARIO. Applied Mathematics 375a Instructor: Matt Davison. Final Examination December 14, :00 12:00 a.m. THE UNIVERSITY OF WESTERN ONTARIO London Ontario Applied Mathematics 375a Instructor: Matt Davison Final Examination December 4, 22 9: 2: a.m. 3 HOURS Name: Stu. #: Notes: ) There are 8 question worth

More information

ECS332: Midterm Examination (Set I) Seat

ECS332: Midterm Examination (Set I) Seat Sirindhorn International Institute of Technology Thammasat University at Rangsit School of Information, Computer and Communication Technology ECS33: Midterm Examination (Set I) COURSE : ECS33 (Principles

More information

Fourier series: Additional notes

Fourier series: Additional notes Fourier series: Additional notes Linking Fourier series representations for signals Rectangular waveform Require FS expansion of signal y(t) below: 1 y(t) 1 4 4 8 12 t (seconds) Period T = 8, so ω = 2π/T

More information

Representation of 1D Function

Representation of 1D Function Bioengineering 280A Principles of Biomedical Imaging Fall Quarter 2005 Linear Systems Lecture 2 Representation of 1D Function From the sifting property, we can write a 1D function as g(x) = g(ξ)δ(x ξ)dξ.

More information

Systems Analysis and Control

Systems Analysis and Control Systems Analysis and Control Matthew M. Peet Arizona State University Lecture 5: Calculating the Laplace Transform of a Signal Introduction In this Lecture, you will learn: Laplace Transform of Simple

More information

THE WAVE EQUATION (5.1)

THE WAVE EQUATION (5.1) THE WAVE EQUATION 5.1. Solution to the wave equation in Cartesian coordinates Recall the Helmholtz equation for a scalar field U in rectangular coordinates U U r, ( r, ) r, 0, (5.1) Where is the wavenumber,

More information

e iωt dt and explained why δ(ω) = 0 for ω 0 but δ(0) =. A crucial property of the delta function, however, is that

e iωt dt and explained why δ(ω) = 0 for ω 0 but δ(0) =. A crucial property of the delta function, however, is that Phys 53 Fourier Transforms In this handout, I will go through the derivations of some of the results I gave in class (Lecture 4, /). I won t reintroduce the concepts though, so if you haven t seen the

More information

Introduction to Signals and Systems Lecture #4 - Input-output Representation of LTI Systems Guillaume Drion Academic year

Introduction to Signals and Systems Lecture #4 - Input-output Representation of LTI Systems Guillaume Drion Academic year Introduction to Signals and Systems Lecture #4 - Input-output Representation of LTI Systems Guillaume Drion Academic year 2017-2018 1 Outline Systems modeling: input/output approach of LTI systems. Convolution

More information