Wavenumber-Frequency Space. Material drawn from Sec. 2.5
|
|
- Evan Thompson
- 5 years ago
- Views:
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 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 informationDelay-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 informationWave 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 information6.003 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 information06/12/ 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 informationMicrophone-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 informationProblem 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 informationContinuous 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 informationECE 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 informationThe 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 informationSEISMIC 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 informationPractice 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 informationGEORGIA 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 informationEECE 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 informationChapter 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 informationLTI 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 informationPath 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 informationSignals 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 informationNov : 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 informationProblem 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 informationESS 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 informationPractice 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 information1 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 informationMAE143A 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 information27. 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 informationEECE 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 informationENSC327 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 informationGEORGIA 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 informationECE 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 informationMath 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 information2.161 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 informationELEG 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 informationLINEAR 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 information2.161 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
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 informationCS-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 informationAMS 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 informationPART 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 informationFourier 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 informationEE 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 informationReview 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 informationBasic 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 informationLecture 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 informationContinuous-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
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 informatione 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 information2. 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 informationsinc 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 informationHomework 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 informationFOURIER 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 informationEE 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 informationLecture 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 informatione 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 informationA.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 informationLecture 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 informationMATH 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 informationWave 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 informationL2 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 informationEA2.3 - 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 informationChapter 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 informationSolution 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 informationAn 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 informationAdvanced 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 informationECE 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 informationTherefore 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 informationENGIN 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 informationThe 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 informationCorrelation, 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 informationSignals & 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 information2 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 informationESS 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 information26. 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 informationThe 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 informationMIT 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
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 informationSignals 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 informationE2.5 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 information06EC44-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 informationECG782: 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 informationLecture 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 informationMath 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 information19. 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 informationChapter 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 informationCommunication 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 informationCh 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 informationIntroduction 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 informationFourier 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 information3.2 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 information2.161 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 informationUnit 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 informationLecture 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 informationG52IVG, 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 informationTHE 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 informationECS332: 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 informationFourier 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 informationRepresentation 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 informationSystems 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 informationTHE 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 informatione 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 informationIntroduction 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