EECE 301 Signals & Systems Prof. Mark Fowler

Similar documents
Signals and Systems Fourier Series Representation of Periodic Signals

Introduction to Medical Imaging. Signal Processing Basics. Strange Effects. Ever tried to reduce the size of an image and you got this?

Lab 12. Speed Control of a D.C. motor. Controller Design

4.5 COLLEGE ALGEBRA 11/5/2015. Property of Logarithms. Solution. If x > 0, y > 0, a > 0, and a 1, then. x = y if and only if log a x = log a y.

Introduction to Digital Signal Processing

3G Evolution. OFDM Transmission. Outline. Chapter: Subcarriers in Time Domain. Outline

Engineering 1620: High Frequency Effects in BJT Circuits an Introduction Especially for the Friday before Spring Break

Chapter 2 Fundamentals of OFDM

Defeating a Scarcity Mindset

CH 7. Synchronization Techniques for OFDM Systems

EECE 301 Signals & Systems Prof. Mark Fowler

1/24/2017. Electrical resistance

Department of Humanities & Religious Studies Assessment Plan (REV 6/16)

WPCA AMEREN ESP. SEMINAR Understanding ESP Controls. By John Knapik. 2004, General Electric Company

cos The points in an Argand diagram which represent the numbers (iii) Write down a polynomial equation of degree 5 which is satisfied by w.

ANALYSIS ON THE COVERAGE CHARACTERISTICS OF GLONASS CONSTELLATION

In this project you ll learn how to create a game in which you have to save the Earth from space monsters.

RETURN TO MAIN MENU ver-increasing computer calculation speed used for games such as Tomb Raider

The Trouton Rankine Experiment and the End of the FitzGerald Contraction

DETERMINATION OF ELECTRONIC DISTANCE MEASUREMENT ZERO ERROR USING KALMAN FILTER

More Fun with D/A Converters

TALLINN UNIVERSITY OF TECHNOLOGY. IRO0140 Advanced Space Time-Frequency Signal Processing. Individual Work

EECE 301 Signals & Systems Prof. Mark Fowler

CSE 554 Lecture 1: Binary Pictures

Pacing Guide for Kindergarten Version GLE Checks for Understanding Vocabulary Envision Textbook Materials

Digital Signal Processing, Fall 2009

Polyphase Modulation Using a FPGA for High-Speed Applications

Performance Analysis of BLDC Motor for Sinusoidal and Trapezoidal Back-Emf using MATLAB/SIMULINK Environment

Lecture 19: Common Emitter Amplifier with Emitter Degeneration.

3-Dimensions. 3-Dimensions. 3D Shapes. Recognise 3-D Shapes and know their properties. 2 Dimensional. 3 Dimensional. Exercise 1

GV60 VALORSTAT PLUS OPERATING INSTRUCTIONS. VALORSTAT PLUS GV60 Electronic Ignition Remote Control

Logic Design 2013/9/26. Outline. Implementation Technology. Transistor as a Switch. Transistor as a Switch. Transistor as a Switch

Assembly Instructions for Model: VMAA18

Final Exam Solutions June 7, 2004

Digital Signal Processing Fourier Analysis of Continuous-Time Signals with the Discrete Fourier Transform

j e c t s A m P r o a z i n g P h o t o A guide to running your own 10 week after-school photography club

Assembly Instructions for Model: VMDD26

Common Collector & Common Base Amplifier Circuits

EMD3 / UMD3N / IMD3A V CC I C(MAX.) R 1 R 2. 50V 100mA. 10k. 10k. 50V 100mA. 10k. 10k. Datasheet

RClamp2451ZA. Ultra Small RailClamp 1-Line, 24V ESD Protection

Red Room Poetry. Find out more at redroomcompany.org

EMD4 / UMD4N V CC I C(MAX.) R 1 R 2. 50V 100mA. 47kW. V CC -50V -100mA 10kW. Datasheet

EMA5 / UMA5N / FMA5A. V CC -50V -100mA 2.2kW 47kW I C(MAX.) R 1 R 2. Datasheet

HSMS-2823 RF mixer/detector diode

90 and 180 Phase Shifter Using an Arbitrary Phase-Difference Coupled-line Structure

Engagement Schedule. Schedule M-3 Tutorial. December 07 United States

DTA123E series V CC I C(MAX.) R 1 R 2. 50V 100mA 2.2k 2.2k. Datasheet. PNP -100mA -50V Digital Transistors (Bias Resistor Built-in Transistors)

Final Exam Solutions June 14, 2006

SGM Ω, 300MHz Bandwidth, Dual, SPDT Analog Switch

1.1 Transmission line basic concepts: Introduction to narrow-band matching networks

Real Time Speed Control of a DC Motor Based on its Integer and Non-Integer Models Using PWM Signal

Digital Signal Processing

DSP First, 2/e. y[n] a 1. y[n 1] a 2. y[n 2] b k. x[n k] This Lecture: Lecture 25 Second-Order IIR Filters: 3-Domains.

A simple automatic classifier of PSK and FSK signals using characteristic cyclic spectrum

SECTION ONE: SOCIAL ENTREpRENEURSHIP BEGINS WITH YOU

UMH8N / IMH8A V CEO I C R 1. 50V 100mA 10k. Datasheet. Outline. Inner circuit

A Pilot Aided Averaging Channel Estimator for DVB-T2

NMR Part IV, Apodization and Zero Filling

It is the speed and discrete nature of the FFT that allows us to analyze a signal's spectrum with MATLAB.

x(at) 1 x(t t d ) e jωt d X( jω ) x(t)e jω 0t X( j(ω ω 0 )) READING ASSIGNMENTS Table of Easy FT Properties LECTURE OBJECTIVES

SPX mA Low Drop Out Voltage Regulator with Shutdown FEATURES Output 3.3V, 5.0V, at 400mA Output Very Low Quiescent Current Low Dropout Voltage

Content Skills Assessments Lessons. Assessments 9/1/2012

READING ASSIGNMENTS LECTURE OBJECTIVES. DOMAINS: Time & Frequency. ELEG-212 Signal Processing and Communications. This Lecture:

ESX10-10x-DC24V-16A-E electronic circuit protector

Migration ATV11 - ATV12

FAN A, 1.2V Low Dropout Linear Regulator for VRM8.5. Features. Description. Applications. Typical Application.

Theory and Proposed Method for Determining Large Signal Return Loss or Hot S22 for Power Amplifiers Using Phase Information

US6H23 / IMH23 V CEO 20V V EBO 12V. 600mA R k. Datasheet. Outline Parameter Tr1 and Tr2 TUMT6 SMT6

4NPA. Low Frequency Interface Module for Intercom and Public Address Systems. Fig. 4NPA (L- No )

ECEN3250 Lab 8 Audio Power Amplifier

Square VLF Loop Antenna, 1.2 m Diagonal ~ Mechanical and Electrical Characteristics and Construction Details ~ Whitham D. Reeve

Pip Ahoy! Song Lyrics

ECE 429 / 529 Digital Signal Processing

16 th Coherent Laser Radar Conference (June 20, 2011, Long Beach CA, USA)

Online Publication Date: 15 th Jun, 2012 Publisher: Asian Economic and Social Society. Computer Simulation to Generate Gaussian Pulses for UWB Systems

Safety Technique. Multi-Function Safety System SAFEMASTER M Output Module With Output Contacts BG 5912

RECOMMENDATION ITU-R M.1828

Hardware Manual. STR4 & STR8 Step Motor Drives

Impact Analysis of Damping Resistors in Damped Type Double Tuned Filter on Network Harmonic Impedance

3A High Current, Low Dropout Voltage Regulator

A Quadrature Signals Tutorial: Complex, But Not Complicated. by Richard Lyons

ELT COMMUNICATION THEORY

Making carrier frequency offset an advantage for orthogonal frequency division multiplexing

Digital Filters IIR (& Their Corresponding Analog Filters) Week Date Lecture Title

DTD114GK V CEO I C R. 50V 500mA 10kW. Datasheet. NPN 500mA 50V Digital Transistors (Bias Resistor Built-in Transistors) Outline Parameter Value SMT3

Package: H: TO-252 P: TO-220 S: TO-263. Output Voltage : Blank = Adj 12 = 1.2V 15 = 1.5V 18 = 1.8V 25 = 2.5V 33 = 3.3V 50 = 5.0V 3.3V/3A.

Fuzzy Anti-Windup Schemes for PID Controllers

3A High Current, Low Dropout Voltage Regulator Adjustable, Fast Response Time

η = ; (3) QUANTITATIVE INTERPRETATION OF PRECIPITATION RADAR 7R.3 MEASUREMENTS AT VHF BAND Edwin F. Campos 1*, Frédéric Fabry 1, and Wayne Hocking 2

Prototype based languages

CS3291: Digital Signal Processing

SGM8521/2/4 150kHz, 4.7µA, Rail-to-Rail I/O CMOS Operational Amplifiers

Data Acquisition Systems. Signal DAQ System The Answer?

Determination of Antenna Q from the Reflection-Coefficient Data

Lecture 2 Review of Signals and Systems: Part 1. EE4900/EE6720 Digital Communications

2. Doodle-Offs: This is everything you ll need to kit out your 3Doodler workshop and facilitate some great. 2 x power strips and extension cords

ECE 2713 Homework 7 DUE: 05/1/2018, 11:59 PM

LABORATORY - FREQUENCY ANALYSIS OF DISCRETE-TIME SIGNALS

Making the Leap: Achieving Centimeter-Range Accuracy with UAVs. Francois Gervaix Product Manager, Surveying

Transient Voltage Suppressors / ESD Protectors

Transcription:

EECE 301 Signals & Systms Prof. Mark Fowlr ot St #25 D-T Signals: Rlation btwn DFT, DTFT, & CTFT Rading Assignmnt: Sctions 4.2.4 & 4.3 of Kamn and Hck 1/22

Cours Flow Diagram Th arrows hr show concptual flow btwn idas. ot th paralll structur btwn th pink blocks (C-T Frq. Analysis) and th blu blocks (D-T Frq. Analysis). w Signal Modls Ch. 1 Intro C-T Signal Modl Functions on Ral Lin Systm Proprtis LTI Causal Etc Ch. 3: CT Fourir Signal Modls Fourir Sris Priodic Signals Fourir Transform (CTFT) on-priodic Signals Ch. 2 Diff Eqs C-T Systm Modl Diffrntial Equations D-T Signal Modl Diffrnc Equations Zro-Stat Rspons Ch. 5: CT Fourir Systm Modls Frquncy Rspons Basd on Fourir Transform w Systm Modl Ch. 2 Convolution C-T Systm Modl Convolution Intgral Ch. 6 & 8: Laplac Modls for CT Signals & Systms Transfr Function w Systm Modl w Systm Modl D-T Signal Modl Functions on Intgrs w Signal Modl Powrful Analysis Tool Zro-Input Rspons Charactristic Eq. Ch. 4: DT Fourir Signal Modls DTFT (for Hand Analysis) DFT & FFT (for Computr Analysis) D-T Systm Modl Convolution Sum Ch. 5: DT Fourir Systm Modls Frq. Rspons for DT Basd on DTFT w Systm Modl Ch. 7: Z Trans. Modls for DT Signals & Systms Transfr Function w Systm Modl 2/22

W can us th DFT to implmnt numrical FT procssing This nabls us to numrically analyz a signal to find out what frquncis it contains!!! sampls ar FFT algorithm A CT signal coms in through a snsor & lctronics (.g., a microphon & amp) dumpd into a mmory array computs DFT valus ADC x (t) x[n] Th ADC crats sampls (takn at an appropriat F s ) Insid Computr x[0] x[1] x[2] x[-1] mmory array DFT Procssing (via FFT) H/W or S/W on procssor [0] [1] [2] [-1] mmory array DFT valus in mmory array (thy can b plottd or usd to do somthing nat ) 3/22

If w ar doing this DFT procssing to s what th original CT signal x(t) looks lik in th frquncy domain w want th DFT valus to b rprsntativ of th CTFT of x(t) Likwis If w ar doing this DFT procssing to do som nat procssing to xtract som information from x(t) or to modify it in som way w want th DFT valus to b rprsntativ of th CTFT of x(t) So w nd to undrstand what th DFT valus tll us about th CTFT of x(t) W nd to undrstand th rlations btwn CTFT, DTFT, and DFT 4/22

W ll mathmatically xplor th link btwn DTFT & DFT in two cass: 1. For x[n] of finit duration: 0 0 x[0] x[1] x[2]... [ 1] 0 0 non-zro trms (of cours, w could hav som of th intrior valus 0) For this cas w ll assum that th signal is zro outsid th rang that w hav capturd. So w hav all of th maningful signal data. This cas hardly vr happns but it s asy to analyz and provids a prspctiv for th 2 nd cas 2. For x[n] of infinit duration or at last of duration longr than what w can gt into our DFT Procssor insid our computr. So w don t hav all th maningful signal data. This is th practical cas. What ffct dos that hav? How much data do w nd for a givn goal? 5/22

DFT & DTFT: Finit Duration Cas If x[n] 0 for n < 0 and n thn th DTFT is: ( Ω) n x[ n] jωn 1 n 0 x[ n] jωn w can lav out trms that ar zro -π ow if w tak ths sampls and comput th DFT (using th FFT, prhaps) w gt: 1 j2πkn / [ k] x[ n] k 0,1,2,..., 1 n 0 2π Comparing ths w s that for th finit-duration signal cas: [ k] ( k ) (Ω) -π/2 [k] 0 1 2 3 4 5 6 7 π/2 π 2π k Ω DTFT & DFT : DFT points li xactly on th finit-duration signal s DTFT!!! 6/22

Summary of DFT & DTFT for a finit duration x[n] x[n] DTFT DFT (Ω) [ k] k 2π Points of DFT ar sampls of DTFT of x[n] Th numbr of sampls sts how closly spacd ths sampls ar on th DTFT sms to b a limitation. Zro-Padding Trick Aftr w collct our sampls, w tack on som additional zros at th nd to trick th DFT Procssing into thinking thr ar rally mor sampls. (Sinc ths ar zros tackd on thy don t chang th valus in th DFT sums) If w now hav a total of Z sampls (including th tackd on zros), thn th spacing btwn DFT points is 2π/ Z which is smallr than 2π/ 7/22

Ex. 4.11 DTFT & DFT of puls 1, n 0,1,2,...2q x[ n] 0, othrwis Rcall : 1, p q [ n] 0, n q,, 1, othrwis 0, 1,, q Thn x[ n] pq[ n q] ot: w ll nd th dlay proprty for DTFT From DTFT Tabl: p q [ n] P ( Ω) q sin[( q + 0.5) Ω] sin[ Ω / 2] From DTFT Proprty Tabl (Dlay Proprty): ( Ω) sin[( q + 0.5) Ω] sin[ Ω / 2] jqω Sinc x[n] is a finit-duration signal thn th DFT of th 2q+1 non-zro sampls is just sampls of th DTFT: [ k] k 2π [ k] sin[( q +.5)2πk / sin[ πk / ] ] jq2πk / 8/22

ot that if w don t zro pad, thn all but th k 0 DFT valus ar zro!!! That dosn t show what th DTFT looks lik! So w nd to us zro-padding. Hr ar two numrically computd xampls, both for th cas of q 5: For th cas of zropadding 11 zros onto th nd of th signal th DFT points still don t rally show what th DTFT looks lik! For th cas of zropadding 77 zros onto th nd of th signal OW th DFT points rally show what th DTFT looks lik! DFTs wr computd using matlab s fft command s cod on nxt slid 9/22

Comput th DTFT Equation drivd for th puls. Using ps adds a vry small numbr to avoid gtting Ω 0 and thn dividing by 0 omgaps+(-1:0.0001:1)*pi; q5; % usd to st puls lngth to 11 points sin((q+0.5)*omga)./sin(omga/2); subplot(2,1,1) plot(omga/pi,abs()); % plot magn of DTFT xlabl('\omga/\pi (ormalizd rad/sampl)') ylabl(' (\Omga) and [k] ') hold on xzros(1,22); % Initially fill x with 22 zros x(1:(2*q+1))1; % Thn fill first 11 pts with ons kfftshift(fft(x)); % fft computs th DFT and fftshift r-ordrs points % to btwn -pi and pi omga_k(-11:10)*2*pi/22; % comput DFT frquncis, xcpt mak thm % btwn -pi and pi stm(omga_k/pi,abs(k)); % plot DFT vs. normalizd frquncis hold off subplot(2,1,2) plot(omga/pi,abs()); xlabl('\omga/\pi (ormalizd rad/sampl)') ylabl(' (\Omga) and [k] ') hold on xzros(1,88); x(1:(2*q+1))1; kfftshift(fft(x)); omga_k(-44:43)*2*pi/88; stm(omga_k/pi,abs(k)); hold off Mak th zro-paddd signal Comput th DFT Comput th DFT point s frquncy valus and plot th DFT 10/22

Important Points for Finit-Duration Signal Cas DFT points li on th DTFT curv prfct viw of th DTFT But only if th DFT points ar spacd closly nough Zro-Padding dosn t chang th shap of th DFT It just givs a dnsr st of DFT points all of which li on th tru DTFT Zro-padding provids a bttr viw of this prfct viw of th DTFT 11/22

DFT & DTFT: Infinit Duration Cas As w said in a computr w cannot dal with an infinit numbr of signal sampls. So say thr is som signal that gos on forvr (or at last continus on for longr than w can or ar willing to grab sampls) x[n] n, -3, -2, -1, 0, 1, 2, 3, W only grab sampls: x[n], n 0,, 1 W v lost som information! W can dfin an imagind finit-duration signal: W can comput th DFT of th collctd sampls: x[ n], x [ n] 0, [ k] n 1 0 x [ n] n lswhr j2πnk / 0,1,2,..., k 1 0,1,..., 1 Q: How dos this DFT of th truncatd signal rlat to th tru DTFT of th full-duration x[n]? which is what w rally want to s!! 12/22

"Tru" DTFT : ( Ω) n x[ n] jωn What w want to s DTFT of truncatd signal : ( Ω) n 1 n 0 x x[ n] [ n] jωn jωn A distortd vrsion of what w want to s DFT of collctd signal data : [ k] 1 n 0 x[ n] j2πkn / What w can s DFT givs sampls of So DFT of collctd data givs sampls of DTFT of truncatd signal Tru DTFT DFT of collctd data dos not prfctly show DTFT of complt signal. Instad, th DFT of th data shows th DTFT of th truncatd signal So our goal is to undrstand what kinds of rrors ar in th truncatd DTFT thn w ll know what rrors ar in th computd DFT of th data ( Ω) 13/22

To s what th DFT dos show w nd to undrstand how (Ω) rlats to (Ω) First, w not that: x [ n] x[ n] p q [ n q] DTFT P ( Ω) q sin sin [ Ω / 2] [ Ω / 2] j( 1) Ω / 2 with 2q+1 From mult. in tim domain proprty in DTFT Proprty Tabl: ( ) ( Ω) P ( Ω) Ω q Convolution causs smaring of (Ω) So (Ω) which w can s via th DFT [k] is a smard vrsion of (Ω) Fact : Th mor data you collct, th lss smaring bcaus P q (Ω) bcoms mor lik δ(ω) 14/22

Suppos th infinit-duration signal s DTFT is: (Ω) DTFT of infinitduration signal 2π π π 2π Ω Thn it gts smard into somthing that might look lik this: (Ω) DTFT of truncatd signal 2π π π 2π Ω Thn th DFT computd from th data points is: [k] 2π π π 2π Ω Th DFT points ar shown aftr uppr points ar movd (.g., by matlab s fftshift 15/22

Exampl: Infinit-Duration Complx Sinusoid & DFT Suppos w hav th signal jωon x[ n] n..., 3, 2, 1,0,1,2,... and w want to comput th DFT of collctd sampls (n 0, 1, 2,, -1). This is an important xampl bcaus in practic w oftn hav signals that consists of a fw significant sinusoids among som othr signals (.g. radar and sonar). In practic w just gt th sampls and w comput th DFT but bfor w do that w nd to undrstand what th DFT of th sampls will show. So w first nd to thortically find th DTFT of th infinit-duration signal. From DTFT Tabl w hav: (Ω) ( Ω) δ ( Ω Ω0), π < Ω < π priodic lswhr 2π π Ω o π 2π Ω 16/22

From our prvious rsults w know that th DTFT of th collctd data is: ( Ω) sin ( Ω) sin [ Ω / 2] j( 1) Ω / 2 [ Ω / 2] P q (Ω) Just Dlta s in hr Us Sifting Proprty!! δ ( Ω Ω0), π < Ω < π ( Ω) priodic lswhr Just a shiftd vrsion of P q (Ω) ( Ω) ( Ω Ω0) sin 2 ( Ω Ω0) sin 2 priodic lswhr j( 1)( Ω Ω 0 ) / 2, π < Ω < π This is th DTFT on which our data-computd DFT points will li so looking at this DTFT shows us what w can xpct from our DFT procssing!!! 17/22

Tru DTFT of Infinit Duration Complx Sinusoid ( Ω) δ ( Ω Ω0), π < Ω < π priodic lswhr Ω Ω 0 DTFT of Finit umbr of Sampls of a Complx Sinusoid ( Ω Ω0) sin 2 ( Ω) ( Ω Ω0) sin 2 priodic lswhr j( 1)( Ω Ω 0 ) / 2, π < Ω < π Digital Frquncy Ω (rad/sampl) Ω 0 Th computd DFT would giv points on this curv th spacing of points is controlld through zro padding 18/22

So what ffct dos our choic of hav??? To answr that w can simply look at P q (Ω) for diffrnt valus of 2q+1 D(θ,41) P q (Ω) P D(θ,11) q (Ω) 15 10 5 0-5 -3-2 -1 0 1 2 3 θ/π 60 40 41 20 0 11-20 -3-2 -1 0 1 2 3 θ/π As grows looks mor lik a dlta!! So lss smaring of (Ω)!! 19/22

Important points for Infinit-Duration Signal Cas 1. DTFT of finit collctd data is a smard vrsion of th DTFT of th infinit-duration data 2. Th computd DFT points li on th smard DTFT curv not th tru DTFT a. This givs an imprfct viw of th tru DTFT! 3. Zro-padding givs dnsr st of DFT points a bttr viw of this imprfct viw of th dsird DTFT!!! 20/22

Connctions btwn th CTFT, DTFT, & DFT x(t) ADC x[n] Fs / 2 ( f ) CTFT Fs / 2 (Ω)Full Aliasing f DTFT x[0] x[1] x[2] x[-1] Insid Computr DFT procssing [0] [1] [2] [-1] π π Look hr to s aliasd viw of CTFT Ω (Ω) Truncatd Smaring DTFT [ k] Computd DFT π π Ω π π Ω 21/22

Errors in a Computd DFT CTFT DTFT DTFT DFT Aliasing Error control through F s choic (i.. through propr sampling) Smaring Error control through Grid Error control through choic window choic choic zro padding S DSP cours This is th only thing w can comput from data and it has all ths rrors in it!! Th thory covrd hr allows an nginr to undrstand how to control th amount of thos rrors!!! Zro padding trick Collct sampls dfins (Ω) Tack M zros on at th nd of th sampls Tak ( + M)pt. DFT givs points on (Ω) spacd by 2π/(+M) (rathr than 2π/) 22/22