Spectral Analysis. Joseph Fourier The two representations of a signal are connected via the Fourier transform. Z x(t)exp( j2πft)dt

Size: px
Start display at page:

Download "Spectral Analysis. Joseph Fourier The two representations of a signal are connected via the Fourier transform. Z x(t)exp( j2πft)dt"

Transcription

1 Specral Analysis Asignalx may be represened as a funcion of ime as x() or as a funcion of frequency X(f). This is due o relaionships developed by a French mahemaician, physicis, and Egypologis, Joseph Fourier(768-83). Boh he Fourier ransform and he closely associaed Fourier series are named in his honor. Even he elegraph hadn been invened in his lifeime and were he alive oday he would be asonished a he number of algorihms, sofware, and elecronic es insrumens ha bear his name. The fac ha he lived o accomplish he foundaion of specral analysis is miraculous since he was he las of eigheen children and escaped he guilloine several imes during he French Revoluion. Joseph Fourier The wo represenaions of a signal are conneced via he Fourier ransform X(f) =z{x()} = Z x()exp( j2πf)d Many of he signals of ineres in elecrical engineering are periodic funcions of ime. A periodic funcion is one for which x() =x( ± nt )

2 where n is any ineger and T is he smalles inerval of ime for which his relaionship is rue. The inerval T is called he period of he periodic funcion. If he periodic funcion saisfies consrains known as he Dirichle condiions (which are saisfied by any funcion produced by naure) i may be expanded in a Fourier series X x() = c n exp(jnω p ) n= where ω p =2πf p and f p =/T is he frequency in Herz of he periodic funcion. This is known as he complex Fourier series represenaion of a periodic funcion. The expansion coefficien c n are complex consans which can be deermined from x() as c n = Z α+t x()exp( jnω p )d T α where α is any real number. The erms in he Fourier series for which n is an even ineger are known as he even harmonics and he erms for which n is an odd ineger are known as odd harmonics. The erm for which n = ± are known as he fundamenal. Alernaive represenaion of he Fourier series is he real rigonomeric series where a n = 2 T Z α+t α x() =a o /2+ x()cos(nω p )d X [a n cos(nω p )+b n sin(nω p )] n= b n = 2 T Z α+t α x()sin(nω p )d These are no differen series; jus wo ways of expressing he same resul. The expansion coefficiens are relaed by c n = a n jb n 2 a n =2Re(c n ) b n = 2Im(c n ) The Fourier ransform of a periodic funcion is hen given by Z n= X(f) = z{x()} = X Z c n exp(jnω p )exp( j2πf)d = x()exp( j2πf)d = X n= c n δ(f nf p ) which is a line specra. The funcion δ is he Dirac dela which makes he specra zero everywhere exceps a frequencies which are inegral muliples of f p. The lines have ampliudes or weighs of c n. If a plo is made of he magniude of he specra for only posiive frequencies i would consiss of lines a f = nf p 2

3 and he heigh of each line would be 2 c n If he specra is o be ploed in rms each line would be 2 c n. A opic angenial o Fourier or Specral analysis is Toal Harmonic Disorion (THD) which measures how much a signal differs from a perfec sine wave. I is defined as (in percen) as v ux THD = c n 2 Sine Wave n=2 c A sine wave wih ampliude A and frequency f p =/T is given by x() =A sin(ω p ) x () Sine Wave is paricularly simple since exp(jθ) exp( jθ) sin θ = 2j so 2j n = c n = 2j n = n 6= ± and he specra is given by 3

4 2 dd k f k Specra of Sine Wave. where he frequencies and ampliudes have been normalized o uniy for simpliciy. So he Fourier series represenaion of a perfec sine wave is a perfec sine wave. Which makes he THD =which means ha here is no harmonic disorion or, anoher way of puing i, nohing looks like a sine wave more han a sine wave. Square Wave A symmeric square wave wih a dc level of zero is one which is +A half he ime and A he oher half. The choice of he ime origin is arbirary by a common one is A T/2 << x() = +A <<T/2 x( ± nt ) elsewhere 4

5 x () 2 3 Symmeric Square Wave. where A =and T =in he figure. The complex Fourier expansion coefficiens are c n = 4 π n n odd n even.5 2 ddk.5 The normalized specra is Specra of Symmeric Square Wave c n c = n n odd n even As a comparison of how well he Fourier series represens a square wave a plo can be made of he square wave and he firs five harmonics x() =a o /2+ fk 5X [a n cos(nω p )+b n sin(nω p )] n= 5

6 2 y () x () Square Wave and Fourier Approximaion using Firs 5 Terms. The ringing ha occurs where he square wave is swiching levels is known as he Gibbs phenomenon. Using he firs 9 componens he THD for he square wave is % which simply means a square wave doesn look very much like asinewave. Triangular Wave A symmeric riangular wave consiss of alernaing sraigh lines wih slopes of equal magniudes and a dc level of zero. 4A T T/4 4A x() = T +2A T/4 3T/4 4A T 4A 3T/4 T x( ± nt ) where A is he ampliude and T he period of he riangular wave. 6

7 x () 2 3 Symmeric Triangular Wave. The complex Fourier expansion coefficiens are nπ 4sin 2 c n = ja π 2 n 2 which are zero for n even and roll off as /n 2 for n odd. The specra for he riangular wave is dd k f k Specra for Symmeric Triangular Wave. A plo of he riangular wave and he firs 3 componens shows hey are almos indisinguishable 7

8 y () x () 2 3 Triangular Wave and Approximaion by Firs 3 Componens. The THD is only 2.48% which means ha a riangular wave is reasonable closeoasinewave. Ramp A ramp or sawooh wave is one for which 2A T T 2 < T 2 x() = x( ± nt ) n any ineger x () The expansion coefficiens are 2 3 Ramp Wave. c n = ja cos(nπ) nπ 8

9 The specra is given by.6 2 dd k f k Specra of Ramp. Using he firs 3 componens he approximaion and he ramp are y () x () 2 3 Ramp and Approximaion. The THD using he firs 9 componens is %. Recangular Pulse Train A recangular pulse rain is similar o a square wave in ha i swiches beween wo levels bu he duy cycle is no 5%. The duy cycle is he percenage of he ime he waveform is in he high sae. The pulse rain is A τ 2 τ x() = 2 << T x( ± nt ) n any ineger so he duy cycle is d = τ/t. 9

10 .5 p ().5.5 Pulse Train wih Duy Cycle.5. The Fourier expansion coefficiens are wih specra c n = Ad sin(πnd) πnd dd k f k Specra of Pulse Train which is, of course, a line specra bu he envelope of he specra has a sin(x)/x

11 behavior. The approximaion of he pulse rain as he firs 2 erms of he Fourier series is.5 y () x () Approximaion of pulse rain as firs 2 Terms of Fourier Series. for which he THD is 39 which means his really doesn look like a sine wave. If he duy cycle d =.5 his becomes a symmeric square wave. RF Pulse Train A rf pulse rain is a recangular pulse rain muliplied o a sinusoidal wih a frequency much higher han ha of he rain. Mahemaically i is given by A cos(ω c ) τ 2 x() = x( ± nt ) n is an ineger The duraion of he pulse is τ. I is assumed ha f c is an inegral muliple of /T. The number of cycles in he pulse N = τf c which is assumed o be an ineger. This is he sor of signal used in radar.

12 .5 x ( i ) i RF Pulse Train. The Fourier expansion coefficiens are given by c n = T Z T 2 T 2 x()exp(j2πf p n)d = T Z τ 2 τ 2 A cos(2πf c )exp(j2πf p n)d = Z τ 2 A cos(2πf c )cos(2πf p n)d = T τ 2 Z τ 2 A cos(2πf c )cos(2πf p n)d = Aτ T τ 2T 2 sin π(fc nf p ) π(f c nf p ) + sin π(f c + nf p ) π(f c + nf p ) which shows ha he specra of he rf pulse rain is jus ha of he recangular pulse rain shifed up o f = f c anddownof = f c, The specra is 2

13 db k f k RF Pulse Train Specra. The disance f c o he firs null is τ/2 where τ = N/f c where N is he number of cycles in he rf pulse. So he specra of all of hese signals is a line specra which is a direc consequence of heir periodiciy. However, he value of he expansion coefficiens is a funcion of he shape. The envelope of he specra has he ypical sin(u)/u shape. 3

14 Specra of RF Pulse Cenered abou High Frequency Carrier. The specra has nulls cenered abou he carrier a frequencies f = f c /τ f 2 = f c +/τ so he difference beween he firs wo nulls abou he cener of he sin(u)/u is given by f =2/τ =2f c /N where N is he number of cycles in he rf pulse. The specra of a signal is imporan for a number of reasons. Mos imporanly i deermines he bandwidh ha would have o be used o pass or ransmi he signal wihou disorion. I is fundamenal in signal processing and elecommunicaions. 4

Chapter 4 The Fourier Series and Fourier Transform

Chapter 4 The Fourier Series and Fourier Transform Represenaion of Signals in Terms of Frequency Componens Chaper 4 The Fourier Series and Fourier Transform Consider he CT signal defined by x () = Acos( ω + θ ), = The frequencies `presen in he signal are

More information

23.2. Representing Periodic Functions by Fourier Series. Introduction. Prerequisites. Learning Outcomes

23.2. Representing Periodic Functions by Fourier Series. Introduction. Prerequisites. Learning Outcomes Represening Periodic Funcions by Fourier Series 3. Inroducion In his Secion we show how a periodic funcion can be expressed as a series of sines and cosines. We begin by obaining some sandard inegrals

More information

Chapter 2 : Fourier Series. Chapter 3 : Fourier Series

Chapter 2 : Fourier Series. Chapter 3 : Fourier Series Chaper 2 : Fourier Series.0 Inroducion Fourier Series : represenaion of periodic signals as weighed sums of harmonically relaed frequencies. If a signal x() is periodic signal, hen x() can be represened

More information

CHAPTER 2 Signals And Spectra

CHAPTER 2 Signals And Spectra CHAPER Signals And Specra Properies of Signals and Noise In communicaion sysems he received waveform is usually caegorized ino he desired par conaining he informaion, and he undesired par. he desired par

More information

( ) ( ) if t = t. It must satisfy the identity. So, bulkiness of the unit impulse (hyper)function is equal to 1. The defining characteristic is

( ) ( ) if t = t. It must satisfy the identity. So, bulkiness of the unit impulse (hyper)function is equal to 1. The defining characteristic is UNIT IMPULSE RESPONSE, UNIT STEP RESPONSE, STABILITY. Uni impulse funcion (Dirac dela funcion, dela funcion) rigorously defined is no sricly a funcion, bu disribuion (or measure), precise reamen requires

More information

6.003 Homework #9 Solutions

6.003 Homework #9 Solutions 6.00 Homework #9 Soluions Problems. Fourier varieies a. Deermine he Fourier series coefficiens of he following signal, which is periodic in 0. x () 0 0 a 0 5 a k sin πk 5 sin πk 5 πk for k 0 a k 0 πk j

More information

ADDITIONAL PROBLEMS (a) Find the Fourier transform of the half-cosine pulse shown in Fig. 2.40(a). Additional Problems 91

ADDITIONAL PROBLEMS (a) Find the Fourier transform of the half-cosine pulse shown in Fig. 2.40(a). Additional Problems 91 ddiional Problems 9 n inverse relaionship exiss beween he ime-domain and freuency-domain descripions of a signal. Whenever an operaion is performed on he waveform of a signal in he ime domain, a corresponding

More information

Chapter One Fourier Series and Fourier Transform

Chapter One Fourier Series and Fourier Transform Chaper One I. Fourier Series Represenaion of Periodic Signals -Trigonomeric Fourier Series: The rigonomeric Fourier series represenaion of a periodic signal x() x( + T0 ) wih fundamenal period T0 is given

More information

6.003 Homework #9 Solutions

6.003 Homework #9 Solutions 6.003 Homework #9 Soluions Problems. Fourier varieies a. Deermine he Fourier series coefficiens of he following signal, which is periodic in 0. x () 0 3 0 a 0 5 a k a k 0 πk j3 e 0 e j πk 0 jπk πk e 0

More information

6.003: Signal Processing

6.003: Signal Processing 6.003: Signal Processing Coninuous-Time Fourier Transform Definiion Examples Properies Relaion o Fourier Series Sepember 5, 08 Quiz Thursday, Ocober 4, from 3pm o 5pm. No lecure on Ocober 4. The exam is

More information

EECE 301 Signals & Systems Prof. Mark Fowler

EECE 301 Signals & Systems Prof. Mark Fowler EECE 3 Signals & Sysems Prof. Mark Fowler Noe Se #2 Wha are Coninuous-Time Signals??? Reading Assignmen: Secion. of Kamen and Heck /22 Course Flow Diagram The arrows here show concepual flow beween ideas.

More information

SINUSOIDAL WAVEFORMS

SINUSOIDAL WAVEFORMS SINUSOIDAL WAVEFORMS The sinusoidal waveform is he only waveform whose shape is no affeced by he response characerisics of R, L, and C elemens. Enzo Paerno CIRCUIT ELEMENTS R [ Ω ] Resisance: Ω: Ohms Georg

More information

6.003 Homework #13 Solutions

6.003 Homework #13 Solutions 6.003 Homework #3 Soluions Problems. Transformaion Consider he following ransformaion from x() o y(): x() w () w () w 3 () + y() p() cos() where p() = δ( k). Deermine an expression for y() when x() = sin(/)/().

More information

System Processes input signal (excitation) and produces output signal (response)

System Processes input signal (excitation) and produces output signal (response) Signal A funcion of ime Sysem Processes inpu signal (exciaion) and produces oupu signal (response) Exciaion Inpu Sysem Oupu Response 1. Types of signals 2. Going from analog o digial world 3. An example

More information

Outline Chapter 2: Signals and Systems

Outline Chapter 2: Signals and Systems Ouline Chaper 2: Signals and Sysems Signals Basics abou Signal Descripion Fourier Transform Harmonic Decomposiion of Periodic Waveforms (Fourier Analysis) Definiion and Properies of Fourier Transform Imporan

More information

EELE Lecture 3,4 EE445 - Outcomes. Physically Realizable Waveforms. EELE445 Montana State University. In this lecture you:

EELE Lecture 3,4 EE445 - Outcomes. Physically Realizable Waveforms. EELE445 Montana State University. In this lecture you: EELE445 Monana Sae Universiy Lecure 3,4 EE445 - Oucomes EELE445-4 Lecure 3,4 Poer, Energy, ime average operaor secion. In his lecure you: be able o use he ime average operaor [] for finie ime duraion signals

More information

EECE 301 Signals & Systems Prof. Mark Fowler

EECE 301 Signals & Systems Prof. Mark Fowler EECE 3 Signals & Sysems Prof. Mark Fowler Noe Se # Wha are Coninuous-Time Signals??? /6 Coninuous-Time Signal Coninuous Time (C-T) Signal: A C-T signal is defined on he coninuum of ime values. Tha is:

More information

Chapter 1 Fundamental Concepts

Chapter 1 Fundamental Concepts Chaper 1 Fundamenal Conceps 1 Signals A signal is a paern of variaion of a physical quaniy, ofen as a funcion of ime (bu also space, disance, posiion, ec). These quaniies are usually he independen variables

More information

III-A. Fourier Series Expansion

III-A. Fourier Series Expansion Summer 28 Signals & Sysems S.F. Hsieh III-A. Fourier Series Expansion Inroducion. Divide and conquer Signals can be decomposed as linear combinaions of: (a) shifed impulses: (sifing propery) Why? x() x()δ(

More information

A complex discrete (or digital) signal x(n) is defined in a

A complex discrete (or digital) signal x(n) is defined in a Chaper Complex Signals A number of signal processing applicaions make use of complex signals. Some examples include he characerizaion of he Fourier ransform, blood velociy esimaions, and modulaion of signals

More information

Guest Lectures for Dr. MacFarlane s EE3350 Part Deux

Guest Lectures for Dr. MacFarlane s EE3350 Part Deux Gues Lecures for Dr. MacFarlane s EE3350 Par Deux Michael Plane Mon., 08-30-2010 Wrie name in corner. Poin ou his is a review, so I will go faser. Remind hem o go lisen o online lecure abou geing an A

More information

Representing a Signal. Continuous-Time Fourier Methods. Linearity and Superposition. Real and Complex Sinusoids. Jean Baptiste Joseph Fourier

Representing a Signal. Continuous-Time Fourier Methods. Linearity and Superposition. Real and Complex Sinusoids. Jean Baptiste Joseph Fourier Represening a Signal Coninuous-ime ourier Mehods he convoluion mehod for finding he response of a sysem o an exciaion aes advanage of he lineariy and imeinvariance of he sysem and represens he exciaion

More information

Notes 04 largely plagiarized by %khc

Notes 04 largely plagiarized by %khc Noes 04 largely plagiarized by %khc Convoluion Recap Some ricks: x() () =x() x() (, 0 )=x(, 0 ) R ț x() u() = x( )d x() () =ẋ() This hen ells us ha an inegraor has impulse response h() =u(), and ha a differeniaor

More information

Lecture #6: Continuous-Time Signals

Lecture #6: Continuous-Time Signals EEL5: Discree-Time Signals and Sysems Lecure #6: Coninuous-Time Signals Lecure #6: Coninuous-Time Signals. Inroducion In his lecure, we discussed he ollowing opics:. Mahemaical represenaion and ransormaions

More information

Lecture 2: Optics / C2: Quantum Information and Laser Science

Lecture 2: Optics / C2: Quantum Information and Laser Science Lecure : Opics / C: Quanum Informaion and Laser Science Ocober 9, 8 1 Fourier analysis This branch of analysis is exremely useful in dealing wih linear sysems (e.g. Maxwell s equaions for he mos par),

More information

EE 435. Lecture 31. Absolute and Relative Accuracy DAC Design. The String DAC

EE 435. Lecture 31. Absolute and Relative Accuracy DAC Design. The String DAC EE 435 Lecure 3 Absolue and Relaive Accuracy DAC Design The Sring DAC . Review from las lecure. DFT Simulaion from Malab Quanizaion Noise DACs and ADCs generally quanize boh ampliude and ime If convering

More information

6.003: Signals and Systems. Fourier Representations

6.003: Signals and Systems. Fourier Representations 6.003: Signals and Sysems Fourier Represenaions Ocober 27, 20 Fourier Represenaions Fourier series represen signals in erms of sinusoids. leads o a new represenaion for sysems as filers. Fourier Series

More information

Signal processing. A. Sestieri Dipartimento di Meccanica e Aeronautica University La Sapienza, Rome

Signal processing. A. Sestieri Dipartimento di Meccanica e Aeronautica University La Sapienza, Rome Signal processing A. Sesieri Diparimeno di Meccanica e Aeronauica Universiy La Sapienza, Rome Presenaion layou - Fourier series and Fourier ransforms - Leakage - Aliasing - Analog versus digial signals

More information

The Fourier Transform.

The Fourier Transform. The Fourier Transform. Consider an energy signal x(). Is energy is = E x( ) d 2 x() x () T Such signal is neiher finie ime nor periodic. This means ha we canno define a "specrum" for i using Fourier series.

More information

Chapter Q1. We need to understand Classical wave first. 3/28/2004 H133 Spring

Chapter Q1. We need to understand Classical wave first. 3/28/2004 H133 Spring Chaper Q1 Inroducion o Quanum Mechanics End of 19 h Cenury only a few loose ends o wrap up. Led o Relaiviy which you learned abou las quarer Led o Quanum Mechanics (1920 s-30 s and beyond) Behavior of

More information

9/9/99 (T.F. Weiss) Signals and systems This subject deals with mathematical methods used to describe signals and to analyze and synthesize systems.

9/9/99 (T.F. Weiss) Signals and systems This subject deals with mathematical methods used to describe signals and to analyze and synthesize systems. 9/9/99 (T.F. Weiss) Lecure #: Inroducion o signals Moivaion: To describe signals, boh man-made and naurally occurring. Ouline: Classificaion ofsignals Building-block signals complex exponenials, impulses

More information

R.#W.#Erickson# Department#of#Electrical,#Computer,#and#Energy#Engineering# University#of#Colorado,#Boulder#

R.#W.#Erickson# Department#of#Electrical,#Computer,#and#Energy#Engineering# University#of#Colorado,#Boulder# .#W.#Erickson# Deparmen#of#Elecrical,#Compuer,#and#Energy#Engineering# Universiy#of#Colorado,#Boulder# Chaper 2 Principles of Seady-Sae Converer Analysis 2.1. Inroducion 2.2. Inducor vol-second balance,

More information

Continuous Time. Time-Domain System Analysis. Impulse Response. Impulse Response. Impulse Response. Impulse Response. ( t) + b 0.

Continuous Time. Time-Domain System Analysis. Impulse Response. Impulse Response. Impulse Response. Impulse Response. ( t) + b 0. Time-Domain Sysem Analysis Coninuous Time. J. Robers - All Righs Reserved. Edied by Dr. Rober Akl 1. J. Robers - All Righs Reserved. Edied by Dr. Rober Akl 2 Le a sysem be described by a 2 y ( ) + a 1

More information

ES.1803 Topic 22 Notes Jeremy Orloff

ES.1803 Topic 22 Notes Jeremy Orloff ES.83 Topic Noes Jeremy Orloff Fourier series inroducion: coninued. Goals. Be able o compue he Fourier coefficiens of even or odd periodic funcion using he simplified formulas.. Be able o wrie and graph

More information

Voltage/current relationship Stored Energy. RL / RC circuits Steady State / Transient response Natural / Step response

Voltage/current relationship Stored Energy. RL / RC circuits Steady State / Transient response Natural / Step response Review Capaciors/Inducors Volage/curren relaionship Sored Energy s Order Circuis RL / RC circuis Seady Sae / Transien response Naural / Sep response EE4 Summer 5: Lecure 5 Insrucor: Ocavian Florescu Lecure

More information

ME 452 Fourier Series and Fourier Transform

ME 452 Fourier Series and Fourier Transform ME 452 Fourier Series and Fourier ransform Fourier series From Joseph Fourier in 87 as a resul of his sudy on he flow of hea. If f() is almos any periodic funcion i can be wrien as an infinie sum of sines

More information

KINEMATICS IN ONE DIMENSION

KINEMATICS IN ONE DIMENSION KINEMATICS IN ONE DIMENSION PREVIEW Kinemaics is he sudy of how hings move how far (disance and displacemen), how fas (speed and velociy), and how fas ha how fas changes (acceleraion). We say ha an objec

More information

Fourier Transforms. L is given by

Fourier Transforms. L is given by Royal Holloway Universiy of ondon Fourier Transforms Inroducion The imporan discovery of Fourier was ha an arbirary waveform could be consruced as a superposiion of sine (and cosine) funcions. We have

More information

Chapter 4 AC Network Analysis

Chapter 4 AC Network Analysis haper 4 A Nework Analysis Jaesung Jang apaciance Inducance and Inducion Time-Varying Signals Sinusoidal Signals Reference: David K. heng, Field and Wave Elecromagneics. Energy Sorage ircui Elemens Energy

More information

Bernoulli numbers. Francesco Chiatti, Matteo Pintonello. December 5, 2016

Bernoulli numbers. Francesco Chiatti, Matteo Pintonello. December 5, 2016 UNIVERSITÁ DEGLI STUDI DI PADOVA, DIPARTIMENTO DI MATEMATICA TULLIO LEVI-CIVITA Bernoulli numbers Francesco Chiai, Maeo Pinonello December 5, 206 During las lessons we have proved he Las Ferma Theorem

More information

Week 1 Lecture 2 Problems 2, 5. What if something oscillates with no obvious spring? What is ω? (problem set problem)

Week 1 Lecture 2 Problems 2, 5. What if something oscillates with no obvious spring? What is ω? (problem set problem) Week 1 Lecure Problems, 5 Wha if somehing oscillaes wih no obvious spring? Wha is ω? (problem se problem) Sar wih Try and ge o SHM form E. Full beer can in lake, oscillaing F = m & = ge rearrange: F =

More information

6.003 Homework #8 Solutions

6.003 Homework #8 Solutions 6.003 Homework #8 Soluions Problems. Fourier Series Deermine he Fourier series coefficiens a k for x () shown below. x ()= x ( + 0) 0 a 0 = 0 a k = e /0 sin(/0) for k 0 a k = π x()e k d = 0 0 π e 0 k d

More information

( ) = Q 0. ( ) R = R dq. ( t) = I t

( ) = Q 0. ( ) R = R dq. ( t) = I t ircuis onceps The addiion of a simple capacior o a circui of resisors allows wo relaed phenomena o occur The observaion ha he ime-dependence of a complex waveform is alered by he circui is referred o as

More information

Some Basic Information about M-S-D Systems

Some Basic Information about M-S-D Systems Some Basic Informaion abou M-S-D Sysems 1 Inroducion We wan o give some summary of he facs concerning unforced (homogeneous) and forced (non-homogeneous) models for linear oscillaors governed by second-order,

More information

DEPARTMENT OF ELECTRICAL AND ELECTRONIC ENGINEERING EXAMINATIONS 2008

DEPARTMENT OF ELECTRICAL AND ELECTRONIC ENGINEERING EXAMINATIONS 2008 [E5] IMPERIAL COLLEGE LONDON DEPARTMENT OF ELECTRICAL AND ELECTRONIC ENGINEERING EXAMINATIONS 008 EEE/ISE PART II MEng BEng and ACGI SIGNALS AND LINEAR SYSTEMS Time allowed: :00 hours There are FOUR quesions

More information

10. State Space Methods

10. State Space Methods . Sae Space Mehods. Inroducion Sae space modelling was briefly inroduced in chaper. Here more coverage is provided of sae space mehods before some of heir uses in conrol sysem design are covered in he

More information

EE 435. Lecture 35. Absolute and Relative Accuracy DAC Design. The String DAC

EE 435. Lecture 35. Absolute and Relative Accuracy DAC Design. The String DAC EE 435 Lecure 35 Absolue and Relaive Accuracy DAC Design The Sring DAC Makekup Lecures Rm 6 Sweeney 5:00 Rm 06 Coover 6:00 o 8:00 . Review from las lecure. Summary of ime and ampliude quanizaion assessmen

More information

Linear Response Theory: The connection between QFT and experiments

Linear Response Theory: The connection between QFT and experiments Phys540.nb 39 3 Linear Response Theory: The connecion beween QFT and experimens 3.1. Basic conceps and ideas Q: How do we measure he conduciviy of a meal? A: we firs inroduce a weak elecric field E, and

More information

EECS 2602 Winter Laboratory 3 Fourier series, Fourier transform and Bode Plots in MATLAB

EECS 2602 Winter Laboratory 3 Fourier series, Fourier transform and Bode Plots in MATLAB EECS 6 Winer 7 Laboraory 3 Fourier series, Fourier ransform and Bode Plos in MATLAB Inroducion: The objecives of his lab are o use MATLAB:. To plo periodic signals wih Fourier series represenaion. To obain

More information

Section 7.4 Modeling Changing Amplitude and Midline

Section 7.4 Modeling Changing Amplitude and Midline 488 Chaper 7 Secion 7.4 Modeling Changing Ampliude and Midline While sinusoidal funcions can model a variey of behaviors, i is ofen necessary o combine sinusoidal funcions wih linear and exponenial curves

More information

6.003 Homework 1. Problems. Due at the beginning of recitation on Wednesday, February 10, 2010.

6.003 Homework 1. Problems. Due at the beginning of recitation on Wednesday, February 10, 2010. 6.003 Homework Due a he beginning of reciaion on Wednesday, February 0, 200. Problems. Independen and Dependen Variables Assume ha he heigh of a waer wave is given by g(x v) where x is disance, v is velociy,

More information

Fourier Series Approximation of a Square Wave *

Fourier Series Approximation of a Square Wave * OpenSax-CNX module: m4 Fourier Series Approximaion of a Square Wave * Don Johnson his work is produced by OpenSax-CNX and licensed under he Creaive Commons Aribuion License. Absrac Shows how o use Fourier

More information

For example, the comb filter generated from. ( ) has a transfer function. e ) has L notches at ω = (2k+1)π/L and L peaks at ω = 2π k/l,

For example, the comb filter generated from. ( ) has a transfer function. e ) has L notches at ω = (2k+1)π/L and L peaks at ω = 2π k/l, Comb Filers The simple filers discussed so far are characeried eiher by a single passband and/or a single sopband There are applicaions where filers wih muliple passbands and sopbands are required The

More information

Two Coupled Oscillators / Normal Modes

Two Coupled Oscillators / Normal Modes Lecure 3 Phys 3750 Two Coupled Oscillaors / Normal Modes Overview and Moivaion: Today we ake a small, bu significan, sep owards wave moion. We will no ye observe waves, bu his sep is imporan in is own

More information

Fourier Series & The Fourier Transform. Joseph Fourier, our hero. Lord Kelvin on Fourier s theorem. What do we want from the Fourier Transform?

Fourier Series & The Fourier Transform. Joseph Fourier, our hero. Lord Kelvin on Fourier s theorem. What do we want from the Fourier Transform? ourier Series & The ourier Transfor Wha is he ourier Transfor? Wha do we wan fro he ourier Transfor? We desire a easure of he frequencies presen in a wave. This will lead o a definiion of he er, he specru.

More information

Physics 235 Chapter 2. Chapter 2 Newtonian Mechanics Single Particle

Physics 235 Chapter 2. Chapter 2 Newtonian Mechanics Single Particle Chaper 2 Newonian Mechanics Single Paricle In his Chaper we will review wha Newon s laws of mechanics ell us abou he moion of a single paricle. Newon s laws are only valid in suiable reference frames,

More information

Physics 180A Fall 2008 Test points. Provide the best answer to the following questions and problems. Watch your sig figs.

Physics 180A Fall 2008 Test points. Provide the best answer to the following questions and problems. Watch your sig figs. Physics 180A Fall 2008 Tes 1-120 poins Name Provide he bes answer o he following quesions and problems. Wach your sig figs. 1) The number of meaningful digis in a number is called he number of. When numbers

More information

Class Meeting # 10: Introduction to the Wave Equation

Class Meeting # 10: Introduction to the Wave Equation MATH 8.5 COURSE NOTES - CLASS MEETING # 0 8.5 Inroducion o PDEs, Fall 0 Professor: Jared Speck Class Meeing # 0: Inroducion o he Wave Equaion. Wha is he wave equaion? The sandard wave equaion for a funcion

More information

1. VELOCITY AND ACCELERATION

1. VELOCITY AND ACCELERATION 1. VELOCITY AND ACCELERATION 1.1 Kinemaics Equaions s = u + 1 a and s = v 1 a s = 1 (u + v) v = u + as 1. Displacemen-Time Graph Gradien = speed 1.3 Velociy-Time Graph Gradien = acceleraion Area under

More information

Kinematics Vocabulary. Kinematics and One Dimensional Motion. Position. Coordinate System in One Dimension. Kinema means movement 8.

Kinematics Vocabulary. Kinematics and One Dimensional Motion. Position. Coordinate System in One Dimension. Kinema means movement 8. Kinemaics Vocabulary Kinemaics and One Dimensional Moion 8.1 WD1 Kinema means movemen Mahemaical descripion of moion Posiion Time Inerval Displacemen Velociy; absolue value: speed Acceleraion Averages

More information

Traveling Waves. Chapter Introduction

Traveling Waves. Chapter Introduction Chaper 4 Traveling Waves 4.1 Inroducion To dae, we have considered oscillaions, i.e., periodic, ofen harmonic, variaions of a physical characerisic of a sysem. The sysem a one ime is indisinguishable from

More information

2 Signals. 2.1 Elementary algebra on signals

2 Signals. 2.1 Elementary algebra on signals 2 Signals We usually use signals o represen quaniies ha vary wih ime. An example of a signal is he size of he sea swell a some locaion in False Bay: a any paricular ime he waves in he bay have an ampliude

More information

6.003: Signals and Systems. Relations among Fourier Representations

6.003: Signals and Systems. Relations among Fourier Representations 6.003: Signals and Sysems Relaions among Fourier Represenaions April 22, 200 Mid-erm Examinaion #3 W ednesday, April 28, 7:30-9:30pm. No reciaions on he day of he exam. Coverage: Lecures 20 Reciaions 20

More information

Vehicle Arrival Models : Headway

Vehicle Arrival Models : Headway Chaper 12 Vehicle Arrival Models : Headway 12.1 Inroducion Modelling arrival of vehicle a secion of road is an imporan sep in raffic flow modelling. I has imporan applicaion in raffic flow simulaion where

More information

Introduction to AC Power, RMS RMS. ECE 2210 AC Power p1. Use RMS in power calculations. AC Power P =? DC Power P =. V I = R =. I 2 R. V p.

Introduction to AC Power, RMS RMS. ECE 2210 AC Power p1. Use RMS in power calculations. AC Power P =? DC Power P =. V I = R =. I 2 R. V p. ECE MS I DC Power P I = Inroducion o AC Power, MS I AC Power P =? A Solp //9, // // correced p4 '4 v( ) = p cos( ω ) v( ) p( ) Couldn' we define an "effecive" volage ha would allow us o use he same relaionships

More information

Chapter 2: Principles of steady-state converter analysis

Chapter 2: Principles of steady-state converter analysis Chaper 2 Principles of Seady-Sae Converer Analysis 2.1. Inroducion 2.2. Inducor vol-second balance, capacior charge balance, and he small ripple approximaion 2.3. Boos converer example 2.4. Cuk converer

More information

EE 224 Signals and Systems I Complex numbers sinusodal signals Complex exponentials e jωt phasor addition

EE 224 Signals and Systems I Complex numbers sinusodal signals Complex exponentials e jωt phasor addition EE 224 Signals and Sysems I Complex numbers sinusodal signals Complex exponenials e jω phasor addiion 1/28 Complex Numbers Recangular Polar y z r z θ x Good for addiion/subracion Good for muliplicaion/division

More information

Delhi Noida Bhopal Hyderabad Jaipur Lucknow Indore Pune Bhubaneswar Kolkata Patna Web: Ph:

Delhi Noida Bhopal Hyderabad Jaipur Lucknow Indore Pune Bhubaneswar Kolkata Patna Web:     Ph: Serial : 0. ND_NW_EE_Signal & Sysems_4068 Delhi Noida Bhopal Hyderabad Jaipur Lucknow Indore Pune Bhubaneswar Kolkaa Pana Web: E-mail: info@madeeasy.in Ph: 0-4546 CLASS TEST 08-9 ELECTRICAL ENGINEERING

More information

Ground Rules. PC1221 Fundamentals of Physics I. Kinematics. Position. Lectures 3 and 4 Motion in One Dimension. A/Prof Tay Seng Chuan

Ground Rules. PC1221 Fundamentals of Physics I. Kinematics. Position. Lectures 3 and 4 Motion in One Dimension. A/Prof Tay Seng Chuan Ground Rules PC11 Fundamenals of Physics I Lecures 3 and 4 Moion in One Dimension A/Prof Tay Seng Chuan 1 Swich off your handphone and pager Swich off your lapop compuer and keep i No alking while lecure

More information

2.4 Cuk converter example

2.4 Cuk converter example 2.4 Cuk converer example C 1 Cuk converer, wih ideal swich i 1 i v 1 2 1 2 C 2 v 2 Cuk converer: pracical realizaion using MOSFET and diode C 1 i 1 i v 1 2 Q 1 D 1 C 2 v 2 28 Analysis sraegy This converer

More information

V The Fourier Transform

V The Fourier Transform V he Fourier ransform Lecure noes by Assaf al 1. Moivaion Imagine playing hree noes on he piano, recording hem (soring hem as a.wav or.mp3 file), and hen ploing he resuling waveform on he compuer: 100Hz

More information

Signals and Systems Prof. Brian L. Evans Dept. of Electrical and Computer Engineering The University of Texas at Austin

Signals and Systems Prof. Brian L. Evans Dept. of Electrical and Computer Engineering The University of Texas at Austin EE 345S Real-Time Digial Signal Processing Lab Spring 26 Signals and Sysems Prof. Brian L. Evans Dep. of Elecrical and Compuer Engineering The Universiy of Texas a Ausin Review Signals As Funcions of Time

More information

Chapter 7: Solving Trig Equations

Chapter 7: Solving Trig Equations Haberman MTH Secion I: The Trigonomeric Funcions Chaper 7: Solving Trig Equaions Le s sar by solving a couple of equaions ha involve he sine funcion EXAMPLE a: Solve he equaion sin( ) The inverse funcions

More information

EECE251. Circuit Analysis I. Set 4: Capacitors, Inductors, and First-Order Linear Circuits

EECE251. Circuit Analysis I. Set 4: Capacitors, Inductors, and First-Order Linear Circuits EEE25 ircui Analysis I Se 4: apaciors, Inducors, and Firs-Order inear ircuis Shahriar Mirabbasi Deparmen of Elecrical and ompuer Engineering Universiy of Briish olumbia shahriar@ece.ubc.ca Overview Passive

More information

Continuous Time Linear Time Invariant (LTI) Systems. Dr. Ali Hussein Muqaibel. Introduction

Continuous Time Linear Time Invariant (LTI) Systems. Dr. Ali Hussein Muqaibel. Introduction /9/ Coninuous Time Linear Time Invarian (LTI) Sysems Why LTI? Inroducion Many physical sysems. Easy o solve mahemaically Available informaion abou analysis and design. We can apply superposiion LTI Sysem

More information

2 Frequency-Domain Analysis

2 Frequency-Domain Analysis requency-domain Analysis Elecrical engineers live in he wo worlds, so o speak, of ime and frequency. requency-domain analysis is an exremely valuable ool o he communicaions engineer, more so perhaps han

More information

15. Vector Valued Functions

15. Vector Valued Functions 1. Vecor Valued Funcions Up o his poin, we have presened vecors wih consan componens, for example, 1, and,,4. However, we can allow he componens of a vecor o be funcions of a common variable. For example,

More information

RC, RL and RLC circuits

RC, RL and RLC circuits Name Dae Time o Complee h m Parner Course/ Secion / Grade RC, RL and RLC circuis Inroducion In his experimen we will invesigae he behavior of circuis conaining combinaions of resisors, capaciors, and inducors.

More information

Answers to Exercises in Chapter 7 - Correlation Functions

Answers to Exercises in Chapter 7 - Correlation Functions M J Robers - //8 Answers o Exercises in Chaper 7 - Correlaion Funcions 7- (from Papoulis and Pillai) The random variable C is uniform in he inerval (,T ) Find R, ()= u( C), ()= C (Use R (, )= R,, < or

More information

Physics for Scientists & Engineers 2

Physics for Scientists & Engineers 2 Direc Curren Physics for Scieniss & Engineers 2 Spring Semeser 2005 Lecure 16 This week we will sudy charges in moion Elecric charge moving from one region o anoher is called elecric curren Curren is all

More information

k The function Ψ(x) is called wavelet function and shows band-pass behavior. The wavelet coefficients d a,b

k The function Ψ(x) is called wavelet function and shows band-pass behavior. The wavelet coefficients d a,b Wavele Transform Wavele Transform The wavele ransform corresponds o he decomposiion of a quadraic inegrable funcion sx ε L 2 R in a family of scaled and ranslaed funcions Ψ,l, ψ, l 1/2 = ψ l The funcion

More information

3. Alternating Current

3. Alternating Current 3. Alernaing Curren TOPCS Definiion and nroducion AC Generaor Componens of AC Circuis Series LRC Circuis Power in AC Circuis Transformers & AC Transmission nroducion o AC The elecric power ou of a home

More information

ψ(t) = V x (0)V x (t)

ψ(t) = V x (0)V x (t) .93 Home Work Se No. (Professor Sow-Hsin Chen Spring Term 5. Due March 7, 5. This problem concerns calculaions of analyical expressions for he self-inermediae scaering funcion (ISF of he es paricle in

More information

INDEX. Transient analysis 1 Initial Conditions 1

INDEX. Transient analysis 1 Initial Conditions 1 INDEX Secion Page Transien analysis 1 Iniial Condiions 1 Please inform me of your opinion of he relaive emphasis of he review maerial by simply making commens on his page and sending i o me a: Frank Mera

More information

Math 2142 Exam 1 Review Problems. x 2 + f (0) 3! for the 3rd Taylor polynomial at x = 0. To calculate the various quantities:

Math 2142 Exam 1 Review Problems. x 2 + f (0) 3! for the 3rd Taylor polynomial at x = 0. To calculate the various quantities: Mah 4 Eam Review Problems Problem. Calculae he 3rd Taylor polynomial for arcsin a =. Soluion. Le f() = arcsin. For his problem, we use he formula f() + f () + f ()! + f () 3! for he 3rd Taylor polynomial

More information

Matlab and Python programming: how to get started

Matlab and Python programming: how to get started Malab and Pyhon programming: how o ge sared Equipping readers he skills o wrie programs o explore complex sysems and discover ineresing paerns from big daa is one of he main goals of his book. In his chaper,

More information

BEng (Hons) Telecommunications. Examinations for / Semester 2

BEng (Hons) Telecommunications. Examinations for / Semester 2 BEng (Hons) Telecommunicaions Cohor: BTEL/14/FT Examinaions for 2015-2016 / Semeser 2 MODULE: ELECTROMAGNETIC THEORY MODULE CODE: ASE2103 Duraion: 2 ½ Hours Insrucions o Candidaes: 1. Answer ALL 4 (FOUR)

More information

28. Narrowband Noise Representation

28. Narrowband Noise Representation Narrowband Noise Represenaion on Mac 8. Narrowband Noise Represenaion In mos communicaion sysems, we are ofen dealing wih band-pass filering of signals. Wideband noise will be shaped ino bandlimied noise.

More information

Mon Apr 9 EP 7.6 Convolutions and Laplace transforms. Announcements: Warm-up Exercise:

Mon Apr 9 EP 7.6 Convolutions and Laplace transforms. Announcements: Warm-up Exercise: Mah 225-4 Week 3 April 9-3 EP 7.6 - convoluions; 6.-6.2 - eigenvalues, eigenvecors and diagonalizabiliy; 7. - sysems of differenial equaions. Mon Apr 9 EP 7.6 Convoluions and Laplace ransforms. Announcemens:

More information

Section 3.5 Nonhomogeneous Equations; Method of Undetermined Coefficients

Section 3.5 Nonhomogeneous Equations; Method of Undetermined Coefficients Secion 3.5 Nonhomogeneous Equaions; Mehod of Undeermined Coefficiens Key Terms/Ideas: Linear Differenial operaor Nonlinear operaor Second order homogeneous DE Second order nonhomogeneous DE Soluion o homogeneous

More information

EE456 Digital Communications

EE456 Digital Communications EE456 Digial Communicaions Professor Ha Nguyen Sepember 6 EE456 Digial Communicaions Inroducion o Basic Digial Passband Modulaion Baseband ransmission is conduced a low frequencies. Passband ransmission

More information

23.5. Half-Range Series. Introduction. Prerequisites. Learning Outcomes

23.5. Half-Range Series. Introduction. Prerequisites. Learning Outcomes Half-Range Series 2.5 Inroducion In his Secion we address he following problem: Can we find a Fourier series expansion of a funcion defined over a finie inerval? Of course we recognise ha such a funcion

More information

Nature Neuroscience: doi: /nn Supplementary Figure 1. Spike-count autocorrelations in time.

Nature Neuroscience: doi: /nn Supplementary Figure 1. Spike-count autocorrelations in time. Supplemenary Figure 1 Spike-coun auocorrelaions in ime. Normalized auocorrelaion marices are shown for each area in a daase. The marix shows he mean correlaion of he spike coun in each ime bin wih he spike

More information

Phase and Frequency Modulation

Phase and Frequency Modulation Angle Modulaion Phase and Frequency Modulaion Consider a signal of he form x c = A c cos 2π f c + φ θ i ( ) where A c and f c are consans. The envelope is a consan so he message canno be in he envelope.

More information

Designing Information Devices and Systems I Spring 2019 Lecture Notes Note 17

Designing Information Devices and Systems I Spring 2019 Lecture Notes Note 17 EES 16A Designing Informaion Devices and Sysems I Spring 019 Lecure Noes Noe 17 17.1 apaciive ouchscreen In he las noe, we saw ha a capacior consiss of wo pieces on conducive maerial separaed by a nonconducive

More information

Signal and System (Chapter 3. Continuous-Time Systems)

Signal and System (Chapter 3. Continuous-Time Systems) Signal and Sysem (Chaper 3. Coninuous-Time Sysems) Prof. Kwang-Chun Ho kwangho@hansung.ac.kr Tel: 0-760-453 Fax:0-760-4435 1 Dep. Elecronics and Informaion Eng. 1 Nodes, Branches, Loops A nework wih b

More information

NCSS Statistical Software. , contains a periodic (cyclic) component. A natural model of the periodic component would be

NCSS Statistical Software. , contains a periodic (cyclic) component. A natural model of the periodic component would be NCSS Saisical Sofware Chaper 468 Specral Analysis Inroducion This program calculaes and displays he periodogram and specrum of a ime series. This is someimes nown as harmonic analysis or he frequency approach

More information

Hall effect. Formulae :- 1) Hall coefficient RH = cm / Coulumb. 2) Magnetic induction BY 2

Hall effect. Formulae :- 1) Hall coefficient RH = cm / Coulumb. 2) Magnetic induction BY 2 Page of 6 all effec Aim :- ) To deermine he all coefficien (R ) ) To measure he unknown magneic field (B ) and o compare i wih ha measured by he Gaussmeer (B ). Apparaus :- ) Gauss meer wih probe ) Elecromagne

More information

2.3 SCHRÖDINGER AND HEISENBERG REPRESENTATIONS

2.3 SCHRÖDINGER AND HEISENBERG REPRESENTATIONS Andrei Tokmakoff, MIT Deparmen of Chemisry, 2/22/2007 2-17 2.3 SCHRÖDINGER AND HEISENBERG REPRESENTATIONS The mahemaical formulaion of he dynamics of a quanum sysem is no unique. So far we have described

More information