6.003 Homework #9 Solutions

Size: px
Start display at page:

Download "6.003 Homework #9 Solutions"

Transcription

1 6.003 Homework #9 Soluions Problems. Fourier varieies a. Deermine he Fourier series coefficiens of he following signal, which is periodic in 0. x () a 0 5 a k a k 0 πk j3 e 0 e j πk 0 jπk πk e 0 d sin 3πk 5 3 sin πk 5 πk πk e 0 d 0 πk πk 3 e 0 e 0 jπk πk e 0 πk 0 3πk sin 5 sin πk 5 πk + 0 πk e 0 πk 0 for k 0 3 b. Deermine he Fourier ransform of he following signal, which is zero ouside he indicaed range. x () 3 X (j) sin 3 sin X (j) e d + e j3 e j j 3 e d e + e e 3 j + e sin 3 sin 3

2 6.003 Homework #9 Soluions / Fall 0 c. Wha is he relaion beween he answers o pars a and b? In paricular, derive an expression for a k (he soluion o par a) in erms of X (j) (he soluion o par b). ( a k 0 X j πk ) 0 he Fourier series coefficiens a k are a k 0 X (j) πk 0 d. Deermine he ime waveform ha corresponds o he following Fourier ransform, which is zero ouside he indicaed range. X 3 (j) 3 x 3 () sin 3 sin π x 3 () π e e 3 jπ e j d + π 3 + e j3 e j jπ e j d e j jπ sin 3 sin π + e j jπ 3 e. Wha is he relaion beween he answers o pars b and d? In paricular, derive an expression for x 3 () (he soluion o par d) in erms of X (j) (he soluion o par b). x 3 () π X ( ) j( ) or π X ( ) j() he relaion beween his answer and ha of he previous par is dualiy. x 3 () π X (j) Since X (j) is real and even, π X (j) would also work.

3 6.003 Homework #9 Soluions / Fall 0 3. Fourier ransform properies Le X(j) represen he Fourier ransform of { x() e 0 < < 0 oherwise. Express he Fourier ransforms of each of he following signals in erms of X(j). x () X (j) X(j) + X() x () x() + x( ) x( ) x( )e d X (j) X(j) + X() x () x()e j d X() X (j) X(j) X() x () x() x( ) X (j) X(j) X() x 3 () X 3 (j) ( + e j )X(j) x 3 () x() + x( + ) x( + ) x( + )e d X 3 (j) ( + e j )X(j) x()e ( ) d e j X(j)

4 6.003 Homework #9 Soluions / Fall Fourier ransforms Find he Fourier ransforms of he following signals. a. x () e cos() X (j) + ( ) + + ( + ) e u() + j e + j + j + cos() πδ( ) + πδ( + ) herefore, by he muliplicaion propery, e cos() + ( ) + + ( + ) b. x () sin(π) π( ) X (j) e (u( + π) u( π)) sin(π) π u( + π) u( π) sin(π) sin(π π) sin(π( )) e (u( + π) u( π)) π( ) π( ) π( )

5 6.003 Homework #9 Soluions / Fall 0 5 { c. x 3 () 0 < < 0 oherwise X 3 (j) j e + e + j ( e ) 3 x 3 () (u() u( )) u() j + πδ() u() u( ) + πδ() e j j e πδ() e j f() j d d F (j) f() d d F (j) ( u() u( ) ) d d ( ) e j x 3 () j e + e + j 3 ( e ) d. x 4 () ( ) u( + )u( ) X 4 (j) ( cos ) Le p() u( + 0.5) u( 0.5). hen x 4 () p() p() and X 4 (j) P (j). P (j) e d e sin X 4 (j) 4 sin ( cos ) You could also solve his by differeniaing wice in he ime domain o ge a sequence of dela funcions, compuing he ransform, and muliplying wice by j [he dela funcions in he inegraion propery have zero weigh].

6 6.003 Homework #9 Soluions / Fall 0 6 Engineering Design Problem 4. Parseval s heorem Parseval s heorem relaes ime- and frequency-domain mehods for calculaing he average energy of a signal as follows: x() d a k where a k represens he Fourier series coefficiens of he periodic signal x() wih period. a. We can derive Parseval s heorem from he properies of C Fourier series.. Le y() x(). Find he Fourier series coefficiens b k of y(). [Hin: x() x()x ().] x() x () b k a k e j π k a π ke k π y()e k d l l m l m m a k+m a m a l e j π l a l a m m a l a mδ[k l + m] x()x π ()e k d a π me m π e k d π e (k l+m) d. Use he resul from he previous par o derive Parseval s heorem. x() d b 0 a k a k a k b. Le x () represen he inpu o an LI sysem, where x () α k e jk π 4 for 0 < α <. he frequency response of he sysem is { H(j) < W 0 oherwise.

7 6.003 Homework #9 Soluions / Fall 0 7 Wha is he minimum value of W so ha he average energy in he oupu signal will be a leas 90% of ha in he inpu signal. he signal x () is periodic wih period 8 and has Fourier series coefficiens a k α k. he average energy in he inpu signal is x() d a k α k α + α + α α he lowpass filer passes some number K of he harmonic componens of x() so ha he average energy in he oupu signals is K y() d α k αk+ α + αk+ α + α α K+ α k K o make he energy in he oupu a leas 90% of ha in he inpu + α α K+ ( ) + α α 0.9 α + α α K+ 0.9( + α ) α K+ 0.( + α ) (K + ) log α log + α 0 K > log +α 0 log α (inequaliy swiches because he logs are negaive) Because he harmonics are spaced a π ( ) log +α 0 π W > log α 8 inervals in frequency

8 6.003 Homework #9 Soluions / Fall Filering he poin of his quesion is o undersand how he magniude of a filer affecs he oupu and how he angle of a filer affecs he oupu. Consider he following RC circui as a filer. R + v + i v o C Assume ha he inpu v i () is he following square wave. v i () 0 If he fundamenal frequency of he square wave ( π ) is equal o he cuoff frequency of he RC circui ( RC ) hen he oupu v o() will have he following form. v o () 0 We can hink of he RC circui as filering he square wave as shown below. H(j) H(j) π /RC /RC he RC filer has wo effecs: () he ampliudes of he Fourier componens of he inpu (verical red lines in upper panel) are muliplied by he magniude of he frequency response ( H(j) ). () he phase of he Fourier componens (red dos in lower panel) are shifed by he phase of he frequency response ( H(j)). a. Deermine (using whaever mehod you find convenien) he oupu ha would resul if v i () were passed hrough a filer whose magniude is H(j) (as above) bu whose phase funcion is 0 for all frequencies. Compare he resul wih v o () above.

9 6.003 Homework #9 Soluions / Fall 0 9 he following plo shows he sum of he firs 46 erms of he series expansion for he square wave, wih each erm filered by he magniude (bu no he phase) of he RC lowpass filer. he original oupu is also shown (dashed green) for reference. v o () 0 he asymmery in he charging and discharging porions of he RC response is gone. he effec of he filer is o reduce he magniudes of he higher harmonics wihou adding phase delay. b. Deermine (using whaever mehod you find convenien) he oupu ha would resul if v i () were passed hrough a filer whose phase funcion is H(j) (as above) bu whose magniude funcion is for all frequencies. Compare he resul wih v o () above. he following plo shows he sum of he firs 46 erms of he series expansion for he square wave, wih each erm filered by he phase (bu no he magniude) of he RC lowpass filer. v o () 0 he asymmery in he charging and discharging porions of he RC response is even more pronounced han before. Because he magniudes of he higher harmonics are no aenuaed, hey now accumulae o make a subsanial peak ha was no seen in he RC response. We can undersand he large overshoo as follows. Each harmonic componen in he square wave is a sinusoid: v i () k k odd πk sin ( ) πk. Excep for he fundamenal, he phase shifs are nearly a quarer cycle. Delaying each of he harmonic componens by a quarer cycle aligns he peaks of each componen a as shown below

10 6.003 Homework #9 Soluions / Fall 0 0 v x () 0 where v x () k k odd ( ) πk πk sin π/.

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

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

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

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

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

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

6.003: Signals and Systems

6.003: Signals and Systems 6.3: Signals and Sysems Lecure 7 April 8, 6.3: Signals and Sysems C Fourier ransform C Fourier ransform Represening signals by heir frequency conen. X(j)= x()e j d ( analysis equaion) x()= π X(j)e j d

More information

EECE 301 Signals & Systems Prof. Mark Fowler

EECE 301 Signals & Systems Prof. Mark Fowler EECE 31 Signals & Sysems Prof. Mar Fowler Noe Se #1 C-T Signals: Circuis wih Periodic Sources 1/1 Solving Circuis wih Periodic Sources FS maes i easy o find he response of an RLC circui o a periodic source!

More information

SOLUTIONS TO ECE 3084

SOLUTIONS TO ECE 3084 SOLUTIONS TO ECE 384 PROBLEM 2.. For each sysem below, specify wheher or no i is: (i) memoryless; (ii) causal; (iii) inverible; (iv) linear; (v) ime invarian; Explain your reasoning. If he propery is no

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

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

EE 315 Notes. Gürdal Arslan CLASS 1. (Sections ) What is a signal?

EE 315 Notes. Gürdal Arslan CLASS 1. (Sections ) What is a signal? EE 35 Noes Gürdal Arslan CLASS (Secions.-.2) Wha is a signal? In his class, a signal is some funcion of ime and i represens how some physical quaniy changes over some window of ime. Examples: velociy of

More information

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

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

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

Problem Set 11(Practice Problems) Spring 2014

Problem Set 11(Practice Problems) Spring 2014 Issued: Wednesday April, ECE Signals and Sysems I Problem Se (Pracice Problems) Spring Due: For pracice only. No due. Reading in Oppenheim/Willsky/Nawab /9/ Secions 6.-6. // Secions 6.-6. See revised schedule

More information

KEY. Math 334 Midterm III Winter 2008 section 002 Instructor: Scott Glasgow

KEY. Math 334 Midterm III Winter 2008 section 002 Instructor: Scott Glasgow KEY Mah 334 Miderm III Winer 008 secion 00 Insrucor: Sco Glasgow Please do NOT wrie on his exam. No credi will be given for such work. Raher wrie in a blue book, or on your own paper, preferably engineering

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

Q1) [20 points] answer for the following questions (ON THIS SHEET):

Q1) [20 points] answer for the following questions (ON THIS SHEET): Dr. Anas Al Tarabsheh The Hashemie Universiy Elecrical and Compuer Engineering Deparmen (Makeup Exam) Signals and Sysems Firs Semeser 011/01 Final Exam Dae: 1/06/01 Exam Duraion: hours Noe: means convoluion

More information

Laplace Transforms. Examples. Is this equation differential? y 2 2y + 1 = 0, y 2 2y + 1 = 0, (y ) 2 2y + 1 = cos x,

Laplace Transforms. Examples. Is this equation differential? y 2 2y + 1 = 0, y 2 2y + 1 = 0, (y ) 2 2y + 1 = cos x, Laplace Transforms Definiion. An ordinary differenial equaion is an equaion ha conains one or several derivaives of an unknown funcion which we call y and which we wan o deermine from he equaion. The equaion

More information

6.003: Signals and Systems Lecture 20 November 17, 2011

6.003: Signals and Systems Lecture 20 November 17, 2011 6.3: Signals and Sysems Lecure November 7, 6.3: Signals and Sysems Applicaions of Fourier ransforms Filering Noion of a filer. LI sysems canno creae new frequencies. can only scale magniudes and shif phases

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

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

e 2t u(t) e 2t u(t) =?

e 2t u(t) e 2t u(t) =? EE : Signals, Sysems, and Transforms Fall 7. Skech he convoluion of he following wo signals. Tes No noes, closed book. f() Show your work. Simplify your answers. g(). Using he convoluion inegral, find

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

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

( ) = 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

( ) ( ) 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

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

6.2 Transforms of Derivatives and Integrals.

6.2 Transforms of Derivatives and Integrals. SEC. 6.2 Transforms of Derivaives and Inegrals. ODEs 2 3 33 39 23. Change of scale. If l( f ()) F(s) and c is any 33 45 APPLICATION OF s-shifting posiive consan, show ha l( f (c)) F(s>c)>c (Hin: In Probs.

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

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

2 int T. is the Fourier transform of f(t) which is the inverse Fourier transform of f. i t e

2 int T. is the Fourier transform of f(t) which is the inverse Fourier transform of f. i t e PHYS67 Class 3 ourier Transforms In he limi T, he ourier series becomes an inegral ( nt f in T ce f n f f e d, has been replaced by ) where i f e d is he ourier ransform of f() which is he inverse ourier

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

B Signals and Systems I Solutions to Midterm Test 2. xt ()

B Signals and Systems I Solutions to Midterm Test 2. xt () 34-33B Signals and Sysems I Soluions o Miderm es 34-33B Signals and Sysems I Soluions o Miderm es ednesday Marh 7, 7:PM-9:PM Examiner: Prof. Benoi Boule Deparmen of Elerial and Compuer Engineering MGill

More information

δ (τ )dτ denotes the unit step function, and

δ (τ )dτ denotes the unit step function, and ECE-202 Homework Problems (Se 1) Spring 18 TO THE STUDENT: ALWAYS CHECK THE ERRATA on he web. ANCIENT ASIAN/AFRICAN/NATIVE AMERICAN/SOUTH AMERICAN ETC. PROVERB: If you give someone a fish, you give hem

More information

Differential Equations

Differential Equations Mah 21 (Fall 29) Differenial Equaions Soluion #3 1. Find he paricular soluion of he following differenial equaion by variaion of parameer (a) y + y = csc (b) 2 y + y y = ln, > Soluion: (a) The corresponding

More information

V L. DT s D T s t. Figure 1: Buck-boost converter: inductor current i(t) in the continuous conduction mode.

V L. DT s D T s t. Figure 1: Buck-boost converter: inductor current i(t) in the continuous conduction mode. ECE 445 Analysis and Design of Power Elecronic Circuis Problem Se 7 Soluions Problem PS7.1 Erickson, Problem 5.1 Soluion (a) Firs, recall he operaion of he buck-boos converer in he coninuous conducion

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

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

6.003: Signals and Systems

6.003: Signals and Systems 6.003: Signals and Sysems Relaions among Fourier Represenaions November 5, 20 Mid-erm Examinaion #3 Wednesday, November 6, 7:30-9:30pm, No reciaions on he day of he exam. Coverage: Lecures 8 Reciaions

More information

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

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

More information

( ) ( ) ( ) () () Signals And Systems Exam#1. 1. Given x(t) and y(t) below: x(t) y(t) (A) Give the expression of x(t) in terms of step functions.

( ) ( ) ( ) () () Signals And Systems Exam#1. 1. Given x(t) and y(t) below: x(t) y(t) (A) Give the expression of x(t) in terms of step functions. Signals And Sysems Exam#. Given x() and y() below: x() y() 4 4 (A) Give he expression of x() in erms of sep funcions. (%) x () = q() q( ) + q( 4) (B) Plo x(.5). (%) x() g() = x( ) h() = g(. 5) = x(. 5)

More information

Solutions for homework 12

Solutions for homework 12 y Soluions for homework Secion Nonlinear sysems: The linearizaion of a nonlinear sysem Consider he sysem y y y y y (i) Skech he nullclines Use a disincive marking for each nullcline so hey can be disinguished

More information

ENGI 9420 Engineering Analysis Assignment 2 Solutions

ENGI 9420 Engineering Analysis Assignment 2 Solutions ENGI 940 Engineering Analysis Assignmen Soluions 0 Fall [Second order ODEs, Laplace ransforms; Secions.0-.09]. Use Laplace ransforms o solve he iniial value problem [0] dy y, y( 0) 4 d + [This was Quesion

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

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

Solutions - Midterm Exam

Solutions - Midterm Exam DEPARTMENT OF ELECTRICAL AND COMPUTER ENGINEERING, THE UNIVERITY OF NEW MEXICO ECE-34: ignals and ysems ummer 203 PROBLEM (5 PT) Given he following LTI sysem: oluions - Miderm Exam a) kech he impulse response

More information

dt = C exp (3 ln t 4 ). t 4 W = C exp ( ln(4 t) 3) = C(4 t) 3.

dt = C exp (3 ln t 4 ). t 4 W = C exp ( ln(4 t) 3) = C(4 t) 3. Mah Rahman Exam Review Soluions () Consider he IVP: ( 4)y 3y + 4y = ; y(3) = 0, y (3) =. (a) Please deermine he longes inerval for which he IVP is guaraneed o have a unique soluion. Soluion: The disconinuiies

More information

Spring Ammar Abu-Hudrouss Islamic University Gaza

Spring Ammar Abu-Hudrouss Islamic University Gaza Chaper 7 Reed-Solomon Code Spring 9 Ammar Abu-Hudrouss Islamic Universiy Gaza ١ Inroducion A Reed Solomon code is a special case of a BCH code in which he lengh of he code is one less han he size of he

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

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

KEY. Math 334 Midterm III Fall 2008 sections 001 and 003 Instructor: Scott Glasgow

KEY. Math 334 Midterm III Fall 2008 sections 001 and 003 Instructor: Scott Glasgow KEY Mah 334 Miderm III Fall 28 secions and 3 Insrucor: Sco Glasgow Please do NOT wrie on his exam. No credi will be given for such work. Raher wrie in a blue book, or on your own paper, preferably engineering

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

Simulation-Solving Dynamic Models ABE 5646 Week 2, Spring 2010

Simulation-Solving Dynamic Models ABE 5646 Week 2, Spring 2010 Simulaion-Solving Dynamic Models ABE 5646 Week 2, Spring 2010 Week Descripion Reading Maerial 2 Compuer Simulaion of Dynamic Models Finie Difference, coninuous saes, discree ime Simple Mehods Euler Trapezoid

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

ENEE 420 FALL 2010 COMMUNICATIONS SYSTEMS ANSWER KEY TO TEST # 2:

ENEE 420 FALL 2010 COMMUNICATIONS SYSTEMS ANSWER KEY TO TEST # 2: ENEE /FALL ENEE FALL COMMUNICAIONS SYSEMS ANSWER KEY O ES # :..a Fix in R. I is plain ha z = Φy AM = aa c + k A m cos πf c + ba c + k A m cos πf c = aa c + k A m cos πf c + b A c + k Am + cos πf c = aa

More information

Homework-8(1) P8.3-1, 3, 8, 10, 17, 21, 24, 28,29 P8.4-1, 2, 5

Homework-8(1) P8.3-1, 3, 8, 10, 17, 21, 24, 28,29 P8.4-1, 2, 5 Homework-8() P8.3-, 3, 8, 0, 7, 2, 24, 28,29 P8.4-, 2, 5 Secion 8.3: The Response of a Firs Order Circui o a Consan Inpu P 8.3- The circui shown in Figure P 8.3- is a seady sae before he swich closes a

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

Chapter 7 Response of First-order RL and RC Circuits

Chapter 7 Response of First-order RL and RC Circuits Chaper 7 Response of Firs-order RL and RC Circuis 7.- The Naural Response of RL and RC Circuis 7.3 The Sep Response of RL and RC Circuis 7.4 A General Soluion for Sep and Naural Responses 7.5 Sequenial

More information

Echocardiography Project and Finite Fourier Series

Echocardiography Project and Finite Fourier Series Echocardiography Projec and Finie Fourier Series 1 U M An echocardiagram is a plo of how a porion of he hear moves as he funcion of ime over he one or more hearbea cycles If he hearbea repeas iself every

More information

Laplace transfom: t-translation rule , Haynes Miller and Jeremy Orloff

Laplace transfom: t-translation rule , Haynes Miller and Jeremy Orloff Laplace ransfom: -ranslaion rule 8.03, Haynes Miller and Jeremy Orloff Inroducory example Consider he sysem ẋ + 3x = f(, where f is he inpu and x he response. We know is uni impulse response is 0 for

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

Y 0.4Y 0.45Y Y to a proper ARMA specification.

Y 0.4Y 0.45Y Y to a proper ARMA specification. HG Jan 04 ECON 50 Exercises II - 0 Feb 04 (wih answers Exercise. Read secion 8 in lecure noes 3 (LN3 on he common facor problem in ARMA-processes. Consider he following process Y 0.4Y 0.45Y 0.5 ( where

More information

From Complex Fourier Series to Fourier Transforms

From Complex Fourier Series to Fourier Transforms Topic From Complex Fourier Series o Fourier Transforms. Inroducion In he previous lecure you saw ha complex Fourier Series and is coeciens were dened by as f ( = n= C ne in! where C n = T T = T = f (e

More information

h[n] is the impulse response of the discrete-time system:

h[n] is the impulse response of the discrete-time system: Definiion Examples Properies Memory Inveribiliy Causaliy Sabiliy Time Invariance Lineariy Sysems Fundamenals Overview Definiion of a Sysem x() h() y() x[n] h[n] Sysem: a process in which inpu signals are

More information

Laplace Transform and its Relation to Fourier Transform

Laplace Transform and its Relation to Fourier Transform Chaper 6 Laplace Transform and is Relaion o Fourier Transform (A Brief Summary) Gis of he Maer 2 Domains of Represenaion Represenaion of signals and sysems Time Domain Coninuous Discree Time Time () [n]

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

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

2.7. Some common engineering functions. Introduction. Prerequisites. Learning Outcomes

2.7. Some common engineering functions. Introduction. Prerequisites. Learning Outcomes Some common engineering funcions 2.7 Inroducion This secion provides a caalogue of some common funcions ofen used in Science and Engineering. These include polynomials, raional funcions, he modulus funcion

More information

System of Linear Differential Equations

System of Linear Differential Equations Sysem of Linear Differenial Equaions In "Ordinary Differenial Equaions" we've learned how o solve a differenial equaion for a variable, such as: y'k5$e K2$x =0 solve DE yx = K 5 2 ek2 x C_C1 2$y''C7$y

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

Block Diagram of a DCS in 411

Block Diagram of a DCS in 411 Informaion source Forma A/D From oher sources Pulse modu. Muliplex Bandpass modu. X M h: channel impulse response m i g i s i Digial inpu Digial oupu iming and synchronizaion Digial baseband/ bandpass

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

Lab 10: RC, RL, and RLC Circuits

Lab 10: RC, RL, and RLC Circuits Lab 10: RC, RL, and RLC Circuis In his experimen, we will invesigae he behavior of circuis conaining combinaions of resisors, capaciors, and inducors. We will sudy he way volages and currens change in

More information

Linear Time-invariant systems, Convolution, and Cross-correlation

Linear Time-invariant systems, Convolution, and Cross-correlation Linear Time-invarian sysems, Convoluion, and Cross-correlaion (1) Linear Time-invarian (LTI) sysem A sysem akes in an inpu funcion and reurns an oupu funcion. x() T y() Inpu Sysem Oupu y() = T[x()] An

More information

Let us start with a two dimensional case. We consider a vector ( x,

Let us start with a two dimensional case. We consider a vector ( x, Roaion marices We consider now roaion marices in wo and hree dimensions. We sar wih wo dimensions since wo dimensions are easier han hree o undersand, and one dimension is a lile oo simple. However, our

More information

6.003: Signals and Systems

6.003: Signals and Systems 6.003: Signals and Sysems Fourier Series November 1, 2011 1 Las Time: Describing Signals by Frequency Conen Harmonic conen is naural way o describe some kinds of signals. Ex: musical insrumens (hp://heremin.music.uiowa.edu/mis.hml)

More information

EECS20n, Solution to Midterm 2, 11/17/00

EECS20n, Solution to Midterm 2, 11/17/00 EECS20n, Soluion o Miderm 2, /7/00. 0 poins Wrie he following in Caresian coordinaes (i.e. in he form x + jy) (a) 2 poins j 3 j 2 + j += j ++j +=2 (b) 2 poins ( j)/( + j) = j (c) 2 poins cos π/4+jsin π/4

More information

A Bayesian Approach to Spectral Analysis

A Bayesian Approach to Spectral Analysis Chirped Signals A Bayesian Approach o Specral Analysis Chirped signals are oscillaing signals wih ime variable frequencies, usually wih a linear variaion of frequency wih ime. E.g. f() = A cos(ω + α 2

More information

STATE PLANE ANALYSIS, AVERAGING,

STATE PLANE ANALYSIS, AVERAGING, CHAPER 3 SAE PLAE AALYSIS, AVERAGIG, AD OHER AALYICAL OOLS he sinusoidal approximaions used in he previous chaper break down when he effecs of harmonics are significan. his is a paricular problem in he

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

Homework 4 SOLUTION EE235, Summer 2012

Homework 4 SOLUTION EE235, Summer 2012 Homework 4 SOLUTION EE235, Summer 202. Causal and Sable. These are impulse responses for LTI sysems. Which of hese LTI sysem impulse responses represen BIBO sable sysems? Which sysems are causal? (a) h()

More information

9. Alternating currents

9. Alternating currents WS 9. Alernaing currens 9.1 nroducion Besides ohmic resisors, capaciors and inducions play an imporan role in alernaing curren (AC circuis as well. n his experimen, one shall invesigae heir behaviour in

More information

LAPLACE TRANSFORM AND TRANSFER FUNCTION

LAPLACE TRANSFORM AND TRANSFER FUNCTION CHBE320 LECTURE V LAPLACE TRANSFORM AND TRANSFER FUNCTION Professor Dae Ryook Yang Spring 2018 Dep. of Chemical and Biological Engineering 5-1 Road Map of he Lecure V Laplace Transform and Transfer funcions

More information

SEC. 6.3 Unit Step Function (Heaviside Function). Second Shifting Theorem (t-shifting) 217. Second Shifting Theorem (t-shifting)

SEC. 6.3 Unit Step Function (Heaviside Function). Second Shifting Theorem (t-shifting) 217. Second Shifting Theorem (t-shifting) SEC. 6.3 Uni Sep Funcion (Heaviside Funcion). Second Shifing Theorem (-Shifing) 7. PROJECT. Furher Resuls by Differeniaion. Proceeding as in Example, obain (a) and from his and Example : (b) formula, (c),

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

8. Basic RL and RC Circuits

8. Basic RL and RC Circuits 8. Basic L and C Circuis This chaper deals wih he soluions of he responses of L and C circuis The analysis of C and L circuis leads o a linear differenial equaion This chaper covers he following opics

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 #7. EECS490: Digital Image Processing. Image Processing Example Fuzzy logic. Fourier Transform. Basics Image processing examples

Lecture #7. EECS490: Digital Image Processing. Image Processing Example Fuzzy logic. Fourier Transform. Basics Image processing examples Lecure #7 Image Processing Example Fuzzy logic Basics Image processing examples Fourier Transorm Inner produc, basis uncions Fourier series Image Processing Example original image Laplacian o image (c)

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 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

EE 313 Linear Signals & Systems (Fall 2018) Solution Set for Homework #8 on Continuous-Time Signals & Systems

EE 313 Linear Signals & Systems (Fall 2018) Solution Set for Homework #8 on Continuous-Time Signals & Systems EE 33 Linear Signals & Sysems (Fall 08) Soluion Se for Homework #8 on Coninuous-Time Signals & Sysems By: Mr. Houshang Salimian & Prof. Brian L. Evans Here are several useful properies of he Dirac dela

More information

Course Notes for EE227C (Spring 2018): Convex Optimization and Approximation

Course Notes for EE227C (Spring 2018): Convex Optimization and Approximation Course Noes for EE7C Spring 018: Convex Opimizaion and Approximaion Insrucor: Moriz Hard Email: hard+ee7c@berkeley.edu Graduae Insrucor: Max Simchowiz Email: msimchow+ee7c@berkeley.edu Ocober 15, 018 3

More information

L1, L2, N1 N2. + Vout. C out. Figure 2.1.1: Flyback converter

L1, L2, N1 N2. + Vout. C out. Figure 2.1.1: Flyback converter page 11 Flyback converer The Flyback converer belongs o he primary swiched converer family, which means here is isolaion beween in and oupu. Flyback converers are used in nearly all mains supplied elecronic

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

t is a basis for the solution space to this system, then the matrix having these solutions as columns, t x 1 t, x 2 t,... x n t x 2 t...

t is a basis for the solution space to this system, then the matrix having these solutions as columns, t x 1 t, x 2 t,... x n t x 2 t... Mah 228- Fri Mar 24 5.6 Marix exponenials and linear sysems: The analogy beween firs order sysems of linear differenial equaions (Chaper 5) and scalar linear differenial equaions (Chaper ) is much sronger

More information

Demodulation of Digitally Modulated Signals

Demodulation of Digitally Modulated Signals Addiional maerial for TSKS1 Digial Communicaion and TSKS2 Telecommunicaion Demodulaion of Digially Modulaed Signals Mikael Olofsson Insiuionen för sysemeknik Linköpings universie, 581 83 Linköping November

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

t + t sin t t cos t sin t. t cos t sin t dt t 2 = exp 2 log t log(t cos t sin t) = Multiplying by this factor and then integrating, we conclude that

t + t sin t t cos t sin t. t cos t sin t dt t 2 = exp 2 log t log(t cos t sin t) = Multiplying by this factor and then integrating, we conclude that ODEs, Homework #4 Soluions. Check ha y ( = is a soluion of he second-order ODE ( cos sin y + y sin y sin = 0 and hen use his fac o find all soluions of he ODE. When y =, we have y = and also y = 0, so

More information