Convolution. Lecture #6 2CT.3 8. BME 333 Biomedical Signals and Systems - J.Schesser

Size: px
Start display at page:

Download "Convolution. Lecture #6 2CT.3 8. BME 333 Biomedical Signals and Systems - J.Schesser"

Transcription

1 Convoluion Lecure #6 C.3 8

2 Deiniion When we compue he ollowing inegral or τ and τ we say ha he we are convoluing wih g d his says: ae τ, lip i convolve in ime -τ, hen displace i in ime by seconds -τ, and muliply i by τ. Finally, inegrae he produc over all τ.

3 Properies o Convoluion Firs some shorhand: Commuaive: Associaive: Disribuive: d ] [ ] [ 3 3 ] [ ] [ ] [ 3 3

4 A Graphical Example o How o Perorm a Convoluion =A- τ/ A B hτ C h d h-τ B h-τ B τ τ - τ -- τ We need o loo a 5 cases: <, < <, 3 < <, 4 < < 3, & 5 > 3 For case and case 5, C= since here is no overlap. B h-τ, < A h-τ, >3 A B τ 3 τ 3

5 Graphical Convoluion Example Coninued Case : < < Case 3: < < Case 4: < < 3 AB/ 3 h-τ, < < B A -- h-τ, < < 3 A B -- 3 h-τ, < < B A -- C τ C τ τ AB d AB AB / AB d C AB[ AB / AB AB d ] 4 AB

6 Malab Code clear all; endpulse=; s=.; endpoin=; n=-endpoin:s:endpoin; nn=-endpoin*:s:endpoin*; pulser=n>=&n<=endpulse; pulse=*pulser; ripulse=n>=&n<=; ri=-n.*ripulse; subplo,, plon,ri,'r',n,pulse,'b'; ile'signals'; xlabel'ime Seconds'; axis[- min[minri minpulse].*max[maxri maxpulse]]; c=convpulse,ri*s; subplo,, plonn,c; ile'convoluion'; xlabel'shi Seconds'; axis[- minc.*maxc]; ime Seconds Convoluion.4. Signals Shi Seconds 5

7 Inegraion o Convoluion Inegral Case : C AB d AB AB { } AB { } AB { } AB { } AB{ } Case 3: From he inegraion rom Case C AB d AB AB{ } AB{ } AB Case 4: 3 From he inegraion rom Case C AB d AB AB { } AB { } AB { } AB { } AB{ } 6

8 7 Convoluion and Sysems For an LI sysem, le s deine h as he sysem response o a uni impulse source, δ. hen he ollowing mus be rue: superposiion scalar ime invariance h x x h x x h h y x

9 Convoluion and Sysems Coninued Consruc x as he sum o uni impulse slices. he irs expression represens he slices o he source oaled as approaches ininiy. Le Side Equals lim x approaches x d x x x h x x h d y Consruc y as he sum o slices o he response due o an uni impulse uncion: xδh-δ. he inegral on he righ is he convoluion o x and h. Righ Side Equals lim x h approaches x h d his resul is very imporan since i says ha i one nows he impulse response o a sysem hen he oupu response or any given inpu source can be ound by convolving he inpu wih he impulse response. 8

10 Impulse Response and Causaliy y o does no depend on x i ha occur a imes aer o, i > o. y h x d h x d h x d o o o o For his inegral h x d, is posiive,, and or x x we have i o i o o o o For his inegral h x d, is negaive,, and or x x we have i o i o hereore or y o be causal his h x d mus be zero. For his o happen h,. o o o o 9

11 Calculaing he Uni Impulse Response, h R Vs i L Le s irs loo a mehods:. Narrow Pulse approximaion. Diereniaing u Beer Mehods o Come 3

12 Narrow Pulse Approximaion ε = widh o pulse ε heigh o pulse As ε, he narrow pulse = /ε [u-u-ε] δ So hen le s irs loo a he response o u: di Vs u i R L d i = Vs/R -e -R/L u Now we consruc he narrow pulse response: i = Vs/R /ε {-e -R/L u - e -R/L-ε u-ε} 3

13 Narrow Pulse Approximaion Coninued i = Vs/R/ε [-e -R/L u -e -R/L-ε u-ε] OR, or < i= Vs/R/ε [-e -R/L ] or < ε Vs/R/ε [-e -R/L ] -e -R/L-ε ], or > ε OR, or < i= Vs/R/ε [-e -R/L ], or < ε Vs/R/ε [e R/Lε e -R/L ], or > ε 3

14 aylor Series Approximaion or e x ax ax e! or x, e ax ax! 3 ax ax ax...!! 3! we can drop he higher order erms : ax! ax 33

15 Narrow Pulse Approximaion Coninued Applying he approximaion or e x, Vs= he uni impulse uncion and α = R/L, or < i= Vs/R/ε [-e -R/L ]= Vs/R/εR/L=Vs/Rα/ε, or < ε Vs/R/ε [e +R/Lε e -R/L ]= Vs/R/εαεe -α, or > ε, or < i= /Rα/ε=α/R, or <ε /R αe -α, or > ε h lim i e R u 34

16 Diereniaing he Uni Sep uncion he response due o a Uni Sep uncion is i = Vs/R -e -α u and since du, hen d h di d h [ e u e ] R [ e e u ] R e R u 35

17 Convoluion or Discree Sysems For an LI sysem, le s deine h[n] as he sysem response o a uni impulse source, δ[n]. δ[n]=, n= and or n We have: x[n]= Σ x[m]δ[n-m] y[n]=σ x[m] h[n-m] In addiion he same convoluion properies hold: Commuaive Associaive Disribuive [ n] [ n] [ n] [ n] [ n] { [ n] 3[ n]} { [ n] [ n]} 3[ n] [ n] { [ n] 3[ n]} { [ n] [ n]} { [ n] 3[ ]} 36

18 Sabiliy o Sysems Coninuous Sysems I a sysem is sable, i.e., Bounded Inpu, Bounded Oupu BIBO, he ollowing mus be rue: x, hen y y h x d h x d x max max OR However, his is no always he case. Posiive Feedbac causes insabiliy h d h d 37

19 Wha is needed or BIBO For a coninuous ime sysem, he poles o Hp mus lie wihin he le hand complex plane such ha Re s i < where s i are he poles o Hp. his will assure ha he ree response will be damped and no grow exponenially. HIS IS WHY WE SUDIED SOLUIONS OF LINEAR ODE IN ERMS OF SOURCE-FREE AND SOURCE COMPONENS. HIS IMPLIES HA Hp AND HE IMPULSE RESPONSE, h, MAY BE RELAED. 38

20 Sabiliy o Sysems Discree Sysems I a sysem is sable, i.e., Bounded Inpu, Bounded Oupu BIBO, he ollowing mus be rue: x[n], hen y[n] y[n] h[m]x[n m] x max h[m] OR h[m] However, his is no always he case. Posiive Feedbac causes insabiliy 39

21 Wha is needed or BIBO For a discree ime sysem, he eigenvalues o hn [ ] mus lie wihin he uni circle n such ha z where z are he eigenvalues o h[ n] Az. i i i i i his will assure ha ree response will no diverge and hn [ ] Using he ormula or he parial sums o a geomeric series, where N is he number o roos o he Characerisic Equaion L L N N L N L N n n i lim hn [ ] lim zi lim zi L L L L n n i i n i zi i z i z, lim lim i L N N L n i zi L L n i i zi z z angle z i i i L L z z Langle z i i i L n n i A z i n i <. z lim ; provided zi z L lim z lim{ z Langle z } lim{ z } { Langle z } i i i i L L L L lim{ z } ; z andlim{ z } ; z L i i i i L L L i i 4

22 Homewor Convoluion Veriy your all your resuls o hese convoluion problems using Malab and is conv uncion. Problem Assume ha a sysem response is given by he ollowing: h Sech he response o a u, b u-u-a or a=.5, a=, and a=5, and c evaluae e - u a = and = Problem Assume ha a sysem response is given by he ollowing: h /3 3 Evaluae he response o e - u a = and =3

23 Homewor Sabiliy Deermine he sabiliy o he ollowing sysems wih poles in he complex plane, describe he orm o he ransien response: Imaginary axis Imaginary axis Imaginary axis Real axis Real axis Real axis C.3., C.3. 4

Signals and Systems Linear Time-Invariant (LTI) Systems

Signals and Systems Linear Time-Invariant (LTI) Systems Signals and Sysems Linear Time-Invarian (LTI) Sysems Chang-Su Kim Discree-Time LTI Sysems Represening Signals in Terms of Impulses Sifing propery 0 x[ n] x[ k] [ n k] k x[ 2] [ n 2] x[ 1] [ n1] x[0] [

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

Signals and Systems Profs. Byron Yu and Pulkit Grover Fall Midterm 1 Solutions

Signals and Systems Profs. Byron Yu and Pulkit Grover Fall Midterm 1 Solutions 8-90 Signals and Sysems Profs. Byron Yu and Pulki Grover Fall 07 Miderm Soluions Name: Andrew ID: Problem Score Max 0 8 4 6 5 0 6 0 7 8 9 0 6 Toal 00 Miderm Soluions. (0 poins) Deermine wheher he following

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

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

( ) ( ) 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

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

Theory of! Partial Differential Equations!

Theory of! Partial Differential Equations! hp://www.nd.edu/~gryggva/cfd-course/! Ouline! Theory o! Parial Dierenial Equaions! Gréar Tryggvason! Spring 011! Basic Properies o PDE!! Quasi-linear Firs Order Equaions! - Characerisics! - Linear and

More information

Chapter 8 The Complete Response of RL and RC Circuits

Chapter 8 The Complete Response of RL and RC Circuits Chaper 8 The Complee Response of RL and RC Circuis Seoul Naional Universiy Deparmen of Elecrical and Compuer Engineering Wha is Firs Order Circuis? Circuis ha conain only one inducor or only one capacior

More information

Theory of! Partial Differential Equations-I!

Theory of! Partial Differential Equations-I! hp://users.wpi.edu/~grear/me61.hml! Ouline! Theory o! Parial Dierenial Equaions-I! Gréar Tryggvason! Spring 010! Basic Properies o PDE!! Quasi-linear Firs Order Equaions! - Characerisics! - Linear and

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

The Laplace Transform

The Laplace Transform The Laplace Transform Previous basis funcions: 1, x, cosx, sinx, exp(jw). New basis funcion for he LT => complex exponenial funcions LT provides a broader characerisics of CT signals and CT LTI sysems

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

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

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

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

EE 301 Lab 2 Convolution

EE 301 Lab 2 Convolution EE 301 Lab 2 Convoluion 1 Inroducion In his lab we will gain some more experience wih he convoluion inegral and creae a scrip ha shows he graphical mehod of convoluion. 2 Wha you will learn This lab will

More information

5. Response of Linear Time-Invariant Systems to Random Inputs

5. Response of Linear Time-Invariant Systems to Random Inputs Sysem: 5. Response of inear ime-invarian Sysems o Random Inpus 5.. Discree-ime linear ime-invarian (IV) sysems 5... Discree-ime IV sysem IV sysem xn ( ) yn ( ) [ xn ( )] Inpu Signal Sysem S Oupu Signal

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

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

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

EECE 301 Signals & Systems Prof. Mark Fowler

EECE 301 Signals & Systems Prof. Mark Fowler EECE 31 Signals & Sysems Prof. Mark Fowler Noe Se #1 C-T Sysems: Convoluion Represenaion Reading Assignmen: Secion 2.6 of Kamen and Heck 1/11 Course Flow Diagram The arrows here show concepual flow beween

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

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

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

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

Ordinary dierential equations

Ordinary dierential equations Chaper 5 Ordinary dierenial equaions Conens 5.1 Iniial value problem........................... 31 5. Forward Euler's mehod......................... 3 5.3 Runge-Kua mehods.......................... 36

More information

Homework 6 AERE331 Spring 2019 Due 4/24(W) Name Sec. 1 / 2

Homework 6 AERE331 Spring 2019 Due 4/24(W) Name Sec. 1 / 2 Homework 6 AERE33 Spring 9 Due 4/4(W) Name Sec / PROBLEM (5p In PROBLEM 4 of HW4 we used he frequency domain o design a yaw/rudder feedback conrol sysem for a plan wih ransfer funcion 46 Gp () s The conroller

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

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

MAE143A Signals & Systems - Homework 2, Winter 2014 due by the end of class Thursday January 23, 2014.

MAE143A Signals & Systems - Homework 2, Winter 2014 due by the end of class Thursday January 23, 2014. MAE43A Signals & Sysems - Homework, Winer 4 due by he end of class Thursday January 3, 4. Quesion Zener diode malab [Chaparro Quesion.] A zener diode circui is such ha an oupu corresponding o an inpu v

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

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

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

in Engineering Prof. Dr. Michael Havbro Faber ETH Zurich, Switzerland Swiss Federal Institute of Technology

in Engineering Prof. Dr. Michael Havbro Faber ETH Zurich, Switzerland Swiss Federal Institute of Technology Risk and Saey in Engineering Pro. Dr. Michael Havbro Faber ETH Zurich, Swizerland Conens o Today's Lecure Inroducion o ime varian reliabiliy analysis The Poisson process The ormal process Assessmen o he

More information

d 1 = c 1 b 2 - b 1 c 2 d 2 = c 1 b 3 - b 1 c 3

d 1 = c 1 b 2 - b 1 c 2 d 2 = c 1 b 3 - b 1 c 3 and d = c b - b c c d = c b - b c c This process is coninued unil he nh row has been compleed. The complee array of coefficiens is riangular. Noe ha in developing he array an enire row may be divided or

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

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

1 1 + x 2 dx. tan 1 (2) = ] ] x 3. Solution: Recall that the given integral is improper because. x 3. 1 x 3. dx = lim dx.

1 1 + x 2 dx. tan 1 (2) = ] ] x 3. Solution: Recall that the given integral is improper because. x 3. 1 x 3. dx = lim dx. . Use Simpson s rule wih n 4 o esimae an () +. Soluion: Since we are using 4 seps, 4 Thus we have [ ( ) f() + 4f + f() + 4f 3 [ + 4 4 6 5 + + 4 4 3 + ] 5 [ + 6 6 5 + + 6 3 + ]. 5. Our funcion is f() +.

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

SUFFICIENT CONDITIONS FOR EXISTENCE SOLUTION OF LINEAR TWO-POINT BOUNDARY PROBLEM IN MINIMIZATION OF QUADRATIC FUNCTIONAL

SUFFICIENT CONDITIONS FOR EXISTENCE SOLUTION OF LINEAR TWO-POINT BOUNDARY PROBLEM IN MINIMIZATION OF QUADRATIC FUNCTIONAL HE PUBLISHING HOUSE PROCEEDINGS OF HE ROMANIAN ACADEMY, Series A, OF HE ROMANIAN ACADEMY Volume, Number 4/200, pp 287 293 SUFFICIEN CONDIIONS FOR EXISENCE SOLUION OF LINEAR WO-POIN BOUNDARY PROBLEM IN

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

(1) (2) Differentiation of (1) and then substitution of (3) leads to. Therefore, we will simply consider the second-order linear system given by (4)

(1) (2) Differentiation of (1) and then substitution of (3) leads to. Therefore, we will simply consider the second-order linear system given by (4) Phase Plane Analysis of Linear Sysems Adaped from Applied Nonlinear Conrol by Sloine and Li The general form of a linear second-order sysem is a c b d From and b bc d a Differeniaion of and hen subsiuion

More information

Chapter #1 EEE8013 EEE3001. Linear Controller Design and State Space Analysis

Chapter #1 EEE8013 EEE3001. Linear Controller Design and State Space Analysis Chaper EEE83 EEE3 Chaper # EEE83 EEE3 Linear Conroller Design and Sae Space Analysis Ordinary Differenial Equaions.... Inroducion.... Firs Order ODEs... 3. Second Order ODEs... 7 3. General Maerial...

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

THE 2-BODY PROBLEM. FIGURE 1. A pair of ellipses sharing a common focus. (c,b) c+a ROBERT J. VANDERBEI

THE 2-BODY PROBLEM. FIGURE 1. A pair of ellipses sharing a common focus. (c,b) c+a ROBERT J. VANDERBEI THE 2-BODY PROBLEM ROBERT J. VANDERBEI ABSTRACT. In his shor noe, we show ha a pair of ellipses wih a common focus is a soluion o he 2-body problem. INTRODUCTION. Solving he 2-body problem from scrach

More information

4/9/2012. Signals and Systems KX5BQY EE235. Today s menu. System properties

4/9/2012. Signals and Systems   KX5BQY EE235. Today s menu. System properties Signals and Sysems hp://www.youube.com/v/iv6fo KX5BQY EE35 oday s menu Good weeend? Sysem properies iy Superposiion! Sysem properies iy: A Sysem is if i mees he following wo crieria: If { x( )} y( ) and

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

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

F This leads to an unstable mode which is not observable at the output thus cannot be controlled by feeding back.

F This leads to an unstable mode which is not observable at the output thus cannot be controlled by feeding back. Lecure 8 Las ime: Semi-free configuraion design This is equivalen o: Noe ns, ener he sysem a he same place. is fixed. We design C (and perhaps B. We mus sabilize if i is given as unsable. Cs ( H( s = +

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

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

CE 395 Special Topics in Machine Learning

CE 395 Special Topics in Machine Learning CE 395 Special Topics in Machine Learning Assoc. Prof. Dr. Yuriy Mishchenko Fall 2017 DIGITAL FILTERS AND FILTERING Why filers? Digial filering is he workhorse of digial signal processing Filering is a

More information

EECE.3620 Signal and System I

EECE.3620 Signal and System I EECE.360 Signal and Sysem I Hengyong Yu, PhD Associae Professor Deparmen of Elecrical and Compuer Engineering Universiy of Massachuses owell EECE.360 Signal and Sysem I Ch.9.4. Geomeric Evaluaion of he

More information

Characteristics of Linear System

Characteristics of Linear System Characerisics o Linear Sysem h g h : Impulse response F G : Frequency ranser uncion Represenaion o Sysem in ime an requency. Low-pass iler g h G F he requency ranser uncion is he Fourier ransorm o he impulse

More information

Math Week 14 April 16-20: sections first order systems of linear differential equations; 7.4 mass-spring systems.

Math Week 14 April 16-20: sections first order systems of linear differential equations; 7.4 mass-spring systems. Mah 2250-004 Week 4 April 6-20 secions 7.-7.3 firs order sysems of linear differenial equaions; 7.4 mass-spring sysems. Mon Apr 6 7.-7.2 Sysems of differenial equaions (7.), and he vecor Calculus we need

More information

Math Final Exam Solutions

Math Final Exam Solutions Mah 246 - Final Exam Soluions Friday, July h, 204 () Find explici soluions and give he inerval of definiion o he following iniial value problems (a) ( + 2 )y + 2y = e, y(0) = 0 Soluion: In normal form,

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

Calculus Tricks #1. So if you understand derivatives, you ll understand the course material much better. a few preliminaries exponents

Calculus Tricks #1. So if you understand derivatives, you ll understand the course material much better. a few preliminaries exponents Calculus Tricks # Eric Doviak Calculus is no a pre-requisie or his course. However, he oundaions o economics are based on calculus, so wha we ll be discussing over he course o he semeser is he inuiion

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

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

Topic Astable Circuits. Recall that an astable circuit has two unstable states;

Topic Astable Circuits. Recall that an astable circuit has two unstable states; Topic 2.2. Asable Circuis. Learning Objecives: A he end o his opic you will be able o; Recall ha an asable circui has wo unsable saes; Explain he operaion o a circui based on a Schmi inverer, and esimae

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

KEEE313(03) Signals and Systems. Chang-Su Kim

KEEE313(03) Signals and Systems. Chang-Su Kim KEEE313(03) Signals and Sysems Chang-Su Kim Course Informaion Course homepage hp://mcl.korea.ac.kr Lecurer Chang-Su Kim Office: Engineering Bldg, Rm 508 E-mail: changsukim@korea.ac.kr Tuor 허육 (yukheo@mcl.korea.ac.kr)

More information

Linear Circuit Elements

Linear Circuit Elements 1/25/2011 inear ircui Elemens.doc 1/6 inear ircui Elemens Mos microwave devices can be described or modeled in erms of he hree sandard circui elemens: 1. ESISTANE () 2. INDUTANE () 3. APAITANE () For he

More information

Communication Systems, 5e

Communication Systems, 5e Communicaion Sysems, 5e Chaper : Signals and Specra A. Bruce Carlson Paul B. Crilly The McGraw-Hill Companies Chaper : Signals and Specra Line specra and ourier series Fourier ransorms Time and requency

More information

10/10/2011. Signals and Systems EE235. Today s menu. Chicken

10/10/2011. Signals and Systems EE235. Today s menu. Chicken Signals and Sysems EE35 Today s menu Homework 1 Due omorrow Ocober 14 h Lecure will be online Sysem properies Lineariy Time invariance Sabiliy Inveribiliy Causaliy Los of examples! Chicken Why did he chicken

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

Solutions to Assignment 1

Solutions to Assignment 1 MA 2326 Differenial Equaions Insrucor: Peronela Radu Friday, February 8, 203 Soluions o Assignmen. Find he general soluions of he following ODEs: (a) 2 x = an x Soluion: I is a separable equaion as we

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

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

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

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

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 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

Module 4: Time Response of discrete time systems Lecture Note 2

Module 4: Time Response of discrete time systems Lecture Note 2 Module 4: Time Response of discree ime sysems Lecure Noe 2 1 Prooype second order sysem The sudy of a second order sysem is imporan because many higher order sysem can be approimaed by a second order model

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

11/16/99 (T.F. Weiss) Lecture #18: Continuous time periodic signals. Outline: Fourier series of periodic functions. Motivation:

11/16/99 (T.F. Weiss) Lecture #18: Continuous time periodic signals. Outline: Fourier series of periodic functions. Motivation: /6/99 F Weiss) Lecure #8: Coninuous ime periodic signals Moivaion: Represenaion o coninuous ime, periodic signals in he requency domain Periodic signals occur requenly moion o planes and heir saellies,

More information

on the interval (x + 1) 0! x < ", where x represents feet from the first fence post. How many square feet of fence had to be painted?

on the interval (x + 1) 0! x < , where x represents feet from the first fence post. How many square feet of fence had to be painted? Calculus II MAT 46 Improper Inegrals A mahemaician asked a fence painer o complee he unique ask of paining one side of a fence whose face could be described by he funcion y f (x on he inerval (x + x

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

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

Tutorial Sheet #2 discrete vs. continuous functions, periodicity, sampling

Tutorial Sheet #2 discrete vs. continuous functions, periodicity, sampling 2.39 Tuorial Shee #2 discree vs. coninuous uncions, periodiciy, sampling We will encouner wo classes o signals in his class, coninuous-signals and discree-signals. The disinc mahemaical properies o each,

More information

Time Domain Transfer Function of the Induction Motor

Time Domain Transfer Function of the Induction Motor Sudies in Engineering and Technology Vol., No. ; Augus 0 ISSN 008 EISSN 006 Published by Redfame Publishing URL: hp://se.redfame.com Time Domain Transfer Funcion of he Inducion Moor N N arsoum Correspondence:

More information

6/27/2012. Signals and Systems EE235. Chicken. Today s menu. Why did the chicken cross the Möbius Strip? To get to the other er um

6/27/2012. Signals and Systems EE235. Chicken. Today s menu. Why did the chicken cross the Möbius Strip? To get to the other er um Signals and Sysems EE35 Chicken Why did he chicken cross he Möbius Srip? To ge o he oher er um Today s menu Sysem properies Lineariy Time invariance Sabiliy Inveribiliy Causaliy Los of examples! 1 Sysem

More information

Solutions of Sample Problems for Third In-Class Exam Math 246, Spring 2011, Professor David Levermore

Solutions of Sample Problems for Third In-Class Exam Math 246, Spring 2011, Professor David Levermore Soluions of Sample Problems for Third In-Class Exam Mah 6, Spring, Professor David Levermore Compue he Laplace ransform of f e from is definiion Soluion The definiion of he Laplace ransform gives L[f]s

More information

+ γ3 A = I + A 1 (1 + γ + γ2. = I + A 1 ( t H O M E W O R K # 4 Sebastian A. Nugroho October 5, 2017

+ γ3 A = I + A 1 (1 + γ + γ2. = I + A 1 ( t H O M E W O R K # 4 Sebastian A. Nugroho October 5, 2017 THE UNIVERSITY OF TEXAS AT SAN ANTONIO EE 543 LINEAR SYSTEMS AND CONTROL H O M E W O R K # 4 Sebasian A. Nugroho Ocober 5, 27 The objecive of his homework is o es your undersanding of he conen of Module

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

Lectures 29 and 30 BIQUADRATICS AND STATE SPACE OP AMP REALIZATIONS. I. Introduction

Lectures 29 and 30 BIQUADRATICS AND STATE SPACE OP AMP REALIZATIONS. I. Introduction EE-202/445, 3/18/18 9-1 R. A. DeCarlo Lecures 29 and 30 BIQUADRATICS AND STATE SPACE OP AMP REALIZATIONS I. Inroducion 1. The biquadraic ransfer funcion has boh a 2nd order numeraor and a 2nd order denominaor:

More information

Sub Module 2.6. Measurement of transient temperature

Sub Module 2.6. Measurement of transient temperature Mechanical Measuremens Prof. S.P.Venkaeshan Sub Module 2.6 Measuremen of ransien emperaure Many processes of engineering relevance involve variaions wih respec o ime. The sysem properies like emperaure,

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

Unsteady Flow Problems

Unsteady Flow Problems School of Mechanical Aerospace and Civil Engineering Unseady Flow Problems T. J. Craf George Begg Building, C41 TPFE MSc CFD-1 Reading: J. Ferziger, M. Peric, Compuaional Mehods for Fluid Dynamics H.K.

More information

Math 333 Problem Set #2 Solution 14 February 2003

Math 333 Problem Set #2 Solution 14 February 2003 Mah 333 Problem Se #2 Soluion 14 February 2003 A1. Solve he iniial value problem dy dx = x2 + e 3x ; 2y 4 y(0) = 1. Soluion: This is separable; we wrie 2y 4 dy = x 2 + e x dx and inegrae o ge The iniial

More information

Chapter #1 EEE8013 EEE3001. Linear Controller Design and State Space Analysis

Chapter #1 EEE8013 EEE3001. Linear Controller Design and State Space Analysis Chaper EEE83 EEE3 Chaper # EEE83 EEE3 Linear Conroller Design and Sae Space Analysis Ordinary Differenial Equaions.... Inroducion.... Firs Order ODEs... 3. Second Order ODEs... 7 3. General Maerial...

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

u(x) = e x 2 y + 2 ) Integrate and solve for x (1 + x)y + y = cos x Answer: Divide both sides by 1 + x and solve for y. y = x y + cos x

u(x) = e x 2 y + 2 ) Integrate and solve for x (1 + x)y + y = cos x Answer: Divide both sides by 1 + x and solve for y. y = x y + cos x . 1 Mah 211 Homework #3 February 2, 2001 2.4.3. y + (2/x)y = (cos x)/x 2 Answer: Compare y + (2/x) y = (cos x)/x 2 wih y = a(x)x + f(x)and noe ha a(x) = 2/x. Consequenly, an inegraing facor is found wih

More information

Basic definitions and relations

Basic definitions and relations Basic definiions and relaions Lecurer: Dmiri A. Molchanov E-mail: molchan@cs.u.fi hp://www.cs.u.fi/kurssi/tlt-2716/ Kendall s noaion for queuing sysems: Arrival processes; Service ime disribuions; Examples.

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

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

Zürich. ETH Master Course: L Autonomous Mobile Robots Localization II

Zürich. ETH Master Course: L Autonomous Mobile Robots Localization II Roland Siegwar Margaria Chli Paul Furgale Marco Huer Marin Rufli Davide Scaramuzza ETH Maser Course: 151-0854-00L Auonomous Mobile Robos Localizaion II ACT and SEE For all do, (predicion updae / ACT),

More information