Leo Lam © 2010-2012 Signals and Systems EE235. Leo Lam © 2010-2012 Convergence Two mathematicians are studying a convergent series. The first one says:

Slides:



Advertisements
Similar presentations
Leo Lam © Signals and Systems EE235. Leo Lam © Pet Q: Has the biomedical imaging engineer done anything useful lately? A: No, he's.
Advertisements

Leo Lam © Signals and Systems EE235 Lecture 16.
Leo Lam © Signals and Systems EE235 October 14 th Friday Online version.
Leo Lam © Signals and Systems EE235. Transformers Leo Lam ©
Leo Lam © Signals and Systems EE235. Fourier Transform: Leo Lam © Fourier Formulas: Inverse Fourier Transform: Fourier Transform:
Leo Lam © Signals and Systems EE235. Leo Lam © Futile Q: What did the monserous voltage source say to the chunk of wire? A: "YOUR.
Lecture 5: Linear Systems and Convolution
Continuous-Time Convolution EE 313 Linear Systems and Signals Fall 2005 Initial conversion of content to PowerPoint by Dr. Wade C. Schwartzkopf Prof. Brian.
Leo Lam © Signals and Systems EE235. Leo Lam © Fourier Transform Q: What did the Fourier transform of the arbitrary signal say to.
Leo Lam © Signals and Systems EE235 Lecture 27.
Leo Lam © Signals and Systems EE235. Leo Lam © Today’s menu Homework 2 due now Convolution!
Leo Lam © Signals and Systems EE235. So stable Leo Lam ©
Leo Lam © Signals and Systems EE235. Leo Lam © Arthur’s knights Who was the largest knight at King Arthur’s round table? Sir Cumfrence,
Leo Lam © Signals and Systems EE235 Lecture 31.
Leo Lam © Signals and Systems EE235 Leo Lam.
Leo Lam © Signals and Systems EE235. Leo Lam © x squared equals 9 x squared plus 1 equals y Find value of y.
Leo Lam © Signals and Systems EE235. Leo Lam © Fourier Transform Q: What did the Fourier transform of the arbitrary signal say to.
Leo Lam © Signals and Systems EE235 Lecture 14.
Leo Lam © Signals and Systems EE235. Leo Lam © Today’s menu Good weekend? System properties –Time Invariance –Linearity –Superposition!
Recall: RC circuit example x y x(t) R C y(t) + _ + _ assuming.
The Convolution Integral
Leo Lam © Signals and Systems EE235. Leo Lam © Surgery Five surgeons were taking a coffee break and were discussing their work. The.
Leo Lam © Signals and Systems EE235. Leo Lam © Today’s menu Fourier Series (Exponential form) Fourier Transform!
Leo Lam © Signals and Systems EE235. Leo Lam © Pet Q: Has the biomedical imaging engineer done anything useful lately? A: No, he's.
Leo Lam © Signals and Systems EE235. Summary: Convolution Leo Lam © Draw x() 2.Draw h() 3.Flip h() to get h(-) 4.Shift forward.
Continuous-Time Convolution Impulse Response Impulse response of a system is response of the system to an input that is a unit impulse (i.e., a.
Leo Lam © Signals and Systems EE235. Leo Lam © Stanford The Stanford Linear Accelerator Center was known as SLAC, until the big earthquake,
Chapter 2 Time Domain Analysis of CT System Basil Hamed
Leo Lam © Signals and Systems EE235 Leo Lam.
Leo Lam © Signals and Systems EE235 Lecture 20.
Leo Lam © Signals and Systems EE235 Lecture 25.
Leo Lam © Signals and Systems EE235. Leo Lam © Fourier Transform Q: What did the Fourier transform of the arbitrary signal say to.
( II ) This property is known as “convolution” ( الإلتواء التفاف ) From Chapter 2, we have Proof is shown next Chapter 3.
Leo Lam © Signals and Systems EE235. Leo Lam © Surgery Five surgeons were taking a coffee break and were discussing their work. The.
Leo Lam © Signals and Systems EE235 Leo Lam © Working with computers.
Leo Lam © Signals and Systems EE235 KX5BQY.
Leo Lam © Signals and Systems EE235 Leo Lam.
Leo Lam © Signals and Systems EE235 Leo Lam.
Leo Lam © Signals and Systems EE235 Leo Lam.
Leo Lam © Signals and Systems EE235. Leo Lam © Today’s menu Today: Fourier Series –“Orthogonality” –Fourier Series etc.
1 LTI Systems; Convolution September 4, 2000 EE 64, Section 1 ©Michael R. Gustafson II Pratt School of Engineering.
Leo Lam © Signals and Systems EE235 Lecture 25.
ENEE 322: Continuous-Time Fourier Transform (Chapter 4)
Leo Lam © Signals and Systems EE235. Leo Lam © Today’s menu LTI System – Impulse response Lead in to Convolution.
Leo Lam © Signals and Systems EE235 Lecture 26.
EE 309 Signal and Linear System Analysis
Signal Processing First
Signals and Systems EE235 Leo Lam ©
Signals and Systems EE235 Lecture 26 Leo Lam ©
Signals and Systems EE235 Leo Lam ©
Signals and Systems EE235 Lecture 13 Leo Lam ©
Signals and Systems EE235 Lecture 31 Leo Lam ©
Signals and Systems EE235 Leo Lam ©
Signals and Systems EE235 Leo Lam ©
Lecture 5: Linear Systems and Convolution
Signals and Systems EE235 Leo Lam Leo Lam ©
Signals and Systems EE235 Leo Lam ©
Chapter 3 Convolution Representation
Signals and Systems EE235 Leo Lam Leo Lam ©
Signals and Systems EE235 Leo Lam Leo Lam ©
Signals and Systems EE235 Lecture 14 Leo Lam ©
Signals and Systems EE235 Leo Lam ©
Signals and Systems EE235 Lecture 23 Leo Lam ©
Signals and Systems EE235 Leo Lam Leo Lam ©
Signals and Systems EE235 Leo Lam ©
Signals and Systems EE235 Leo Lam ©
Signals and Systems EE235 Leo Lam ©
Signals and Systems EE235 Lecture 14 Leo Lam ©
Signals and Systems EE235 Leo Lam Leo Lam ©
From Chapter 2, we have ( II ) Proof is shown next
Presentation transcript:

Leo Lam © Signals and Systems EE235

Leo Lam © Convergence Two mathematicians are studying a convergent series. The first one says: "Do you realize that the series converges even when all the terms are made positive?" The second one asks: "Are you sure?" "Absolutely!"

Leo Lam © Today’s menu Lab 3 this week Convolution!

Convolution Integral Leo Lam © Standard Notation The output of a system is its input convolved with its impulse response

Convolution (mathematically) Leo Lam © Use sampling property of delta: Evaluate integral to arrive at output signal: Does this make sense physically?

Convolution (graphically) Leo Lam © τ y(t=-5) -5 t Does not move wrt t -2 Goal: Find y(t) x( τ ) and h(t- τ ) no overlap, y(t)=0

Convolution (graphically) Leo Lam © τ t 2 -2 Overlapped at τ =0 y(t=-1)

Convolution (graphically) Leo Lam © Both overlapped y(t=1)

Convolution (graphically) Leo Lam © Overlapped at τ =2 y(t=3) Does it make sense?

Convolution (mathematically) Leo Lam © Using Linearity Let’s focus on this part

Convolution (mathematically) Leo Lam © Consider this part: Recall that: And the integral becomes:

Convolution (mathematically) Leo Lam © Same answer as the graphically method Apply delta rules:

Summary: Convolution Leo Lam © Draw x() 2.Draw h() 3.Flip h() to get h(-) 4.Shift forward in time by t to get h(t-) 5.Multiply x() and h(t-) for all values of  6.Integrate (add up) the product x()h(t-) over all  to get y(t) for this particular t value (you have to do this for every t that you are interested in)

Summary: Convolution Leo Lam © Flip Shift Multiply Integrate

Leo Lam © Summary Convolution!

y(t) at specific time t 0 Leo Lam © Flip Shift Multiply Integrate Here t 0 =3/4 y(t 0 =3/4)= ?3/4

y(t) at all t Leo Lam © At all t t<0 The product of these two signals is zero where they don’t overlap ShiftMultiplyIntegrate

y(t) at all t Leo Lam © At all t 0≤t<1 ShiftMultiplyIntegrate

y(t) at all t Leo Lam © At all t 1≤t<2 y(t)=2-t for 1≤t<2 ShiftMultiplyIntegrate

y(t) at all t Leo Lam © At all t t≥2 y(t)=0 for t≥2 (same as t<0, no overlap) ShiftMultiplyIntegrate

y(t) at all t Leo Lam © Combine it all –y(t)=0 for t 2 –y(t)=t for 0≤t<1 –y(t)=2-t for 1≤t<2

Leo Lam © Summary Convolution and first few examples