mit eecs: 6.003 signals and systems lecture notes …web.mit.edu/6.003/f11/www/handouts/lec08.pdfdt...
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![Page 1: MIT EECS: 6.003 Signals and Systems lecture notes …web.mit.edu/6.003/F11/www/handouts/lec08.pdfDT Convolution: Summary Representing an LTI system by a single signal. x[n] h[n] y[n]](https://reader034.vdocuments.us/reader034/viewer/2022042620/5f40ecf96746820fe1439514/html5/thumbnails/1.jpg)
6.003: Signals and Systems
Convolution
October 4, 2011
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Mid-term Examination #1
Wednesday, October 5, 7:30-9:30pm.
Rooms 26-302, 26-310, 26-322, 26-328.
No recitations on the day of the exam.
Coverage: CT and DT Systems, Z and Laplace Transforms
Lectures 1–7
Recitations 1–7
Homeworks 1–4
Homework 4 will not collected or graded. Solutions are posted.
Closed book: 1 page of notes (812 × 11 inches; front and back).
No calculators, computers, cell phones, music players, or other aids.
Designed as 1-hour exam; two hours to complete.
Prior term midterm exams have been posted on the 6.003 website.
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Multiple Representations of CT and DT Systems
Verbal descriptions: preserve the rationale.
Difference/differential equations: mathematically compact.
y[n] = x[n] + z0 y[n− 1] y(t) = x(t) + s0 y(t)
Block diagrams: illustrate signal flow paths.
+
Rz0
X Y + A
s0
X Y
Operator representations: analyze systems as polynomials.
Y
X= 1
1− z0RY
X= A
1− s0A
Transforms: representing diff. equations with algebraic equations.
H(z) = z
z − z0H(s) = 1
s− s0
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Convolution
Representing a system by a single signal.
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Responses to arbitrary signals
Although we have focused on responses to simple signals (δ[n], δ(t))we are generally interested in responses to more complicated signals.
How do we compute responses to a more complicated input signals?
No problem for difference equations / block diagrams.
→ use step-by-step analysis.
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Check Yourself
Example: Find y[3]
+ +
R R
X Y
when the input isx[n]
n
1
1. 1 2. 2 3. 3 4. 4 5. 5
0. none of the above
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Responses to arbitrary signals
Example.
+ +
R R
0
0 0
0
x[n]
n
y[n]
n
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Responses to arbitrary signals
Example.
+ +
R R
1
0 0
1
x[n]
n
y[n]
n
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Responses to arbitrary signals
Example.
+ +
R R
1
1 0
2
x[n]
n
y[n]
n
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Responses to arbitrary signals
Example.
+ +
R R
1
1 1
3
x[n]
n
y[n]
n
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Responses to arbitrary signals
Example.
+ +
R R
0
1 1
2
x[n]
n
y[n]
n
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Responses to arbitrary signals
Example.
+ +
R R
0
0 1
1
x[n]
n
y[n]
n
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Responses to arbitrary signals
Example.
+ +
R R
0
0 0
0
x[n]
n
y[n]
n
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Check Yourself
What is y[3]? 2
+ +
R R
0
0 0
0
x[n]
n
1y[n]
n
1. 1 2. 2 3. 3 4. 4 5. 5
0. none of the above
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Superposition
Break input into additive parts and sum the responses to the parts.
n
x[n]
y[n]
n
=
+
+
+
+
=
n
−1 0 1 2 3 4 5n
n
n
n
−1 0 1 2 3 4 5n
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Superposition
Break input into additive parts and sum the responses to the parts.
n
x[n]
y[n]
n
=
+
+
+
+
=
n
−1 0 1 2 3 4 5n
n
n
n
−1 0 1 2 3 4 5n
Superposition works because the system is linear.
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Linearity
A system is linear if its response to a weighted sum of inputs is equal
to the weighted sum of its responses to each of the inputs.
Given
systemx1[n] y1[n]
and
systemx2[n] y2[n]
the system is linear if
systemαx1[n] + βx2[n] αy1[n] + βy2[n]
is true for all α and β.
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Superposition
Break input into additive parts and sum the responses to the parts.
n
x[n]
y[n]
n
=
+
+
+
+
=
n
−1 0 1 2 3 4 5n
n
n
n
−1 0 1 2 3 4 5n
Superposition works if the system is linear.
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Superposition
Break input into additive parts and sum the responses to the parts.
n
x[n]
y[n]
n
=
+
+
+
+
=
n
−1 0 1 2 3 4 5n
n
n
n
−1 0 1 2 3 4 5n
Reponses to parts are easy to compute if system is time-invariant.
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Time-Invariance
A system is time-invariant if delaying the input to the system simply
delays the output by the same amount of time.
Given
systemx[n] y[n]
the system is time invariant if
systemx[n− n0] y[n− n0]
is true for all n0.
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Superposition
Break input into additive parts and sum the responses to the parts.
n
x[n]
y[n]
n
=
+
+
+
+
=
n
−1 0 1 2 3 4 5n
n
n
n
−1 0 1 2 3 4 5n
Superposition is easy if the system is linear and time-invariant.
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Structure of Superposition
If a system is linear and time-invariant (LTI) then its output is the
sum of weighted and shifted unit-sample responses.
systemδ[n] h[n]
systemδ[n− k] h[n− k]
systemx[k]δ[n− k] x[k]h[n− k]
systemx[n] =∞∑
k=−∞x[k]δ[n− k] y[n] =
∞∑k=−∞
x[k]h[n− k]
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Convolution
Response of an LTI system to an arbitrary input.
LTIx[n] y[n]
y[n] =∞∑
k=−∞x[k]h[n− k] ≡ (x ∗ h)[n]
This operation is called convolution.
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Notation
Convolution is represented with an asterisk.
∞∑k=−∞
x[k]h[n− k] ≡ (x ∗ h)[n]
It is customary (but confusing) to abbreviate this notation:
(x ∗ h)[n] = x[n] ∗ h[n]
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Notation
Do not be fooled by the confusing notation.
Confusing (but conventional) notation:∞∑
k=−∞x[k]h[n− k] = x[n] ∗ h[n]
x[n] ∗ h[n] looks like an operation of samples; but it is not!
x[1] ∗ h[1] 6= (x ∗ h)[1]
Convolution operates on signals not samples.
Unambiguous notation:∞∑
k=−∞x[k]h[n− k] ≡ (x ∗ h)[n]
The symbols x and h represent DT signals.
Convolving x with h generates a new DT signal x ∗ h.
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Structure of Convolution
y[n] =∞∑
k=−∞x[k]h[n− k]
−2−1 0 1 2 3 4 5n
x[n] h[n]∗
−2−1 0 1 2 3 4 5n
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Structure of Convolution
y[0] =∞∑
k=−∞x[k]h[0− k]
−2−1 0 1 2 3 4 5n
x[n] h[n]∗
−2−1 0 1 2 3 4 5n
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Structure of Convolution
y[0] =∞∑
k=−∞x[k]h[0− k]
−2−1 0 1 2 3 4 5k
x[k] h[k]∗
−2−1 0 1 2 3 4 5k
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Structure of Convolution
y[0] =∞∑
k=−∞x[k]h[0− k]
−2−1 0 1 2 3 4 5k
x[k] h[k]
h[−k]
∗flip
−2−1 0 1 2 3 4 5k
−2−1 0 1 2 3 4 5k
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Structure of Convolution
y[0] =∞∑
k=−∞x[k]h[0− k]
−2−1 0 1 2 3 4 5k
x[k] h[k]
h[0− k]
∗shift
−2−1 0 1 2 3 4 5k
−2−1 0 1 2 3 4 5k
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Structure of Convolution
y[0] =∞∑
k=−∞x[k]h[0− k]
−2−1 0 1 2 3 4 5k
x[k] h[k]
h[0− k]h[0− k]
∗multiply
−2−1 0 1 2 3 4 5k
−2−1 0 1 2 3 4 5k
−2−1 0 1 2 3 4 5k
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Structure of Convolution
y[0] =∞∑
k=−∞x[k]h[0− k]
k
x[k] h[k]
h[0− k]h[0− k]
x[k]h[0− k]
∗multiply
−2−1 0 1 2 3 4 5k
k−2−1 0 1 2 3 4 5
k
−2−1 0 1 2 3 4 5k
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Structure of Convolution
y[0] =∞∑
k=−∞x[k]h[0− k]
k
x[k] h[k]
h[0− k]h[0− k]
x[k]h[0− k]
∗sum
∞∑k=−∞
k
k−2−1 0 1 2 3 4 5
k
−2−1 0 1 2 3 4 5k
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Structure of Convolution
y[0] =∞∑
k=−∞x[k]h[0− k]
k
x[k] h[k]
h[0− k]h[0− k]
x[k]h[0− k]
∗
∞∑k=−∞
k
k−2−1 0 1 2 3 4 5
k
−2−1 0 1 2 3 4 5k = 1
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Structure of Convolution
y[1] =∞∑
k=−∞x[k]h[1− k]
k
x[k] h[k]
h[1− k]h[1− k]
x[k]h[1− k]
∗
∞∑k=−∞
k
k−2−1 0 1 2 3 4 5
k
−2−1 0 1 2 3 4 5k = 2
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Structure of Convolution
y[2] =∞∑
k=−∞x[k]h[2− k]
k
x[k] h[k]
h[2− k]h[2− k]
x[k]h[2− k]
∗
∞∑k=−∞
k
k−2−1 0 1 2 3 4 5
k
−2−1 0 1 2 3 4 5k = 3
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Structure of Convolution
y[3] =∞∑
k=−∞x[k]h[3− k]
k
x[k] h[k]
h[3− k]h[3− k]
x[k]h[3− k]
∗
∞∑k=−∞
k
k−2−1 0 1 2 3 4 5
k
−2−1 0 1 2 3 4 5k = 2
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Structure of Convolution
y[4] =∞∑
k=−∞x[k]h[4− k]
k
x[k] h[k]
h[4− k]h[4− k]
x[k]h[4− k]
∗
∞∑k=−∞
k
k−2−1 0 1 2 3 4 5
k
−2−1 0 1 2 3 4 5k = 1
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Structure of Convolution
y[5] =∞∑
k=−∞x[k]h[5− k]
k
x[k] h[k]
h[5− k]h[5− k]
x[k]h[5− k]
∗
∞∑k=−∞
k
k−2−1 0 1 2 3 4 5
k
−2−1 0 1 2 3 4 5k = 0
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Check Yourself
1 ∗ 1
Which plot shows the result of the convolution above?
1.1
2.1
3.1
4.1
5. none of the above
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Check Yourself
1 ∗ 1
Express mathematically:((23
)nu[n]
)∗((
23
)nu[n]
)=
∞∑k=−∞
((23
)ku[k]
)×
((23
)n−ku[n− k]
)
=n∑k=0
(23
)k×(
23
)n−k=
n∑k=0
(23
)n=(
23
)n n∑k=0
1
= (n+ 1)(
23
)nu[n]
= 1, 43 ,
43 ,
3227 ,
8081 , . . .
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Check Yourself
1 ∗ 1
Which plot shows the result of the convolution above? 3
1.1
2.1
3.1
4.1
5. none of the above
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DT Convolution: Summary
Representing an LTI system by a single signal.
h[n]x[n] y[n]
Unit-sample response h[n] is a complete description of an LTI system.
Given h[n] one can compute the response y[n] to any arbitrary input
signal x[n]:
y[n] = (x ∗ h)[n] ≡∞∑
k=−∞x[k]h[n− k]
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CT Convolution
The same sort of reasoning applies to CT signals.
t
x(t)
x(t) = lim∆→0
∑k
x(k∆)p(t− k∆)∆
where
t
p(t)
∆
1∆
As ∆→ 0, k∆→ τ , ∆→ dτ , and p(t)→ δ(t):
x(t)→∫ ∞−∞
x(τ)δ(t− τ)dτ
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Structure of Superposition
If a system is linear and time-invariant (LTI) then its output is the
integral of weighted and shifted unit-impulse responses.
systemδ(t) h(t)
systemδ(t− τ) h(t− τ)
systemx(τ)δ(t− τ) x(τ)h(t− τ)
systemx(t) =∫ ∞−∞
x(τ)δ(t− τ)dτ y(t) =∫ ∞−∞
x(τ)h(t− τ)dτ
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CT Convolution
Convolution of CT signals is analogous to convolution of DT signals.
DT: y[n] = (x ∗ h)[n] =∞∑
k=−∞x[k]h[n− k]
CT: y(t) = (x ∗ h)(t) =∫ ∞−∞
x(τ)h(t− τ)dτ
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Check Yourself
t
e−tu(t)
∗t
e−tu(t)
Which plot shows the result of the convolution above?
1.t
2.t
3.t
4.t
5. none of the above
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Check Yourself
Which plot shows the result of the following convolution?
t
e−tu(t)
∗t
e−tu(t)
(e−tu(t)
)∗(e−tu(t)
)=∫ ∞−∞
e−τu(τ)e−(t−τ)u(t− τ)dτ
=∫ t
0e−τe−(t−τ)dτ = e−t
∫ t
0dτ = te−tu(t)
t
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Check Yourself
t
e−tu(t)
∗t
e−tu(t)
Which plot shows the result of the convolution above? 4
1.t
2.t
3.t
4.t
5. none of the above
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Convolution
Convolution is an important computational tool.
Example: characterizing LTI systems
• Determine the unit-sample response h[n].• Calculate the output for an arbitrary input using convolution:
y[n] = (x ∗ h)[n] =∑
x[k]h[n− k]
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Applications of Convolution
Convolution is an important conceptual tool: it provides an impor-
tant new way to think about the behaviors of systems.
Example systems: microscopes and telescopes.
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Microscope
Images from even the best microscopes are blurred.
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Microscope
A perfect lens transforms a spherical wave of light from the target
into a spherical wave that converges to the image.
target image
Blurring is inversely related to the diameter of the lens.
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Microscope
A perfect lens transforms a spherical wave of light from the target
into a spherical wave that converges to the image.
target image
Blurring is inversely related to the diameter of the lens.
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Microscope
A perfect lens transforms a spherical wave of light from the target
into a spherical wave that converges to the image.
target image
Blurring is inversely related to the diameter of the lens.
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Microscope
Blurring can be represented by convolving the image with the optical
“point-spread-function” (3D impulse response).
target image
∗ =
Blurring is inversely related to the diameter of the lens.
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Microscope
Blurring can be represented by convolving the image with the optical
“point-spread-function” (3D impulse response).
target image
∗ =
Blurring is inversely related to the diameter of the lens.
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Microscope
Blurring can be represented by convolving the image with the optical
“point-spread-function” (3D impulse response).
target image
∗ =
Blurring is inversely related to the diameter of the lens.
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Microscope
Measuring the “impulse response” of a microscope.
Image diameter ≈ 6 times target diameter: target → impulse.
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Microscope
Images at different focal planes can be assembled to form a three-
dimensional impulse response (point-spread function).
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Microscope
Blurring along the optical axis is better visualized by resampling the
three-dimensional impulse response.
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Microscope
Blurring is much greater along the optical axis than it is across the
optical axis.
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Microscope
The point-spread function (3D impulse response) is a useful way to
characterize a microscope. It provides a direct measure of blurring,
which is an important figure of merit for optics.
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Hubble Space Telescope
Hubble Space Telescope (1990-)
http://hubblesite.org
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Hubble Space Telescope
Why build a space telescope?
Telescope images are blurred by the telescope lenses AND by at-
mospheric turbulence.
ha(x, y) hd(x, y)X Y
atmosphericblurring
blur due tomirror size
ht(x, y) = (ha ∗ hd)(x, y)X Y
ground-basedtelescope
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Hubble Space Telescope
Telescope blur can be respresented by the convolution of blur due
to atmospheric turbulence and blur due to mirror size.
−2 −1 0 1 2θ −2 −1 0 1 2θ −2 −1 0 1 2θ
−2 −1 0 1 2θ −2 −1 0 1 2θ −2 −1 0 1 2θ
ha(θ)
ha(θ)
hd(θ)
hd(θ)
ht(θ)
ht(θ)
∗
∗
=
=
d = 12cm
d = 1m
[arc-seconds]
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Hubble Space Telescope
The main optical components of the Hubble Space Telescope are
two mirrors.
http://hubblesite.org
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Hubble Space Telescope
The diameter of the primary mirror is 2.4 meters.
http://hubblesite.org
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Hubble Space Telescope
Hubble’s first pictures of distant stars (May 20, 1990) were more
blurred than expected.
expected early Hubble
point-spread image of
function distant star
http://hubblesite.org
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Hubble Space Telescope
The parabolic mirror was ground 2.2 µm too flat!
http://hubblesite.org
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Hubble Space Telescope
Corrective Optics Space Telescope Axial Replacement (COSTAR):
eyeglasses for Hubble!
Hubble COSTAR
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Hubble Space Telescope
Hubble images before and after COSTAR.
before after
http://hubblesite.org
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Hubble Space Telescope
Hubble images before and after COSTAR.
before after
http://hubblesite.org
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Hubble Space Telescope
Images from ground-based telescope and Hubble.
http://hubblesite.org
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Impulse Response: Summary
The impulse response is a complete description of a linear, time-
invariant system.
One can find the output of such a system by convolving the input
signal with the impulse response.
The impulse response is an especially useful description of some
types of systems, e.g., optical systems, where blurring is an impor-
tant figure of merit.