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Fourier Series and Fourier Transforms
Houshou Chen
Dept. of Electrical Engineering,
National Chung Hsing University
E-mail: [email protected]
H.S. Chen Fourier Series and Fourier Transforms 1
Why Fourier
1. eigenfunction ejwt
2. x(t) periodic ⇒ x(t) =∑∞
k=−∞ ckejkw0t
3. ek1w0t and ek2w0t (k1 6= k1) are orthogonal over [0, T ] ⇒
ck =1
T
∫ T
0
x(t)e−jkw0tdt
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H.S. Chen Fourier Series and Fourier Transforms 2
• Representing signals as superpositions of complex sinusoids not
only leads to a useful expression for the system output, but also
provides an insightful characterization of signals and systems.
• We represent the signal as the ’linear sum’ of the sinusoids. The
weight associated with a sinusoid of a given frequency represents
the contribution of that sinusoids to the overall signal.
• The study of signals and systems using sinusoidal representations
is termed Fourier analysis after Joseph Fourier (1768–1830).
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H.S. Chen Fourier Series and Fourier Transforms 3
• There are four distinct Fourier representations, each applicable to
a different class of signals, determined by the periodicity
properties of the signal and whether the signal is discrete or
continuous in time.
• Fourier series (FS) for periodic continuous-time signals
• Discrete time Fourier series (DTFS) for periodic discrete-time
signals
• Fourier transform (FT) for aperiodic continuous-time signals
• Discrete time Fourier transform (DTFT) for aperiodic
discrete-time signals
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H.S. Chen Fourier Series and Fourier Transforms 4
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H.S. Chen Fourier Series and Fourier Transforms 5
Fourier Series
• Consider representing a periodic signal as a weighted
superposition of complex sinusoids. Since the weighted
superposition must have the same period as the signal, each
sinusoid in the superposition must have the same period as the
signal.
• This implies that the frequency of each sinusoid must be an
integer multiple of the signal’s fundamental frequency.
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H.S. Chen Fourier Series and Fourier Transforms 6
• If x(t) is a continuous-time signal with fundamental period T ,
then we seek to represent x(t) by the FS:
x(t) =∑
k
A[k]ejkw0t,
where w0 = 2πT .
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H.S. Chen Fourier Series and Fourier Transforms 7
Fourier Transform
• In contrast to the case of the periodic signal, there are no
restrictions on the period of the sinusoids used to represent
aperiodic signals.
• Hence, the Fourier transform representations employ complex
sinusoids having a continuum of frequencies.
• The signal is represented as a weighted integral of complex
sinusoids where the variable of integration is the sinusoid’s
frequency.
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H.S. Chen Fourier Series and Fourier Transforms 8
• Continuous-time sinusoids with distinct frequencies are distinct,
so the FT involves frequencies from −∞ to ∞:
x(t) =1
2π
∫ ∞
−∞
X(jw)ejwtdw
• Here, X(jw)2π is the weight at frequency w.
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H.S. Chen Fourier Series and Fourier Transforms 9
FS
We will do the following step by step
1. The derivation of FS
2. Properties of FS
3. Some examples of FS
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H.S. Chen Fourier Series and Fourier Transforms 10
• Continuous-time periodic signals are represented by the Fourier
series (FS). We write the FS of a signal x(t) with fundamental
period T (fundamental frequency w0 = 2πT ) as
x(t) =
∞∑
k=−∞
X[k]ejkw0t
where
X[k] =1
T
∫ T
0
x(t)e−jkw0tdt
are the FS coefficients of the signal x(t).
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H.S. Chen Fourier Series and Fourier Transforms 11
• We say that x(t) and X[k] are an FS pair and denote this
relationship as
x(t)FS;w0←→ X[k]
• In some problem it is advantageous to represent the signal in the
time domain as x(t), while in others the FS coefficients X[k] offer
a more convenient description.
• The FS coefficients are known as a frequency domain
representation of x(t) because X[k] is the coefficient associated
with complex sinusoid at frequency kw0.
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H.S. Chen Fourier Series and Fourier Transforms 12
• The reason that x(t) and X[k] = ck with the following
relationship
x(t) =
∞∑
k=−∞
ckejkw0t
and
X[k] = ck =1
T
∫ T
0
x(t)e−jkw0tdt
The reason is that any two signals ek1w0t ek2w0t at distinct
frequencies k1w0 and k2w0 are orthogonal over [0, T ].
• The derivation is as follows.
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H.S. Chen Fourier Series and Fourier Transforms 13
since
∫ T
0
ejk1w0te−jk2w0tdt
=
∫ T
0
ej(k1−k2)w0tdt
=
1j(k1−k2)w0
ej(k1−k2)w0t|T0 ,if k1 6= k2∫ T
01dt ,if k1 = k2
=
1j(k1−k2)w0
(ej(k1−k2)2π − 1) ,if k1 6= k2
T ,if k1 = k2
=
0 ,if k1 6= k2
T ,if k1 = k2
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H.S. Chen Fourier Series and Fourier Transforms 14
I.e., any two signals ek1w0t ek2w0t at distinct frequencies k1w0 and
k1w0 are orthogonal over [0, T ], or in general over any period
[t0, t0 + T0]
We said that {ejkw0t}∞k=−∞ are orthogonal basis for x(t) whose
fundamental period is T0
Apply this fact to x(t), we have
x(t) =
∞∑
k=−∞
ckejkw0t
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H.S. Chen Fourier Series and Fourier Transforms 15
(x(t), ejmw0t) =
∫ T0
0
x(t)e−jmw0tdt
=
∫ T0
0
∞∑
k=−∞
ckejkw0te−jmw0tdt
=
∞∑
k=−∞
ck
∫ T0
0
ej(k−m)w0tdt
= T0cm
⇒ cm =1
T0
∫ T0
0
x(t)e−jmw0tdt for any m0 ∈ Z
I.e., we have the Fourier series pair for a periodic signal x(t) of
fundamental period T0.
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H.S. Chen Fourier Series and Fourier Transforms 16
⇒
x(t) =∑∞
k=−∞ ckejkw0t
ck = 1T0
(x(t), ejkw0t) = 1T0
∫
T0x(t)e−jkw0tdt
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H.S. Chen Fourier Series and Fourier Transforms 17
Recall:
V = 〈v1, ..., vn〉
v ∈ V ⇒ v =
n∑
i=1
aivi
if {v1, ..., vn} is an orthogonal basis
i.e.
(vi, vj) =
A ,i = j
0 ,i 6= j
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H.S. Chen Fourier Series and Fourier Transforms 18
then
(v, vj) = (
n∑
i=1
aivi, vj) =
n∑
i=1
ai(vi, vj) = Aaj
i.e. aj =1
A(v, vj) for ∀j
Therefore the coefficient aj of an orthogonal basis can be obtained
from the inner product between v and vj .
v =∑n
i=1 aivi
ai = 1A (v, vi)
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H.S. Chen Fourier Series and Fourier Transforms 19
For the space of periodic signals with period T0, we have
V = 〈ejkw0t〉∞k=−∞ w0 =2π
T0
Now 〈ejkw0t〉∞k=−∞ is an orthogonal basis
since
(ejk1w0t, ejk2w0t) =
T0 ,k1 = k2
0 ,k1 6= k2
⇒
x(t) =∑∞
k=−∞ ckejkw0t
ck = 1T0
(x(t), ejkw0t) = 1T0
∫
〈T0〉x(t)e−jkw0tdt
as we expect.
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H.S. Chen Fourier Series and Fourier Transforms 20
• If x(t) is real, we can use {cos(kw0t), sin(kw0t)}∞k=0 as the
orthogonal basis for x(t).
Original basis:
{ejkw0t}∞k=−∞
Since
ejkw0t+e−jkw0t
2 = cos(kw0t)
ejkw0t−e−jkw0t
2j = sin(kw0t)
we can replace {ejkw0t, e−jkw0t}∞k=1 by {cos(kw0t), sin(kw0t)}∞k=1
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H.S. Chen Fourier Series and Fourier Transforms 21
Now if x(t) is real, we have
ck =1
T0
∫ T0
0
x(t)e−jkw0tdt
c−k =1
T0
∫ T0
0
x(t)e−j(−k)w0tdt
=1
T0
∫ T0
0
x(t)ejkw0tdt
= (1
T0
∫ T0
0
x(t)e−jkw0tdt)∗
= c∗k
= |ck|e−j∠c−k
i.e. c−k = |c−k|ej∠c−k = |ck|e
−j∠ck⇒ |c−k| = |ck| and ∠c−k = −∠ck
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H.S. Chen Fourier Series and Fourier Transforms 22
x(t) =
∞∑
k=−∞
ckejkw0t
= c0 +
−1∑
k=−∞
ckejkw0t +
∞∑
k=1
ckejkw0t
= c0 +
∞∑
k=1
c−ke−jkw0t +
∞∑
k=1
ckejkw0t
= c0 +
∞∑
k=1
(c∗−ke−jkw0t)∗ +
∞∑
k=1
ckejkw0t
= c0 +
∞∑
k=1
[(cke−jkw0t)∗ + ckejkw0t]
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H.S. Chen Fourier Series and Fourier Transforms 23
= c0 +
∞∑
k=1
2Re{ckejkw0t}
= c0 +
∞∑
k=1
2Re{(Re{ck}+ jIm{ck})(cos(kw0t) + jsin(kw0t))}
= c0 +
∞∑
k=1
2(Re{ck}cos(kw0t)− Im{ck}sinkw0t)
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H.S. Chen Fourier Series and Fourier Transforms 24
⇒ x(t) = c0 +
∞∑
k=1
Akcos(kw0t) + Bksin(kw0t)
where Ak = 2Re{ck}
= 2Re{1
T0
∫ T0
0
x(t)e−jkw0tdt}
=2
T0
∫ T0
0
x(t)cos(kw0t)dt
Bk = −2Im{ck}
= −2Im{1
T0
∫ T0
0
x(t)e−jkw0tdt}
=2
T0
∫ T0
0
x(t)sin(kw0t)dt
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H.S. Chen Fourier Series and Fourier Transforms 25
Properties of Fourier Series
x(t)FS;w0←→ ak
y(t)FS;w0←→ bk
1.linearity
⇒ Ax(t) + By(t)FS;w0←→ Aak + Bbk
c(t) =∞∑
k=−∞
ckejkw0t
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H.S. Chen Fourier Series and Fourier Transforms 26
and
c(t) = Ax(t) + By(t) = A∑
k
akejkw0t + B∑
k
bkejkw0t
=∑
k
(Aak + Bbk)ejkw0t
⇒ ck = Aak + Bbk
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H.S. Chen Fourier Series and Fourier Transforms 27
2. time shifting
x(t)↔ ak
x(t− t0)↔ ake−jkw0t0
︸ ︷︷ ︸
bk
bk =1
T
∫
〈T 〉
x(t− t0)e−jkw0tdt
=1
T
∫ T
0
x(t− t0)e−jkw0tdt
=1
T
∫ T−t0
−t0
x(τ)e−jkw0(τ+t0)dτ
= (1
T
∫
〈T 〉
x(τ)e−jkw0τdτ)e−jkw0t0
= ake−jkw0t0
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H.S. Chen Fourier Series and Fourier Transforms 28
3. frequency shifting
x(t)↔ ak
x(t)ejMw0t ↔ bk = ak−M
bk =1
T
∫
〈T 〉
x(t)ejMw0te−jkw0tdt =1
T
∫
〈T 〉
x(t)e−j(k−M)w0tdt
= ak−M
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H.S. Chen Fourier Series and Fourier Transforms 29
4. conjugation
x(t)↔ ak
x∗(t)↔ bk = a∗−k
bk =1
T
∫
〈T 〉
x∗(t)e−jkw0tdt = (1
T
∫
〈T 〉
x(t)e−j(−k)w0tdt)∗ = a∗−k
in particular if x(t) is real ⇒ x(t) = x∗(t)
therefore ak = a∗−k
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H.S. Chen Fourier Series and Fourier Transforms 30
5. multiplication
x(t)↔ ak
y(t)↔ bk
x(t)y(t)↔ ak ∗ bk = hk
i.e. hk =
∞∑
l=−∞
albk−l
x(t) =∞∑
k=−∞
akejkw0t
y(t) =
∞∑
l=−∞
blejlw0t
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H.S. Chen Fourier Series and Fourier Transforms 31
Finally, we have
x(t)y(t) =
∞∑
k=−∞
∞∑
l=−∞
akblej(k+l)w0t
=
∞∑
m=−∞
(
∞∑
l=−∞
am−lbl)ejmw0t
=
∞∑
m=−∞
hmejmw0t
where m = k + l.
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H.S. Chen Fourier Series and Fourier Transforms 32
6. convolution
z(t) = x(t) ∗ y(t)↔ zk = T0akbk
proof:
z(t) = x(t) ∗ y(t) =
∫
τ∈〈T0〉
x(τ)y(t− τ)dτ
⇒ zk =1
T0
∫
t∈〈T0〉
z(t)e−jkw0tdt
=1
T0
∫
t∈〈T0〉
∫
τ∈〈T0〉
x(τ)y(t− τ)dτe−jkw0tdt
=1
T0
∫
τ∈〈T0〉
x(τ)
∫
t∈〈T0〉
y(t− τ)e−jkw0tdtdτ
= T01
T0
∫
τ∈〈T0〉
x(τ)e−jkw0τdτ1
T0
∫
λ∈〈T0〉
y(λ)e−jkw0λdλ
= T0akbk
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H.S. Chen Fourier Series and Fourier Transforms 33
7. Parseval’s theorem for F.S.
P =1
T0
∫ T0
0
|x(t)|2dt =
∞∑
k=−∞
|ck|2
I.e., to find the average power P of x(t), we can either calculate in
time domain or in frequency domain.
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H.S. Chen Fourier Series and Fourier Transforms 34
proof:
1
T0
∫ T0
0
|x(t)|2dt
=1
T0
∫ T
0
∞∑
k=−∞
ckejkw0t∞∑
m=−∞
c∗me−jmw0tdt
=1
T0
∞∑
k=−∞
∞∑
m=−∞
ckc∗m
∫
〈T0〉
ej(k−m)w0tdt
=1
T0
∞∑
k=−∞
ckc∗kT0
=∞∑
k=−∞
|ck|2
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H.S. Chen Fourier Series and Fourier Transforms 35
For real signal x(t), we have |c−k| = |ck|
⇒ P = |c0|2 + 2
∞∑
k=1
|ck|2
also we have
Ak = 2Re{ck}
Bk = −2Im{ck}
therefore
|ck|2 = Re2{ck}+ Im2{ck} =
A2k
4+
B2k
4
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H.S. Chen Fourier Series and Fourier Transforms 36
Finally for real signal, we have another form:
P = |c0|2 +
∞∑
k=1
A2k
2+
B2k
2
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H.S. Chen Fourier Series and Fourier Transforms 37
FT
We will do the following step by step
1. The derivation of FT
2. Some examples of FT
3. Properties of FT
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H.S. Chen Fourier Series and Fourier Transforms 38
Fourier Transform: from F.S. to F.T.
f(t)FT←→ F (jw)
F (jw) = F{f(t)} and f(t) = F−1{f(jw)}
F (jw) =∫ ∞
−∞f(t)e−jwtdt
f(t) = 12π
∫ ∞
−∞F (jw)ejwtdw
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H.S. Chen Fourier Series and Fourier Transforms 39
Fourier transform pair for some common signals
Example 1:
f(t) =
1, −T1 ≤ t ≤ T1
0, otherwise
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H.S. Chen Fourier Series and Fourier Transforms 40
F (jw) =
∫ ∞
−∞
f(t)e−jwtdt
=
∫ T1
−T1
1 · e−jwtdt
=1
−jwe−jwt|T1
−T1
=1
+jw(ejT1w−e−jT1w
)
=2T1
wT1
ejT1w − e−jT1w
2j
= 2T1sin(T1w)
wT1
= 2T1sinc(T1w)
therefore F(jw) is the envelop function of T0 · Ck
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H.S. Chen Fourier Series and Fourier Transforms 41
i.e.
rect(t
w)←→Wsinc(
wW
2)
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H.S. Chen Fourier Series and Fourier Transforms 42
Example 2: x(t) = e−atu(t) (a > 0)
=⇒ X(jw) =
∫ ∞
−∞
x(t)e−jwtdt
=
∫ ∞
−∞
e−atu(t)e−jwtdt
=
∫ ∞
0
e−(jw+a)tdt
=1
−(jw + a)e−(jw+a)t|0−∞
=1
jw + a(ifa > 0)
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H.S. Chen Fourier Series and Fourier Transforms 43
Example 3: x(t) = e−a|t| (a > 0)
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H.S. Chen Fourier Series and Fourier Transforms 44
=⇒ X(jw) =
∫ ∞
−∞
x(t)e−jwtdt
=
∫ ∞
−∞
e−a|t|e−jwtdt
=
∫ 0
−∞
eate−jwtdt +
∫ ∞
0
e−ate−jwtdt
=
∫ 0
−∞
e(a−jw)tdt +
∫ ∞
0
e−(a+jw)tdt
=1
a− jwe(a−jw)t|∞0 +
1
jw + a
=1
a− jw+
1
jw + aif a > 0
=2a
a2 + w2if a > 0
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H.S. Chen Fourier Series and Fourier Transforms 45
Example 4:
x(t) = δ(t)
=⇒ X(jw) =
∫ ∞
−∞
x(t)e−jwtdt
=
∫ ∞
−∞
δ(t)e−jwtdt = e−jwt|t=0 = 1
i.e.
δ(t)←→ 1
similarly if x(t) = δ(t− t0)
=⇒ X(jw) =
∫ ∞
−∞
δ(t− t0)e−jwtdt
= e−jwt|t=t0 = e−jwt0
δ(t− t0)←→ 1 · e−jwt0
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H.S. Chen Fourier Series and Fourier Transforms 46
Example 5:
X(jw) = 2πδ(w)
=⇒ x(t) =1
2π
∫ ∞
−∞
X(jw)ejwtdw
=1
2π
∫ ∞
−∞
2πδ(w)ejwtdw
= ejwt|w=0 = 1
i.e.
1←→ 2πδ(w)
similarly if X(jw) = 2πδ(w − w0)
=⇒ x(t) =1
2π
∫ ∞
−∞
2πδ(w − w0)ejwtdw
= ejwt|w=w0= ejw0t
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H.S. Chen Fourier Series and Fourier Transforms 47
i.e.
1 · ejw0t ←→ 2πδ(w − w0) (frequency shifting) )
With this, we can represent the periodic signal by FT:
x(t) =
∞∑
k=−∞
Ckejkw0t
=⇒ X(jw) = F{x(t)} = F{
∞∑
k=−∞
Ckejkw0t}
=
∞∑
k=−∞
CkF{ejkw0t}
=∞∑
k=−∞
2πCkδ(w − kw0)
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H.S. Chen Fourier Series and Fourier Transforms 48
i.e.
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H.S. Chen Fourier Series and Fourier Transforms 49
Example 6:
x(t) = cosw0t =1
2ejw0t +
1
2e−jw0t
=⇒ X(jw) =1
22πδ(w − w0) +
1
22πδ(w + w0)
= πδ(w − w0) + πδ(w + w0)
x(t) = sinw0t =1
2jejw0t −
1
2je−jw0t
=⇒ X(jw) =1
2j2πδ(w − w0)−
1
2j2πδ(w + w0)
= jπδ(w + w0)− jπδ(w − w0)
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H.S. Chen Fourier Series and Fourier Transforms 50
In conclusion
x(t) ←→ X(jw)
1 ←→ 2πδ(w)
ejw0t ←→ 2πδ(w − w0)
δ(t) ←→ 1
δ(t− t0) ←→ e−jwt0
e−atu(t) ←→1
jw + a(a > 0)
e−a|t| ←→2σ
a2 + w2(a > 0)
∞∑
k=−∞
Ckejkw0t ←→∞∑
k=−∞
2πCkδ(w − kw0)
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H.S. Chen Fourier Series and Fourier Transforms 51
cosw0t ←→ πδ(w + w0) + πδ(w − w0)
sinw0t ←→π
jδ(w − w0)−
π
jδ(w + w0)
u(t) ←→ πδ(w) +1
jw
sgn(t) ←→2
jw
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H.S. Chen Fourier Series and Fourier Transforms 52
Properties of FT
x(t) ←→ X(jw)
y(t) ←→ Y (jw)
ax(t) + by(t) ←→ aX(jw) + bY (jw)
x∗(t) ←→ X∗(−jw)
x(at) ←→1
|a|X(
w
a)
x(t− t0) ←→ X(jw)e−jwt0
x(t)ejw0t ←→ X(j(w − w0))
X(t) ←→ 2πx(−w)
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H.S. Chen Fourier Series and Fourier Transforms 53
1. linearity
x(t) ←→ X(jw)
y(t) ←→ Y (jw)
ax(t) + by(t) ←→ aX(jw) + bY (jw)
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H.S. Chen Fourier Series and Fourier Transforms 54
2. conjugation
x(t) ←→ X(jw)
x∗(t) ←→ X∗(−jw)
∫ ∞
−∞
x∗(t)e−jwtdt = (
∫ ∞
−∞
x(t)e−(−jwt))∗ = X∗(−jw)
if x(t) = x∗(t) real =⇒ X∗(−jw) = X(jw)
=⇒
Re{X(−jw)} = Re{X(jw)}
Im{X(−jw)} = −Im{X(jw)}
|X(−jw)| = |X(jw)|
∠X(−jw) = −∠X(−jw)
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3. Time scaling
f(αt)F←→
1
|α|F (
w
α)
Interpretation:
• If α > 1, we obtain that compression in the time domain
corresponds to expansion in the frequency domain.
• If 0 < α < 1, we obtain that compression in the time domain
corresponds to expansion in the frequency domain.
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4. Time shifting
f(t− t0)F←→ e−jwt0F (w)
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Example: Recall the Fourier transforms of δ(t) and δ(t− t0).
Proof:
F [f(t− t0)] =
∫ ∞
−∞
f(t− t0)e−jwtdt
=
∫ ∞
−∞
f(σ)e−jw(σ+t0)dσ
= e−jwt0F (w)
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Combining Time Scaling and Time shifting :
f(at + t0)F←→
1
|a|F (
w
a)e+jt0w/a
This is obtained by (i) shifting f(t) by t0 and (ii) scaling the result of
(i) by a.
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5. Frequency shifting
ejw0tf(t)F←→ F (w − w0)
for any real w0.
Example: Recall the Fourier transforms of 1 and ejw0t, and u(t) and
cos(w0t)u(t). Also ,examine the ”modulation” property below.
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Proof:
F [ejw0tf(t)] =
∫ ∞
−∞
ejw0tf(t)e−jwtdt
=
∫ ∞
−∞
f(t)e−j(w−w0)tdt
= F (w − w0)
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6. Duality
F (−t)←→ 2πf(w)
F (t)←→ 2πf(−w)
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Proof:
f(t) =1
2π
∫ ∞
−∞
F (w)ejwtdw
2πf(−t) =
∫ ∞
−∞
F (w)e−jwtdw
2πf(−w) =
∫ ∞
−∞
F (t)e−jwtdt = F [F (t)]
where the last line is obtained by interchanging t with w.
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7. Convolution
If all the involved Fourier transforms exist, then
x(t) ∗ h(t) F←→ X(w)H(w)
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Proof:
F [x(t) ∗ h(t)] =
∫ ∞
−∞
[
∫ ∞
−∞
h(t− τ)x(τ)dτ ]e−jwtdt
=
∫ ∞
−∞
x(τ)[
∫ ∞
−∞
h(t− τ)e−jwtdt]dτ
=
∫ ∞
−∞
x(τ)[e−jwtH(w)]dτ
= H(w)
∫ ∞
−∞
x(τ)e−jwtdτ
= H(w)X(w)
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8. Modulation
If all the involved Fourier transforms exist, then
x(t)m(t)F←→
1
2πX(w) ∗M(w)
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Proof:
F [x(t)m(t)] =
∫ ∞
−∞
x(t)[1
2π
∫ ∞
−∞
M(w′)ejw′tdw′]e−jwtdt
=1
2π
∫ ∞
−∞
M(w′)[
∫ ∞
−∞
x(t)e−jwte+jw′tdt]dw′
=1
2π
∫ ∞
−∞
M(w′)[X(w − w′)]dw′
=1
2πM(w) ∗X(w)
Example: This result can be used to calculate F [cos(w0t)u(t)].
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9. Time Differentiation
If f(t) is continuous and if f(t) and f ′(t) = df(t)dt are both absolutely
integrable (and thus satisfy the Dirichlet conditions),then
df(t)
dt
F←→ jwF (w)
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Proof:∫ ∞
−∞
f ′(t)e−jwtdt = f(t)e−jwt|∞−∞ + jw
∫ ∞
−∞
f(t)e−jwtdt
The key step now is to observe that since f(t) is assumed to be
absolutely integrable, then f(t)→ 0 as t→ ±∞ and thus the first
term on the RHS evaluates to zero. The second term is simply
jwF (w)
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10. Parseval’s Theorem
Parseval’s Theorem∫ ∞
−∞
|f(t)|2dt =1
2π
∫ ∞
−∞
|F (w)|2dw
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Proof:∫ ∞
−∞
|f(t)|2dt =
∫ ∞
−∞
f(t)f∗(t)dt
=
∫ ∞
−∞
f(t)[1
2π
∫ ∞
−∞
F ∗(w)e−jwtdw]dt
=1
2π
∫ ∞
−∞
F ∗(w)[
∫ ∞
−∞
f(t)e−jwtdt]dw
=1
2π
∫ ∞
−∞
F ∗(w)F (w)dw
=1
2π
∫ ∞
−∞
|F (w)|2dw
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