The document provides an overview of Fourier transforms. It begins by introducing Fourier series which deals with continuous-time periodic signals and results in discrete frequency spectra. It then discusses how the Fourier integral and continuous Fourier transform can deal with aperiodic signals by providing continuous spectra. The continuous Fourier transform represents a function as an integral of its frequencies, while the inverse transform uses this representation to recover the original function. The properties of the Fourier transform discussed include linearity, time scaling, time reversal, time shifting, and frequency shifting. Real functions have special properties where the Fourier transform is always real or pure imaginary. Examples are provided to illustrate how to calculate the Fourier transform of simple functions.
This presentation describes the Fourier Transform used in different mathematical and physical applications.
The presentation is at an Undergraduate in Science (math, physics, engineering) level.
Please send comments and suggestions to improvements to solo.hermelin@gmail.com.
More presentations can be found at my website http://www.solohermelin.com.
Its states Periodic function, Fourier series for disontinous function, Fourier series, Intervals, Odd and even functions, Half range fourier series etc. Mostly used as active learning assignment in Degree 3rd sem students.
This presentation describes the Fourier Transform used in different mathematical and physical applications.
The presentation is at an Undergraduate in Science (math, physics, engineering) level.
Please send comments and suggestions to improvements to solo.hermelin@gmail.com.
More presentations can be found at my website http://www.solohermelin.com.
Its states Periodic function, Fourier series for disontinous function, Fourier series, Intervals, Odd and even functions, Half range fourier series etc. Mostly used as active learning assignment in Degree 3rd sem students.
What is Fourier Transform
Spatial to Frequency Domain
Fourier Transform
Forward Fourier and Inverse Fourier transforms
Properties of Fourier Transforms
Fourier Transformation in Image processing
What is Fourier Transform
Spatial to Frequency Domain
Fourier Transform
Forward Fourier and Inverse Fourier transforms
Properties of Fourier Transforms
Fourier Transformation in Image processing
On Twisted Paraproducts and some other Multilinear Singular IntegralsVjekoslavKovac1
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5. Review of Fourier Series
Deal
with continuous-time periodic signals.
Discrete frequency spectra.
A Periodic Signal
A Periodic Signal
f(t)
t
T
2T
3T
6. Two Forms for Fourier Series
Sinusoidal
a0 ∞
2πnt ∞
2πnt
f (t ) = + ∑ an cos
+ ∑ bn sin
Form
2 n =1
T
T
n =1
2 T /2
a0 = ∫
f (t )dt
−T / 2
T
Complex
Form:
f (t ) =
∞
∑ cn e
n = −∞
jnω0t
2 T /2
an = ∫
f (t ) cos nω0tdt
T −T / 2
2 T /2
bn = ∫
f (t ) sin nω0tdt
T −T / 2
1
cn =
T
∫
T /2
−T / 2
f (t )e − jnω0t dt
7. How to Deal with Aperiodic Signal?
A Periodic Signal
A Periodic Signal
f(t)
t
T
If T→∞, what happens?
9. Fourier Integral
fT (t ) =
∞
∑c e
n = −∞
n
jnω0t
1
cn =
T
∫
T /2
−T / 2
fT (t )e − jnω0t dt
∞
1 T /2
=∑ ∫
fT (τ)e − jnω0 τ dτ e jnω0t
−T / 2
n = −∞ T
1 ∞ T /2
=
fT (τ)e − jnω0 τ dτ ω0 e jnω0t
∑
2π n = −∞ ∫−T / 2
1 ∞ T /2
=
fT (τ)e − jnω0 τ dτ e jnω0t ∆ω
∑
2π n = −∞ ∫−T / 2
1 ∞ ∞
=
fT (τ)e − jωτ dτ e jωt dω
2π ∫−∞ ∫−∞
ω0 =
2π
T
1 ω0
=
T 2π
Let ∆ω = ω0 =
2π
T
T → ∞ ⇒ dω = ∆ω ≈ 0
10. Fourier Integral
1 ∞ ∞
− jωτ
e jωt dω
f (t ) =
∫−∞ ∫−∞ f (τ)e dτ
2π
F(jω )
1 ∞
jω t
f (t ) =
∫−∞ F ( jω)e dω
2π
∞
F ( jω) = ∫ f (t )e
−∞
− jω t
dt
Synthesis
Analysis
11. Fourier Series vs. Fourier Integral
Fourier
Series:
f (t ) =
cn e jnω0t
∑
Period Function
n = −∞
1
cn =
T
Fourier
Integral:
∞
∫
T /2
−T / 2
Discrete Spectra
fT (t )e − jnω0t dt
1 ∞
f (t ) =
F ( jω)e jωt dω
2π ∫−∞
∞
F ( jω) = ∫ f (t )e − jωt dt
−∞
Non-Period
Function
Continuous Spectra
21. Notation
F [ f (t )] = F ( jω)
F [ F ( jω)] = f (t )
-1
f (t ) ←
→ F ( jω)
F
22. Linearity
a1 f1 (t ) + a2 f 2 (t ) ←
→ a1 F1 ( jω) + a2 F2 ( jω)
F
orrk !!
Wo k
H om e W
!!Home
23. Time Scaling
1 ω
f (at ) ←
→
F j
|a| a
F
orrk !!
Wo k
H om e W
!!Home
24. Time Reversal
f ( −t ) ←
→ F ( − jω)
F
Pf) F [ f (−t )] = ∞ f (−t )e − jωt dt = t =∞ f (−t )e − jωt dt
∫−∞
∫t =−∞
=∫
− t =∞
−t = −∞
= −∫
=∫
f (t )e jωt d ( −t )
f (t )e d ( −t )
−t = −∞
t = −∞
t =∞
∞
− t =∞
j ωt
f (t )e dt = ∫
j ωt
t =∞
t = −∞
f (t )e jωt dt
= ∫ f (t )e jωt dt = F (− jω)
−∞
25. Time Shifting
f (t − t0 ) ←
→ F ( jω) e
F
− jωt 0
Pf) F [ f (t − t )] = ∞ f (t − t )e − jωt dt = t =∞ f (t − t )e − jωt dt
0
0
0
∫−∞
∫t =−∞
=∫
t +t0 =∞
=e
− j ωt 0
=e
− j ωt 0
t + t 0 = −∞
f (t )e − jω(t +t0 ) d (t + t0 )
∫
t =∞
∫
∞
t = −∞
−∞
f (t )e − jωt dt
− jω t
f (t )e − jωt dt = F ( jω)e 0
26. Frequency Shifting (Modulation)
f (t )e
Pf)
jω0t
¬ F [ j (ω − ω0 ) ]
→
F [ f (t )e
F
jω 0 t
∞
] = ∫ f (t )e jω0t e − jωt dt
−∞
∞
= ∫ f (t )e − j ( ω−ω0 )t dt
−∞
= F [ j (ω − ω0 )]
27. Symmetry Property
F [ F ( jt )] = 2πf (−ω)
Proof
∞
2πf (t ) = ∫ F ( jω)e jωt dω
−∞
∞
2πf (−t ) = ∫ F ( jω)e − jωt dω
−∞
Interchange symbols ω and t
∞
2πf (−ω) = ∫ F ( jt )e − jωt dt = F [ F ( jt )]
−∞
28. Fourier Transform for
Real Functions
If f(t) is a real function, and F(jω) = FR(jω) + jFI(jω)
F(−jω) = F*(jω)
∞
F ( jω) = ∫ f (t )e
− jωt
−∞
∞
dt
F * ( jω) = ∫ f (t )e dt = F (− jω)
−∞
jωt
29. Fourier Transform for
Real Functions
If f(t) is a real function, and F(jω) = FR(jω) + jFI(jω)
F(−jω) = F*(jω)
FR(jω) is even, and FI(jω) is odd.
F R jω ) = F R F (− jω ) = − F (jω )
(−
(jω ) I
I
Magnitude spectrum |F(jω)| is even, and
phase spectrum φ(ω) is odd.
30. Fourier Transform for
Real Functions
If f(t) is real and even
F(jω) is real
Pf)
Even
If f(t) is real and odd
√
f (t ) = f (−t )
F(jω) is pure imaginary
Pf)
Odd
F ( jω) = F (− jω)
Real
F (− jω) = F * ( jω)
F ( jω) = F * ( jω)
√
f (t ) = − f (−t )
F ( jω) = − F (− jω)
Real
F (− jω) = F * ( jω)
F ( jω) = − F * ( jω)
31. Example:
F [ f (t )] = F ( jω)
Sol)
F [ f (t ) cos ω0t ] = ?
1
f (t ) cos ω0t = f (t )(e jω0t + e − jω0t )
2
1
1
jω 0 t
F [ f (t ) cos ω0t ] = F [ f (t )e ] + F [ f (t )e − jω0t ]
2
2
1
1
= F [ j (ω − ω0 )] + F [ j (ω + ω0 )]
2
2
34. 1
Example:
sin at
f (t ) =
πt
Sol)
wd(t)
t
−d/2
d/2
F ( jω) = ?
2 ωd
Answer is
Wd ( jω) = sin
just
ω 2
opposite to
as expected
2 td
F [Wd ( jt )] = F sin = 2πwd (−ω)
2
t
0 ω <| a |
sin at
F [ f (t )] = F
= w2 a (−ω) = 1 ω >| a |
πt
35. Fourier Transform of f’(t)
f (t ) ←
→ F ( jω) and lim f (t ) = 0
F
t → ±∞
f ' (t ) ←F jωF ( jω)
→
Pf) F [ f ' (t )] = ∞ f ' (t )e − jωt dt
∫−∞
= f (t )e
− j ωt ∞
−∞
= jωF ( jω)
∞
+ jω∫ f (t )e − jωt dt
−∞
36. Fourier Transform of f (t)
(n)
f (t ) ←
→ F ( jω) and lim f (t ) = 0
F
t → ±∞
f ( n ) (t ) ←F ( jω) n F ( jω)
→
orrk !!
Wo k
H om e W
!!Home
37. Fourier Transform of f (t)
(n)
f (t ) ←
→ F ( jω) and lim f (t ) = 0
F
t → ±∞
f ( n ) (t ) ←F ( jω) n F ( jω)
→
orrk !!
Wo k
H om e W
!!Home
38. Fourier Transform of Integral
f (t ) ←
→ F ( jω) and
F
∫
∞
−∞
f (t )dt = F ( 0 ) = 0
t f ( x)dx = 1 F ( jω)
F ∫
−∞
jω
Let φ(t ) =
∫
t
−∞
f ( x)dx
lim φ(t ) = 0
t →∞
F [φ' (t )] = F [ f (t )] = F ( jω) = jωΦ ( jω)
1
Φ ( jω) =
F ( jω)
jω
39. The Derivative of Fourier Transform
dF ( jω)
F [− jtf (t )] ←
→
dω
F
Pf)
∞
F ( jω) = ∫ f (t )e − jωt dt
−∞
∞
dF ( jω) d ∞
∂ − j ωt
− j ωt
=
∫−∞ f (t )e dt = ∫−∞ f (t ) ∂ω e dt
dω
dω
∞
= ∫ [− jtf (t )]e − jωt dt = F [− jtf (t )]
−∞