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Wave PhenomenaPhysics 15c
Lecture 11Fourier Analysis
(H&L Sections 13.1–4)(Georgi Chapter 10)
What We Did Last Time
Studied reflection of mechanical wavesSimilar to reflection of electromagnetic wavesMechanical impedance is defined by
For transverse/longitudinal waves:Useful in analyzing reflection
Studied standing wavesCreated by reflecting sinusoidal wavesOscillation pattern has nodes and antinodesMusical instruments use standing waves to produce their distinct sound
Z TF Zv±=
[ or ] lK ρ=
Goals For Today
Define Fourier integralFourier series is defined for repetitive functions
Discreet values of frequencies contribute
Extend the definition to include non-repetitive functionsSum becomes an integral
Discuss pulses and wave packetsSending information using wavesSignal speed and bandwidthConnection with Quantum Mechanics
( )01
( ) cos sinn n n nn
f t a a t b tω ω∞
=
= + +∑
Looking Back
In Lecture #5, we solved the wave equation
Normal-mode solutions Using Fourier series, we can make any arbitrary waveformwith linear combination of the normal modes
Example: forward-going repetitive waves
Non-repetitive waves also OK if we make T ∞This makes ω continuous
2 22
2 2( , ) ( , )wx t c x tt x
ξ ξ∂ ∂=
∂ ∂( )
0( , ) i kx tx t e ωξ ξ ±= wckω
=
( )1
( , ) ( ) cos( ) sin( )w n n n n n nn
x t f x c t a k x t b k x tξ ω ω∞
=
= − = − + −∑2
nn
Tπω =
n w nk c ω=
A little math work needed
Fourier Series
For repetitive function f(t)
Express cosωnt and sinωnt with complex exponentials
( )01
( ) cos sinn n n nn
f t a a t b tω ω∞
=
= + +∑
∫=T
dttfT
a00 )(1
0
2 ( )cosT
n na f t tdtT
ω= ∫ 0
2 ( )sinT
n nb f t tdtT
ω= ∫
2n
nTπω =
( )1 1
1
1
cos sin2 2
2 2
n n
m n
i t i tn n n nn n n n
n n
i t i tm m n n
m n
a ib a iba t b t e e
a ib a ibe e
ω ω
ω ω
ω ω∞ ∞
−
= =
− ∞− −
=−∞ =
− + + = +
+ + = +
∑ ∑
∑ ∑
m nω ω= −m nb b= −m na a=m n= −
Fourier Series
Define and
How do we calculate Fn?
It’s useful later if I shift the integration range here
Now we take it to the continuous limit…
( ) ni tn
nf t F e ω
∞−
=−∞
= ∑
0 0
1 ( )T
F f t dtT
= ∫
0 0 0
1 2 2 1( )cos ( )sin ( )2
nT T T i t
n n nF f t tdt i f t tdt f t e dT T T
ωω ω = + = ∫ ∫ ∫
2n n
na ibF +
= 0 0F a=
same
2
2
1 ( ) nT i t
n TF f t e dt
Tω
−= ∫ OK because f(t)
is repetitive
Sum includes n = 0
t
Fourier Integral
Make T ∞
F(ω) is the Fourier integral of f(t)
2n
nTπω =( ) ni t
nn
f t F e ω∞
−
=−∞
= ∑2
2
1 ( ) nT i t
n TF f t e dt
Tω
−= ∫
( ) lim lim
lim2
( )
ni t in tnnT Tn n
i tnT
i t
Ff t F e e
T F e d
F e d
ω ω
ω
ω
ωω
ωπ
ω ω
∞ ∞− − ∆
→∞ →∞=−∞ =−∞
∞ −
−∞→∞
∞ −
−∞
= = ∆∆
=
=
∑ ∑
∫
∫
2Tπω∆ ≡
1( ) lim ( )2 2
i tnT
TF F f t e dtωωπ π
∞
−∞→∞≡ = ∫
( ) ( ) i tf t F e dωω ω∞ −
−∞= ∫
1( ) ( )2
i tF f t e dtωωπ
∞
−∞= ∫
Fourier Integral
Fourier integral F(ω) isA decomposition of f(t) into different frequenciesAn alternative, complete representation of f(t)
One can convert f(t) into F(ω) and vice versa
f(t) is in the time domainF(ω) is in the frequency domain
( ) ( ) i tf t F e dωω ω∞ −
−∞= ∫
1( ) ( )2
i tF f t e dtωωπ
∞
−∞= ∫
F(ω) and f(t) are two equally-good representationsof a same function
Warning
Different conventions exist in Fourier integrals
Watch out when you read other textbooks
( ) ( ) i tf t F e dωω ω∞ −
−∞= ∫
1( ) ( )2
i tf t F e dωω ωπ
∞ −
−∞= ∫
1( ) ( )2
i tF f t e dtωωπ
∞
−∞= ∫
( ) ( ) i tF f t e dtωω∞
−∞= ∫
and
and
1( ) ( )2
i tf t F e dωω ωπ
∞ −
−∞= ∫ and
1( ) ( )2
i tF f t e dtωωπ
∞
−∞= ∫
Square Pulse
Consider a short pulse with unit area
F(ω) is a bunch of little ripplesaround ω = 0Height is 1/2πArea is 1/T
T
1T
12
2
( )0
TT
T
tf t
t <= >
2
2
1 1 1( ) ( ) sin2 2 2
Ti t i t
T
TF f t e dt e dtT T
ω ω ωωπ π πω
∞
−∞ −= = =∫ ∫Fourier
ω0
2Tπ
1(0)2
Fπ
=
Pulse Width
Pulse of duration T
The shorter the pulse, the wider the F(ω)(width in t) × (width in ω) = 2π = const
This is a general feature of Fourier transformationExample: Gaussian function
1( ) sin2TF
Tωω
πω= “width”
2Tπ
2
221( )2
tTf t e
Tπ
−=
2 2
21( )2
T
F eω
ωπ
−=
T1T
Sending Information
Consider sending information using wavesVoice in the airVoice converted into EM signals on a phone cableVideo signals through a TV cable
You can’t do it with pure sine waves cos(kx – ωt)It just goes on Completely predictable No informationYou need waves that change patterns with time
What you really need are pulsesPulse width T determines the speed
Pulses must be separated by at least T
Amplitude Modulation
Audio signals range from 20 to 20 kHzToo low for efficient radio transmissionUse a better frequency and modulate amplitude
Modulated waves are no longer pure sine wavesWhat is the frequency composition?
Carrier wave
Audio signal
Amplitude-modulatedwaves
Wave Packet
Consider carrier waves modulated by a pulseThis makes a short train of waves
A wave packet
T = 1/(20 kHz) for audio signals
Fourier integral is
T
( )f t
02
2
( )0
i t T
T
e tf t
t
ω− <= >
02 02
0
( )1 1( ) sin2 ( ) 2
T i t i t
T
TF e e dtω ω ω ωωπ π ω ω
−
−
−= =
−∫
Wave Packet
Similar to the square pulseWidth is 2π/TCentered at ω = ω0
This is called the bandwidth of your radio stationThis limits how close the frequencies of radio stations can be
You need 20 kHz for HiFi audioIt’s more like 5 kHz in commercial AM stations
0
0
( )1( ) sin( ) 2
TF ω ωωπ ω ω
−=
−
ω0ω
2Tπ
To send pulses every T second, your signal must have a minimum spread of 2π/T in ω, which corresponds to 1/T in frequency
Bandwidth
Speed of information transfer = # of pulses / secondDetermined by the pulse width in the time domainTranslated into bandwidth in the frequency domainWe say “bandwidth” to mean “speed of communication”
“Broadband” means “fast communication”
Each medium has its maximum bandwidthYou can split it into smaller bandwidth “channels”
Radio wave frequencies Regulated by the governmentCable TV 750 MHz / 6 MHz = 125 channels
You want to minimize the bandwidth of each channelTelephones carry only between 400 and 3400 Hz
Delta Function
Take the square pulse againMake it narrower by T → 0The height grows 1/T → ∞
We get an infinitely narrow pulse with unit areaDirac’s delta function δ(t)
For any function f(t)
T
1T
0( )
0 0t
tt
δ∞ =
= ≠( ) 1t dtδ
∞
−∞=∫
( ) ( ) (0)f t t dt fδ∞
−∞=∫ 0 0( ) ( ) ( )f t t t dt f tδ
∞
−∞− =∫
and
Delta Function
What is the Fourier integral of δ(t)?
δ(t) contains all frequencies equally
1 1( ) ( )2 2
i tF t e dtωω δπ π
∞
−∞= =∫
You can get this also bymaking T 0 in
1( ) sin2TF
Tωω
πω=
1( )2
i tt e dωδ ωπ
∞ −
−∞= ∫ Another way of defining δ(t)
Pure Sine Waves
Consider pure sine waves with angular frequency ω00( ) i tf t e ω−=
0 0( )0
1 1( ) ( )2 2
i t i ti tF e e dt e dtω ω ωωω δ ω ωπ π
∞ ∞− −
−∞ −∞= = = −∫ ∫
t
ω0ω
( )f t ( )F ω
How Things Fit Together
T0infinitet width
1/Tinfinite0ω width
F(ω)f(t)Finite pulse and everything else
uniformδ(t)δ pulseδ(ω0 – ω)uniformSinusoidalω domaint domainWaveform
Pure sine waves and δ pulses are the two extreme cases of all waves
Everything falls in betweenWidths in t and ω are inversely proportional to each other
Wait… Did I prove it?
Arbitrary Signal Width
Now we consider a signal with an arbitrary shape
Let’s define the average time and the average frequency
Because (energy density) ∝ (amplitude)2
Now we define the r.m.s. widths in t and ω
( )f t ( )F ωFourier
2
2
( )
( )
t f t dtt
f t dt
∞
−∞∞
−∞
= ∫∫
2
2
( )
( )
F d
F d
ω ω ωω
ω ω
∞
−∞∞
−∞
= ∫∫
( ) ( )22t t t∆ = − ( ) ( )22ω ω ω∆ = −
r.m.s. = root mean square
Arbitrary Signal Width
( ) ( )( )2 2
22
2
( )
( )
t t f t dtt t t
f t dt
∞
−∞∞
−∞
−∆ = − = ∫
∫
( ) ( )( )2 2
22
2
( )
( )
F d
F d
ω ω ω ωω ω ω
ω ω
∞
−∞∞
−∞
−∆ = − = ∫
∫
What can we do with this mess??
We can express F(ω) with f(t) as2 * ( )
2
*
2
1( ) ( ) ( )41 ( ) ( ) ( )
21 ( )
2
i t sF d f t f s e d dtds
f t f s t s dtds
f t dt
ωω ω ωπ
δπ
π
∞ ∞ ∞ ∞ −
−∞ −∞ −∞ −∞
∞ ∞
−∞ −∞
∞
−∞
=
= −
=
∫ ∫ ∫ ∫
∫ ∫
∫
12( ) ( ) i tF f t e dtωπω
∞
−∞= ∫
Arbitrary Signal Width
Next we take
We can use this to construct
( ) ( ) i tf t F e dωω ω∞ −
−∞= ∫
Differentiatewith t [ ]( ) ( ) i td f t i F e d
dtωω ω ω
∞ −
−∞= − ∫
[ ]( ) ( )i t dF e d i f tdt
ωω ω ω∞ −
−∞=∫
( ) ( ) ( ) ( )
( ) ( ) 0
2 2 *
( )*
2
( ) ( ) ( )
1 ( ) ( )21 ( )
2
i t
F d F F d d
F F e d d d
di f t dtdt
ω ω
ω ω ω ω ω ω ω ω ω ω δ ω ω ω ω
ω ω ω ω ω ω ω ωπ
ωπ
∞ ∞ ∞
−∞ −∞ −∞
∞ ∞ ∞ − −
−∞ −∞ −∞
∞
−∞
′ ′ ′ ′ ′− = − − −
′ ′ ′ ′= − −
= −
∫ ∫ ∫
∫ ∫ ∫
∫
t
Arbitrary Signal Width
Now we have
Here comes the trick: we calculate the integral
It’s a positive number divided by a positive numberκ is a real number
( )( ) 2
2
2
( )
( )
t t f t dtt
f t dt
∞
−∞∞
−∞
−∆ = ∫
∫( )
2
2
2
( )
( )
di f t dtdt
f t dt
ωω
∞
−∞
∞
−∞
− ∆ =
∫
∫
( )
2
2
( )0
( )
dt t i i f t dtdt
If t dt
κ ωκ
∞
−∞
∞
−∞
− − − = >
∫
∫
Arbitrary Signal Width
The integral in the denominator becomes
Integrate the first term in parts
( ) ( )* *( ) ( ) ( ) ( )d dt t f t f t f t t t f t dtdt dt
κ∞
−∞
− + − ∫
( ) ( )2 * *( ) ( ) ( ) ( ) ( )d dt t f t t t f t f t f t t t f t ddt dt
∞ ∞
−∞−∞
− + − − + − ∫
= 0 because the pulse has a finite extent
( ) 2* *( ) ( ) ( ) ( ) ( )d dtf t f t f t tf t dt f t dtdt dt
κ κ∞ ∞
−∞ −∞
− + = − ∫ ∫
( ) ( ) ( )( ) ( )* *
2 22
2
( ) ( ) ( ) ( )
( )
d dt t f t i i f t i i f t t t f t dtdt dt
I tf t dt
κ ω κ ωκ κ ω
∞
−∞
∞
−∞
− − − + − − − = ∆ + ∆ +
∫
∫
( ) tκ κ
Arbitrary Signal Width
We’ve come a long wayNow we gotIf a quadratic function of κ is always positive,
( ) ( ) ( )2 22 0I tκ κ ω κ= ∆ + ∆ − >
( ) ( )2 21 4 0D t ω= − ∆ ∆ <12
t ω∆ ∆ >finally!
For any signal, the product of the r.m.s. widths ∆t and ∆ωin the time and frequency domain is greater than 1/2
Space and Wavenumber
We have studied Fourier transformation in time t and frequency ω
We can also do it in space x and wavenumber kEverything works the same way
In particular, for any signal traveling in space
Why is it important?
( ) ( ) ikxf x F k e dk∞ −
−∞= ∫
1( ) ( )2
ikxF k f x e dxπ
∞
−∞= ∫
12
x k∆ ∆ >
Uncertainty Principle
In Quantum Mechanics, particles are wave packetsUnlike a classical particle, wave packet has a lengthThe position cannot be determined more accurately than ∆x
Momentum is related to the wavenumber by
This means
p k= h2hπ
=h Planck’s constant = 6.63 × 10−34 J s
2x p x k∆ ∆ = ∆ ∆ >
hh Heisenberg’s Uncertainty Principle
Summary
Defined Fourier integral
f(t) and F(ω) represent a function in time/frequency domainsAnalyzed pulses and wave packets
Time resolution ∆t and bandwidth ∆ω related byProved for arbitrary waveform
Rate of information transmission ∝ bandwidthDirac’s δ(t) a limiting case of infinitely fast pulseConnection with Heisenberg’s Uncertainty Principle in QM
( ) ( ) i tf t F e dωω ω∞ −
−∞= ∫
1( ) ( )2
i tF f t e dtωωπ
∞
−∞= ∫
12
t ω∆ ∆ >