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Course Outline (Tentative). Fundamental Concepts of Signals and Systems Signals Systems Linear Time-Invariant (LTI) Systems Convolution integral and sum Properties of LTI Systems … Fourier Series Response to complex exponentials Harmonically related complex exponentials … - PowerPoint PPT Presentation
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Course Outline Course Outline (Tentative)(Tentative)
Fundamental Concepts of Signals and Systems Signals Systems
Linear Time-Invariant (LTI) Systems Convolution integral and sum Properties of LTI Systems …
Fourier Series Response to complex exponentials Harmonically related complex exponentials …
Fourier Integral Fourier Transform & Properties … Modulation (An application example)
Discrete-Time Frequency Domain Methods DT Fourier Series DT Fourier Transform Sampling Theorem
Laplace Transform Z Transform
Chapter IVChapter IVFourier Integral
Continuous–Time Fourier Continuous–Time Fourier TransformTransform
So far, we have seen periodic signals and their representation in terms of linear combination of complex exponentials.
Practical meaning: superposition of harmonically related complex
exponentials.
How about aperiodic signals?
Fourier Transform Fourier Transform Representation of Aperiodic Representation of Aperiodic SignalsSignals
(An aperiodic signal is a periodic signal with infinite period.) Let us consider continuous-time periodic square wave
2/ ,0
,1)(
1
1
TtT
Tttx
T2T1T 1T
2T
T
............
)(tx
Fourier Transform Fourier Transform Representation of Aperiodic Representation of Aperiodic SignalsSignals
Recall that the FS coefficients are:
Let us look at it as:
0 10
0
2sin( ) 2, for kk Ta
k T T
0
12sin : samples of an envelope functionk kTTa
Fourier Transform Fourier Transform Representation of Aperiodic Representation of Aperiodic SignalsSignals
as , closer samples, faster rate!as , periodic square wave rectangular pulse (aperiodic
signal) , FS coefficients x T envelope itself
Think of aperiodic signal as the limit of a periodic signal
kTa
0 , T T
Fourier Transform Fourier Transform Representation of Aperiodic Representation of Aperiodic SignalsSignals
Examine the limiting behaviour ofExamine the limiting behaviour of FS FS representation of this signal representation of this signal
)(tx 1 ,0)( Tttx
t
)(tx
1T1T
Consider a signal that is of finite Consider a signal that is of finite duration,duration,
Fourier Transform Fourier Transform Representation of Aperiodic Representation of Aperiodic SignalsSignals
We can construct a periodic signal We can construct a periodic signal out of with period out of with period For For
)(~ tx )(tx T).()(~ , txtxT
1T1T tT2T
2T T
......
)(~ tx
0 0
/ 2
0/ 2
1 2( ) , ( ) , for T
jk t jk tk k
k T
x t a e a x t e dtT T
FS representation of periodic signal
Fourier Transform Fourier Transform Representation of Aperiodic Representation of Aperiodic SignalsSignals
Since 2
for 0 x(t)and 2
for )()(~ TtTttxtx
0 0
/ 2
/ 2
1 1( ) ( )T
jk jk tk
T
a x t e dt x t e dtT T
(Recall and envelope case)kTa
( ) ( ) j tX x t e dt
kTa
00
1( ) ( ) jk t
k
x t X k eT
0 01 ( ) (for )ka X k kT
Define as the envelope of
Fourier Transform Fourier Transform Representation of Aperiodic Representation of Aperiodic SignalsSignals
k
tjkekXtx 000).(
21)(~
,k ,0 ,T 00 As )(~ txx
Hence,
deXtx tj)(21)(
dtetxX tj )()(
: inverse Fourier transform
: Fourier transform (spectrum)
Fourier Transform pair
information needed for describing x(t) as a linear combination of sinusoids
0T2
For
Convergence of Fourier Convergence of Fourier TransformTransform
dttxtx )()(i) x(t) is absolutely integrable,
ii) x(t) has a fiinite number of maxima and minima within any finite interval
iii) x(t) has a finite number of discontinuities within any finite interval. Further more each of these discontinuities must be finite.
deXtx tj)(21)( at any
t except at discontinuities (similar to periodic case).
Dirichlet conditions guarantee that
ExampleExample
0a ),(e x(t) -at tuConsider
0
)( dteeX tjat 0
1 )(
tjaeja
0a ,1
ja
,1)(22
a
wX )(tan)( 1
aX
a1 2
2
ExampleExample
x t ta)
1
1
1,
0,
t Tx t
t T
b)Rectangular pulsesignal
1
1
12 sinTj t
T
TX e dt
X(ω)2T1
1T
ω
-T1 T1
x(t)
t
-
-
( ) 1j tX t e dt
Example (cont’d)Example (cont’d)
1,
0,
Wx t X
W
c) Consider with FT X(ω)
ω-W W
1 sin2
Wj t
W
Wtx t e dt
x(t)
t
W
W
W
NoteNote
sinsinc
important function
1 11
2sin 2 sincT TX T
sin sincWt W Wtx tt
Narrow in Time Domain have Broad FT!Broad in Time Domain Narrow FT!
(Scaling property)
X(ω)2T1
1T
ω
x(t)
tW
W
W
X(ω)
ω
-W W
-T1 T1
x(t)
t
Fourier Transform For Periodic Fourier Transform For Periodic SignalsSignals
Consider a signal x(t) with Fourier Transform(i.e., a signal impulse of area 2 at ω=ω0)
0( ) 2 . ( )X
Let us find the signal
tjtj edetx 0)( 221)( 0
if X(ω) is a linear combination of impulses equally spaced in frequency, i.e.,
k
k kaX )( 2)( 0
: FS representation of periodic signal
k
tjkkeatx 0)( the
n
Fourier Transform For Periodic Fourier Transform For Periodic SignalsSignals
Hence, FT of periodic signal is weighted impulse train occuring at integer multiples of ω0
Example:a) Periodic square wave:
kTkak
10sin )(sin2)( 0
10 k
kk
TkX
Recall FS coefficients of periodicsquare wave (from previous chapter)
Fourier Transform For Periodic Fourier Transform For Periodic SignalsSignals
b) x(t)=sinω0t
o/w ,0
-1k ,21
1 ,21
j
kj
ak )()()( 00 jX
x(t)=cosω0t
)()()( 00 X
c)
k
kTttx )()( impulse train
k T
kX )2( T2)(
X(ω)
-ω0 ω0
Tdtet
Ta
T
T
tjkk
1)(1 2
2
0
FT is again impulse train in frequency domain with period T
2
Properties of CT Fourier Properties of CT Fourier TransformTransform
Notations: ),( )( F Xtx ( ) ( )or X F x t
1. Linearity:
FIf ( ) ( ),x t X ),( )( F Yty
then ( ) ( ) ( ) ( ),Fax t by t aX bY
Prove it as an exercise!
Properties of CT Fourier Properties of CT Fourier TransformTransform
2. Time Shifting:
If ( ) ( ),Fx t X 00then ( ) ( )j tFx t t e X
Proof:
deXtx tj)(21)(
deXttx ttj )(0
0)(21)(
01 ( )
2j t j te X e d
F{x(t-t0)}
Properties of CT Fourier Properties of CT Fourier TransformTransformExampleExample
X(t)
1 2 3 4
11.5
t t
1
X1(t)
21 2
1 t
1
X2(t)
23 2
3
1 21( ) ( 2.5) ( 2.5)2
x t x t x t
, )2sin(2
)(1
X
)23sin(2
)(2 X
52
3sin 2sin2 2( )j
X e
By linearity andtime-shifting propertiesof FT
Properties of CT Fourier Properties of CT Fourier TransformTransform
3. Conjugation / Conjugate Symmetry:( ) ( )Fx t X ( ) ( )Fx t X
)()( XX Opp. pg. 303 for proof !for real x(t)
4. Differentiation & Integration:
deXjdttdx tj)(
21)( ( ) ( )Fdx t j X
dt
* Important in solving linear differential equations:Multiplication in frequency domain
t
F XXj
dx )()0( )(1)(
Properties of CT Fourier Properties of CT Fourier TransformTransformExampleExample
Consider
)()()( txtydttdy
Take FT of both sides;
)()( XYj )(1)(
Xj
Y
For )()( ttx 1)( X
j
H
1)(
)()( tueth t
Properties of CT Fourier Properties of CT Fourier TransformTransformExampleExample
Consider (unit step))()( tutx
For ),()( ttg
t
dgtu )()(
1( ) ( ) (0) ( )Fu t G Gj
1( ) ( )Fu tj
Properties of CT Fourier Properties of CT Fourier TransformTransform
5. Time and Frequency Scaling:
If ( ) ( ),Fx t X 1then ( ) ( )Fx at Xa a
(Prove using FT integral)
Remark:
- Inverse relation between time and frequency domains:- A signal varying rapidly will have a transform occupying wider frequency band, and vice versa
)( )( Xtx F-
Properties of CT Fourier Properties of CT Fourier TransformTransform
6. Duality:Observe the FT and inverse FT integrals:
; )()(
dtetxX tj
deXtx tj)(21)(
Recall
1
11 t ,0
t ,1)(
T
Ttx F
1
1sin2)( TX
tWttx
sin)(2 F
W
WX
,0
,1)(2
Symmetry between the FT pairs!
In general;
if ( ) ( ), then ( ) 2 (- )F Fg t f f t g
Properties of CT Fourier Properties of CT Fourier TransformTransform
Example: (t))( tx F 1)X(
By duality;
1)( tx F )( 2 (prove it!)
Dual of the properties:
F
ddX )()(tjtx
F
F)(0 txe tj
)()0( )(1 txtxjt
dX )(
)( 0 X
(frequency differentiation)(frequency shifting)
Properties of CT Fourier Properties of CT Fourier TransformTransform
7. Parseval’s Relation:
if ( ) ( )Fx t X 2 2
-
1then ( ) ( )2
x t dt X d
(Check the proof in Opp. pg.312) total energy in x(t)
energy density spectrum: )( 2X
Properties of CT Fourier Properties of CT Fourier TransformTransformConvolution PropertyConvolution Property
-
)()(y(t) dthx
Take FT.
-
)()()Y( dtedthx tj
-
)()( ddtethx tj
dexHdHex jj )()()()(-
)()()( XHY
(Additional & very important properties of FT, in terms of LTI systems)
Properties of CT Fourier Properties of CT Fourier TransformTransformConvolution PropertyConvolution Property
)()()()( XHtxth F
Convolution of two signals in time domain is equivalent to multiplication of their spectrums in frequency domain
:)(H frequency response of the system
Example:a) Consider a CT, LTI system with
)()( 0ttth
Frequency response is
0)( tjeH
)()()()( 0 XeXHY tj )()( 0ttxty
Recall the time-shiftingproperty of FT
Properties of CT Fourier Properties of CT Fourier TransformTransformConvolution PropertyConvolution Property
b) Frequency-selective filtering: achieved by an LTI system whose frequency response
H(ω) passes desired range of frequencies and stops (attenuates) other frequencies, e.g.,
; ,0
,1)(
c
cH
ttth c
sin)(
1
0-ωc
ωωc
H(ω)
Passband
stopband stopband
ideal lowpass filter
Properties of CT Fourier Properties of CT Fourier TransformTransformMultiplication PropertyMultiplication Property
From convolution property and duality, multiplication in time domain corresponds to convolution in frequency domain.
)()(21)(
PSR )()()( tptstr F
Amplitude Modulation (multiplication of two signals) important in telecommunications !
Example:
Let s(t) has spectrum S(ω)
A
-ω1
ωω1
S(ω)
Baseband signal
Properties of CT Fourier Properties of CT Fourier TransformTransformMultiplication PropertyMultiplication Property
p(t)=cosω0t
)()()( 00 P-ω0
ωω0
P(ω)
1( ) ( ) ( )2
R S P
R(ω)
-ω0-ω0-ω1 -ω0+ω1 ω0ω0-ω1 ω0+ω1
2A
- Information (spectral content) in s(t) is preserved but shifted to higher frequencies (more suitable for transmission)
Properties of CT Fourier Properties of CT Fourier TransformTransformMultiplication PropertyMultiplication Property
To recover:
-Multiply r(t) with p(t)=cosω0t g(t)=r(t).p(t)-Apply lowpass filter!
Application of Fourier Application of Fourier TheoryTheoryCommunication SystemsCommunication Systems
Modulation: Embedding an info-bearing signal into a second signal.
Definitions:
Demodulation: Extracting the information bearing signal from the second signal.
Info-bearing signal x(t): The signal to be transmitted (modulating signal).
Carrier signal c(t): The signal which carries the info-bearing signal (usually a sinusoidal signal).
Application of Fourier Application of Fourier TheoryTheoryCommunication SystemsCommunication Systems
Modulated signal y(t) is then the product of x(t) and c(t)
y(t)=x(t).c(t)
Objective: To produce a signal whose frequency range is suitable for transmission over communication channel. e.g.:
individual voice signals are in 200Hz-4kHz telephony (long-distance) over microwave or
satellite links in 300MHz-300GHz (microwave), 300MHz-40GHz (satellite)
Information in voice signals must be shifted into these higher ranges of frequency
Application of Fourier Theory Application of Fourier Theory Amplitude Modulation with Complex Exponential Carrier
)()( cctjetc ωc: carrier frequency
Consider θc=0
tj cetxty )()(
From multiplication property
)()(21)(
CXY
)( 2)( cC )()( cXY
Application of Fourier Application of Fourier Theory Theory Amplitude Modulation with Complex Exponential Carrier
-ωm ωm
1 X(ω)
ω *ωc
C(ω)
ω2
ωc+ ωm
1Y(ω)
ωωcωc- ωm
To recover x(t) from y(t):
tj cetytx )()(
(Demodulation)
(Shift the spectrum back)
Application of Fourier Application of Fourier Theory Theory Amplitude Modulation with Sinusoidal Carrier
)cos()( ccttc )cos()( ccttc
y(t)
x(t)
For θc=0 , y(t)=x(t)cosωct , )()()( ccC
)()(21)( cc XXY
-ωc- ωm
Y(ω)
ωc+ ωm
ωωc- ωm ωc-ωc -ωc+ ωm
21
Application of Fourier Application of Fourier Theory Theory Amplitude Modulation with a Sinusoidal Carrier
To recover x(t) from y(t), the condition ωc>ωm must be satisfied! Otherwise, the replicas will overlap.
Example:
-ωm ωm
1 X(ω)
-ωc ωc
1 Y(ω)
ωm+ ωc-ωm- ωc
21
2 m
cFor
Application of Fourier Application of Fourier Theory Theory Demodulation for Sinusoidal Amplitude Modulation
To recover the information-bearing signal x(t) at the To recover the information-bearing signal x(t) at the receiverreceiver
Synchronous Demodulation:
x(t) can be recovered by modulating y(t) with the same sinusoidal carrier and applying a lowpass filter.
ttx c2cos)(
: cos)()( ttxty c
cos)()( ttytw c
ttx c2cos
21
21)(
ttxtxtw c2cos)(21)(
21)(
(need to get rid of the 2nd term in RHS)
Transmitter and receiver are synchronized in phase
Application of Fourier Application of Fourier Theory Theory Demodulation for Sinusoidal Amplitude Modulation
ωc
Y(ω)
ωc+ ωm
ωωc- ωm-ωc
21
ωc
C(ω)
ω
-ωc
Apply lowpass filter (H(ω)) with a gain of 2 and cutoff frequency (ωco)ωm<ωco<2ωc-ωm
W(ω)
ω2ωc- ωm
2ωc2ωc
21
41
41
H(ω)
-ωm ωm
Application of Fourier Application of Fourier Theory Theory Demodulation for Sinusoidal Amplitude Modulation
)cos( cct
w(t)y(t
) -ωco ωc
o
2
H(ω)
x(t)
lowpass filter
In general,
)cos( cct
y(t)
x(t)
Application of Fourier Application of Fourier Theory Theory Demodulation for Sinusoidal Amplitude Modulation
Assume that modulator and demodulator are not synchronized;θc : phase of modulator
φc : phase of demodulator
)cos()cos()()( cccc tttxtw
)2cos(
21)cos(
21)( ccccc ttx
)2cos()(21)()cos(
21)( ccccc ttxtxtw
Application of Fourier Application of Fourier Theory Theory Demodulation for Sinusoidal Amplitude Modulation
When we apply lowpass filter
)()cos( txcc
output x(t)ccif
2c c output 0
cc must be maintained over time
requires synchronization
Application of Fourier Application of Fourier Theory Theory Demodulation for Sinusoidal Amplitude Modulation
Asynchronous Demodulation: Avoids the need for synchronization between the
modulator and demodulator If the message signal x(t) is positive, and carrier frequency
ωc is much higher than ωm (the highest frequency in the modulating signal), then envelope of y(t) is a very close approximation to x(t)
envelopey(t)
t
Application of Fourier Application of Fourier Theory Theory Demodulation for Sinusoidal Amplitude Modulation
Envelope Detector :Envelope Detector :
to assure positivity add DC to message signal, i.e., x(t)to assure positivity add DC to message signal, i.e., x(t)+A > 0+A > 0 x(t) vary slowly compared to x(t) vary slowly compared to ωωcc (to track envelope) (to track envelope)
A
y(t)=(A+x(t)) cosωctx(t)
Tradeoff : simpler demodulator, but requires transmission of redundancy (higher power)
half-wave rectifier!
+
–y(t) C R
+
–w(t)
cos ωct
Application of Fourier Theory Application of Fourier Theory Single-Sideband (SSB) Sinusoidal Amplitude Modulation
For ωm is the highest frequency in x(t), total bandwidth of the original signal 2ωm.
With sinusoidal carrier: spectrum shifted to ωc and -ωc twice bandwidth is required.
Redundancy in modulated signal!
ωm
X(ω)
2ωm
4ωm Y(ω)
ωc-ωc
Solution: Use SSB modulation
Application of Fourier Application of Fourier Theory Theory Single-Sideband (SSB) Sinusoidal Amplitude Modulation
ωm
X(ω)
(DSB)
(SSB)
(SSB)YL(ω)
ωc-ωc
YU(ω)
ωc-ωc
Spectrum with upper sidebands
Spectrum with lower sidebands
Y(ω)
ωc-ωc ωc +ωmlower sideband
upper sideband
upper sideband
lower sideband
Application of Fourier Application of Fourier Theory Theory Single-Sideband (SSB) Sinusoidal Amplitude Modulation
For upper sidebands: Apply y(t) to a sharp cutoff bandpass/highpass filter.
-ωc ωc
H(ω)
y(t) yU(t)
Y(ω)
ωc
-ωc
YU(ω)
ωc-ωc
Application of Fourier Application of Fourier Theory Theory Single-Sideband (SSB) Sinusoidal Amplitude Modulation
For lower sidebands: Use 90o phase-shift network.
00
)ωj,ωj,
(ωH
x(t)
cos ωct
sin ωct
xp(t)
y1(t)
y2(t)
y(t)
(Trace the operation as exercise!)
AM-DSB/WCAM-DSB/SC
, AM-SSB/WCAM-SSB/SC
: AM, Double (Single) SB, with carrier: AM, Double (Single) SB, suppressed carrier
YL(ω)
ωc-ωc