Signal Processing Fundamentals - EIEenpklun/EIE327/Intro.pdf1 THE HONG KONG POLYTECHNIC UNIVERSITY...
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THE HONG KONG POLYTECHNIC UNIVERSITYDepartment of Electronic and Information Engineering
EIE 327EIE 327
Signal Processing FundamentalsSignal Processing Fundamentals
Part I: Spectrum Analysis and Filtering Part I: Spectrum Analysis and Filtering –– Dr Daniel Dr Daniel LunLun, EIE, EIE
Part II: Statistical Signal ProcessingPart II: Statistical Signal Processing–– Dr Bonnie Law, EIEDr Bonnie Law, EIE
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THE HONG KONG POLYTECHNIC UNIVERSITYDepartment of Electronic and Information Engineering
EIE 327EIE 327Signal Processing Fundamentals Signal Processing Fundamentals
PartPart--IISpectrum Analysis and Filtering Spectrum Analysis and Filtering
Lecturer:Lecturer: Dr. Daniel PakDr. Daniel Pak--Kong LUNKong LUN
Room:Room: DE637DE637 Tel:Tel: 2766625527666255EE--Mail:Mail: enpklunenpklun@@polyupolyu..eduedu..hkhkWeb page:Web page: www.www.eieeie..polyupolyu..eduedu..hkhk/~/~enpklunenpklun/EIE327//EIE327/
EIE327.htmlEIE327.html
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THE HONG KONG POLYTECHNIC UNIVERSITYDepartment of Electronic and Information Engineering
Contents• Signals and Systems• Sinusoids and Complex Numbers• Spectrum Representation• Sampling and Aliasing• Fourier Transform and Spectrum Analysis• Fast Fourier Transform• Convolution, FIR Filters and Z-transform
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THE HONG KONG POLYTECHNIC UNIVERSITYDepartment of Electronic and Information Engineering
References• DSP First – A Multimedia Approach
J.H. McClellan, R.W. Schafer and M.A. YoderPrentice Hall, 1998Comment: Entry level with lots of multimedia illustrations
• Introduction to Digital Signal ProcessingA.L. Paul and W. FuerstJohn Wiley and Sons Inc, 2nd Ed. 1998Comment: More formal treatment
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1. 1. Signals and SystemsSignals and Systems
Signal Processing Fundamentals – Part ISpectrum Analysis and Filtering
1. Signals and Systems
Input Output
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What is Signal?“Something” that carries information
Signal Processing Fundamentals – Part ISpectrum Analysis and Filtering
1. Signals and Systems
“Something” ⇒ pattern of variations of physical quantity that can be manipulated, stored, or transmitted by physical process
e.g. Speech signals, audio signals, image signals, video signals, radar signals, etc.
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Examples of Signals
Speech Audio
Image
Video Multimedia
Signal Processing Fundamentals – Part ISpectrum Analysis and Filtering
1. Signals and Systems
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• Signals can change to other physical form, e.g. electricity
wave
change of voltage
mic
Light
Lens
CCD
Object
change of voltage
Signal Processing Fundamentals – Part ISpectrum Analysis and Filtering
1. Signals and Systems
Facilitate processing by electrical equipment
Facilitate processing by electrical equipment
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Signal Processing Fundamentals – Part ISpectrum Analysis and Filtering
1. Signals and Systems
Continuous-time Signals• Many signals can be considered as pattern
changing with time, e.g. speech
• For every time instant, signal is found• Hence continuous-time signal (or analogue signal)• Most natural signals, such as speech, audio, etc.
are continuous-time signals
timevoltage
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Signal Processing Fundamentals – Part ISpectrum Analysis and Filtering
1. Signals and Systems
Discrete-time Signals• With the advance of computer technology,
we want to process signals by computer• Computer can only handle data, but not
continuous signals• Need sampling⇒ extract signal at some time instants
timevoltage Signal is found only at some instantsDiscrete-time signal
244.2
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Signal Processing Fundamentals – Part ISpectrum Analysis and Filtering
1. Signals and Systems
Typical Signal Processing Systems
Mic
Sound card with sampler(or A/D converter)
Processed signalD/A converter speaker
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Why signal processing?• Change the signal characteristic
• e.g. increase or decrease the loudness of speech• Signals in their original form may not be
manipulated easily• e.g. Speech compression – reduce the effort for
storage and transmission of speech• To understand the signal
• e.g. Speech recognition – to recognize the content of the speech
Signal Processing Fundamentals – Part ISpectrum Analysis and Filtering
1. Signals and Systems
Since we often process discrete-time signals, such processing is called digital signal processing (DSP)
Since we often process discrete-time signals, such processing is called digital signal processing (DSP)
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Signal Processing Fundamentals – Part ISpectrum Analysis and Filtering
1. Signals and Systems
Examples of DSP Systems
Video Object tracking system
Speech compression
system
10 times compressed
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Steps to construct a DSP system
1. Develop signal processing algorithm• Express input and output in mathematical form• Using mathematics to figure out the solution to
the problem2. Realize the signal processing algorithm
• Translate the algorithm into computer program• Execute the program with the computer
Input Output
Signal Processing Fundamentals – Part ISpectrum Analysis and Filtering
1. Signals and Systems
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Mathematic Representation of Continuous-time Signals
x(t)
x: a symbol represents this signal
t: a real number variable
x(t) is defined for all value of t, hence continuouse.g. x(0) = 0; x(70.5) = -90.5; x(100.23) = 80
t = 100.23
t = 70.5t = 0
We use () to indicate that x is continuous
Signal Processing Fundamentals – Part ISpectrum Analysis and Filtering
1. Signals and Systems
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Mathematic Representation of Discrete-time Signals
x[n]
x: a symbol represents this signal
n: an integer variable
We use [] to indicate that x is discrete
n = 0
n = 100
n = 70
x[n] is defined only at some instants of time, since n is integer
e.g. x[0] = 0; x[70] = -90.5; x[100] = 80
Signal Processing Fundamentals – Part ISpectrum Analysis and Filtering
1. Signals and Systems
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Signal Processing Fundamentals – Part ISpectrum Analysis and Filtering
1. Signals and Systems
Ts
x(t)
x[n]
x[n] = x(n*Ts)x[n] = x(n*Ts)
x[0] = x(0)x[1] = x(Ts)x[2] = x(2Ts)x[3] = x(3Ts)
: :
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Simplest DSP System - Amplifier Input
(256 samples)
1. Develop signal processing algorithm• Input – x[n]; Output – y[n]• y[n] = x[n] × 2 for all n = 1 to 256 (algorithm)
2. Realize the signal processing algorithmmain() { int n, x[], y[];
for (n=1; n<=256; n++) y[n] = x[n]*2;}
Output(256 samples) Our task:
Output = Input x 2
Our task: Output = Input x 2
Signal Processing Fundamentals – Part ISpectrum Analysis and Filtering
1. Signals and Systems
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Signal Processing Fundamentals – Part ISpectrum Analysis and Filtering
1. Signals and Systems
Mathematic Representation of Systems
• A signal processing system transforms signals into new signals or different signal representations
• Mathematically
y(t) = T{x(t)}y(t) = T{x(t)}Output signal Input signal
System or Operator
• T, in this case, works on continuous-time signals, hence it is a continuous-time system
e.g. y(t) = 2*x(t)
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Signal Processing Fundamentals – Part ISpectrum Analysis and Filtering
1. Signals and Systems
Input Output
System T⇓
x 2
x[n] y[n]
⇒ y[n] = 2*x[n]
• T now works on discrete-time signals, hence it is a discrete-time system
y[n] = T{x[n]}y[n] = T{x[n]}
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Signal Processing Fundamentals – Part ISpectrum Analysis and Filtering
1. Signals and Systems
Other Simple System Examples
System T⇓
sampling
x(t) y(t) = T{x(t)} = x(nTs)
System T⇓
square
x[n] y[n] = T{x[n]} = x[n]2
{0, 2, -1, -3, 4…} {0, 4, 1, 9, 16…}
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Signal Processing Fundamentals – Part ISpectrum Analysis and Filtering
2. Sinusoids and Complex Numbers
2. 2. Sinusoids and Complex NumbersSinusoids and Complex Numbers
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Signal Processing Fundamentals – Part ISpectrum Analysis and Filtering
2. Sinusoids and Complex Numbers
• Sinusoidal signals, or more concisely, sinusoids, are the most basic signals in signal processing
What are Sinusoids?
)tcos(A)t(x o φω +=
A continuous-time signal
Amplitudecosine function
radian frequency = 2π f
phase-shift
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)tcos(A)t(x o φω += ⇒ x(t) = 10 cos (2π1000t + 0)
• The signal above represents a cosine function with amplitude 10 and frequency 1000 Hz with no phase shift
e.g. x(0) = 10 cos(2π0) = 10x(0.5*10-3) = 10 cos(2π0.5) = -10x(1*10-3) = 10 cos(2π) = 10x(2*10-3) = 10 cos(2π2) = 10x(3*10-3) = 10 cos(2π3) = 10
Signal Processing Fundamentals – Part ISpectrum Analysis and Filtering
2. Sinusoids and Complex Numbers
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x(t) = 10 cos (2π1000t + 0)
x(t) = 5 cos (2π1000t + 0)
Change of Amplitude
Change of Amplitude
Signal Processing Fundamentals – Part ISpectrum Analysis and Filtering
2. Sinusoids and Complex Numbers
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Change of FrequencyChange of Frequency
x(t) = 10 cos (2π1000t + 0)
x(t) = 10 cos (2π500t + 0)
1 period = 2ms
1 period = 1ms
Signal Processing Fundamentals – Part ISpectrum Analysis and Filtering
2. Sinusoids and Complex Numbers
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Change of Phase shiftChange of Phase shift
x(t) = 10 cos (2π1000t + 0)
x(t) = 10 cos (2π1000t + pi/2)
Signal will shift 1 period if the cosine function advances by 2πHence shifting pi/2 means shifting ¼period
Signal Processing Fundamentals – Part ISpectrum Analysis and Filtering
2. Sinusoids and Complex Numbers
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Signal Processing Fundamentals – Part ISpectrum Analysis and Filtering
2. Sinusoids and Complex Numbers
Sinusoids are Real Signals• Although cosine are mathematical functions,
sinusoids can be generated by real instrument• e.g. Tuning fork
• Tuning fork vibrates in air at 440Hz and generates wave at the same frequency
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Signal Processing Fundamentals – Part ISpectrum Analysis and Filtering
2. Sinusoids and Complex Numbers
Why Sinusoids are so Important?• In 1807, a famous mathematician, Fourier, showed
that Almost all signals can be constructed by the summation of sinusoids of different frequencies, amplitudes and phase shifts
• The larger is the number of sinusoids, the closer is the square wave
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• The larger is the number of sinusoids, the closer is the waveform to square wave
A0+A1cos(ωot )
A0 + A1cos(ωot ) + A3cos(3ωot )
A0 + A1cos(ωot ) + A3cos(3ωot ) + A5cos(5ωot ) + A7cos(7ωot )
Signal Processing Fundamentals – Part ISpectrum Analysis and Filtering
2. Sinusoids and Complex Numbers
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Signal Processing Fundamentals – Part ISpectrum Analysis and Filtering
2. Sinusoids and Complex Numbers
• Sometimes, calculation involving cosine functions is not that convenient
1.7cos(2π10t + 70π /180) + 1.9cos (2π10t + 200π/180) = ???
• Solution: use complex numbers
jbaA +=
Complex number A The real
part of A
The imaginary part of A
1−=j
Phasor additionPhasor addition
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Signal Processing Fundamentals – Part ISpectrum Analysis and Filtering
2. Sinusoids and Complex Numbers
• Euler’s formula
θθθ sincos je j += { }θθ jeRecos =or
( ) ( )2211 coscos φωφω +++ tAtA• Consider
{ } { }( ){ } ( ){ }
{ } { }{ } ( )φωφω
ωφω
ωφφω
φωφω
+====
+=+=
+=
+
++
tAAeXeAee
XXeeAeAeeAeA
tj
tjjtj
tjjjtj
tjtj
cosReReRe
ReRe
ReRe
)(
2121
)(2
)(1
21
21
Phasors
X = X1 + X2
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1.7cos(2π10t + 70π /180) + 1.9cos (2π10t + 200π/180) = ???
597.15814.07.1 180/70
1
jeX j
+== π
6498.0785.19.1 180/200
2
jeX j
−−== π
180/79.14121
532.1
9476.0204.1πje
jXXX=
+−=+=
( )180/79.141102cos532.1: ππ +tSolution
Signal Processing Fundamentals – Part ISpectrum Analysis and Filtering
2. Sinusoids and Complex Numbers
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Exercise
Signal Processing Fundamentals – Part ISpectrum Analysis and Filtering
2. Sinusoids and Complex Numbers
( )( )4/31002cos4)(
4/1002cos8)(
2
1
ππππ
−=+=ttxttx
What is the sum of x1 and x2?
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Signal Processing Fundamentals – Part ISpectrum Analysis and Filtering
2. Sinusoids and Complex Numbers
Solution
)7071.07071.0(88 4/
1
jeX j
+== π
)7071.07071.0(44 4/3
2
jeX j
−−== − π
4/21
4
)7071.07071.0(4πje
jXXX
=
+=+=
( )4/1002cos4: ππ +tSolution