Electronic instrumentation Signalsbrd4.ort.org.il/~ksamuel/ElIn.31361/Lectures/013 Analog... ·...
Transcript of Electronic instrumentation Signalsbrd4.ort.org.il/~ksamuel/ElIn.31361/Lectures/013 Analog... ·...
Electronic instrumentationAnalog Signals in FD
Lecturer: Dr. Samuel Kosolapov
Items to be discussed
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• Idea of decomposition• Fourier approach• Spectrum concept• Examples of spectrum usage• Laboratory Device: spectrum analyzer
This presentation deals with Analog Signals in FDDigital Signals in FD will be discussed later
Real-life Analog Signal: Graphic Presentation in the TD
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Example: Real-life Audio Signal. Digitized short music fragment stored in Windows “ding.wav” file
Graphic Presentation in TD reveals that math description of audio signals in TD is not trivial
Real-life Medical Analog Signals: Graphic Presentation in the TD
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Examples: ECG (ElectroCardioGram ) & EEG (ElectroEncephaloGram– important medical diagnostic tools
Graphic Presentation in TD reveals that math description of those IMPORTANT signal in TD is not trivial
Bright Idea: Decompose “sophisticated” signal to sum of basis(elementary) functions
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When set of “basis functions” is known, set of factors provides alternative description of the X(t)
a1j1
Signal X (t)
a2j2
anjn
𝑋 𝑡 = a𝑖j𝑖 𝑡
Constant factor
Basis Function
Fourier Analysis
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Jean Baptiste Joseph Fourier, 1768-1830French Physicist
Any (even bad-behaved) function can be representedas infinite series of harmonic sinusoids
ATTENTION: Be aware of different forms of modern Fourier analysis:
Fourier Series (real / complex),Integral Fourier (Fourier Transform)Digital Fourier Transform Fast Fourier Transform (FFT)n-dimensional Fourier Transform Image Processing, Cosine Fourier Transform DCFFT … JPEG, MP3 …
Signal Decomposition: Fourier Series
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We’ll start with Fourier Series now and will finish with Fourier Transform.
If the function X(t) is defined in the “t” range [0..T], then it can be expressed as a Fourier Series of the form:
;)2sin()2cos(
...)2sin(...)32sin()22sin()2sin(
...)2cos(...)32cos()22cos()2cos(
)(
1
000
0030201
0030201
0
n
nn
n
n
tnfbtnfaa
tnfbtfbtfbtfb
tnfatfatfatfa
atX
Where:constant a0 is the DC component (average value of X(t) ),constants {an } and {bn } are the “Fourier coefficients”.f0 = 1/T – fundamental frequency – (the lowest AC frequency)
Signal Decomposition: Fourier Series
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Calculation of the Fourier coefficients:
Technical problem (for advanced students):Mathematicians require “Dirichlet conditions”: X(t) must be periodic function with period T.
Modern approach is: ANY function can be presented using Fourier series.
The price is Gibbs phenomenon – spikes at X(t) presentation.
T
n
T
n
T
dtTnttXT
bdtTnttXT
a
dttXT
a
00
0
0
;)/2sin()(2
;)/2cos()(2
;)(1
Not a “theoretical declaration” but a practical way of coefficients calculationExplain why any reasonable function can be decomposed by Fourier: Even Dirac function can be integrated
Explain why a0 is called DC
Signal Decomposition: Complex Fourier Series
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Fourier Series: mess of sin and cos:
Alternative is to use “complex Fourier series”:
;)sin()cos()(
2;2
;)2sin()2cos()(
1
000
000
1
000
n
nn
n
nn
tnbtnaatX
Tf
tfnbtfnaatX
dtetXT
dtetXT
cectXtjn
T
T
tjn
T
n
tjnn
n
n000
2/
2/0
)(1
)(1
;)(
Fourier Series: “Exact” or “Approximation”
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If we take only limited set of cos/sin or complex exponents,(that is {n=1 to k} and k< infinity),
then we have approximation of X(t) by Fourier series.
In case we KNOW that our signal does not contain frequencies larger than “k*fo”, then “approximation” is “exact function presentation”
"";)2sin()2cos()(
"";)2sin()2cos()(
1
000
1
000
ionApproximattfnbtfnaatX
ExacttfnbtfnaatX
k
n
nn
n
nn
"";)(
}"{";)(
0
0
ionApproximatectX
ExactectX
tjnkn
kn
n
tjnn
n
n
Fourier Series: “Exact” or “Approximation”
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Suppose we definitely know that some specific signaldoes not contains components above some max frequency.
For example, Human cannot produce sounds above ~ 8 kHz there is no need to allow high frequencies (>8 kHz) in the telephone apparatus It is bad idea to listen music by (old) telephone
"";)(
}"{";)(
0
0
ionApproximatectX
ExactectX
tjnkn
kn
n
tjnn
n
n
Nyquist Frequency and Nyquist Rate
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Wikipedia claims that the Nyquist frequency should not be confused with the Nyquist rate.
Thus, Nyquist rate is a property of a continuous-time signal, whereas Nyquist frequency is a property of a discrete-time system.
We will discuss Nyquist frequency later
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Example: Good Looking Polynomial
X:=proc(t)
> 4*t*(1-t):
> end:
Considering T=1, let’s approximate X(t)
Using n=2 only !!!
n:=2:
> for i from -n to +n do
> c[i]:=evalf( int( X(t)*exp(-I*2*Pi*i*t/T ),t=0..1) );
> end;
C[-2] = - 0.051C[-1]= - 0.203C[0] = 0.667C[1] = - 0.203C[2] = - 0.051
Example: Approximation by Fourier SeriesQ: What is common between Parabola and sin ???
Reminder from Math:Coefficients C[i] are Real and “symmetrical” in this case
C[0] is “DC” level
X(t)
Quality of Fourier Series Approximation
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N=16. Looks good except …
X(t); n = 16X(t); n = 2
Problem with Fourier Series Approximation
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Mathematicians have told us…. (that X must be periodical)
. Gibbs Effect: X(t) is not periodical .But Fourier Approximation
is “Periodical” Additionally:
1st Derivatives now have “Jump”near 0 and 1
Approximation is the best possible (but according to Least squares method)
X(t); n = 16
Example” Fourier Series Approximation
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Another example: SAWTOOTH signal:
n=16
Explain: Approx(0) = Approx(1) =0.5
For t=0 is function 0 or 1?“Fourier Auto Decision”:
(0+1)/2=0.5
Extremely strong MACROGibbs effect.High n does not help: Error is not 0
X(t); n = 16
Important Concept: Spectrum
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Frequency, Wavelength
Spectrum of Ideal “White” light
“Filtered” white light == some color
Use Graph of Energy versus wavelength/frequency
to “describe” light “content”
~ Energy
White light is dispersed/decomposed
by a prism to a number of colors
Spectrum example: Astronomy: {Energy- Wavelength} graphical presentation
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~ “White” light(~ Equal energy for any wavelength)
~ Dirac functionSignificant Energy absorption in the narrow frequency bandShape of the spectrum is very informative
Spectral analysis enable to knowwhat is the chemical content of the star
we will never visit
Spectrum example: Chemistry: {Energy- Wavelength} graphical presentation
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By analyzing absorbance spectrum one can estimate amount of specific substance
Start to think how to build primitive (but useful) spectrometer by using:Arduino board,
set of LEDs of different colors,photoresistor
See pulse oximeter example:http://www.howequipmentworks.com/pulse_oximeter/
Important EE Concept: Spectrum
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Set of Coefficients { C[n] } is good function presentation.
Each function has its own unique set of { C[n] }
What is the meaning of each coefficient C[i]– weight (importance) of specific frequency i*0
More exactly: Energy about the frequency i*0
Analogy with OPTICS Graphic presentation {C[n], n} describes signal in FD
In most our cases C[-i] = C[i] use range [0..n] (explain *2) + omission of some technical details
We need “Energy” ~C2,
but in many cases we’ll speak about voltage amplitude |C|
Because any function can be reconstructed
(well, approximately reconstructed)By using known set of {C[n]} )
BTW: DSP can do this very fast (press “play” button
and listen music immediately)
Example of Spectrum: Signal Presentation in FD
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Parabola Sawtooth
Spectrum (set of {C[n]}) is different for different functions. {Compare DC}
{ X(n) , n } { X(n) , n }
Extremely Important feature C[n] 0 for large n
That is: most of the energy is in DC
and in LOW frequencies.
Fast explanation about MP2 compression for audio signals. Details later
Example of Important Analog Voltage Signal:Square wave (compare with sine wave)
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Fourier coefficient ~ 1/n
Example of Important Analog Voltage Signal:Square wave: TD FD
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Example of Important Analog Voltage Signal:Triangle wave
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:
:
~ 1/k2
Compare with 1/k for Square Wave
Example of Analog Voltage Signal:Sawtooth wave
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Explain duty cycle practical limitations
Important Laboratory Instrument:Spectrum Analyzer
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Presents spectrum (signal in FD). For many signals this presentation is more convenient than that of TD
The amplitude of the signal at the input is plotted
against the frequency of the signal.
Arduino-based spectrum analyzer will be discussed later
Virtual Laboratory. Simulation ExampleSpectrum Analyzer Demo
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Sin wave selected
Spectrum Analyzer Symbol in Multisim
~ Single base frequency is seen (spectrum of sin)Explain why not ideal: “digitization …
Mind “lin”, “dB”, dBmoptions
Virtual Laboratory. Simulation ExampleSpectrum Analyzer Demo
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Spectrum of the Square Wave.Basic frequency (= ?)
Pulse/Rectangle wave is selected
Harmonics are seen Amplitude ~ 1/n
This “peak” must be smaller. Defect of “digital” simulation. Beware.