Lecture 4 1 Lecture 4 Practical sampling and reconstruction
Lecture 4 1
Lecture 4
Practical sampling and reconstruction
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Outline
Practical sampling– Aperture effect– Non ideal filters– Non-band limited input signals
Practical reconstruction Practical digital systems Discrete time Fourier transform
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Practical sampling
Practical sampling differs from ideal in the following respects– The sample (impulse) train actually consists of
pulses of duration – Real signal are time limited, therefore cannot
be band limited (the uncertainty principle of Fourier transform)
– Reconstruction filters are not ideal
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xa(t)
n=
s(t) = (tnT)
xs(t) = xa(nT)(tnT)n=
sample andhold filter
1h(t)
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Xa(j)1
s>2
1/T
/T
|Hs(j)|
/ /
H (j) = sin (/2) e-j/2
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s>2
|Xs(j)|/T
/T
Xs(j) = H(j) . 1T Xa(jjks )k= -
If / there is no significant distortion over signal band (otherwise equalization can compensate distortion)
Aperture effect
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Non ideal filters
The problem of non-ideal filtering can be combatted by increasing 1/T
Effectively, we are only using the middle portion of the filter, where it is closer to perfect
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Non-band limited signals
We may be only interested in a low frequency portion of a wide-band signal, e.g. speech only needs upto 3-4 Khz
There may be high frequency, wideband additive noise in the input signal
So we prefilter |c 1
0, |Haa(j) =
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Anti-aliasing filter Filter prior to sampling removes higher
frequency components which could have been moved into the lower frequency range by aliasing
Of course, we are distorting the signal, but this is in a frequency range in which we are not interested
If we were, we would need to use a higher sampling frequency
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Practical D/A conversion
We don’t have perfect interpolation Sample and hold The impulse response of a sample and hold filter is h(t)
1
h(t)
T
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Frequency domain
We need to compensate by adding a compensated reconstruction filter after the sample and hold process
|Hs(j)|
/ /
Ho(j) = sin (/2) e-j/2
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|Hr(j)|
/
e j/2, |
sin (/2)
~
Compensated reconstruction filter
0, |
~
Hr(j) =
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Ideal system
A/D D/Ah(n)
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Practical system
h(n)A/D
T
Antialiasing pre-filter
Sample and Hold
TSample and Hold
CompensatedReconstructionFilter
Haa(j)
D/A
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Effective frequency response
Heff(j) = Hr(j) H0(j) H(e jT) Haa(j)
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xa(t)
n=
S(t) = (tnT)
xs(t) = xa(nT)(tnT)n=
convert todiscretesequence x[n] = xa(nT)
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Back to sampling
Let xa(t)aperiodic
xs(t) = xa(nT)(tnT) aperiodic
Xs(j) = xa(nT)(tnT) e -
jtdt
n=
n=
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Xs(j) = xa(nT)(tnT) e -
jtdt
xa(nT) e -jnT
= x[n] e -jn
where =T X(e j) = x[n] e -jn is defined as the
discrete time Fourier transform of x[n]
n=
n=
n=
n=
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From the sampling theorem,
Xs(j) = Xa(jkjs)
thus,
X(e j) = Xa(jj )
In other words, on going from the continuous domain to the discrete domain, we undergo a scaling or normalization in frequency from =s to = 2
There is also a corresponding time normalization from t=T to n=1
n=
1T
k=
1T
T
2k T
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Discrete time Fourier transform
X(e j) = x[n] e -jn
– continuous and periodic with period 2
x[n] = X(e j) e jnd– discrete and aperiodic
n=
1
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Example
Let x[n] = u[n]-u[n-M]
= [n-k]
Then X(e j) = x[n] e -jn=e -jn
= = e -jM-1)/2
k=0
M-1
n=0
M-1
n=
1-e-jM
1-e-j
sin(M/2)
sin(/2)
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sin(M/2)
sin(/2)|X(e j)| =
/ / 0......
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Reading
Discrete time signals: Sections Sampling: Sections 8.2 Reconstruction, quantization, coding:
Sections 8.2 - p.363 Practical sampling and reconstruction:
Sections 8.2 - p.355 Fourier transform: Chapter 4