4. SAMPLING PROCESS
The Sampling process of converting a continuous
time signal into an equivalent discrete time signal
.
An analog signal is converted into a
corresponding sequence of samples that are
usually spaced uniformly in time.
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The continuous signal is represented
with a green colored line while the
discrete samples are indicated by the
blue vertical lines.
6. SAMPLING THEOREM
A Continuous time signal can be completely
represented in its samples & it can be recovered
back if the sampling frequency is twice of the
highest frequency content of the signal.
fs > 2fm
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8. RECONSTRUCTION
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The process of reconstructing a continuous time
signal x(t) from its samples is known
as interpolation. In the sampling theorem we
saw that a signal x(t) band limited to D Hz can be
reconstructed from its samples. This
reconstruction is accomplished by passing the
sampled signal through an ideal low pass filter of
bandwidth D Hz.
10. TYPES OF SAMPLING
Ideal sampling
Practical sampling
a) Natural sampling
b) Flat top sampling
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11. NYGUIST RATE
When the sampling rate becomes exactly equal
to 2fm samples/sec, for a given signal then it is
called as nyquist rate.
fs = 2fm
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13. NYQUIST INTERVAL
The time interval between any two adjacent
samples when sampling rate is nyquist rate.
Ts = 1/2fm
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14. ALIASING
If the sampling frequency is less than the nyquist
rate then the high frequency component in the
spectrum of the sampled signal interferes with low
frequency & appears as low frequency signal
then it is called as aliasing.
fs < 2fm
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16. Elimination of aliasing
It can be eliminated by using low pass filter.
This low pass filter is also called anti aliasing
filter.
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17. QUANTIZATION
It is the approximated or rounded off to the finite
number of nearest standard predefined voltage
level or quantization levels is called as
quantization.
a)Uniform quantization
b)Non uniform quantization
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19. CONCLUSION
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This result is summarised by the Sampling
Theorem which states that we can collect all the
information in a signal by sampling at a rate ,
where B is the signal bandwidth. Given this
information we can, therefore, reconstruct the
actual shape of the original continuous signal at
any instant ‘in between’ the sampled instants. It
should also be clear that this reconstruction is not
a guess but a true reconstruction.
