SIGNALS AND SYSTEMS
EEE F243 / INSTR F243
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Lecture 16
Sampling
Todays Session
Fourier Analysis of Sampled Signals
Ideal Low Pass Filter
Signal Reconstruction
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Signal Reconstruction
Practical Difficulties in Sampling
Fourier Analysis of Sampled Signals
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Sampling Realization
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Spectrum of Sampled Signal
( ) ( ) ( ) ( ) ( )
===
nTS nTtnTfttftf
( ) ( )
= SS nFF 1
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( ) ( )=
=n
SS nFT
F 1
Sampling Theorem: Nyquist Rate ,
Nyquist Interval,
( ) BFS 2=
BT
2
1=
Example
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Interpolation
The Process of signal reconstruction or signal recovery of a continuous-
time signal from its samples is known as interpolation.
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Signal reconstruction is achieved through ideal low pass filter (LPF).
Ideal Low Pass Filter
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Physical Realization of
Interpolation
Simple Interpolation using a zero- order hold circuit:
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Improvement over Interpolation
First order hold circuit:
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( )
=B
TrectH
4
( ) ( ) ( ) =k
kBtctftf 2sin
Generalized Interpolation
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Signal values between samples as a weighted sum at the sample values.
Practical Difficulties
Causality of the ideal low pass filter.
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Filter gain must be zero beyond the first cycle.
Practical Difficulties
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Treachery of Aliasing
Solution:Anti Aliasing Filter: Band widthT
FS
2
1
2=
Practical Difficulties
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Features of Sampling
Sampling a signal in time domain introduces periodicity in the
frequency domain.
Sampling at a rate less than the Nyquist rate yield Aliasing.
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Sampling at a rate less than the Nyquist rate yield Aliasing.
Signal should be band - limited before it is send to sampler.
Band limiting is achieved by passing the signal through a low pass
filter.
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