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Sampling and
reconstruction of
discrete time signal
BY :- KARAN SATI
Discrete time signal
• A discrete signal or discrete-time signal is a time series consisting of
a sequence of quantities.
• Discrete-time signals may have several origins, but can usually be
classified into one of two groups:-
• By acquiring values of an analog signal at constant or variable rate.
This process is called samling
• By observing an inherently discrete-time process, such as the weekly
peak value of a particular economic indicator.
Sampling :-
• Sampling is a process of breakage of continuous signal to
discrete signal.
• The output of system is recorded at different intervals of
time.
• Time interval between two consecutive sampling intervals
is called Sampling period or Sample interval.
• The sampling frequency or sampling rate, fs, is the average
number of samples obtained in one second (samples per
second), thus fs = 1/T.
Sampling theorem
 The sampling theorem was published in 1949 in a scientific article of C.E. Shannon
Let x(t) be a band (frequency)-limited signal
X(jw) = 0 for |w|>wM.
Then x(t) is uniquely determined by its samples {x(nT)} when the sampling frequency satisfies:
Ms ww 2
2wM is known as the Nyquist rate, as it represents the largest frequency
that can be reproduced with the sample time.
Sampling Effects: Frequency Domain
Xc(j)

N-N
XS(j)

N-N S-S 2S-2S

S-S 2S-2S
S > 2 N
S < 2 N (aliasing)
FourierTransform of
continuous function
FourierTransform of sampled
function
Reconstruction
Reconstruction :-
• In signal processing, reconstruction usually means the
determination of an original continuous signal from a sequence of
equally spaced samples.
Reconstruction
• Since spectrum of sampled signal consists of baseband spectrum
and spectral images shifted at multiples of 2π/T, reconstruction
means isolating the baseband image
• Concept: lowpass filter to pass baseband while removing images
XS(j)

N-N S-S 2S-2S
Reconstruction :
• Multiplication by rectangular pulse in frequency
domain (LPF) corresponds to convolution by sinc( )
function in time domain
• Because sinc( ) is non-causal and of infinite extent,
practical reconstruction requires an approximation
to the ideal case
Reconstruction
The process of reconstructing a continuous time
signal x(t) from its samples is known as interpolation.
Reconstruction is accomplished by passing the sampled signal
through an ideal low pass filter.
1
1/T
T
1
When the sampling frequency ws is less
than twice the band-limited frequency wM,
there is no overlaps the spectrum X(jw)
If this is true, the original signal x(t) can be
recovered from the impulse sampled xp(t),
by passing it through a low pass filter H(jw)
with gain T and cutoff frequency
between wM and ws-wM.
Thank you

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SAMPLING & RECONSTRUCTION OF DISCRETE TIME SIGNAL

  • 1. Sampling and reconstruction of discrete time signal BY :- KARAN SATI
  • 2. Discrete time signal • A discrete signal or discrete-time signal is a time series consisting of a sequence of quantities. • Discrete-time signals may have several origins, but can usually be classified into one of two groups:- • By acquiring values of an analog signal at constant or variable rate. This process is called samling • By observing an inherently discrete-time process, such as the weekly peak value of a particular economic indicator.
  • 3. Sampling :- • Sampling is a process of breakage of continuous signal to discrete signal. • The output of system is recorded at different intervals of time. • Time interval between two consecutive sampling intervals is called Sampling period or Sample interval. • The sampling frequency or sampling rate, fs, is the average number of samples obtained in one second (samples per second), thus fs = 1/T.
  • 4. Sampling theorem  The sampling theorem was published in 1949 in a scientific article of C.E. Shannon Let x(t) be a band (frequency)-limited signal X(jw) = 0 for |w|>wM. Then x(t) is uniquely determined by its samples {x(nT)} when the sampling frequency satisfies: Ms ww 2 2wM is known as the Nyquist rate, as it represents the largest frequency that can be reproduced with the sample time.
  • 5. Sampling Effects: Frequency Domain Xc(j)  N-N XS(j)  N-N S-S 2S-2S  S-S 2S-2S S > 2 N S < 2 N (aliasing) FourierTransform of continuous function FourierTransform of sampled function
  • 7. Reconstruction :- • In signal processing, reconstruction usually means the determination of an original continuous signal from a sequence of equally spaced samples.
  • 8. Reconstruction • Since spectrum of sampled signal consists of baseband spectrum and spectral images shifted at multiples of 2π/T, reconstruction means isolating the baseband image • Concept: lowpass filter to pass baseband while removing images XS(j)  N-N S-S 2S-2S
  • 9. Reconstruction : • Multiplication by rectangular pulse in frequency domain (LPF) corresponds to convolution by sinc( ) function in time domain • Because sinc( ) is non-causal and of infinite extent, practical reconstruction requires an approximation to the ideal case
  • 10. Reconstruction The process of reconstructing a continuous time signal x(t) from its samples is known as interpolation. Reconstruction is accomplished by passing the sampled signal through an ideal low pass filter.
  • 11. 1 1/T T 1 When the sampling frequency ws is less than twice the band-limited frequency wM, there is no overlaps the spectrum X(jw) If this is true, the original signal x(t) can be recovered from the impulse sampled xp(t), by passing it through a low pass filter H(jw) with gain T and cutoff frequency between wM and ws-wM.