Discrete Time Fourier Transform Engineering Mathematics Notes | EduRev

Engineering Mathematics : Discrete Time Fourier Transform Engineering Mathematics Notes | EduRev

 Page 1


Module 3 : Sampling and Reconstruction 
Lecture 28 : Discrete time Fourier transform and its Properties
 
Objectives:
Scope of this Lecture:
In the previous lecture we defined digital signal processing and understood its features. The general procedure is to convert the
Continuous Time signal into Discrete Time signal. Then we try to obtain back the original signal. In this lecture we will study the concepts
of Discrete time Fourier Transform and Signal Representation.
Representation of discrete time periodic signal .
Discrete Time Fourier Transform (DTFT) of an aperiodic discrete time signal .
Another way of representing DTFT of a periodic discrete time signal.
Properties of DTFT
 
Representation of Discrete periodic signal.
A periodic discrete time signal x[n] with period N can be represented as a Fourier series:
 where 
Here the summation ranges over any consecutive N integers of  x[n],
where N is the period of the discrete time signal x[n].
Here equation (i) is called the Synthesis Equation and equation (ii) is called the Analysis Equation.
Now since x[n] is periodic with period N; the Fourier series coefficients are related as;
 
Discrete Time Fourier Transform of an aperiodic discrete time signal
Given a general aperiodic signal  of finite duration, that is; for some integer N, . From this aperiodic signal we
can construct a periodic signal  for which  is one period. As we chose period N to be larger than the duration of ,  is
identical to . As the period ,  for any finite value of n.
Flash File 
 
 
 
 
Page 2


Module 3 : Sampling and Reconstruction 
Lecture 28 : Discrete time Fourier transform and its Properties
 
Objectives:
Scope of this Lecture:
In the previous lecture we defined digital signal processing and understood its features. The general procedure is to convert the
Continuous Time signal into Discrete Time signal. Then we try to obtain back the original signal. In this lecture we will study the concepts
of Discrete time Fourier Transform and Signal Representation.
Representation of discrete time periodic signal .
Discrete Time Fourier Transform (DTFT) of an aperiodic discrete time signal .
Another way of representing DTFT of a periodic discrete time signal.
Properties of DTFT
 
Representation of Discrete periodic signal.
A periodic discrete time signal x[n] with period N can be represented as a Fourier series:
 where 
Here the summation ranges over any consecutive N integers of  x[n],
where N is the period of the discrete time signal x[n].
Here equation (i) is called the Synthesis Equation and equation (ii) is called the Analysis Equation.
Now since x[n] is periodic with period N; the Fourier series coefficients are related as;
 
Discrete Time Fourier Transform of an aperiodic discrete time signal
Given a general aperiodic signal  of finite duration, that is; for some integer N, . From this aperiodic signal we
can construct a periodic signal  for which  is one period. As we chose period N to be larger than the duration of ,  is
identical to . As the period ,  for any finite value of n.
Flash File 
 
 
 
 
 
The Fourier series representation of  is :
Since over a period that includes the interval  , it is convenient to choose the interval of summation to be this
period, so that  can be replaced by  in the summation. Therefore,
 
 
 
Flash File 
 
Another way of representing DTFT of a periodic discrete signal
In continuous time, the fourier transform of  is an impulse at  .However in discrete time ,for signal    the
discrete time fourier transform is periodic in with period  . The DTFT of  is a train of impulses at 
 i.e Fourier Transform can be written as :
Consider a periodic sequence x[n] with period N and with fourier series representation
Then discrete time Fourier Transform of a periodic signal x[n] with period N can be written as :
Page 3


Module 3 : Sampling and Reconstruction 
Lecture 28 : Discrete time Fourier transform and its Properties
 
Objectives:
Scope of this Lecture:
In the previous lecture we defined digital signal processing and understood its features. The general procedure is to convert the
Continuous Time signal into Discrete Time signal. Then we try to obtain back the original signal. In this lecture we will study the concepts
of Discrete time Fourier Transform and Signal Representation.
Representation of discrete time periodic signal .
Discrete Time Fourier Transform (DTFT) of an aperiodic discrete time signal .
Another way of representing DTFT of a periodic discrete time signal.
Properties of DTFT
 
Representation of Discrete periodic signal.
A periodic discrete time signal x[n] with period N can be represented as a Fourier series:
 where 
Here the summation ranges over any consecutive N integers of  x[n],
where N is the period of the discrete time signal x[n].
Here equation (i) is called the Synthesis Equation and equation (ii) is called the Analysis Equation.
Now since x[n] is periodic with period N; the Fourier series coefficients are related as;
 
Discrete Time Fourier Transform of an aperiodic discrete time signal
Given a general aperiodic signal  of finite duration, that is; for some integer N, . From this aperiodic signal we
can construct a periodic signal  for which  is one period. As we chose period N to be larger than the duration of ,  is
identical to . As the period ,  for any finite value of n.
Flash File 
 
 
 
 
 
The Fourier series representation of  is :
Since over a period that includes the interval  , it is convenient to choose the interval of summation to be this
period, so that  can be replaced by  in the summation. Therefore,
 
 
 
Flash File 
 
Another way of representing DTFT of a periodic discrete signal
In continuous time, the fourier transform of  is an impulse at  .However in discrete time ,for signal    the
discrete time fourier transform is periodic in with period  . The DTFT of  is a train of impulses at 
 i.e Fourier Transform can be written as :
Consider a periodic sequence x[n] with period N and with fourier series representation
Then discrete time Fourier Transform of a periodic signal x[n] with period N can be written as :
 
Properties of DTFT
Periodicity:
Linearity:
The DTFT is linear.
If
and
then
 
Stability:
The DTFT is an unstable system   i.e   the input x[n] gives an unbounded output.
Example :
If   x[n] = 1 for all n
then DTFT diverges i.e Unbounded output. 
Time Shifting and Frequency Shifting:
If,
then,
and,
 
Time and Frequency Scaling:
Time reversal  
Let us find the DTFT of x[-n]
Page 4


Module 3 : Sampling and Reconstruction 
Lecture 28 : Discrete time Fourier transform and its Properties
 
Objectives:
Scope of this Lecture:
In the previous lecture we defined digital signal processing and understood its features. The general procedure is to convert the
Continuous Time signal into Discrete Time signal. Then we try to obtain back the original signal. In this lecture we will study the concepts
of Discrete time Fourier Transform and Signal Representation.
Representation of discrete time periodic signal .
Discrete Time Fourier Transform (DTFT) of an aperiodic discrete time signal .
Another way of representing DTFT of a periodic discrete time signal.
Properties of DTFT
 
Representation of Discrete periodic signal.
A periodic discrete time signal x[n] with period N can be represented as a Fourier series:
 where 
Here the summation ranges over any consecutive N integers of  x[n],
where N is the period of the discrete time signal x[n].
Here equation (i) is called the Synthesis Equation and equation (ii) is called the Analysis Equation.
Now since x[n] is periodic with period N; the Fourier series coefficients are related as;
 
Discrete Time Fourier Transform of an aperiodic discrete time signal
Given a general aperiodic signal  of finite duration, that is; for some integer N, . From this aperiodic signal we
can construct a periodic signal  for which  is one period. As we chose period N to be larger than the duration of ,  is
identical to . As the period ,  for any finite value of n.
Flash File 
 
 
 
 
 
The Fourier series representation of  is :
Since over a period that includes the interval  , it is convenient to choose the interval of summation to be this
period, so that  can be replaced by  in the summation. Therefore,
 
 
 
Flash File 
 
Another way of representing DTFT of a periodic discrete signal
In continuous time, the fourier transform of  is an impulse at  .However in discrete time ,for signal    the
discrete time fourier transform is periodic in with period  . The DTFT of  is a train of impulses at 
 i.e Fourier Transform can be written as :
Consider a periodic sequence x[n] with period N and with fourier series representation
Then discrete time Fourier Transform of a periodic signal x[n] with period N can be written as :
 
Properties of DTFT
Periodicity:
Linearity:
The DTFT is linear.
If
and
then
 
Stability:
The DTFT is an unstable system   i.e   the input x[n] gives an unbounded output.
Example :
If   x[n] = 1 for all n
then DTFT diverges i.e Unbounded output. 
Time Shifting and Frequency Shifting:
If,
then,
and,
 
Time and Frequency Scaling:
Time reversal  
Let us find the DTFT of x[-n]
 
Time expansion:
It is very difficult for us to define x[an] when a is not an integer. However if a is an integer other than 1 or -1 then the original signal is
not just speeded up. Since n can take only integer values, the resulting signal consists of samples of x[n] at an.
If k is a positive integer, and we define the signal
then
 
 
Convolution Property :
Let h[n] be the impulse response of a discrete time LSI system. Then the frequency response of the LSI system is
Now
and
If
then
Proof
now put n-k =m, for fixed k, 
Page 5


Module 3 : Sampling and Reconstruction 
Lecture 28 : Discrete time Fourier transform and its Properties
 
Objectives:
Scope of this Lecture:
In the previous lecture we defined digital signal processing and understood its features. The general procedure is to convert the
Continuous Time signal into Discrete Time signal. Then we try to obtain back the original signal. In this lecture we will study the concepts
of Discrete time Fourier Transform and Signal Representation.
Representation of discrete time periodic signal .
Discrete Time Fourier Transform (DTFT) of an aperiodic discrete time signal .
Another way of representing DTFT of a periodic discrete time signal.
Properties of DTFT
 
Representation of Discrete periodic signal.
A periodic discrete time signal x[n] with period N can be represented as a Fourier series:
 where 
Here the summation ranges over any consecutive N integers of  x[n],
where N is the period of the discrete time signal x[n].
Here equation (i) is called the Synthesis Equation and equation (ii) is called the Analysis Equation.
Now since x[n] is periodic with period N; the Fourier series coefficients are related as;
 
Discrete Time Fourier Transform of an aperiodic discrete time signal
Given a general aperiodic signal  of finite duration, that is; for some integer N, . From this aperiodic signal we
can construct a periodic signal  for which  is one period. As we chose period N to be larger than the duration of ,  is
identical to . As the period ,  for any finite value of n.
Flash File 
 
 
 
 
 
The Fourier series representation of  is :
Since over a period that includes the interval  , it is convenient to choose the interval of summation to be this
period, so that  can be replaced by  in the summation. Therefore,
 
 
 
Flash File 
 
Another way of representing DTFT of a periodic discrete signal
In continuous time, the fourier transform of  is an impulse at  .However in discrete time ,for signal    the
discrete time fourier transform is periodic in with period  . The DTFT of  is a train of impulses at 
 i.e Fourier Transform can be written as :
Consider a periodic sequence x[n] with period N and with fourier series representation
Then discrete time Fourier Transform of a periodic signal x[n] with period N can be written as :
 
Properties of DTFT
Periodicity:
Linearity:
The DTFT is linear.
If
and
then
 
Stability:
The DTFT is an unstable system   i.e   the input x[n] gives an unbounded output.
Example :
If   x[n] = 1 for all n
then DTFT diverges i.e Unbounded output. 
Time Shifting and Frequency Shifting:
If,
then,
and,
 
Time and Frequency Scaling:
Time reversal  
Let us find the DTFT of x[-n]
 
Time expansion:
It is very difficult for us to define x[an] when a is not an integer. However if a is an integer other than 1 or -1 then the original signal is
not just speeded up. Since n can take only integer values, the resulting signal consists of samples of x[n] at an.
If k is a positive integer, and we define the signal
then
 
 
Convolution Property :
Let h[n] be the impulse response of a discrete time LSI system. Then the frequency response of the LSI system is
Now
and
If
then
Proof
now put n-k =m, for fixed k, 
This is a very useful result.
 
Symmetry Property:
If
then
Proof
Furthermore if x[n] is real then,
 
 
The DTFT of Cross-Correlation Sequence between x[n] and h[n]
If the DTFT of the cross correlation sequence between x[n] and h[n] exists then,
In particular,
 
 
 
 
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