Wolfram Demonstrations Project

The purpose of this Demonstration is to show the relation between the continuous-time Fourier transform (CTFT) of a signal

and the corresponding discrete-time Fourier transform (DTFT) of the signal

generated from

) by sampling.

This Demonstration also illustrates the use of different algorithms to reconstruct

from

The time domain signal

consists of two harmonics. You can vary the amplitude and the frequency of each harmonic as well as the sampling frequency, the duration of

, and the delay time. The magnitude and the phase spectra of

and of

are also displayed. A number of plotting options are available to help analyze the results generated.

It is observed that if the CTFT is an aperiodic and continuous function, the DTFT is a continuous function, periodic in

. The maximum frequency present in the DTFT is

in radians (or

in cycles). In addition, there is a scaling effect: the magnitude spectrum of

smaller than the magnitude spectrum of

, where

is the sampling period. The units of the DTFT are radians, while the units of the CTFT are in radians per second.

Contributed by: Nasser M. Abbasi

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The CTFT of a continuous time signal

is defined as

, where

is in radians per second. The DTFT of a discrete signal

is defined as

, where

is in radians. In Mathematica, the built-in function FourierTransform implements the CTFT and the function FourierSequenceTransform implements the DTFT.

In this Demonstration, a signal

made up of two harmonics is used to analyze the process of sampling and generating the DTFT. The signal is given by

, where

is the frequency of each harmonic in cycles per second (Hz),

is the amplitude of each harmonic in units appropriate to the nature of the signal (such as volts), and

is the delay in seconds. The signal

is of limited duration, which you can vary. Since

is time-limited, its spectrum will not be band-limited. In practice, this is handled by passing the signal through an antialiasing filter before sampling it. This filter has not been implemented in this Demonstration, thus some aliasing will be present even if the sampling frequency is greater than the Nyquist frequency. For the purposes of this Demonstration, it was not necessary to implement an antialiasing filter before sampling.

To reconstruct the time domain signal from the discrete time signal, one of the following algorithms can be selected: sinc interpolation, staircase interpolation, or spline interpolation (of order up to four). It was found that to obtain good reconstruction using the staircase and spline algorithms, the sampling frequency needs to be much higher than the Nyquist frequency.

You can vary the

axis plot range to see more details in the plot. Other plotting options are available to allow the change of units of the spectrum from Hz to radians and to superimpose the samples and the reconstructed signal on top of the original signal

. This will show the effect of sampling.