Figure 1 shows the ideal reconstruction system based on the results of the Sampling theorem proof.

Figure 1 consists of a sampling device which produces a time-discrete sequence

Summary: An overview of sampling and reconstruction on a system level.

Figure 1 shows the ideal reconstruction system based on the results of the Sampling theorem proof.

Figure 1 consists of a sampling device which produces a time-discrete sequence

To be sure that the reconstructed signal is free of aliasing it is customary to apply a lowpass filter, an anti-aliasing filter, before sampling as shown in Figure 2.

But if the anti-aliasing filter removes the "higher" frequencies, (which in fact is the job
of the anti-aliasing filter), we will *never* be able
to *exactly* reconstruct the original signal,

The reconstructed signal,

To make our reconstruction system realizable there are many things to look into. Among them are the fact that any practical reconstruction system must input finite length pulses into the reconstruction filter. This can be accomplished by the hold operation. To alleviate the distortion caused by the hold opeator we apply the output from the hold device to a compensator. The compensation can be as accurate as we wish, this is cost and application consideration.

• Introduction; • Proof; • Illustrations; • Matlab example; • Hold operation; • Aliasing applet; • Exercises