The Sampling Theorem
Sampling and interpolation take us back and forth between discrete and continuous time and vice versa.
However - our reconstructed (interpolated) continuous time signal is by no means guaranteed to be even
close to the original continuous time signal. A major breakthrough for doing this sampling and interpolation
business ’right’ was achieved by Claude Shannon in 1948 with his famous Sampling Theorem. The
sampling theorem states conditions under which a continuous time signal can be reconstructed exactly
from its samples and also defines the interpolation algorithm which should be used to achieve this exact
reconstruction. In loose terms, the sampling theorem states, that the original continuous time signal can
be reconstructed from its samples exactly, when the highest frequency (denoted as fh) present in the signal
(seen as composition of sinosoids) is lower than a half of the sampling frequency: