Understading sampling theorem and aliasing

1. What is aliasing in sampling theorem?
2. How can sampling theorem prevent aliasing?
3. How do you understand the statement of sampling theorem?
4. How is sampling rate and aliasing related?

What is aliasing in sampling theorem?

Aliasing is the effect of new frequencies appearing in the sampled signal after reconstruction, that were not present in the original signal. It is caused by too low sample rate for sampling a particular signal or too high frequencies present in the signal for a particular sample rate.

How can sampling theorem prevent aliasing?

Aliasing is generally avoided by applying low-pass filters or anti-aliasing filters (AAF) to the input signal before sampling and when converting a signal from a higher to a lower sampling rate.

How do you understand the statement of sampling theorem?

The sampling theorem essentially says that a signal has to be sampled at least with twice the frequency of the original signal. Since signals and their respective speed can be easier expressed by frequencies, most explanations of artifacts are based on their representation in the frequency domain.

How is sampling rate and aliasing related?

When the sampling rate is not large enough (not larger than 2B Hz), then interference among adjacent bands will occur, and this results in the phenomenon of aliasing. In this case, the original signal cannot be recovered from the sampled signal.