What theorem is commonly used to convert a continuous-time signal to a discrete-time signal?

The Sampling Theorem is fundamental when it comes to converting a continuous-time signal into a discrete-time signal. This theorem states that a continuous signal can be completely represented in its discrete form if it is sampled at a rate that is at least twice the highest frequency present in the signal. This minimum rate is referred to as the Nyquist rate.

By adhering to the Sampling Theorem, one ensures that the crucial information in the continuous signal is preserved and accurately represented in the discrete signal. This process is essential in digital signal processing, as it allows for the effective handling and manipulation of signals in a digital format, significantly impacting various applications in telecommunications, audio processing, and video encoding.

In contrast, other options do not pertain to the process of converting continuous-time signals to discrete-time signals in the same way. The coding theorem focuses on the rules regarding how information can be efficiently encoded, while the quantizing theorem relates to the process of converting a signal into a quantized version during the digitization process. Nyquist's Theorem, while closely related and often mentioned in the context of the Sampling Theorem, is specifically about the conditions under which aliasing can be avoided and does not cover the overall procedure of converting the signal from one form to another