What is the Nyquist Sampling Theorem?
It was given by Harry Nyquist Claude, Shannon of Bell Labs first provided the Nyquist-Shannon sampling theorem in the late 1940s. Harry expressed the Nyquist Sampling Theorem which established the principle of using sampling to convert a continuous analog signal to a digital signal. He produced a sample rule that should be followed to determine appropriate sample rates for differing sounds.
It states that to reconstruct a continuous analog signal from its sampled version accurately, the sampling rate must be at least twice the highest frequency present in the signal. This ensures that there are enough samples taken per unit of time to capture all the details of the original waveform without introducing aliasing, which can cause distortion or artifacts in the reconstructed signal.
The Formula for Nyquist Sampling Theorem can be given as
[Tex]f_s >=2f_m[/Tex]
Where,
[Tex]f_s[/Tex] refers to frequency signal
[Tex]f_m[/Tex] refers to max frequency
The Theorem is important in the various fields such as audio and image processing, where analog signals are commonly converted into digital form. By understanding the concept of Nyquist sampling theorem, we can determine the appropriate sampling rates to ensure the accuracy of the digital representation of analog signals.
Nyquist Sampling Theorem
Nyquist Theorem also referred to as the Sampling Theorem is a principle of reproducing a sample rate, that is at least twice the frequency of the original signal. This principle is very important in all analog-to-digital conversion and is applied in digital audio and video to minimize a problem referred to as Aliasing.
In digital communication, signals are representations of information that are transmitted from one point to another in a digital format. Nyquist Sampling is a critical theorem that is used to derive the frequency of the signal to reconstruct without aliasing. Aliasing refers to the distortion or unwanted noise that may destroy a signal’s integral value.