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The Nyquist–Shannon sampling theorem is an essential principle for digital signal processing linking the frequency range of a signal and the sample rate required to avoid a type of distortion called aliasing. The theorem states that the sample rate must be at least twice the bandwidth of the signal to avoid aliasing.
Nyquist–Shannon sampling theorem; S. Schwartz–Zippel lemma; ... Shannon's source coding theorem This page was last edited on 2 February 2012, at 17:02 (UTC). ...
As with the Nyquist–Shannon sampling theorem, this theorem also assumes an idealization of any real-world situation, as it only applies to functions that are sampled over an infinitude of points. Perfect reconstruction is mathematically possible for the idealized model but only an approximation for real-world functions and sampling techniques ...
The Nyquist–Shannon sampling theorem states that a signal can be exactly reconstructed from its samples if the sampling frequency is greater than twice the highest frequency component in the signal. In practice, the sampling frequency is often significantly higher than this. [8]
Norton's theorem was published in November 1926 by Hans Ferdinand Mayer and independently discovered by Edward Lawry Norton who presented it in an internal Bell Labs technical report, dated November 1926. Nyquist–Shannon sampling theorem. The name Nyquist–Shannon sampling theorem honours Harry Nyquist and Claude Shannon, but the theorem was ...
An anti-aliasing filter (AAF) is a filter used before a signal sampler to restrict the bandwidth of a signal to satisfy the Nyquist–Shannon sampling theorem over the band of interest. Since the theorem states that unambiguous reconstruction of the signal from its samples is possible when the power of frequencies above the Nyquist frequency is ...
An early breakthrough in signal processing was the Nyquist–Shannon sampling theorem. It states that if a real signal's highest frequency is less than half of the sampling rate, then the signal can be reconstructed perfectly by means of sinc interpolation. The main idea is that with prior knowledge about constraints on the signal's frequencies ...
Sound cards have a limited sample rate, typically up to 192 kHz. Under the assumptions of the Nyquist–Shannon sampling theorem, this means a maximum signal frequency (bandwidth) of half that: 96 kHz. Real sound cards tend to have a bandwidth smaller than implied by the Nyquist limit from internal filtering.