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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.
Between samples no measurement of the signal is made; the sampling theorem guarantees non-ambiguous representation and recovery of the signal only if it has no energy at frequency f s /2 or higher (one half the sampling frequency, known as the Nyquist frequency); higher frequencies will not be correctly represented or recovered and add aliasing ...
The theorem of Petersen and Middleton can be used to identify the optimal lattice for sampling fields that are wavenumber-limited to a given set . For example, it can be shown that the lattice in ℜ 2 {\displaystyle \Re ^{2}} with minimum spatial density of points that admits perfect reconstructions of fields wavenumber-limited to a circular ...
The principle of sampling was developed during the 1930s in Bell Laboratories by Nyquist, after whom the sampling theorem is named. The first sampling oscilloscope was, however, developed in the late 1950s at the Atomic Energy Research Establishment at Harwell in England by G.B.B. Chaplin, A.R. Owens and A.J. Cole.
Because of this, sampling functions and digital signal processing functions which change sample rates usually require bandlimiting filters to control the amount of aliasing distortion. Bandlimiting filters should be designed carefully to manage other distortions because they alter the signal of interest in both its frequency domain magnitude ...
Nyquist–Shannon sampling theorem; S. Schwartz–Zippel lemma; Shannon–Hartley theorem; Shannon's source coding theorem
The sampling theorem states that sampling frequency would have to be greater than 200 Hz. Sampling at four times that rate requires a sampling frequency of 800 Hz. This gives the anti-aliasing filter a transition band of 300 Hz ((f s /2) − B = (800 Hz/2) − 100 Hz = 300 Hz) instead of 0 Hz if the sampling frequency was 200 Hz. Achieving an ...
Signal sampling representation. The continuous signal S(t) is represented with a green colored line while the discrete samples are indicated by the blue vertical lines. In signal processing, sampling is the reduction of a continuous-time signal to a discrete-time signal. A common example is the conversion of a sound wave to a sequence of "samples".