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Some programs (such as MATLAB toolboxes) that design filters with real-valued coefficients prefer the Nyquist frequency (/) as the frequency reference, which changes the numeric range that represents frequencies of interest from [,] cycle/sample to [,] half-cycle/sample. Therefore, the normalized frequency unit is important when converting ...
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.
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". A sample is a value of the signal at a point in time and/or space; this definition differs from the term's usage in statistics, which refers to a set of such values ...
A simple illustration of aliasing can be obtained by studying low-resolution images. A gray-scale image can be interpreted as a function in two-dimensional space. An example of aliasing is shown in the images of brick patterns in Figure 5. The image shows the effects of aliasing when the sampling theorem's condition is not satisfied.
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 ...
It is an alternative to methods from the Bayesian literature [3] such as bridge sampling and defensive importance sampling. Here is a simple version of the nested sampling algorithm, followed by a description of how it computes the marginal probability density Z = P ( D ∣ M ) {\displaystyle Z=P(D\mid M)} where M {\displaystyle M} is M 1 ...
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 ...