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The term Nyquist Sampling Theorem (capitalized thus) appeared as early as 1959 in a book from his former employer, Bell Labs, [22] and appeared again in 1963, [23] and not capitalized in 1965. [24] It had been called the Shannon Sampling Theorem as early as 1954, [25] but also just the sampling theorem by several other books in the early 1950s.
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 ...
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 ...
Similarly, the accuracy of the sampling timing, or aperture uncertainty of the sampler, frequently the analog-to-digital converter, must be appropriate for the frequencies being sampled 108MHz, not the lower sample rate. If the sampling theorem is interpreted as requiring twice the highest frequency, then the required sampling rate would be ...
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.
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 ...
Fig 1: Typical example of Nyquist frequency and rate. They are rarely equal, because that would require over-sampling by a factor of 2 (i.e. 4 times the bandwidth). In signal processing , the Nyquist rate , named after Harry Nyquist , is a value equal to twice the highest frequency ( bandwidth ) of a given function or signal.
Rice's theorem (recursion theory, computer science) Rice–Shapiro theorem (computer science) Richardson's theorem (mathematical logic) Riemann mapping theorem (complex analysis) Riemann series theorem (mathematical series) Riemann's existence theorem (algebraic geometry) Riemann's theorem on removable singularities (complex analysis)