Search results
Results From The WOW.Com Content Network
The sampling theory of Shannon can be generalized for the case of nonuniform sampling, that is, samples not taken equally spaced in time. The Shannon sampling theory for non-uniform sampling states that a band-limited signal can be perfectly reconstructed from its samples if the average sampling rate satisfies the Nyquist condition. [5]
Papoulis's generalization of the sampling theorem [6] unified many variations of the Nyquist–Shannon sampling theorem into one theorem. [ 7 ] [ 8 ] The Papoulis–Gerchberg algorithm [ 9 ] [ 10 ] [ 11 ] is an iterative signal restoration algorithm that has found widespread use in signal and image processing.
The sampling theory of Shannon can be generalized for the case of nonuniform samples, that is, samples not taken equally spaced in time. The Shannon sampling theory for non-uniform sampling states that a band-limited signal can be perfectly reconstructed from its samples if the average sampling rate satisfies the Nyquist condition. [1]
Early uses of the term Nyquist frequency, such as those cited above, are all consistent with the definition presented in this article.Some later publications, including some respectable textbooks, call twice the signal bandwidth the Nyquist frequency; [6] [7] this is a distinctly minority usage, and the frequency at twice the signal bandwidth is otherwise commonly referred to as the Nyquist rate.
The Whittaker–Shannon interpolation formula or sinc interpolation is a method to construct a continuous-time bandlimited function from a sequence of real numbers. The formula dates back to the works of E. Borel in 1898, and E. T. Whittaker in 1915, and was cited from works of J. M. Whittaker in 1935, and in the formulation of the Nyquist–Shannon sampling theorem by Claude Shannon in 1949.
Download as PDF; Printable version; In other projects ... sampling theory may mean: Nyquist–Shannon sampling theorem, digital signal processing (DSP) Statistical ...
Compressed sensing (also known as compressive sensing, compressive sampling, or sparse sampling) is a signal processing technique for efficiently acquiring and reconstructing a signal, by finding solutions to underdetermined linear systems.
In theoretical sampling the researcher manipulates or changes the theory, sampling activities as well as the analysis during the course of the research. Flexibility occurs in this style of sampling when the researchers want to increase the sample size due to new factors that arise during the research.