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The following functions and variables are used in the table below: δ represents the Dirac delta function. u(t) represents the Heaviside step function. Literature may refer to this by other notation, including () or (). Γ(z) represents the Gamma function. γ is the Euler–Mascheroni constant. t is a real number.
Therefore, the Fourier transform goes from one space of functions to a different space of functions: functions which have a different domain of definition. In general, ξ {\displaystyle \xi } must always be taken to be a linear form on the space of its domain, which is to say that the second real line is the dual space of the first real line.
Affine transformation (Euclidean geometry) Bäcklund transform; Bilinear transform; Box–Muller transform; Burrows–Wheeler transform (data compression) Chirplet transform; Distance transform; Fractal transform; Gelfand transform; Hadamard transform; Hough transform (digital image processing) Inverse scattering transform; Legendre ...
4 Table of basic transformation rules. ... Well-behaved functions, for example smooth functions, have Fourier series that converge to the original function.
The unilateral Laplace transform takes as input a function whose time domain is the non-negative reals, which is why all of the time domain functions in the table below are multiples of the Heaviside step function, u(t). The entries of the table that involve a time delay τ are required to be causal (meaning that τ > 0).
Main examples of transforms that are both well known and widely applicable include integral transforms [1] such as the Fourier transform, the fractional Fourier Transform, [2] the Laplace transform, and linear canonical transformations. [3] These transformations are used in signal processing, optics, and quantum mechanics.
In mathematics, a transformation, transform, or self-map [1] is a function f, usually with some geometrical underpinning, that maps a set X to itself, i.e. f: X → X. [ 2 ] [ 3 ] [ 4 ] Examples include linear transformations of vector spaces and geometric transformations , which include projective transformations , affine transformations , and ...
In mathematics and mathematical optimization, the convex conjugate of a function is a generalization of the Legendre transformation which applies to non-convex functions. It is also known as Legendre–Fenchel transformation, Fenchel transformation, or Fenchel conjugate (after Adrien-Marie Legendre and Werner Fenchel).