Fourier transform
From Academic Kids

The Fourier transform, named after Jean Baptiste Joseph Fourier, is an integral transform that reexpresses a function in terms of sinusoidal basis functions, i.e. as a sum or integral of sinusoidal functions multiplied by some coefficients ("amplitudes"). There are many closelyrelated variations of this transform, summarized below, depending upon the type of function being transformed. See also: List of Fourierrelated transforms.
Contents 
Applications
Fourier transforms have many scientific applications — in physics, number theory, combinatorics, signal processing, probability theory, statistics, cryptography, acoustics, oceanography, optics, geometry, and other areas. (In signal processing and related fields, the Fourier transform is typically thought of as decomposing a signal into its component frequencies and their amplitudes.) This wide applicability stems from several useful properties of the transforms:
 The transforms are linear operators and, with proper normalization, are unitary as well (a property known as Parseval's theorem or, more generally, as the Plancherel theorem, and most generally via Pontryagin duality).
 The transforms are invertible, and in fact the inverse transform has almost the same form as the forward transform.
 The sinusoidal basis functions are eigenfunctions of differentiation, which means that this representation transforms linear differential equations with constant coefficients into ordinary algebraic ones. (For example, in a linear timeinvariant physical system, frequency is a conserved quantity, so the behavior at each frequency can be solved independently.)
 By the convolution theorem, Fourier transforms turn the complicated convolution operation into simple multiplication, which means that they provide an efficient way to compute convolutionbased operations such as polynomial multiplication and multiplying large numbers.
 The discrete version of the Fourier transform (see below) can be evaluated quickly on computers using fast Fourier transform (FFT) algorithms.
Variants of the Fourier transform
Continuous Fourier transform
Most often, the unqualified term "Fourier transform" refers to the continuous Fourier transform, representing any squareintegrable function f(t) as a sum of complex exponentials with angular frequencies ω and complex amplitudes F(ω):
 <math>
f(t) = \mathcal{F}^{1}(F)(t)
= \frac{1}{\sqrt{2\pi}} \int\limits_{\infty}^\infty F(\omega) e^{i\omega t}\,d\omega.
<math>
This is actually the inverse continuous Fourier transform, whereas the Fourier transform expresses F(ω) in terms of f(t); the original function and its transform are sometimes called a transform pair. See continuous Fourier transform for more information, including a table of transforms, discussion of the transform properties, and the various conventions. A generalization of this transform is the fractional Fourier transform, by which the transform can be raised to any real "power".
When f(t) is an even or odd function, the sine or cosine terms disappear and one is left with the cosine transform or sine transform, respectively. Another important case is where f(t) is purely real, where it follows that F(−ω) = F(ω)^{*}. (Similar special cases appear for all other variants of the Fourier transform as well.)
Fourier series
The continuous transform is itself actually a generalization of an earlier concept, a Fourier series, which was specific to periodic (or finitedomain) functions f(x) (with period 2π), and represents these functions as a series of sinusoids:
 <math>f(x) = \sum_{n=\infty}^{\infty} F_n \,e^{inx} ,<math>
where <math>F_n<math> is the (complex) amplitude. Or, for realvalued functions, the Fourier series is often written:
 <math>f(x) = \frac{1}{2}a_0 + \sum_{n=1}^\infty\left[a_n\cos(nx)+b_n\sin(nx)\right],<math>
where a_{n} and b_{n} are the (real) Fourier series amplitudes.
Discrete Fourier transform
For use on computers, both for scientific computation and digital signal processing, one must have functions x_{k} that are defined over discrete instead of continuous domains, again finite or periodic. In this case, one uses the discrete Fourier transform (DFT), which represents x_{k} as the sum of sinusoids:
 <math>x_k = \frac{1}{n} \sum_{j=0}^{n1} f_j e^{2\pi ijk/n} \quad \quad k = 0,\dots,n1<math>
where f_{j} are the Fourier amplitudes. Although applying this formula directly would require O(n^{2}) operations, it can be computed in only O(n log n) operations using a fast Fourier transform (FFT) algorithm (see Big O notation), which makes Fourier transformation a practical and important operation on computers.
Other variants
The DFT is a special case of (and is sometimes used as an approximation for) the discretetime Fourier transform (DTFT), in which the x_{k} are defined over discrete but infinite domains, and thus the spectrum is continuous and periodic. The DTFT is essentially the inverse of the Fourier series.
These Fourier variants can also be generalized to Fourier transforms on arbitrary locally compact abelian topological groups, which are studied in harmonic analysis; there, one transforms from a group to its dual group. This treatment also allows a general formulation of the convolution theorem, which relates Fourier transforms and convolutions. See also the Pontryagin duality for the generalized underpinnings of the Fourier transform.
Timefrequency transforms such as the shorttime Fourier transform, wavelet transforms, chirplet transforms, and the fractional Fourier transform try to obtain frequency information from a signal as a function of time (or whatever the independent variable is), although the ability to simultaneously resolve frequency and time is limited by a (mathematical) uncertainty principle.
Family of Fourier transforms
The following table summarizes the family of Fourier transforms. We see that discreteness in one domain implies periodicity in the conjugate domain and that continuity in one domain implies aperiodicity in the conjugate domain.
Transform  Time  Frequency 

Continuous Fourier transform  Continuous, Aperiodic  Continuous, Aperiodic 
Fourier series  Continuous, Periodic  Discrete, Aperiodic 
Discretetime Fourier transform  Discrete, Aperiodic  Continuous, Periodic 
Discrete Fourier transform  Discrete, Periodic  Discrete, Periodic 
Interpretation in terms of time and frequency
In terms of signal processing, the transform takes a time series representation of a signal function and maps it into a frequency spectrum, where ω is angular frequency. That is, it takes a function in the time domain into the frequency domain; it is a decomposition of a function into harmonics of different frequencies.
When the function f is a function of time and represents a physical signal, the transform has a standard interpretation as the frequency spectrum of the signal. The magnitude of the resulting complexvalued function F represents the amplitudes of the respective frequencies (ω), while the phase shifts are given by arctan(imaginary parts/real parts).
However, it is important to realize that Fourier transforms are not limited to functions of time, and temporal frequencies. They can equally be applied to analyze spatial frequencies, and indeed for nearly any function domain.
References
 Smith, Steven W. The Scientist and Engineer's Guide to Digital Signal Processing, 2nd edition. San Diego: California Technical Publishing, 1999. ISBN 0966017633. (also available online: [1] (http://www.dspguide.com/pdfbook.htm))
 A. D. Polyanin and A. V. Manzhirov, Handbook of Integral Equations, CRC Press, Boca Raton, 1998.
See also
 Numbertheoretic transform
 Laplace transform
 Twosided Laplace transform
 Mellin transform
 Orthogonal functions
 Wavelet
 Chirplet
 Characteristic function (probability theory)
External links
 Online Computation (http://wims.unice.fr/wims/wims.cgi?session=6WA23CFB0C.3&+lang=en&+module=tool%2Fanalysis%2Ffourierlaplace.en) of the transform or inverse transform, wims.unice.fr
 Tables of Integral Transforms (http://eqworld.ipmnet.ru/en/auxiliary/auxinttrans.htm) at EqWorld: The World of Mathematical Equations.de:FourierTransformation
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