The term Fourier transform (named for Jean Baptiste Joseph Fourier) is often taken to refer only to the continuous Fourier transform, treated below, and treated at greater length in the article of that title, which also includes a table of Fourier transforms. The continuous Fourier transform can be thought as a continuous analogous of Fourier series for non-periodic functions. Other Fourier transforms than the "continuous" one are also mentioned below, with links to articles about them.
Fourier transforms have many scientific applications in physics, number theory, combinatorics, signal processing, probability theory, statistics, cryptography, acoustics, oceanography, optics, geometry, and other areas. The book by Dym and McKean cited at the end of this article treats many such applications.
In terms of a signal, the transform takes a time series representation of a signal function and maps it into a frequency spectrum. That is, it takes a function in the time domain into the frequency domain; it may be thought as an decomposition of a function into harmonics of different frequencies.
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2 Discrete Fourier transforms and Fourier series 3 Generalizations 4 Computational Inplementations 5 See also 6 External links |
The Fourier transform is an integral transform (and thus, a linear operator) that maps one complex function of a real variable into another; the original function and its transform are sometimes called a transform pair.
As the transform and its inverse are unique (by the Fourier inversion theorem),
there is exactly one transform pair for each function for which the transform is defined.
The Fourier transform of a function f is defined by an integral,
The inverse Fourier transform, given the above definition for the transform, is a similar integral,
Definition of the Fourier transform
denoting the transformed function as F, and the transformation operator itself by the script F.
Note that the definition is sometimes written with different scaling factors in the exponent or in front of the integral.
There is also a discrete version of
the Fourier transform.
When the function f is a function of time and represents a physical signal,
the transform has a standard interpretation as the spectrum of the signal.
The real parts of the resulting complex-valued function F represent the amplitudes of their respective frequencies (s), while the imaginary parts represent the phase shiftss.
There are also discrete Fourier transforms and Fourier series.
The Fourier transform taking functions with domain A into functions with domain B may be:
Both the continous and discrete Fourier transforms, and also the
Fourier series, are generalized by the Fourier transform on locally compact abelian topological groups, which is studied in harmonic analysis; here, A is the group and B is its dual group. This treatment also allows a general formulation of the convolution theorem, which relates Fourier transforms and convolutions.
The Fourier transform can be viewed as a special case of the Z-transform: the Fourier transform is the Z-transform evaluated at the unit circle in the complex space.
Implementations of Fourier transforms of arbitrary signals are computationally intensive, but the fast Fourier transform can greatly reduce the computation required.
Such transforms are used in some types of RF modulation.
The free software library FFTW is a
C library for computing the discrete Fourier transform, which claims to
be especially fast.
See the Fourier transform in action on the SETI at home project.
Interpretation in terms of time and frequency
Discrete Fourier transforms and Fourier series
Generalizations
Computational Inplementations
See also
External links