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List of Fourier-related transforms; Fourier transform on finite groups; Fractional Fourier transform; Continuous Fourier transform; Fourier operator; Fourier inversion theorem; Sine and cosine transforms; Parseval's theorem; Paley–Wiener theorem; Projection-slice theorem; Frequency spectrum
The trade-off between the compaction of a function and its Fourier transform can be formalized in the form of an uncertainty principle by viewing a function and its Fourier transform as conjugate variables with respect to the symplectic form on the time–frequency domain: from the point of view of the linear canonical transformation, the ...
Conjugate variables are pairs of variables mathematically defined in such a way that they become Fourier transform duals, [1] [2] or more generally are related through Pontryagin duality. The duality relations lead naturally to an uncertainty relation—in physics called the Heisenberg uncertainty principle—between them.
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. This justifies their use in such diverse branches as image processing, heat conduction, and automatic control.
Analogously to the classical Fourier transform, graph Fourier transform provides a way to represent a signal in two different domains: the vertex domain and the graph spectral domain. Note that the definition of the graph Fourier transform and its inverse depend on the choice of Laplacian eigenvectors, which are not necessarily unique. [3]
Finite Fourier transform; Fourier integral operator; Fourier operator; Fourier optics; Fourier transform; Fourier transform on finite groups; Fourier–Bros–Iagolnitzer transform; Fourier-transform infrared spectroscopy; Fourier-transform spectroscopy; Fractional Fourier transform; Fractional wavelet transform; Fraunhofer diffraction equation
The inverse Fourier transform converts the frequency-domain function back to the time-domain function. A spectrum analyzer is a tool commonly used to visualize electronic signals in the frequency domain. A frequency-domain representation may describe either a static function or a particular time period of a dynamic function (signal or system).
The Fourier transform can therefore be seen to relate the coefficients and the values of a polynomial: the coefficients are in the time-domain, and the values are in the frequency domain. Here, of course, it is important that the polynomial is evaluated at the n th roots of unity, which are exactly the powers of α {\displaystyle \alpha } .