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This is a list of linear transformations of functions related to Fourier analysis. Such transformations map a function to a set of coefficients of basis functions, where the basis functions are sinusoidal and are therefore strongly localized in the frequency spectrum. (These transforms are generally designed to be invertible.) In the case of the Fourier transform, each basis function corresponds to a single frequency component.
Contents
Continuous transforms
Applied to functions of continuous arguments, Fourierrelated transforms include:
 Twosided Laplace transform
 Mellin transform, another closely related integral transform
 Laplace transform
 Fourier transform, with special cases:
 Fourier series
 When the input function/waveform is periodic, the Fourier transform output is a Dirac comb function, modulated by a discrete sequence of finitevalued coefficients that are complexvalued in general. These are called Fourier series coefficients. The term Fourier series actually refers to the inverse Fourier transform, which is a sum of sinusoids at discrete frequencies, weighted by the Fourier series coefficients.
 When the nonzero portion of the input function has finite duration, the Fourier transform is continuous and finitevalued. But a discrete subset of its values is sufficient to reconstruct/represent the portion that was analyzed. The same discrete set is obtained by treating the duration of the segment as one period of a periodic function and computing the Fourier series coefficients.
 Sine and cosine transforms: When the input function has odd or even symmetry around the origin, the Fourier transform reduces to a sine or cosine transform.
 Hartley transform
 Shorttime Fourier transform (or shortterm Fourier transform) (STFT)
 Chirplet transform
 Fractional Fourier transform (FRFT)
 Hankel transform: related to the Fourier Transform of radial functions.
Discrete transforms
For usage on computers, number theory and algebra, discrete arguments (e.g. functions of a series of discrete samples) are often more appropriate, and are handled by the transforms (analogous to the continuous cases above):
 Discretetime Fourier transform (DTFT): Equivalent to the Fourier transform of a "continuous" function that is constructed from the discrete input function by using the sample values to modulate a Dirac comb. The DTFT output is always a periodic function. An alternative viewpoint is that the DTFT is a transform to a frequency domain that is bounded (or finite), the length of one period.
 Fourier series, or discrete Fourier transform (DFT):
 When the input sequence is periodic, the (periodic) DTFT output is also a Dirac comb function, modulated by the coefficients of a Fourier series. The coefficients can also be computed directly from the sample values (without actually doing the DTFT), in which case it is more commonly known as DFT. The number of discrete values in one period of the DFT is the same as in one period of the input sequence.
 When the nonzero portion of the input sequence has finite duration, the DTFT is continuous and finitevalued. But a discrete subset of its values is sufficient to reconstruct/represent the portion that was analyzed. The same discrete set is obtained by treating the duration of the segment as one period of a periodic function and computing the Fourier series coefficients / DFT.
 Discrete sine and cosine transforms: When the input sequence has odd or even symmetry around the origin, the DTFT reduces to a discrete sine transform (DST) or discrete cosine transform (DCT).
 Ztransform, a generalization of the DTFT.
 Modified discrete cosine transform (MDCT)
 Discrete Hartley transform (DHT)
 Also the discretized STFT (see above).
 Hadamard transform (Walsh function).
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