In mathematics, the Fourier sine and cosine transforms are forms of the Fourier transform that do not use complex numbers or require negative frequency. They are the forms originally used by Joseph Fourier and are still preferred in some applications, such as signal processing or statistics. The Fourier sine transform of f(t), sometimes denoted by either or , is If t means time, then ξ is frequency in cycles per unit time, but in the abstract, they can be any pair of variables which are dual to each other. This transform is necessarily an odd function of frequency, i.e. for all ξ: The numerical factors in the Fourier transforms are defined uniquely only by their product. Here, in order that the Fourier inversion formula not have any numerical factor, the factor of 2 appears because the sine function has L2 norm of The Fourier cosine transform of f(t), sometimes denoted by either or , is It is necessarily an even function of frequency, i.e. for all ξ: Since positive frequencies can fully express the transform, the non-trivial concept of negative frequency needed in the regular Fourier transform can be avoided. Some authors only define the cosine transform for even functions of t, in which case its sine transform is zero. Since cosine is also even, a simpler formula can be used, Similarly, if f is an odd function, then the cosine transform is zero and the sine transform can be simplified to Just like the Fourier transform takes the form of different equations with different constant factors (see ), other authors also define the cosine transform as and sine as or, the cosine transform as and the sine transform as using as the transformation variable. And while t is typically used to represent the time domain, x is often used alternatively, particularly when representing frequencies in a spatial domain. The original function f can be recovered from its transform under the usual hypotheses, that f and both of its transforms should be absolutely integrable. For more details on the different hypotheses, see Fourier inversion theorem.