 Fourier transform

Fourier transforms Continuous Fourier transform Fourier series Discrete Fourier transform Discretetime Fourier transform Related transforms The Fourier transform is a mathematical operation that decomposes a function into its constituent frequencies, known as a frequency spectrum. For instance, the transform of a musical chord made up of pure notes is a mathematical representation of the amplitudes (and phase) of the individual notes that make it up. The composite waveform depends on time, and therefore is called the time domain representation. The frequency spectrum is a function of frequency and is called the frequency domain representation. Each value of the function is a complex number (called complex amplitude) that encodes both a magnitude and phase component. The term "Fourier transform" refers to both the transform operation and to the complexvalued function it produces.
In the case of a periodic function, like the musical chord, the Fourier transform can be simplified to the calculation of a discrete set of complex amplitudes, called Fourier series coefficients. Also, when a timedomain function is sampled to facilitate storage and/or computerprocessing, it is still possible to recreate a version of the original Fourier transform according to the Poisson summation formula, also known as discretetime Fourier transform. These topics are addressed in separate articles. For an overview of those and other related operations, refer to Fourier analysis or List of Fourierrelated transforms.
Definition
There are several common conventions for defining the Fourier transform of an integrable function ƒ : R → C (Kaiser 1994). This article will use the definition:
 for every real number ξ.
When the independent variable x represents time (with SI unit of seconds), the transform variable ξ represents frequency (in hertz). Under suitable conditions, ƒ can be reconstructed from by the inverse transform:
 for every real number x.
For other common conventions and notations, including using the angular frequency ω instead of the frequency ξ, see Other conventions and Other notations below. The Fourier transform on Euclidean space is treated separately, in which the variable x often represents position and ξ momentum.
Introduction
See also: Fourier analysisThe motivation for the Fourier transform comes from the study of Fourier series. In the study of Fourier series, complicated functions are written as the sum of simple waves mathematically represented by sines and cosines. Due to the properties of sine and cosine it is possible to recover the amount of each wave in the sum by an integral. In many cases it is desirable to use Euler's formula, which states that e^{2πiθ} = cos 2πθ + i sin 2πθ, to write Fourier series in terms of the basic waves e^{2πiθ}. This has the advantage of simplifying many of the formulas involved and providing a formulation for Fourier series that more closely resembles the definition followed in this article. This passage from sines and cosines to complex exponentials makes it necessary for the Fourier coefficients to be complex valued. The usual interpretation of this complex number is that it gives both the amplitude (or size) of the wave present in the function and the phase (or the initial angle) of the wave. This passage also introduces the need for negative "frequencies". If θ were measured in seconds then the waves e^{2πiθ} and e^{−2πiθ} would both complete one cycle per second, but they represent different frequencies in the Fourier transform. Hence, frequency no longer measures the number of cycles per unit time, but is closely related.
There is a close connection between the definition of Fourier series and the Fourier transform for functions ƒ which are zero outside of an interval. For such a function we can calculate its Fourier series on any interval that includes the interval where ƒ is not identically zero. The Fourier transform is also defined for such a function. As we increase the length of the interval on which we calculate the Fourier series, then the Fourier series coefficients begin to look like the Fourier transform and the sum of the Fourier series of ƒ begins to look like the inverse Fourier transform. To explain this more precisely, suppose that T is large enough so that the interval [−T/2,T/2] contains the interval on which ƒ is not identically zero. Then the nth series coefficient c_{n} is given by:
Comparing this to the definition of the Fourier transform it follows that since ƒ(x) is zero outside [−T/2,T/2]. Thus the Fourier coefficients are just the values of the Fourier transform sampled on a grid of width 1/T. As T increases the Fourier coefficients more closely represent the Fourier transform of the function.
Under appropriate conditions the sum of the Fourier series of ƒ will equal the function ƒ. In other words ƒ can be written:
where the last sum is simply the first sum rewritten using the definitions ξ_{n} = n/T, and Δξ = (n + 1)/T − n/T = 1/T.
This second sum is a Riemann sum, and so by letting T → ∞ it will converge to the integral for the inverse Fourier transform given in the definition section. Under suitable conditions this argument may be made precise (Stein & Shakarchi 2003).
In the study of Fourier series the numbers c_{n} could be thought of as the "amount" of the wave in the Fourier series of ƒ. Similarly, as seen above, the Fourier transform can be thought of as a function that measures how much of each individual frequency is present in our function ƒ, and we can recombine these waves by using an integral (or "continuous sum") to reproduce the original function.
The following images provide a visual illustration of how the Fourier transform measures whether a frequency is present in a particular function. The function depicted oscillates at 3 hertz (if t measures seconds) and tends quickly to 0. This function was specially chosen to have a real Fourier transform which can easily be plotted. The first image contains its graph. In order to calculate we must integrate e^{−2πi(3t)}ƒ(t). The second image shows the plot of the real and imaginary parts of this function. The real part of the integrand is almost always positive, this is because when ƒ(t) is negative, then the real part of e^{−2πi(3t)} is negative as well. Because they oscillate at the same rate, when ƒ(t) is positive, so is the real part of e^{−2πi(3t)}. The result is that when you integrate the real part of the integrand you get a relatively large number (in this case 0.5). On the other hand, when you try to measure a frequency that is not present, as in the case when we look at , the integrand oscillates enough so that the integral is very small. The general situation may be a bit more complicated than this, but this in spirit is how the Fourier transform measures how much of an individual frequency is present in a function ƒ(t).
Properties of the Fourier transform
An integrable function is a function ƒ on the real line that is Lebesguemeasurable and satisfies
Basic properties
Given integrable functions f(x), g(x), and h(x), their Fourier transforms are denoted by , , and respectively. The Fourier transform has the following basic properties (Pinsky 2002).
 Linearity
 For any complex numbers a and b, if h(x) = aƒ(x) + bg(x), then
 Translation
 For any real number x_{0}, if h(x) = ƒ(x − x_{0}), then
 Modulation
 For any real number ξ_{0}, if h(x) = e^{2πixξ0}ƒ(x), then .
 Scaling
 For a nonzero real number a, if h(x) = ƒ(ax), then . The case a = −1 leads to the timereversal property, which states: if h(x) = ƒ(−x), then .
 Conjugation
 If , then
 In particular, if ƒ is real, then one has the reality condition
 And if ƒ is purely imaginary, then
 Duality
 If then
 Convolution
 If , then
Uniform continuity and the Riemann–Lebesgue lemma
The Fourier transform may be defined in some cases for nonintegrable functions, but the Fourier transforms of integrable functions have several strong properties.
The Fourier transform of any integrable function ƒ is uniformly continuous and (Katznelson 1976). By the Riemann–Lebesgue lemma (Stein & Weiss 1971),
Furthermore, is bounded and continuous, but need not be integrable. For example, the Fourier transform of the rectangular function, which is integrable, is the sinc function, which is not Lebesgue integrable, because its improper integrals behave analogously to the alternating harmonic series, in converging to a sum without being absolutely convergent.
It is not generally possible to write the inverse transform as a Lebesgue integral. However, when both ƒ and are integrable, the inverse equality
holds almost everywhere. That is, the Fourier transform is injective on L^{1}(R). (But if ƒ is continuous, then equality holds for every x.)
The Plancherel theorem and Parseval's theorem
Let f(x) and g(x) be integrable, and let and be their Fourier transforms. If f(x) and g(x) are also squareintegrable, then we have Parseval's theorem (Rudin 1987, p. 187):
where the bar denotes complex conjugation.
The Plancherel theorem, which is equivalent to Parseval's theorem, states (Rudin 1987, p. 186):
The Plancherel theorem makes it possible to define the Fourier transform for functions in L^{2}(R), as described in Generalizations below. The Plancherel theorem has the interpretation in the sciences that the Fourier transform preserves the energy of the original quantity. It should be noted that depending on the author either of these theorems might be referred to as the Plancherel theorem or as Parseval's theorem.
See Pontryagin duality for a general formulation of this concept in the context of locally compact abelian groups.
Poisson summation formula
Main article: Poisson summation formulaThe Poisson summation formula is an equation that relates the Fourier series coefficients of the periodic summation of a function to values of the function's continuous Fourier transform. It has a variety of useful forms that are derived from the basic one by application of the Fourier transform's scaling and timeshifting properties. One such form leads directly to a proof of the NyquistShannon sampling theorem.
Convolution theorem
Main article: Convolution theoremThe Fourier transform translates between convolution and multiplication of functions. If ƒ(x) and g(x) are integrable functions with Fourier transforms and respectively, then the Fourier transform of the convolution is given by the product of the Fourier transforms and (under other conventions for the definition of the Fourier transform a constant factor may appear).
This means that if:
where ∗ denotes the convolution operation, then:
In linear time invariant (LTI) system theory, it is common to interpret g(x) as the impulse response of an LTI system with input ƒ(x) and output h(x), since substituting the unit impulse for ƒ(x) yields h(x) = g(x). In this case, represents the frequency response of the system.
Conversely, if ƒ(x) can be decomposed as the product of two square integrable functions p(x) and q(x), then the Fourier transform of ƒ(x) is given by the convolution of the respective Fourier transforms and .
Crosscorrelation theorem
Main article: CrosscorrelationIn an analogous manner, it can be shown that if h(x) is the crosscorrelation of ƒ(x) and g(x):
then the Fourier transform of h(x) is:
As a special case, the autocorrelation of function ƒ(x) is:
for which
Eigenfunctions
One important choice of an orthonormal basis for L^{2}(R) is given by the Hermite functions
where He_{n}(x) are the "probabilist's" Hermite polynomials, defined by He_{n}(x) = (−1)^{n}exp(x^{2}/2) D^{n} exp(−x^{2}/2). Under this convention for the Fourier transform, we have that
In other words, the Hermite functions form a complete orthonormal system of eigenfunctions for the Fourier transform on L^{2}(R) (Pinsky 2002). However, this choice of eigenfunctions is not unique. There are only four different eigenvalues of the Fourier transform (±1 and ±i) and any linear combination of eigenfunctions with the same eigenvalue gives another eigenfunction. As a consequence of this, it is possible to decompose L^{2}(R) as a direct sum of four spaces H_{0}, H_{1}, H_{2}, and H_{3} where the Fourier transform acts on He_{k} simply by multiplication by i^{k}. This approach to define the Fourier transform is due to N. Wiener (Duoandikoetxea 2001). The choice of Hermite functions is convenient because they are exponentially localized^{[jargon]} in both frequency and time domains, and thus give rise^{[further explanation needed]} to the fractional Fourier transform used in timefrequency analysis (Boashash 2003).
Fourier transform on Euclidean space
The Fourier transform can be in any arbitrary number of dimensions n. As with the onedimensional case there are many conventions, for an integrable function ƒ(x) this article takes the definition:
where x and ξ are ndimensional vectors, and x · ξ is the dot product of the vectors. The dot product is sometimes written as .
All of the basic properties listed above hold for the ndimensional Fourier transform, as do Plancherel's and Parseval's theorem. When the function is integrable, the Fourier transform is still uniformly continuous and the Riemann–Lebesgue lemma holds. (Stein & Weiss 1971)
Uncertainty principle
For more details on this topic, see Uncertainty principle.Generally speaking, the more concentrated f(x) is, the more spread out its Fourier transform must be. In particular, the scaling property of the Fourier transform may be seen as saying: if we "squeeze" a function in x, its Fourier transform "stretches out" in ξ. It is not possible to arbitrarily concentrate both a function and its Fourier transform.
The tradeoff 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 Fourier transform is rotation by 90° in the time–frequency domain, and preserves the symplectic form.
Suppose ƒ(x) is an integrable and squareintegrable function. Without loss of generality, assume that ƒ(x) is normalized:
It follows from the Plancherel theorem that is also normalized.
The spread around x = 0 may be measured by the dispersion about zero (Pinsky 2002) defined by
In probability terms, this is the second moment of about zero.
The Uncertainty principle states that, if ƒ(x) is absolutely continuous and the functions x·ƒ(x) and ƒ′(x) are square integrable, then
 (Pinsky 2002).
The equality is attained only in the case (hence ) where σ > 0 is arbitrary and C_{1} is such that ƒ is L^{2}–normalized (Pinsky 2002). In other words, where ƒ is a (normalized) Gaussian function with variance σ^{2}, centered at zero, and its Fourier transform is a Gaussian function with variance 1/σ^{2}.
In fact, this inequality implies that:
for any in R (Stein & Shakarchi 2003).
In quantum mechanics, the momentum and position wave functions are Fourier transform pairs, to within a factor of Planck's constant. With this constant properly taken into account, the inequality above becomes the statement of the Heisenberg uncertainty principle (Stein & Shakarchi 2003).
A stronger uncertainty principle is the Hirschman uncertainty principle which is expressed as:
where H(p) is the differential entropy of the probability density function p(x):
where the logarithms may be in any base which is consistent. The equality is attained for a Gaussian, as in the previous case.
Spherical harmonics
Let the set of homogeneous harmonic polynomials of degree k on R^{n} be denoted by A_{k}. The set A_{k} consists of the solid spherical harmonics of degree k. The solid spherical harmonics play a similar role in higher dimensions to the Hermite polynomials in dimension one. Specifically, if f(x) = e^{−πx2}P(x) for some P(x) in A_{k}, then . Let the set H_{k} be the closure in L^{2}(R^{n}) of linear combinations of functions of the form f(x)P(x) where P(x) is in A_{k}. The space L^{2}(R^{n}) is then a direct sum of the spaces H_{k} and the Fourier transform maps each space H_{k} to itself and is possible to characterize the action of the Fourier transform on each space H_{k} (Stein & Weiss 1971). Let ƒ(x) = ƒ_{0}(x)P(x) (with P(x) in A_{k}), then where
Here J_{(n + 2k − 2)/2} denotes the Bessel function of the first kind with order (n + 2k − 2)/2. When k = 0 this gives a useful formula for the Fourier transform of a radial function (Grafakos 2004).
Restriction problems
In higher dimensions it becomes interesting to study restriction problems for the Fourier transform. The Fourier transform of an integrable function is continuous and the restriction of this function to any set is defined. But for a squareintegrable function the Fourier transform could be a general class of square integrable functions. As such, the restriction of the Fourier transform of an L^{2}(R^{n}) function cannot be defined on sets of measure 0. It is still an active area of study to understand restriction problems in L^{p} for 1 < p < 2. Surprisingly, it is possible in some cases to define the restriction of a Fourier transform to a set S, provided S has nonzero curvature. The case when S is the unit sphere in R^{n} is of particular interest. In this case the TomasStein restriction theorem states that the restriction of the Fourier transform to the unit sphere in R^{n} is a bounded operator on L^{p} provided 1 ≤ p ≤ (2n + 2) / (n + 3).
One notable difference between the Fourier transform in 1 dimension versus higher dimensions concerns the partial sum operator. Consider an increasing collection of measurable sets E_{R} indexed by R ∈ (0,∞): such as balls of radius R centered at the origin, or cubes of side 2R. For a given integrable function ƒ, consider the function ƒ_{R} defined by:
Suppose in addition that ƒ is in L^{p}(R^{n}). For n = 1 and 1 < p < ∞, if one takes E_{R} = (−R, R), then ƒ_{R} converges to ƒ in L^{p} as R tends to infinity, by the boundedness of the Hilbert transform. Naively one may hope the same holds true for n > 1. In the case that E_{R} is taken to be a cube with side length R, then convergence still holds. Another natural candidate is the Euclidean ball E_{R} = {ξ : ξ < R}. In order for this partial sum operator to converge, it is necessary that the multiplier for the unit ball be bounded in L^{p}(R^{n}). For n ≥ 2 it is a celebrated theorem of Charles Fefferman that the multiplier for the unit ball is never bounded unless p = 2 (Duoandikoetxea 2001). In fact, when p ≠ 2, this shows that not only may ƒ_{R} fail to converge to ƒ in L^{p}, but for some functions ƒ ∈ L^{p}(R^{n}), ƒ_{R} is not even an element of L^{p}.
Generalizations
Fourier transform on other function spaces
It is possible to extend the definition of the Fourier transform to other spaces of functions. Since compactly supported smooth functions are integrable and dense in L^{2}(R), the Plancherel theorem allows us to extend the definition of the Fourier transform to general functions in L^{2}(R) by continuity arguments. Further : L^{2}(R) → L^{2}(R) is a unitary operator (Stein & Weiss 1971, Thm. 2.3). Many of the properties remain the same for the Fourier transform. The Hausdorff–Young inequality can be used to extend the definition of the Fourier transform to include functions in L^{p}(R) for 1 ≤ p ≤ 2. Unfortunately, further extensions become more technical. The Fourier transform of functions in L^{p} for the range 2 < p < ∞ requires the study of distributions (Katznelson 1976). In fact, it can be shown that there are functions in L^{p} with p>2 so that the Fourier transform is not defined as a function (Stein & Weiss 1971).
Fourier–Stieltjes transform
The Fourier transform of a finite Borel measure μ on R^{n} is given by (Pinsky 2002):
This transform continues to enjoy many of the properties of the Fourier transform of integrable functions. One notable difference is that the Riemann–Lebesgue lemma fails for measures (Katznelson 1976). In the case that dμ = ƒ(x) dx, then the formula above reduces to the usual definition for the Fourier transform of ƒ. In the case that μ is the probability distribution associated to a random variable X, the FourierStieltjes transform is closely related to the characteristic function, but the typical conventions in probability theory take e^{ix·ξ} instead of e^{−2πix·ξ} (Pinsky 2002). In the case when the distribution has a probability density function this definition reduces to the Fourier transform applied to the probability density function, again with a different choice of constants.
The Fourier transform may be used to give a characterization of continuous measures. Bochner's theorem characterizes which functions may arise as the Fourier–Stieltjes transform of a measure (Katznelson 1976).
Furthermore, the Dirac delta function is not a function but it is a finite Borel measure. Its Fourier transform is a constant function (whose specific value depends upon the form of the Fourier transform used).
Tempered distributions
Main article: Tempered distributionsThe Fourier transform maps the space of Schwartz functions to itself, and gives a homeomorphism of the space to itself (Stein & Weiss 1971). Because of this it is possible to define the Fourier transform of tempered distributions. These include all the integrable functions mentioned above, as well as wellbehaved functions of polynomial growth and distributions of compact support, and have the added advantage that the Fourier transform of any tempered distribution is again a tempered distribution.
The following two facts provide some motivation for the definition of the Fourier transform of a distribution. First let ƒ and g be integrable functions, and let and be their Fourier transforms respectively. Then the Fourier transform obeys the following multiplication formula (Stein & Weiss 1971),
Secondly, every integrable function ƒ defines a distribution T_{ƒ} by the relation
 for all Schwartz functions φ.
In fact, given a distribution T, we define the Fourier transform by the relation
 for all Schwartz functions φ.
It follows that
Distributions can be differentiated and the above mentioned compatibility of the Fourier transform with differentiation and convolution remains true for tempered distributions.
Locally compact abelian groups
Main article: Pontryagin dualityThe Fourier transform may be generalized to any locally compact abelian group. A locally compact abelian group is an abelian group which is at the same time a locally compact Hausdorff topological space so that the group operations are continuous. If G is a locally compact abelian group, it has a translation invariant measure μ, called Haar measure. For a locally compact abelian group G it is possible to place a topology on the set of characters so that is also a locally compact abelian group. For a function ƒ in L^{1}(G) it is possible to define the Fourier transform by (Katznelson 1976):
Locally compact Hausdorff space
Main article: Gelfand representationThe Fourier transform may be generalized to any locally compact Hausdorff space, which recovers the topology but loses the group structure.
Given a locally compact Hausdorff topological space X, the space A=C_{0}(X) of continuous complexvalued functions on X which vanish at infinity is in a natural way a commutative C*algebra, via pointwise addition, multiplication, complex conjugation, and with norm as the uniform norm. Conversely, the characters of this algebra A, denoted Φ_{A}, are naturally a topological space, and can be identified with evaluation at a point of x, and one has an isometric isomorphism In the case where X=R is the real line, this is exactly the Fourier transform.
Nonabelian groups
The Fourier transform can also be defined for functions on a nonabelian group, provided that the group is compact. Unlike the Fourier transform on an abelian group, which is scalarvalued, the Fourier transform on a nonabelian group is operatorvalued (Hewitt & Ross 1971, Chapter 8). The Fourier transform on compact groups is a major tool in representation theory (Knapp 2001) and noncommutative harmonic analysis.
Let G be a compact Hausdorff topological group. Let Σ denote the collection of all isomorphism classes of finitedimensional irreducible unitary representations, along with a definite choice of representation U^{(σ)} on the Hilbert space H_{σ} of finite dimension d_{σ} for each σ ∈ Σ. If μ is a finite Borel measure on G, then the Fourier–Stieltjes transform of μ is the operator on H_{σ} defined by
where is the complexconjugate representation of U^{(σ)} acting on H_{σ}. As in the abelian case, if μ is absolutely continuous with respect to the leftinvariant probability measure λ on G, then it is represented as
 dμ = fdλ
for some ƒ ∈ L^{1}(λ). In this case, one identifies the Fourier transform of ƒ with the Fourier–Stieltjes transform of μ.
The mapping defines an isomorphism between the Banach space M(G) of finite Borel measures (see rca space) and a closed subspace of the Banach space C_{∞}(Σ) consisting of all sequences E = (E_{σ}) indexed by Σ of (bounded) linear operators E_{σ} : H_{σ} → H_{σ} for which the norm
is finite. The "convolution theorem" asserts that, furthermore, this isomorphism of Banach spaces is in fact an isomorphism of C^{*} algebras into a subspace of C_{∞}(Σ), in which M(G) is equipped with the product given by convolution of measures and C_{∞}(Σ) the product given by multiplication of operators in each index σ.
The PeterWeyl theorem holds, and a version of the Fourier inversion formula (Plancherel's theorem) follows: if ƒ ∈ L^{2}(G), then
where the summation is understood as convergent in the L^{2} sense.
The generalization of the Fourier transform to the noncommutative situation has also in part contributed to the development of noncommutative geometry.^{[citation needed]} In this context, a categorical generalization of the Fourier transform to noncommutative groups is TannakaKrein duality, which replaces the group of characters with the category of representations. However, this loses the connection with harmonic functions.
Alternatives
In signal processing terms, a function (of time) is a representation of a signal with perfect time resolution, but no frequency information, while the Fourier transform has perfect frequency resolution, but no time information: the magnitude of the Fourier transform at a point is how much frequency content there is, but location is only given by phase (argument of the Fourier transform at a point), and standing waves are not localized in time – a sine wave continues out to infinity, without decaying. This limits the usefulness of the Fourier transform for analyzing signals that are localized in time, notably transients, or any signal of finite extent.
As alternatives to the Fourier transform, in timefrequency analysis, one uses timefrequency transforms or timefrequency distributions to represent signals in a form that has some time information and some frequency information – by the uncertainty principle, there is a tradeoff between these. These can be generalizations of the Fourier transform, such as the shorttime Fourier transform or fractional Fourier transform, or can use different functions to represent signals, as in wavelet transforms and chirplet transforms, with the wavelet analog of the (continuous) Fourier transform being the continuous wavelet transform. (Boashash 2003).
Applications
Analysis of differential equations
Fourier transforms and the closely related Laplace transforms are widely used in solving differential equations. The Fourier transform is compatible with differentiation in the following sense: if f(x) is a differentiable function with Fourier transform , then the Fourier transform of its derivative is given by . This can be used to transform differential equations into algebraic equations. Note that this technique only applies to problems whose domain is the whole set of real numbers. By extending the Fourier transform to functions of several variables partial differential equations with domain R^{n} can also be translated into algebraic equations.
Fourier transform spectroscopy
Main article: Fourier transform spectroscopyThe Fourier transform is also used in nuclear magnetic resonance (NMR) and in other kinds of spectroscopy, e.g. infrared (FTIR). In NMR an exponentiallyshaped free induction decay (FID) signal is acquired in the time domain and Fouriertransformed to a Lorentzian lineshape in the frequency domain. The Fourier transform is also used in magnetic resonance imaging (MRI) and mass spectrometry.
Domain and range of the Fourier transform
It is often desirable to have the most general domain for the Fourier transform possible. The definition of Fourier transform as an integral naturally restricts the domain to the space of integrable functions. Unfortunately, there is no simple characterization of which functions are Fourier transforms of integrable functions (Stein & Weiss 1971). It is possible to extend the domain of the Fourier transform in various ways, as discussed in the generalizations above. The following list details some of the more common domains and ranges on which the Fourier transform is defined.
 The space of Schwartz functions is closed under the Fourier transform. Schwartz functions are rapidly decaying functions and do not include all functions which are relevant for the Fourier transform. More details may be found in (Stein & Weiss 1971).
 The space L^{p} maps into the space L^{q}, where 1/p + 1/q = 1 and 1 ≤ p ≤ 2 (Hausdorff–Young inequality).
 In particular, the space L^{2} is closed under the Fourier transform, but here the Fourier transform is no longer defined by integration.
 The space L^{1} of Lebesgue integrable functions maps into C_{0}, the space of continuous functions that tend to zero at infinity – not just into the space of bounded functions (the Riemann–Lebesgue lemma).
 The set of tempered distributions is closed under the Fourier transform. Tempered distributions are also a form of generalization of functions. It is in this generality that one can define the Fourier transform of objects like the Dirac comb.
Other notations
Other common notations for are these:
Though less commonly other notations are used. Denoting the Fourier transform by a capital letter corresponding to the letter of function being transformed (such as f(x) and F(ξ)) is especially common in the sciences and engineering. In electronics, the omega (ω) is often used instead of ξ due to its interpretation as angular frequency, sometimes it is written as F(jω), where j is the imaginary unit, to indicate its relationship with the Laplace transform, and sometimes it is written informally as F(2πf) in order to use ordinary frequency.
The interpretation of the complex function may be aided by expressing it in polar coordinate form
in terms of the two real functions A(ξ) and φ(ξ) where:
is the amplitude and
is the phase (see arg function).
Then the inverse transform can be written:
which is a recombination of all the frequency components of ƒ(x). Each component is a complex sinusoid of the form e^{2πixξ} whose amplitude is A(ξ) and whose initial phase angle (at x = 0) is φ(ξ).
The Fourier transform may be thought of as a mapping on function spaces. This mapping is here denoted and is used to denote the Fourier transform of the function f. This mapping is linear, which means that can also be seen as a linear transformation on the function space and implies that the standard notation in linear algebra of applying a linear transformation to a vector (here the function f) can be used to write instead of . Since the result of applying the Fourier transform is again a function, we can be interested in the value of this function evaluated at the value ξ for its variable, and this is denoted either as or as . Notice that in the former case, it is implicitly understood that is applied first to f and then the resulting function is evaluated at ξ, not the other way around.
In mathematics and various applied sciences it is often necessary to distinguish between a function f and the value of f when its variable equals x, denoted f(x). This means that a notation like formally can be interpreted as the Fourier transform of the values of f at x. Despite this flaw, the previous notation appears frequently, often when a particular function or a function of a particular variable is to be transformed.
For example, is sometimes used to express that the Fourier transform of a rectangular function is a sinc function,
or is used to express the shift property of the Fourier transform.
Notice, that the last example is only correct under the assumption that the transformed function is a function of x, not of x_{0}.
Other conventions
The Fourier transform can also be written in terms of angular frequency: ω = 2πξ whose units are radians per second.
The substitution ξ = ω/(2π) into the formulas above produces this convention:
Under this convention, the inverse transform becomes:
Unlike the convention followed in this article, when the Fourier transform is defined this way, it is no longer a unitary transformation on L^{2}(R^{n}). There is also less symmetry between the formulas for the Fourier transform and its inverse.
Another convention is to split the factor of (2π)^{n} evenly between the Fourier transform and its inverse, which leads to definitions:
Under this convention, the Fourier transform is again a unitary transformation on L^{2}(R^{n}). It also restores the symmetry between the Fourier transform and its inverse.
Variations of all three conventions can be created by conjugating the complexexponential kernel of both the forward and the reverse transform. The signs must be opposites. Other than that, the choice is (again) a matter of convention.
Summary of popular forms of the Fourier transform ordinary frequency ξ (hertz) unitary
angular frequency ω (rad/s) nonunitary
unitary
The ordinaryfrequency convention (which is used in this article) is the one most often found in the mathematics literature.^{[citation needed]} In the physics literature, the two angularfrequency conventions are more commonly used.^{[citation needed]}
As discussed above, the characteristic function of a random variable is the same as the Fourier–Stieltjes transform of its distribution measure, but in this context it is typical to take a different convention for the constants. Typically characteristic function is defined .
As in the case of the "nonunitary angular frequency" convention above, there is no factor of 2π appearing in either of the integral, or in the exponential. Unlike any of the conventions appearing above, this convention takes the opposite sign in the exponential.
Tables of important Fourier transforms
The following tables record some closed form Fourier transforms. For functions ƒ(x) , g(x) and h(x) denote their Fourier transforms by , , and respectively. Only the three most common conventions are included. It may be useful to notice that entry 105 gives a relationship between the Fourier transform of a function and the original function, which can be seen as relating the Fourier transform and its inverse.
Functional relationships
The Fourier transforms in this table may be found in (Erdélyi 1954) or the appendix of (Kammler 2000).
Function Fourier transform
unitary, ordinary frequencyFourier transform
unitary, angular frequencyFourier transform
nonunitary, angular frequencyRemarks
Definition 101 Linearity 102 Shift in time domain 103 Shift in frequency domain, dual of 102 104 Scaling in the time domain. If is large, then is concentrated around 0 and spreads out and flattens. 105 Duality. Here needs to be calculated using the same method as Fourier transform column. Results from swapping "dummy" variables of and or or . 106 107 This is the dual of 106 108 The notation denotes the convolution of and — this rule is the convolution theorem 109 This is the dual of 108 110 For a purely real Hermitian symmetry. indicates the complex conjugate. 111 For a purely real even function , and are purely real even functions. 112 For a purely real odd function , and are purely imaginary odd functions. Squareintegrable functions
The Fourier transforms in this table may be found in (Campbell & Foster 1948), (Erdélyi 1954), or the appendix of (Kammler 2000).
Function Fourier transform
unitary, ordinary frequencyFourier transform
unitary, angular frequencyFourier transform
nonunitary, angular frequencyRemarks
201 The rectangular pulse and the normalized sinc function, here defined as sinc(x) = sin(πx)/(πx) 202 Dual of rule 201. The rectangular function is an ideal lowpass filter, and the sinc function is the noncausal impulse response of such a filter. 203 The function tri(x) is the triangular function 204 Dual of rule 203. 205 The function u(x) is the Heaviside unit step function and a>0. 206 This shows that, for the unitary Fourier transforms, the Gaussian function exp(−αx^{2}) is its own Fourier transform for some choice of α. For this to be integrable we must have Re(α)>0. 207 For a>0. That is, the Fourier transform of a decaying exponential function is a Lorentzian function. 208 Hyperbolic secant is its own Fourier transform 209
H_{n} is the Hermite's polynomial. If a = 1 then the GaussHermite functions are eigenfunctions of the Fourier transform operator. For a derivation, see Hermite polynomial. The formula reduces to 206 for n = 0. Distributions
The Fourier transforms in this table may be found in (Erdélyi 1954) or the appendix of (Kammler 2000).
Function Fourier transform
unitary, ordinary frequencyFourier transform
unitary, angular frequencyFourier transform
nonunitary, angular frequencyRemarks
301 The distribution δ(ξ) denotes the Dirac delta function. 302 Dual of rule 301. 303 This follows from 103 and 301. 304 This follows from rules 101 and 303 using Euler's formula:
(e^{iax} + e ^{− iax}) / 2.
305 This follows from 101 and 303 using
(e^{iax} − e ^{− iax}) / (2i).
306 307 308 Here, n is a natural number and is the nth distribution derivative of the Dirac delta function. This rule follows from rules 107 and 301. Combining this rule with 101, we can transform all polynomials. 309 Here sgn(ξ) is the sign function. Note that 1/x is not a distribution. It is necessary to use the Cauchy principal value when testing against Schwartz functions. This rule is useful in studying the Hilbert transform. 310
1/x^{n} is the homogeneous distribution defined by the distributional derivative 311 This formula is valid for 0 > α > −1. For α > 0 some singular terms arise at the origin that can be found by differentiating 318. If Re α > −1, then  x  ^{α} is a locally integrable function, and so a tempered distribution. The function is a holomorphic function from the right halfplane to the space of tempered distributions. It admits a unique meromorphic extension to a tempered distribution, also denoted  x  ^{α} for α ≠ −2, −4, ... (See homogeneous distribution.) 312 The dual of rule 309. This time the Fourier transforms need to be considered as Cauchy principal value. 313 The function u(x) is the Heaviside unit step function; this follows from rules 101, 301, and 312. 314 This function is known as the Dirac comb function. This result can be derived from 302 and 102, together with the fact that
as distributions.
315 The function J_{0}(x) is the zeroth order Bessel function of first kind. 316 This is a generalization of 315. The function J_{n}(x) is the nth order Bessel function of first kind. The function T_{n}(x) is the Chebyshev polynomial of the first kind. 317 γ is the Euler–Mascheroni constant. 318 This formula is valid for 1 > α > 0. Use differentiation to drive formula for higher exponents. u is the Heaviside function. Twodimensional functions
Functions (400 to 402) Fourier transform
unitary, ordinary frequencyFourier transform
unitary, angular frequencyFourier transform
nonunitary, angular frequency400
401 402  Remarks
To 400: The variables ξ_{x}, ξ_{y}, ω_{x}, ω_{y}, ν_{x} and ν_{y} are real numbers. The integrals are taken over the entire plane.
To 401: Both functions are Gaussians, which may not have unit volume.
To 402: The function is defined by circ(r)=1 0≤r≤1, and is 0 otherwise. This is the Airy distribution, and is expressed using J_{1} (the order 1 Bessel function of the first kind). (Stein & Weiss 1971, Thm. IV.3.3)
Formulas for general ndimensional functions
Function Fourier transform
unitary, ordinary frequencyFourier transform
unitary, angular frequencyFourier transform
nonunitary, angular frequency500
501
502  Remarks
To 501: The function χ_{[0,1]} is the indicator function of the interval [0, 1]. The function Γ(x) is the gamma function. The function J_{n/2 + δ} is a Bessel function of the first kind, with order n/2 + δ. Taking n = 2 and δ = 0 produces 402. (Stein & Weiss 1971, Thm. 4.13)
To 502: See Riesz potential. The formula also holds for all α ≠ −n, −n − 1, ... by analytic continuation, but then the function and its Fourier transforms need to be understood as suitably regularized tempered distributions. See homogeneous distribution.
See also
 Fourier series
 Fast Fourier transform
 Laplace transform
 Discrete Fourier transform
 Discretetime Fourier transform
 Fourier–Deligne transform
 Fractional Fourier transform
 Linear canonical transform
 Fourier sine transform
 Spacetime Fourier transform
 Shorttime Fourier transform
 Fourier inversion theorem
 Analog signal processing
 Transform (mathematics)
 Integral transform
References
 Boashash, B., ed. (2003), TimeFrequency Signal Analysis and Processing: A Comprehensive Reference, Oxford: Elsevier Science, ISBN 0080443354
 Bochner S., Chandrasekharan K. (1949), Fourier Transforms, Princeton University Press
 Bracewell, R. N. (2000), The Fourier Transform and Its Applications (3rd ed.), Boston: McGrawHill, ISBN 0071160434.
 Campbell, George; Foster, Ronald (1948), Fourier Integrals for Practical Applications, New York: D. Van Nostrand Company, Inc..
 Duoandikoetxea, Javier (2001), Fourier Analysis, American Mathematical Society, ISBN 0821821725.
 Dym, H; McKean, H (1985), Fourier Series and Integrals, Academic Press, ISBN 9780122264511.
 Erdélyi, Arthur, ed. (1954), Tables of Integral Transforms, 1, New Your: McGrawHill
 Fourier, J. B. Joseph (1822), Théorie Analytique de la Chaleur, Paris, http://books.google.com/?id=TDQJAAAAIAAJ&printsec=frontcover&dq=Th%C3%A9orie+analytique+de+la+chaleur&q
 Grafakos, Loukas (2004), Classical and Modern Fourier Analysis, PrenticeHall, ISBN 013035399X.
 Hewitt, Edwin; Ross, Kenneth A. (1970), Abstract harmonic analysis. Vol. II: Structure and analysis for compact groups. Analysis on locally compact Abelian groups, Die Grundlehren der mathematischen Wissenschaften, Band 152, Berlin, New York: SpringerVerlag, MR0262773.
 Hörmander, L. (1976), Linear Partial Differential Operators, Volume 1, SpringerVerlag, ISBN 9783540006626.
 James, J.F. (2011), A Student's Guide to Fourier Transforms (3rd ed.), New York: Cambridge University Press, ISBN 9780521176835.
 Kaiser, Gerald (1994), A Friendly Guide to Wavelets, Birkhäuser, ISBN 0817637117
 Kammler, David (2000), A First Course in Fourier Analysis, Prentice Hall, ISBN 0135787823
 Katznelson, Yitzhak (1976), An introduction to Harmonic Analysis, Dover, ISBN 0486633314
 Knapp, Anthony W. (2001), Representation Theory of Semisimple Groups: An Overview Based on Examples, Princeton University Press, ISBN 9780691090894, http://books.google.com/?id=QCcW1h835pwC
 Pinsky, Mark (2002), Introduction to Fourier Analysis and Wavelets, Brooks/Cole, ISBN 0534376606
 Polyanin, A. D.; Manzhirov, A. V. (1998), Handbook of Integral Equations, Boca Raton: CRC Press, ISBN 0849328764.
 Rudin, Walter (1987), Real and Complex Analysis (Third ed.), Singapore: McGraw Hill, ISBN 0071002766.
 Stein, Elias; Shakarchi, Rami (2003), Fourier Analysis: An introduction, Princeton University Press, ISBN 069111384X.
 Stein, Elias; Weiss, Guido (1971), Introduction to Fourier Analysis on Euclidean Spaces, Princeton, N.J.: Princeton University Press, ISBN 9780691080789.
 Wilson, R. G. (1995), Fourier Series and Optical Transform Techniques in Contemporary Optics, New York: Wiley, ISBN 0471303577.
 Yosida, K. (1968), Functional Analysis, SpringerVerlag, ISBN 3540586547.
External links
 The Discrete Fourier Transformation (DFT): Definition and numerical examples  A Matlab tutorial
 Fourier Series Applet (Tip: drag magnitude or phase dots up or down to change the wave form).
 Stephan Bernsee's FFTlab (Java Applet)
 Stanford Video Course on the Fourier Transform
 Weisstein, Eric W., "Fourier Transform" from MathWorld.
 The DFT “à Pied”: Mastering The Fourier Transform in One Day at The DSP Dimension
 An Interactive Flash Tutorial for the Fourier Transform
Categories: Fundamental physics concepts
 Fourier analysis
 Integral transforms
 Unitary operators
 Joseph Fourier
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