Fourier transform

This article specifically discusses Fourier transformation of functions on the real line; for other kinds of Fourier transformation, see Fourier analysis and list of Fourier-related transforms. In Mathematics, the real line is simply the set R of singleton Real numbers However this term is usually used when R is to be treated as a In mathematics Fourier analysis is a subject area which grew out of the study of Fourier series This is a list of Linear transformations of functions related to Fourier analysis. For generalizations, see fractional Fourier transform and linear canonical transform. In Mathematics, in the area of Harmonic analysis, the fractional Fourier transform ( FRFT) is a Linear transformation generalizing the Fourier Paraxial optical systems implemented entirely with Thin lenses and propagation through free space and/or graded index (GRIN media are Quadratic Phase Systems (QPS

In mathematics, the continuous Fourier transform is one of the specific forms of Fourier analysis. Mathematics is the body of Knowledge and Academic discipline that studies such concepts as Quantity, Structure, Space and In mathematics Fourier analysis is a subject area which grew out of the study of Fourier series As such, it transforms one function into another, which is called the frequency domain representation of the original function (where the original function is often a function in the time-domain). In Mathematics a transform is an Operator applied to a function so that under the transform certain operations are simplified The Mathematical concept of a function expresses dependence between two quantities one of which is given (the independent variable, argument of the function Frequency domain is a term used to describe the analysis of Mathematical functions or signals with respect to frequency Time domain is a term used to describe the analysis of mathematical functions or physical signals with respect to Time. In this specific case, both domains are continuous and unbounded. The term Fourier transform can refer to either the frequency domain representation of a function or to the process/formula that "transforms" one function into the other.

Fourier transforms
Continuous Fourier transform
Fourier series
Discrete Fourier transform
Discrete-time Fourier transform
Related transforms

Definitions

There are several common conventions for defining the Fourier transform of a complex-valued Lebesgue integrable function f:R→C. In Mathematics, a Fourier series decomposes a periodic function into a sum of simple oscillating functions In Mathematics, the discrete Fourier transform (DFT is one of the specific forms of Fourier analysis. In Mathematics, the discrete-time Fourier transform (DTFT is one of the specific forms of Fourier analysis. This is a list of Linear transformations of functions related to Fourier analysis. Complex plane In Mathematics, the complex numbers are an extension of the Real numbers obtained by adjoining an Imaginary unit, denoted In Mathematics, the Integral of a non-negative function can be regarded in the simplest case as the Area between the graph of One common definition is:

$F(\nu) = \int_{-\infty}^{\infty} f(t)\ e^{-2\pi i \nu t}\,dt,$ for every real number ν. In Mathematics, the real numbers may be described informally in several different ways

When the independent variable t represents time (with SI unit of seconds), the transform variable ν represents ordinary frequency (in hertz). The second ( SI symbol s) sometimes abbreviated sec, is the name of a unit of Time, and is the International System of Units Frequency is a measure of the number of occurrences of a repeating event per unit Time. The hertz (symbol Hz) is a measure of Frequency, informally defined as the number of events occurring per Second. If f is Hölder continuous, then it can be reconstructed from F by the inverse transform:

$f(t) = \int_{-\infty}^{\infty} F(\nu)\ e^{2 \pi i \nu t}\,d\nu,$ for every real number t. In Mathematics, a real-valued function f on R n satisfies a Hölder condition, or is Hölder continuous, when there are

Other notations for $F(\nu)\,$ are: $\hat{f}(\nu),$ $\mathcal{F} \big\{f(t)\big\},$ $\mathcal{F}\{f\}(\nu),$ and $(\mathcal{F}f)(\nu).$

The interpretation of $F(\nu)\,$ is aided by expressing it in polar coordinate form: $F(\nu) = A(\nu)\ e^{i \phi (\nu)},\,$ where:

$A(\nu) = |F(\nu)|, \,$ the amplitude

$\phi (\nu) = \arg \big( F(\nu) \big), \,$ the phase. In Mathematics, the polar coordinate system is a two-dimensional Coordinate system in which each point on a plane is determined by Amplitude is the magnitude of change in the oscillating variable with each Oscillation, within an oscillating system The phase of an oscillation or wave is the fraction of a complete cycle corresponding to an offset in the displacement from a specified reference point at time t = 0 (see arg function)

Then the inverse transform can be written:

$f(t) = \int _{-\infty}^{\infty} A(\nu)\ e^{ i(2\pi \nu t +\phi (\nu))}\,d\nu,$

which is a recombination of all the frequency components of f(t). In Mathematics the arg function is a logical function that extracts the angular component of a Complex number or function Each component is a complex sinusoid of the form e^2πiνt whose amplitude is A(ν) and whose initial phase angle (at t=0) is φ(ν). Amplitude is the magnitude of change in the oscillating variable with each Oscillation, within an oscillating system In the context of vectors and phasors, the term phase angle refers to the angular component of the Polar coordinate representation

The Fourier transform is often written in terms of angular frequency: ω = 2πν whose units are radians per second. Do not confuse with Angular velocity In Physics (specifically Mechanics and Electrical engineering) angular frequency The radian is a unit of plane Angle, equal to 180/ π degrees, or about 57

The substitution ν = ω/(2π) into the formulas above produces this convention:

$F(\omega) = \int _{-\infty}^\infty f(t)\ e^{- i\omega t}\,dt$ ^[1]

$f(t) = \frac{1}{2\pi} \int _{-\infty}^{\infty} F(\omega)\ e^{ i\omega t}\,d\omega,$

which is also a bilateral Laplace transform evaluated at s=iω. In Mathematics, the Laplace transform is one of the best known and most widely used Integral transforms It is commonly used to produce an easily soluble algebraic

The 2π factor can be split evenly between the Fourier transform and the inverse, which leads to another popular convention:

$F(\omega) = \frac{1}{\sqrt{2\pi}} \int _{-\infty}^\infty f(t)\ e^{- i\omega t}\,dt$

$f(t) = \frac{1}{\sqrt{2\pi}} \int _{-\infty}^{\infty} F(\omega)\ e^{ i\omega t}\,d\omega.$

This makes the transform a unitary one. In Functional analysis, a branch of Mathematics, a unitary operator is a Bounded linear operator U H →

Variations of all three conventions can be created by conjugating the complex-exponential kernel of both the forward and the reverse transform. In Mathematics, the word kernel has several meanings Kernel may mean a subset associated with a mapping The kernel of a mapping is the set of elements that The signs must be opposites. Other than that, the choice is (again) a matter of convention.

**Summary of popular forms of the Fourier transform**
angular frequency $\omega \,$ (rad/s)	unitary	$F_1(\omega) \ \stackrel{\mathrm{def}}{=}\ \frac{1}{\sqrt{2 \pi}} \int _{-\infty}^{\infty} f(t) \ e^{-i \omega t}\, dt \ = \frac{1}{\sqrt{2 \pi}} F_2(\omega) = \frac{1}{\sqrt{2 \pi}} F_3 \left ( \frac{\omega}{2 \pi} \right )\,$ $f(t) = \frac{1}{\sqrt{2 \pi}} \int _{-\infty}^{\infty} F_1(\omega) \ e^{i \omega t}\, d \omega \$
angular frequency $\omega \,$ (rad/s)	non-unitary	$F_2(\omega) \ \stackrel{\mathrm{def}}{=}\ \int _{-\infty}^{\infty} f(t) \ e^{-i \omega t} \ dt \ = \sqrt{2 \pi}\ F_1(\omega) = F_3 \left ( \frac{\omega}{2 \pi} \right ) \,$ $f(t) = \frac{1}{2 \pi} \int_{-\infty}^{\infty} F_2(\omega) \ e^{i \omega t} \ d \omega \$
ordinary frequency ν (hertz)	unitary	$F_3(\nu) \ \stackrel{\mathrm{def}}{=}\ \int _{-\infty}^{\infty} f(t) \ e^{-i 2 \pi \nu t} \ dt \ = \sqrt{2 \pi}\ F_1(2 \pi \nu) = F_2(2 \pi \nu)\,$ $f(t) = \int_{-\infty}^{\infty} F_3(\nu) \ e^{i 2 \pi \nu t}\, d\nu \$

Some Fourier transform properties

Notation: $f(t) \quad \stackrel{\mathcal{F}}{\Longleftrightarrow}\quad F(\omega)$ denotes that f(t) and F(ω) are a Fourier transform pair.

Linearity

$a\cdot f(t) + b\cdot g(t) \quad \stackrel{\mathcal{F}}{\Longleftrightarrow}\quad a\cdot F(\omega) + b\cdot G(\omega)$

Multiplication

$f(t)\cdot g(t) \,$	$\stackrel{\mathcal{F}}{\Longleftrightarrow}\quad \frac{1}{\sqrt{2\pi}}\cdot (F*G)(\omega) \,$	(unitary normalization convention)
	$\stackrel{\mathcal{F}}{\Longleftrightarrow}\quad \frac{1}{2\pi}\cdot (F*G)(\omega) \,$	(non-unitary convention)
	$\stackrel{\mathcal{F}}{\Longleftrightarrow}\quad (F*G)(\nu) \,$	(ordinary frequency)

Modulation

$\begin{align} f(t)\cdot \cos \omega_{0}t &\quad \stackrel{\mathcal{F}}{\Longleftrightarrow}\quad \frac{1}{2}[F(\omega+\omega_{0})+F(\omega-\omega_{0})],\qquad \omega_{0} \in \mathbb{R} \\ f(t)\cdot \sin \omega_{0}t &\quad \stackrel{\mathcal{F}}{\Longleftrightarrow}\quad \frac{i}{2}[F(\omega+\omega_{0})-F(\omega-\omega_{0})] \\ f(t)\cdot e^{i\omega_{0}t} &\quad \stackrel{\mathcal{F}}{\Longleftrightarrow}\quad F(\omega-\omega_{0}) \end{align} \,$

Convolution

$(f*g)(t) \,$	$\stackrel{\mathcal{F}}{\Longleftrightarrow}\quad \sqrt{2\pi}\cdot F(\omega)\cdot G(\omega) \,$	(unitary convention)
	$\stackrel{\mathcal{F}}{\Longleftrightarrow}\quad F(\omega)\cdot G(\omega) \,$	(non-unitary convention)
	$\stackrel{\mathcal{F}}{\Longleftrightarrow}\quad F(\nu)\cdot G(\nu) \,$	(ordinary frequency)

Integration (example of convolution)

$\int_{-\infty}^{t} f(\tau)\, d\tau = (f*u)(t)$ ^[2] $\ \stackrel{\mathcal{F}}{\Longleftrightarrow}\ \ \frac{1}{i\omega}F(\omega)+\pi F(0)\cdot \delta(\omega), \,$

Conjugation

$\overline{f(t)} \quad \stackrel{\mathcal{F}}{\Longleftrightarrow}\quad \overline{F(-\omega)}$

Scaling

$f(at) \quad \stackrel{\mathcal{F}}{\Longleftrightarrow}\quad \frac{1}{|a|}F\biggl(\frac{\omega}{a}\biggr), \qquad a \in \mathbb{R}, a \ne 0$

Time reversal

$f(-t) \quad \stackrel{\mathcal{F}}{\Longleftrightarrow}\quad F(-\omega)$

Time shift

$f(t-t_0) \quad \stackrel{\mathcal{F}}{\Longleftrightarrow}\quad e^{-i\omega t_0}\cdot F(\omega)$

Parseval's theorem

$\int_{-\infty}^{\infty} f(t)\cdot \overline{g(t)}\, dt \,$	$= \int_{-\infty}^{\infty} F(\omega)\cdot \overline{G(\omega)}\, d\omega \,$	(unitary convention)
	$= \frac{1}{2\pi}\cdot \int_{-\infty}^{\infty} F(\omega)\cdot \overline{G(\omega)}\, d\omega \,$	(non-unitary convention)
	$= \int_{-\infty}^{\infty} F(\nu)\cdot \overline{G(\nu)}\, df \,$	(ordinary frequency)

The section "Table of important Fourier transforms" (below) documents more properties of the continuous Fourier transform.

Generalization

Using two arbitrary real constants a and b, the most general definition of the forward 1-dimensional Fourier transform is given by:

$F(\omega) = \sqrt{\frac{|b|}{(2 \pi)^{1-a}}} \int_{-\infty}^{+\infty} f(t) \cdot e^{-i b \omega t} \, dt,$

and the inverse is given by:

$f(t) = \sqrt{\frac{|b|}{(2 \pi)^{1+a}}} \int_{-\infty}^{+\infty} F(\omega) \cdot e^{i b \omega t} \, d\omega.$

Note that the transform definitions are symmetric; they can be reversed by simply changing the signs of a and b.

The ordinary frequency convention corresponds to (a,b) = (0,2π), and in that case the variable ω is changed to ν. If ν and t carry units, their product must be dimensionless. For example, t may be in units of time, specifically seconds, and ν would be in hertz. The second ( SI symbol s) sometimes abbreviated sec, is the name of a unit of Time, and is the International System of Units The hertz (symbol Hz) is a measure of Frequency, informally defined as the number of events occurring per Second.

The unitary, angular frequency convention is (a,b) = (0,1), and the non-unitary convention (above) is (a,b) = (1,1).

The "forward" and "inverse" transforms are always defined so that the operation of both transforms in either order on a function will return the original function. In other words, the composition of the transform pair is defined to be the identity transformation. In Mathematics, a composite function represents the application of one function to the results of another In Mathematics, an identity function, also called identity map or identity transformation, is a function that always returns the same value that

More properties

Completeness

We define the Fourier transform on the set of integrable complex-valued functions of R and then extend it by continuity to the Hilbert space of square-integrable functions with the usual inner-product. In Functional analysis, it is often convenient to define a Linear transformation on a complete, Normed vector space X by first defining a linear This article assumes some familiarity with Analytic geometry and the concept of a limit. Then $\mathcal{F}$ : L²(R) → L²(R) is a unitary operator. In Functional analysis, a branch of Mathematics, a unitary operator is a Bounded linear operator U H → That is $\mathcal{F}^*=\mathcal{F}^{-1}$ and the transform preserves inner-products (see Parseval's theorem, also described below). In Mathematics, Parseval's theorem usually refers to the result that the Fourier transform is unitary; loosely that the sum (or integral of the square Note that, $\mathcal{F}^*$ refers to adjoint of the Fourier Transform operator. In Mathematics, specifically in Functional analysis, each Linear operator on a Hilbert space has a corresponding adjoint operator.

Moreover we can check that:

$\mathcal{F}^2 = \mathcal{J},\quad \mathcal{F}^3 = \mathcal{F}^* = \mathcal{F}^{-1}, \quad \mbox{and} \quad \mathcal{F}^4 = \mathcal{I},$

where $\mathcal{J}$ is the Time-Reversal operator defined as:

$\mathcal{J}(f)(t) = f(-t),$

and $\mathcal{I}$ is the Identity operator defined as:

$\mathcal{I}(f)(t) = f(t).$

Multi-dimensional version

The Fourier transform, can be expanded to arbitrary dimension $n$ . In the unitary, angular frequency convention, the definition is:

$F(\boldsymbol{\omega}) = \mathcal{F}\{f\}(\boldsymbol{\omega}) \ \stackrel{\mathrm{def}}{=}\ \left(\frac{1}{\sqrt{2\pi}}\right)^{n}\int_{\R^n} f(\mathbf{x})\cdot e^{-i(\boldsymbol{\omega}\cdot \mathbf{x})}\,d\mathbf{x},$

where $\mathbf{x}$ and $\boldsymbol{\omega}$ are $n$ -dimensional vectors, and $\boldsymbol{\omega}\cdot \mathbf{x}$ is the inner product, also written $\left\langle \boldsymbol{\omega},\mathbf{x} \right\rangle,$ of the 2 vectors. In Mathematics, a vector space (or linear space) is a collection of objects (called vectors) that informally speaking may be scaled and added In Mathematics, an inner product space is a Vector space with the additional Structure of inner product. The integration is performed over all $n$ dimensions.

The function $f(\mathbf{x})$ is assumed to belong to the "space" of integrable functions defined on Rⁿ:

$\mathcal{F}:L^1(\mathbb{R}^n)\rightarrow C(\mathbb{R}^n),$

where:

$L^1(\mathbb{R}^n) = \{f: \, \mathbb{R}^n \to \mathbb{C} \;\big|\; \int_{\mathbb{R}^n} |f(\mathbf{x})|\, d\mathbf{x} < \infty\},$

and C(Rⁿ) is the space of continuous functions on Rⁿ. In Mathematics, a continuous function is a function for which intuitively small changes in the input result in small changes in the output

One may now use this to define the Fourier transform for compactly supported smooth functions, which are dense in L²(Rⁿ). The Plancherel theorem then allows us to extend the definition of the Fourier transform to functions on L²(Rⁿ) (even those not compactly supported) by continuity arguments. In Mathematics, the Plancherel theorem is a result in Harmonic analysis, first proved by Michel Plancherel. All the properties and formulas listed on this page apply to the Fourier transform so defined.

Unfortunately, further extensions become more technical. One may use the Hausdorff-Young inequality to define the Fourier transform for f ∈ L^p(Rⁿ) for 1 ≤ p ≤ 2. In Mathematics, the Riesz-Thorin theorem, often referred to as the Riesz-Thorin Interpolation Theorem or the Riesz-Thorin Convexity Theorem is a result The Fourier transform of functions in L^p for the range 2 < p < ∞ requires the study of distributions, since the Fourier transform of some functions in these spaces is no longer a function, but rather a distribution. In Mathematical analysis, distributions (also known as generalized functions) are objects which generalize functions and Probability distributions

The Plancherel theorem and Parseval's theorem

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.

If f(t) and g(t) are square-integrable and F(ω) and G(ω) are their unitary Fourier transforms, then we have Parseval's theorem:

$\int_{\mathbb{R}^n} f(t) \bar{g}(t) \, dt = \int_{\mathbb{R}^n} F(\omega) \bar{G}(\omega) \, d\omega,$

where the bar denotes complex conjugation. In Mathematics, Parseval's theorem usually refers to the result that the Fourier transform is unitary; loosely that the sum (or integral of the square In Mathematics, the complex conjugate of a Complex number is given by changing the sign of the Imaginary part. Therefore, the Fourier transform yields an isometric automorphism of the Hilbert space L²(Rⁿ). In Mathematics, an inner product space is a Vector space with the additional Structure of inner product. In Mathematics, an automorphism is an Isomorphism from a Mathematical object to itself This article assumes some familiarity with Analytic geometry and the concept of a limit.

The Plancherel theorem, which is equivalent to Parseval's theorem, states:

$\int_{\mathbb{R}^n} \left| f(t) \right|^2\, dt = \int_{\mathbb{R}^n} \left| F(\omega) \right|^2\, d\omega.$

This theorem is usually interpreted as asserting the unitary property of the Fourier transform. In Mathematics, the Plancherel theorem is a result in Harmonic analysis, first proved by Michel Plancherel. In Mathematics, Parseval's theorem usually refers to the result that the Fourier transform is unitary; loosely that the sum (or integral of the square In Functional analysis, a branch of Mathematics, a unitary operator is a Bounded linear operator U H → See Pontryagin duality for a general formulation of this concept in the context of locally compact abelian groups. In Mathematics, in particular in Harmonic analysis and the theory of Topological groups Pontryagin duality explains the general properties of the Fourier

Localization property

As a rule of thumb: the more concentrated f(t) is, the more spread out F(ω) is. In particular, if we "squeeze" a function in t, it spreads out in ω and vice-versa; and we cannot arbitrarily concentrate both the function and its Fourier transform.

Therefore a function which equals its Fourier transform strikes a precise balance between being concentrated and being spread out. It is easy in theory to construct examples of such functions (called self-dual functions) because the Fourier transform has order 4 (that is, iterating it four times on a function returns the original function). In Mathematics, duality has numerous meanings Generally speaking duality is a metamathematical involution. In Group theory, a branch of Mathematics, the term order is used in two closely related senses the order of a group is The sum of the four iterated Fourier transforms of any function will be self-dual. There are also some explicit examples of self-dual functions, the most important being constant multiples of the Gaussian function

$f(t) = \exp \left( \frac{-t^2}{2} \right).$

This function is related to Gaussian distributions, and in fact, is an eigenfunction of the Fourier transform operators. In Mathematics, a Gaussian function (named after Carl Friedrich Gauss) is a function of the form f(x = a e^{- { (x-b^2 \over 2 The normal distribution, also called the Gaussian distribution, is an important family of Continuous probability distributions applicable in many fields In Mathematics, an eigenfunction of a Linear operator, A, defined on some Function space is any non-zero function f in Again, it is worth stressing that the mere fact that the Gaussian is self-dual does not make it in any way special: many self-dual functions exist.

The trade-off between the compaction of a function and its Fourier transform can be formalized in the form of a Fourier Uncertainty Principle. Suppose f(t) and F(ω) are a Fourier transform pair for a finite-energy (i. e. square-integrable) function. Without loss of generality, we assume that f(t) is normalized:

$\int_{-\infty}^\infty |f(t)|^2 \,dt=1.$

It follows from Parseval's theorem that F(ω) is also normalized.

Define the expected location^[3] of a particle (with probability density |f(t)|²) as

$u_f \ \stackrel{\mathrm{def}}{=}\ \int_{-\infty}^\infty t|f(t)|^2\,dt.$

and the expectation value of the momentum^[3] of the particle (with probability density |F(ω)|²) as

$\xi_F \ \stackrel{\mathrm{def}}{=}\ \int_{-\infty}^\infty \omega |F(\omega)|^2\,d\omega.$

Also define the variances around the above-defined average values as

$\sigma^2_{f} \ \stackrel{\mathrm{def}}{=}\ \int_{-\infty}^\infty (t-u_f)^2|f(t)|^2\,dt$

and

$\sigma^2_{F} \ \stackrel{\mathrm{def}}{=}\ \int_{-\infty}^\infty (\omega-\xi_F)^2 |F(\omega)|^2\,d\omega.$

Then it can be shown that

$\sigma^2_{f}\, \sigma^2_{F} \ge \frac{1}{4}.$

The equality is achieved for the Gaussian function listed above, which shows that the Gaussian function is maximally concentrated in "time-frequency". In Probability theory and Statistics, the variance of a Random variable, Probability distribution, or sample is one measure of

The most famous practical application of this property is found in quantum mechanics. Quantum mechanics is the study of mechanical systems whose dimensions are close to the Atomic scale such as Molecules Atoms Electrons Following from the axioms of quantum mechanics, the momentum and position wave functions are Fourier transform pairs to within a factor of h/2π and are normalized to unity. The above expression then becomes a statement of the Heisenberg uncertainty principle. In Quantum physics, the Heisenberg uncertainty principle states that locating a particle in a small region of space makes the Momentum of the particle uncertain

The Fourier transform also translates between smoothness and decay. If f(t) is several times differentiable, then F(ω) decays rapidly towards zero for ω → ± ∞.

Eigenfunctions

One important choice of orthonormal (under $L 2$ norm) eigenfunctions of the Fourier transform are the Hermite functions

${\psi}_n(t) = \frac{1}{\sqrt{n!\,2^n\sqrt{\pi}}}\,e^{-t^2/2}H_n(t).\,\!$

where $H n (t)$ is the physicist-defined Hermite polynomial. In Mathematics, an eigenfunction of a Linear operator, A, defined on some Function space is any non-zero function f in In Mathematics, the Hermite polynomials are a classical orthogonal Polynomial sequence that arise in Probability, such as the Edgeworth When using the unitary definition of the Fourier Transform, the relation is

$\mathcal{F} \left\{ {\psi}_n(t) \right\} = (-i)^n {\psi}_n(\omega) \,\!$

However, this choice of eigenfunctions is not unique. Because there are only four different eigenvalues of the Fourier transform (±1 and ±i), each highly degenerate, any linear combination of eigenfunctions with the same eigenvalue gives another eigenfunction. In Mathematics, given a Linear transformation, an of that linear transformation is a nonzero vector which when that transformation is applied to it changes for the degeneracy of a Graph, see Arboricity#Related_concepts. However, the choice of Hermite functions is convenient because they are exponentially localized in both frequency and time domains, and thus give rise to the fractional Fourier transform used in time-frequency analyses. In Mathematics, in the area of Harmonic analysis, the fractional Fourier transform ( FRFT) is a Linear transformation generalizing the Fourier

Analysis of differential equations

Fourier transforms, and the closely related Laplace transforms are widely used in solving differential equations. In Mathematics, the Laplace transform is one of the best known and most widely used Integral transforms It is commonly used to produce an easily soluble algebraic A differential equation is a mathematical Equation for an unknown function of one or several variables that relates the values of the The Fourier transform is compatible with differentiation in the following sense: if f(t) is a differentiable function with Fourier transform F(ω), then the Fourier transform of its derivative is given by iω F(ω). In Calculus, a branch of mathematics the derivative is a measurement of how a function changes when the values of its inputs change 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 (as outlined below), partial differential equations with domain Rⁿ can also be translated into algebraic equations. In Mathematics, partial differential equations ( PDE) are a type of Differential equation, i

Convolution theorem

Main article: Convolution theorem

The Fourier transform translates between convolution and multiplication of functions. In Mathematics, the convolution theorem states that under suitableconditions the Fourier transform of a Convolution is the Pointwise product In Mathematics and in particular Functional analysis, convolution is a mathematical operation on two functions f and If f(t) and h(t) are integrable functions with Fourier transforms F(ω) and H(ω) respectively, and if the convolution of f and h exists and is absolutely integrable, then the Fourier transform of the convolution is given by the product of the Fourier transforms F(ω) H(ω) (possibly multiplied by a constant factor depending on the Fourier normalization convention).

In the unitary normalization convention, this means that if:

$g(t) = \{f*h\}(t) = \int_{-\infty}^\infty f(\tau)h(t - \tau)\,d\tau,$

where * denotes the convolution operation, then:

$G(\omega) = \sqrt{2\pi}\cdot F(\omega)H(\omega).\,$

The above formulas hold true for functions defined on both one- and multi-dimension real space. In linear time invariant (LTI) system theory, it is common to interpret h(t) as the impulse response of an LTI system with input f(t) and output g(t), since substituting the unit impulse for f(t) yields g(t)=h(t). LTI system theory or linear time-invariant system theory is a theory in the field of Electrical engineering, specifically in circuits Signal processing The impulse response of a system is its output when presented with a very brief input signal an impulse The Dirac delta or Dirac's delta is a mathematical construct introduced by the British theoretical physicist Paul Dirac. In this case, H(ω) represents the frequency response of the system. Frequency response is the measure of any system's spectrum response at the output to a signal of varying Frequency (but constant amplitude at its input

Conversely, if f(t) can be decomposed as the product of two other functions p(t) and q(t) such that their product p(t)q(t) is integrable, then the Fourier transform of this product is given by the convolution of the respective Fourier transforms P(ω) and Q(ω), again with a constant scaling factor.

In the unitary normalization convention, this means that if f(t) = p(t) q(t) then:

$F(\omega) = \frac{1}{\sqrt{2\pi}} \bigg( P(\omega) * Q(\omega) \bigg) = \frac{1}{\sqrt{2\pi}} \int_{-\infty}^\infty P(\alpha)Q(\omega - \alpha)\,d\alpha.$

Cross-correlation theorem

In an analogous manner, it can be shown that if $g (t)$ is the cross-correlation of $f (t)$ and $h (t)$ :

$g(t)=(f\star h)(t) = \int_{-\infty}^\infty \bar{f}(\tau)\,h(t+\tau)\,d\tau$

then the Fourier transform of $g (t)$ is:

$G(\omega) = \sqrt{2\pi}\,\overline{F}(\omega)\,H(\omega)$

where capital letters are again used to denote the Fourier transform. In Signal processing, cross-correlation is a measure of similarity of two waveforms as a function of a time-lag applied to one of them

Tempered distributions

The most general and useful context for studying the Fourier transform is given by the tempered distributions; these include all the integrable functions mentioned above and have the added advantage that the Fourier transform of any tempered distribution is again a tempered distribution and the rule for the inverse of the Fourier transform is universally valid. In Mathematical analysis, distributions (also known as generalized functions) are objects which generalize functions and Probability distributions Furthermore, the useful Dirac delta is a tempered distribution but not a function; its Fourier transform is a constant function (whose specific value depends upon the form of the Fourier transform used). The Dirac delta or Dirac's delta is a mathematical construct introduced by the British theoretical physicist Paul Dirac. Distributions can be differentiated and the above mentioned compatibility of the Fourier transform with differentiation and convolution remains true for tempered distributions.

Domain and range of the Fourier transform

The domain and range of the Fourier transform cannot be described as two well-defined sets of functions. Instead, they can be chosen in several different ways depending on exactly what is meant by a function and an integral. Furthermore, for some pair of domain and range which can be described for the Fourier transform, it may sometimes be of interest to consider the restriction of the transform to a proper subset of the domain. In general, however, it is of interest to describe as "large" sets as possible for the domain. Such extensions can be done in different ways and may lead to domains where either one is not a subset of the other. Some examples of domains and ranges which are described in the literature are

The set of Schwartz functions is closed under the Fourier transform. In Mathematics, Schwartz space is the Function space of rapidly decreasing functions Schwartz functions are rapidly decaying functions and does not include all functions which are relevant for the Fourier transform.

$L 2$ is closed under the Fourier transform. This is a much richer set than Schwartz functions but is, technically speaking, not a set of functions but rather equivalence classes of functions. In Mathematics, given a set X and an Equivalence relation ~ on X, the equivalence class of an element a in X

The set of tempered distributions is closed under the Fourier transform. In Mathematical analysis, distributions (also known as generalized functions) are objects which generalize functions and Probability distributions Tempered distributions are also a form a generalization of functions. It is in this generality that one can define the Fourier Transform of objects like the Dirac delta function. The Dirac delta or Dirac's delta is a mathematical construct introduced by the British theoretical physicist Paul Dirac.

Table of important Fourier transforms

The following table records some important Fourier transforms. G and H denote Fourier transforms of g(t) and h(t), respectively. g and h may be integrable functions or tempered distributions. Note that the two most common unitary conventions are included.

Functional relationships

	Signal	Fourier transform unitary, angular frequency	Fourier transform unitary, ordinary frequency	Remarks
	$f(t)\,$	$F(\omega)\!\ \stackrel{\mathrm{def}}{=}\ \!$ $\frac{1}{\sqrt{2 \pi}} \int_{-\infty}^{\infty}\!\!f(t) e^{-i \omega t}\, dt$	$F(\nu)\!\ \stackrel{\mathrm{def}}{=}\$ $\int_{-\infty}^{\infty}\!\!f(t) e^{-i 2\pi \nu t}\, dt$
101	$a\cdot f(t) + b\cdot g(t)\,$	$a\cdot F(\omega) + b\cdot G(\omega)\,$	$a\cdot F(\nu) + b\cdot G(\nu)\,$	Linearity
102	$f(t - a)\,$	$e^{- i a \omega} F(\omega)\,$	$e^{- i 2\pi a \nu} F(\nu)\,$	Shift in time domain
103	$e^{ iat} f(t)\,$	$F(\omega - a)\,$	$F \left(\nu - \frac{a}{2\pi}\right)\,$	Shift in frequency domain, dual of 102
104	$f(a t)\,$	$\frac{1}{\|a\|} F \left( \frac{\omega}{a} \right)\,$	$\frac{1}{\|a\|} F \left( \frac{\nu}{a} \right)\,$	If $\|a\|\,$ is large, then $f(a t)\,$ is concentrated around 0 and $\frac{1}{\|a\|}F \left( \frac{\omega}{a} \right)\,$ spreads out and flattens. It is interesting to consider the limit of this as $\| a \|$ tends to infinity - the delta function
105	$F(t)\,$	$f(-\omega)\,$	$f(-\nu)\,$	Duality property of the Fourier transform. Results from swapping "dummy" variables of $t \,$ and $\omega \,$ .
106	$\frac{d^n f(t)}{dt^n}\,$	$(i\omega)^n F(\omega)\,$	$(i 2\pi f)^n F(\nu)\,$	Generalized derivative property of the Fourier transform
107	$t^n f(t)\,$	$i^n \frac{d^n F(\omega)}{d\omega^n}\,$	$\left (\frac{i}{2\pi}\right)^n \frac{d^n F(\nu)}{d\nu^n}\,$	This is the dual of 106
108	$(f * g)(t)\,$	$\sqrt{2\pi} F(\omega) G(\omega)\,$	$F(\nu) G(\nu)\,$	$f * g\,$ denotes the convolution of $f\,$ and $g\,$ — this rule is the convolution theorem
109	$f(t) g(t)\,$	$(F * G)(\omega) \over \sqrt{2\pi}\,$	$(F * G)(\nu)\,$	This is the dual of 108
110	$f(t)\,$ is purely real, and an even function	$F(\omega)\,$ and $F(\nu)\,$ are purely real, and even functions
111	$f(t)\,$ is purely real, and an odd function	$F(\omega)\,$ and $F(\nu)\,$ are purely imaginary, and odd functions

Square-integrable functions

	Signal	Fourier transform unitary, angular frequency	Fourier transform unitary, ordinary frequency	Remarks
	$f(t) \,$	$F(\omega)\!\ \stackrel{\operatorname{def}}{=}\ \!$ $\frac{1}{\sqrt{2 \pi}} \int_{-\infty}^{\infty}\!\!f(t) e^{-i \omega t} \operatorname{d}t \,$	$F(\nu)\!\ \stackrel{\operatorname{def}}{=}\$ $\int_{-\infty}^{\infty}\!\!f(t) e^{-i 2\pi \nu t} \operatorname{d}t \,$
201	$\operatorname{rect}(a t) \,$	$\frac{1}{\sqrt{2 \pi a^2}}\cdot \operatorname{sinc}\left(\frac{\omega}{2\pi a}\right)$	$\frac{1}{\|a\|}\cdot \operatorname{sinc}\left(\frac{\nu}{a}\right)$	The rectangular pulse and the normalized sinc function, here defined as $\operatorname{sinc}(x) = \frac{\sin(\pi x)}{\pi x}$
202	$\operatorname{sinc}(a t)\,$	$\frac{1}{\sqrt{2\pi a^2}}\cdot \operatorname{rect}\left(\frac{\omega}{2 \pi a}\right)$	$\frac{1}{\|a\|}\cdot \operatorname{rect}\left(\frac{\nu}{a} \right)\,$	Dual of rule 201. In Mathematics and in particular Functional analysis, convolution is a mathematical operation on two functions f and In Mathematics, the convolution theorem states that under suitableconditions the Fourier transform of a Convolution is the Pointwise product In Mathematics, even functions and odd functions are functions which satisfy particular Symmetry relations with respect to taking Additive In Mathematics, even functions and odd functions are functions which satisfy particular Symmetry relations with respect to taking Additive In Mathematics, even functions and odd functions are functions which satisfy particular Symmetry relations with respect to taking Additive Geometric interpretation Geometrically imaginary numbers are found on the vertical axis of the complex number plane In Mathematics, even functions and odd functions are functions which satisfy particular Symmetry relations with respect to taking Additive The rectangular function (also known as the rectangle function, rect function, unit pulse, or the normalized Boxcar function) In Mathematics, the sinc function, denoted by \scriptstyle\mathrm{sinc}(x\ and sometimes as \scriptstyle\mathrm{Sa}(x\ has two definitions sometimes The rectangular function is an ideal low-pass filter, and the sinc function is the non-causal impulse response of such a filter. The rectangular function (also known as the rectangle function, rect function, unit pulse, or the normalized Boxcar function) A low-pass filter is a filter that passes low- Frequency signals but Attenuates (reduces the Amplitude of signals with frequencies In Mathematics, the sinc function, denoted by \scriptstyle\mathrm{sinc}(x\ and sometimes as \scriptstyle\mathrm{Sa}(x\ has two definitions sometimes An anticausal system is a Hypothetical System with outputs and internal states that depend solely on future input values
203	$\operatorname{sinc}^2 (a t) \,$	$\frac{1}{\sqrt{2\pi a^2}}\cdot \operatorname{tri} \left( \frac{\omega}{2\pi a} \right)$	$\frac{1}{\|a\|}\cdot \operatorname{tri} \left( \frac{\nu}{a} \right)$	tri is the triangular function
204	$\operatorname{tri} (a t) \,$	$\frac{1}{\sqrt{2\pi a^2}} \cdot \operatorname{sinc}^2 \left( \frac{\omega}{2\pi a} \right)$	$\frac{1}{\|a\|}\cdot \operatorname{sinc}^2 \left( \frac{\nu}{a} \right) \,$	Dual of rule 203. The triangular function (also known as the triangle function, hat function, or tent function) is defined either as':
205	$e^{- a t} u(t) \,$	$\frac{1}{\sqrt{2 \pi} (a + i \omega)}$	$\frac{1}{a + i 2 \pi \nu}$	$u (t)$ is the Heaviside unit step function and $a > 0$ . The Heaviside step function, H, also called the unit step function, is a discontinuous function whose value is zero for negative
206	$e^{-\alpha t^2}\,$	$\frac{1}{\sqrt{2 \alpha}}\cdot e^{-\frac{\omega^2}{4 \alpha}}$	$\sqrt{\frac{\pi}{\alpha}}\cdot e^{-\frac{(\pi \nu)^2}{\alpha}}$	Shows that the Gaussian function $exp( - α t 2)$ is its own Fourier transform. In Mathematics, a Gaussian function (named after Carl Friedrich Gauss) is a function of the form f(x = a e^{- { (x-b^2 \over 2 For this to be integrable we must have $\operatorname{Re}(\alpha)>0$ .
210	$\operatorname{e}^{-a\|t\|} \,$	$\sqrt{\frac{2}{\pi}} \cdot \frac{a}{a^2 + \omega^2}$	$\frac{2 a}{a^2 + 4 \pi^2 f^2}$	a>0
211	$\frac{1}{\sqrt{\|t\|}} \,$	$\frac{1}{\sqrt{\|\omega\|}}$	$\frac{1}{\sqrt{\|f\|}}$	the transform is the function itself
212	$J_0 (t)\,$	$\sqrt{\frac{2}{\pi}} \cdot \frac{\operatorname{rect} \left( \frac{\omega}{2} \right)}{\sqrt{1 - \omega^2}}$	$\frac{2\cdot \operatorname{rect} (\pi\nu)}{\sqrt{1 - 4 \pi^2 \nu^2}}$	J₀(t) is the Bessel function of first kind of order 0
213	$J_n (t) \,$	$\sqrt{\frac{2}{\pi}} \frac{ (-i)^n T_n (\omega) \operatorname{rect} \left( \frac{\omega}{2} \right)}{\sqrt{1 - \omega^2}}$	$\frac{2 (-i)^n T_n (2 \pi \nu) \operatorname{rect} (\pi \nu)}{\sqrt{1 - 4 \pi^2 \nu^2}}$	it's the generalization of the previous transform; T_n (t) is the Chebyshev polynomial of the first kind. In Mathematics, Bessel functions, first defined by the Mathematician Daniel Bernoulli and generalized by Friedrich Bessel, are Canonical In Mathematics the Chebyshev polynomials, named after Pafnuty Chebyshev, are a sequence of Orthogonal polynomials which are related to
214	$\frac{J_n (t)}{t} \,$	$\sqrt{\frac{2}{\pi}} \frac{i}{n} (-i)^n \cdot U_{n-1} (\omega)\,$ $\cdot \ \sqrt{1 - \omega^2} \operatorname{rect} \left( \frac{\omega}{2} \right)$	$\frac{2 i}{n} (-i)^n \cdot U_{n-1} (2 \pi \nu)\,$ $\cdot \ \sqrt{1 - 4 \pi^2 \nu^2} \operatorname{rect} ( \pi \nu )$	U_n (t) is the Chebyshev polynomial of the second kind
215	$\operatorname{sech}(a t) \,$	$\frac{1}{a}\sqrt{\frac{\pi}{2}}\operatorname{sech} \left( \frac{\pi}{2 a} \omega \right)$	$\frac{\pi}{a} \operatorname{sech} \left( \frac{\pi^2}{ a} \nu \right)$	Hyperbolic secant is its own Fourier transform

Distributions

	Signal	Fourier transform unitary, angular frequency	Fourier transform unitary, ordinary frequency	Remarks
	$f(t) \,$	$F(\omega)\!\ \stackrel{\mathrm{def}}{=}\ \!$ $\frac{1}{\sqrt{2 \pi}} \int_{-\infty}^{\infty}\!\!f(t) e^{-i \omega t}\, dt$	$F(\nu)\!\ \stackrel{\mathrm{def}}{=}\$ $\int_{-\infty}^{\infty}\!\!g(t) e^{-i 2\pi \nu t}\, dt$
301	$1\,$	$\sqrt{2\pi}\cdot \delta(\omega)\,$	$\delta(\nu)\,$	$\displaystyle\delta(\omega)$ denotes the Dirac delta distribution. In Mathematics the Chebyshev polynomials, named after Pafnuty Chebyshev, are a sequence of Orthogonal polynomials which are related to In Mathematics, the hyperbolic functions are analogs of the ordinary trigonometric, or circular functions The Dirac delta or Dirac's delta is a mathematical construct introduced by the British theoretical physicist Paul Dirac.
302	$\delta(t)\,$	$\frac{1}{\sqrt{2\pi}}\,$	$1\,$	Dual of rule 301.
303	$e^{i a t}\,$	$\sqrt{2 \pi}\cdot \delta(\omega - a)\,$	$\delta\left(\nu - \frac{a}{2\pi}\right)\,$	This follows from 103 and 301.
304	$\cos (a t)\,$	$\sqrt{2 \pi} \frac{\delta(\omega\!-\!a)\!+\!\delta(\omega\!+\!a)}{2}\,$	$\frac{\delta(\nu\!-\!\begin{matrix}\frac{a}{2\pi}\end{matrix})\!+\!\delta(\nu\!+\!\begin{matrix}\frac{a}{2\pi}\end{matrix})}{2}\,$	Follows from rules 101 and 303 using Euler's formula: $\displaystyle\cos(a t) = (e^{i a t} + e^{-i a t})/2.$
305	$\sin( at)\,$	$i \sqrt{2 \pi}\frac{\delta(\omega\!+\!a)\!-\!\delta(\omega\!-\!a)}{2}\,$	$i \frac{\delta(\nu\!+\!\begin{matrix}\frac{a}{2\pi}\end{matrix})\!-\!\delta(\nu\!-\!\begin{matrix}\frac{a}{2\pi}\end{matrix})}{2}\,$	Also from 101 and 303 using $\displaystyle\sin(a t) = (e^{i a t} - e^{-i a t})/(2i).$
306	$\cos ( a t^2 ) \,$	$\frac{1}{\sqrt{2 a}} \cos \left( \frac{\omega^2}{4 a} - \frac{\pi}{4} \right)$	$\sqrt{\frac{\pi}{a}} \cos \left( \frac{\pi^2 \nu^2}{a} - \frac{\pi}{4} \right)$
307	$\sin ( a t^2 ) \,$	$\frac{-1}{\sqrt{2 a}} \sin \left( \frac{\omega^2}{4 a} - \frac{\pi}{4} \right)$	$- \sqrt{\frac{\pi}{a}} \sin \left( \frac{\pi^2 \nu^2}{a} - \frac{\pi}{4} \right)$
	$e^{iat^2} = \left. e^{-\alpha t^2}\right\|_{\alpha = -i a} \,$	$\frac{1}{\sqrt{2 a}} \cdot e^{-i \left(\frac{\omega^2}{4 a} -\frac{\pi}{4}\right)}$	$\sqrt{\frac{\pi}{a}} \cdot e^{-i \left(\frac{\pi^2 \nu^2}{a} -\frac{\pi}{4}\right)}$	common in optics
308	$t^n\,$	$i^n \sqrt{2\pi} \delta^{(n)} (\omega)\,$	$\left(\frac{i}{2\pi}\right)^n \delta^{(n)} (\nu)\,$	Here, $\displaystyle n$ is a natural number. This article is about Euler's formula in Complex analysis. For Euler's formula in algebraic topology and polyhedral combinatorics see Euler characteristic In Mathematics, a natural number (also called counting number) can mean either an element of the set (the positive Integers or an $\displaystyle\delta^{(n)}(\omega)$ is the $\displaystyle n$ -th distribution derivative of the Dirac delta. This rule follows from rules 107 and 302. Combining this rule with 101, we can transform all polynomials. In Mathematics, a polynomial is an expression constructed from Variables (also known as indeterminates and Constants using the operations
309	$\frac{1}{t}\,$	$-i\sqrt{\frac{\pi}{2}}\sgn(\omega)\,$	$-i\pi\cdot \sgn(\nu)\,$	Here $\displaystyle\sgn(\omega)$ is the sign function; note that this is consistent with rules 107 and 302.
310	$\frac{1}{t^n}\,$	$-i \begin{matrix} \sqrt{\frac{\pi}{2}}\cdot \frac{(-i\omega)^{n-1}}{(n-1)!}\end{matrix} \sgn(\omega)\,$	$-i\pi \begin{matrix} \frac{(-i 2\pi \nu)^{n-1}}{(n-1)!}\end{matrix} \sgn(\nu)\,$	Generalization of rule 309.
311	$\sgn(t)\,$	$\sqrt{\frac{2}{\pi}}\cdot \frac{1}{i\ \omega }\,$	$\frac{1}{i\pi \nu}\,$	The dual of rule 309.
312	$u(t) \,$	$\sqrt{\frac{\pi}{2}} \left( \frac{1}{i \pi \omega} + \delta(\omega)\right)\,$	$\frac{1}{2}\left(\frac{1}{i \pi \nu} + \delta(\nu)\right)\,$	Here $u (t)$ is the Heaviside unit step function; this follows from rules 101 and 311. The Heaviside step function, H, also called the unit step function, is a discontinuous function whose value is zero for negative
313	$\sum_{n=-\infty}^{\infty} \delta (t - n T) \,$	$\begin{matrix} \frac{\sqrt{2\pi }}{T}\end{matrix} \sum_{k=-\infty}^{\infty} \delta \left( \omega -k \begin{matrix} \frac{2\pi }{T}\end{matrix} \right)\,$	$\frac{1}{T} \sum_{k=-\infty}^{\infty} \delta \left( \nu -\frac{k }{T}\right) \,$	The Dirac comb — helpful for explaining or understanding the transition from continuous to discrete time. In Mathematics, a Dirac comb (also known as an impulse train and sampling function in Electrical engineering) is a periodic

Two-dimensional functions

Signal	Fourier transform unitary, angular frequency	Fourier transform unitary, ordinary frequency	Remarks
$exp( - π(a 2 x 2 + b 2 y 2))$		$\frac{1}{\|ab\|} \exp\left[-\pi\left(\frac{f^2_x}{a^2} + \frac{f^2_y}{b^2}\right)\right]$	Both functions are Gaussian bumps, which may not have unit volume.
$\mathrm{circ}(\sqrt{x^2+y^2})$		$\frac{J_1\left[2 \pi f_r\right]}{f_r}$	The circle has unit radius if we think of circ(t) as step function u(1-t); The Airy distribution is expressed using J₁ (the order 1 Bessel function of the first kind); f_r is the magnitude of the frequency vector {f_x,f_y}.

Three-dimensional functions

Signal	Fourier transform unitary, angular frequency	Fourier transform unitary, ordinary frequency	Remarks
$\mathrm{circ}(\sqrt{x^2+y^2+z^2})$		$4 \pi \frac{\sin[2 \pi f_r] - 2 \pi f_r \cos[2 \pi f_r]}{(2 \pi f_r)^3}$	The sphere has unit radius; f_r is the magnitude of the frequency vector {f_x,f_y,f_z}.

About notation

The Fourier transform is a mapping on a function space. This mapping is here denoted $\mathcal{F}$ and $\mathcal{F}\{s\}$ is used to denote the Fourier transform of the function s. This mapping is linear, which means that $\mathcal{F}$ 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 signal s) can be used to write $\mathcal{F} s$ instead of $\mathcal{F}\{s\}$ . 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 $ω$ for its variable, and this is denoted either as $\mathcal{F}\{s\}(\omega)$ or as $(\mathcal{F} s)(\omega)$ . Notice that in the former case, it is implicitly understood that $\mathcal{F}$ is applied first to s and then the resulting function is evaluated at $ω$ , not the other way around.

In mathematics and various applied sciences it is often necessary to distinguish between a function s and the value of s when its variable equals t, denoted s(t). This means that a notation like $\mathcal{F}\{s(t)\}$ formally can be interpreted as the Fourier transform of the values of s at t, which must be considered as an ill-formed expression since it describes the Fourier transform of a function value rather than of a function. 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, $\mathcal{F}\{ \mathrm{rect}(t) \} = \mathrm{sinc}(\omega)$ is sometimes used to express that the Fourier transform of a rectangular function is a sinc function, or $\mathcal{F}\{s(t+t_{0})\} = \mathcal{F}\{s(t)\} e^{i \omega t_{0}}$ 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 t, not of $t 0$ . If possible, this informal usage of the $\mathcal{F}$ operator should be avoided, in particular when it is not perfectly clear which variable the function to be transformed depends on.

Notes

^ F(ν) and F(ω) represent different, but related, functions, as shown in the table labeled Summary of popular forms of the Fourier transform.
^ $u(t)\,$ is the Heaviside_step_function, and $u(t) \ \ \stackrel{\mathcal{F}}{\Longleftrightarrow}\ \ \sqrt{\frac{\pi}{2}} \left( \frac{1}{i \pi \omega} + \delta(\omega)\right)\,$
^ ^a ^b Location, momentum and particle do not have any physical meaning here; they are simply convenient monikers chosen with analogy to the interpretation used in the Heisenberg Uncertainty Principle. The Heaviside step function, H, also called the unit step function, is a discontinuous function whose value is zero for negative

References

Fourier Transforms from eFunda - includes tables
Dym & McKean, Fourier Series and Integrals. In Mathematics, a Fourier series decomposes a periodic function into a sum of simple oscillating functions In Mathematics, the Laplace transform is one of the best known and most widely used Integral transforms It is commonly used to produce an easily soluble algebraic In Mathematics, the discrete Fourier transform (DFT is one of the specific forms of Fourier analysis. A DFT matrix is an expression of a Discrete Fourier transform (DFT as a matrix multiplication In Mathematics, the discrete-time Fourier transform (DTFT is one of the specific forms of Fourier analysis. In Mathematics, in the area of Harmonic analysis, the fractional Fourier transform ( FRFT) is a Linear transformation generalizing the Fourier Paraxial optical systems implemented entirely with Thin lenses and propagation through free space and/or graded index (GRIN media are Quadratic Phase Systems (QPS Fourier sine transform In Mathematics, the Fourier sine transform is a special case of the Continuous Fourier transform, arising naturally when attempting The short-time Fourier transform ( STFT) or alternatively short-term Fourier transform, is a Fourier-related transform used to determine the sinusoidal Analog signal processing is any Signal processing conducted on Analog signals by analog means In Mathematics a transform is an Operator applied to a function so that under the transform certain operations are simplified (For readers with a background in mathematical analysis. Analysis has its beginnings in the rigorous formulation of Calculus. )
K. Yosida, Functional Analysis, Springer-Verlag, 1968. ISBN 3-540-58654-7
L. Hörmander, Linear Partial Differential Operators, Springer-Verlag, 1976.
A. D. Polyanin and A. V. Manzhirov, Handbook of Integral Equations, CRC Press, Boca Raton, 1998. ISBN 0-8493-2876-4
R. G. Wilson, "Fourier Series and Optical Transform Techniques in Contemporary Optics", Wiley, 1995. ISBN-10: 0471303577
R. N. Bracewell, The Fourier Transform and Its Applications, 3rd ed. , Boston, McGraw Hill, 2000.

External links

Fourier Series Applet (Tip: drag magnitude or phase dots up or down to change the wave form).
Tables of Integral Transforms at EqWorld: The World of Mathematical Equations.
Eric W. Weisstein, Fourier Transform at MathWorld. Eric W Weisstein (born March 18, 1969, in Bloomington Indiana) is an Encyclopedist who created and maintains MathWorld MathWorld is an online Mathematics reference work created and largely written by Eric W
Fourier Transform Module by John H. Mathews

Dictionary

-noun

(analysis) a process that expresses a function as a sum or integral of sinusoidal functions multiplied by coefficients; it has many scientific and industrial applications, especially in signal processing

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