# Fourier analysis

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Fourier analysis, named after Joseph Fourier's introduction of the Fourier series, is the decomposition of a function in terms of a sum of sinusoidal basis functions (vs. their frequencies) that can be recombined to obtain the original function. That process of recombining the sinusoidal basis functions is also called Fourier synthesis (in which case Fourier analysis refers specifically to the decomposition process).

The linear operation that transforms a function into the coefficients of the sinusoidal basis functions is called a Fourier transform in general. However, the transform is usually given a more specific name depending upon the domain and other properties of the function being transformed, as described below. Moreover, the original concept of Fourier analysis has been extended over time to apply to more and more abstract and general situations, and the general field is often known as harmonic analysis. See also: List of Fourier-related transforms.

## Applications

Fourier analysis has many scientific applications — in physics, number theory, combinatorics, signal processing, probability theory, statistics, option pricing, cryptography, acoustics, oceanography, optics and diffraction, geometry, and other areas. (In signal processing and related fields, Fourier analysis is typically thought of as decomposing a signal into its component frequencies and their amplitudes.) This wide applicability stems from many useful properties of the transforms:

## Variants of Fourier analysis

There exists a number of transforms that can be seen as special cases or generalizations of each other. The most popular are summarized in the following table:

Name Time domain Frequency domain Formula
(Continuous) Fourier transform Continuous, Aperiodic Continuous, Aperiodic $s \left( t \right) = \int\limits_{-\infty}^\infty S\left( \omega\right) e^{i\omega t}\,d\omega$
Fourier series Continuous, Periodic Discrete, Aperiodic $s(t) = \sum_{k=-\infty}^{\infty} S_k \cdot e^{i \omega_k t}$
Discrete-time Fourier transform Discrete, Aperiodic Continuous, Periodic $X(\omega) = \sum_{n=-\infty}^{\infty} x[n] \,e^{-i \omega n}$
Discrete Fourier transform Discrete, Periodic Discrete, Periodic $X[k] = \sum_{n=0}^{N-1} x[n] \,e^{-i 2 \pi \frac{k}{N} n}$
Generalization arbitrary locally compact abelian topological group arbitrary locally compact abelian topological group See harmonic analysis

The table shows that

• discreteness in one domain implies periodicity in the opposite transformed domain and the converse is true.
• continuity in one domain implies aperiodicity in the transformed domain and the converse is true.
• pure reality in one domain (i.e. imaginary part is zero everywhere) implies conjugate symmetry in the transformed domain and the converse is true.

There are various ways to include normalization factors; For simplicity, this table uses the definitions without them. Also, this table does not contain multi-dimensional versions.

### Fourier transform

Main article: Fourier transform

Most often, the unqualified term Fourier transform refers to the transform of functions of a continuous real argument, representing any square-integrable function $s \left( t \right)$ as a linear combination of complex exponentials with frequencies $\omega\,$:

$s \left( t \right) = \frac{1}{\sqrt{2\pi}} \int_{-\infty}^{\infty} S\left( \omega\right) e^{i\omega t}\,d\omega.$

The quantity, $S(\omega)\,$, provides both the amplitude and initial phase (as a complex number) of basis function: $e^{i\omega t}\,$.

The function, $S(\omega)\,$, is the Fourier transform of $s(t)\,$, denoted by the operator $\mathcal{F}\,$:

$S(\omega) = \left(\mathcal{F}s\right)(\omega)= \mathcal{F}\{s\}(\omega)\,$

And the inverse transform (shown above) is written:

$s(t) = \left(\mathcal{F}^{-1}S\right)(t)= \mathcal{F}^{-1}\{S\}(t)\,$

Together the two functions are referred to as a transform pair. See continuous Fourier transform for more information, including:

• formula for the forward transform
• tabulated transforms of specific functions
• discussion of the transform properties
• various conventions for amplitude normalization and frequency scaling/units

#### Multi-dimensional version

The formulation for the Fourier transform given above applies in one dimension. The Fourier transform, however, can be expanded to arbitrary dimension $n$. The more generalised version of this transform in dimension $n$, notated by $\mathcal{F}_n$ is:

$s \left( \mathbf{x} \right) = \left( \mathcal{F}^{-1}_n S \right) \left( \mathbf{x} \right) = \frac{1}{(2\pi)^{n/2}} \int S \left( \boldsymbol{\omega} \right) e^{i \left\langle \boldsymbol{\omega} ,\mathbf{x} \right\rangle} \, d \boldsymbol{\omega},$

where $\mathbf{x}$ and $\boldsymbol{\omega}$ are $n$-dimensional vectors, $\left\langle \boldsymbol{\omega} ,\mathbf{x} \right\rangle$ is the inner product of these two vectors, and the integration is performed over all $n$ dimensions.

### Fourier series

Main article: Fourier series

The continuous transform is itself actually a generalization of an earlier concept, a Fourier series, which was specific to periodic (or finite-domain) functions $s \left( t \right)$ (with period $\tau \,$), and represents these functions as a series of sinusoids:

$s(t) = \sum_{k=-\infty}^{\infty} S_k \cdot e^{i \omega_k t}, \,$

where $\omega_k = 2\pi k / \tau \,$, and $S_k \,$ is a (complex) amplitude.

For real-valued $s(t)\,$, an equivalent variation is:

$s(t) = \frac{1}{2}a_0 + \sum_{k=1}^\infty\left[a_k\cdot \cos(\omega_k t)+b_k\cdot \sin(\omega_k t)\right],$

where $a_k = 2\cdot \operatorname{Re}\{S_k\} \,$   and   $b_k = -2\cdot \operatorname{Im}\{S_k\} \,$.

### Discrete-time Fourier transform

Main article: Discrete-time Fourier transform

For use on computers, both for scientific computation and digital signal processing, one must have functions, x[n], that are defined for discrete instead of continuous domains, again finite or periodic. A useful "discrete-time" function can be obtained by sampling a "continuous-time" function, x(t). And similar to the continuous Fourier transform, the function can be represented as a sum of complex sinusoids:

$x[n] = \frac{1}{2 \pi}\int_{-\pi}^{\pi} X(\omega)\cdot e^{i \omega n} \, d \omega.$

But in this case, the limits of integration need only span one period of the periodic function, $X(\omega ) \,$, which is derived from the samples by the discrete-time Fourier transform (DTFT):

$X(\omega) = \sum_{n=-\infty}^{\infty} x[n] \,e^{-i \omega n}.\,$

### Discrete Fourier transform

Main article: Discrete Fourier transform

The DTFT is defined on a continuous domain. So despite its periodicity, it still cannot be numerically evaluated for every unique frequency. But a very useful approximation can be made by evaluating it at regularly-spaced intervals, with arbitrarily small spacing. Due to periodicity, the number of unique coefficients (N) to be evaluated is always finite, leading to this simplification:

$X[k] = X\left(\frac{2 \pi }{N} k\right)= \sum_{n=-\infty}^{\infty} x[n] \,e^{-i 2 \pi \frac{k}{N} n},$     for $k = 0, 1, \dots, N-1. \,$

When the portion of x[n] between n=0 and n=N-1 is a good (or exact) representation of the entire x[n] sequence, it is useful to compute:

$X[k] = \sum_{n=0}^{N-1} x[n] \,e^{-i 2 \pi \frac{k}{N} n},$

which is called discrete Fourier transform (DFT). Commonly the length of the x[n] sequence is finite, and a larger value of N is chosen. Effectively, the x[n] sequence is padded with zero-valued samples, referred to as zero padding.

The inverse DFT represents x[n] as the sum of complex sinusoids:

$x[n] = \frac{1}{N} \sum_{k=0}^{N-1} X[k] e^{i 2 \pi \frac{k}{N} n}, \quad \quad n = 0, 1, \dots, N-1. \,$

The table below will note that this actually produces a periodic x[n]. If the original sequence was not periodic to begin with, this phenomenon is the time-domain consequence of approximating the continuous-domain DTFT function with the discrete-domain DFT function.

The DFT can be computed using a fast Fourier transform (FFT) algorithm, which makes it a practical and important transformation on computers.

### Fourier transforms on arbitrary locally compact abelian topological groups

The Fourier variants can also be generalized to Fourier transforms on arbitrary locally compact abelian topological groups, which are studied in harmonic analysis; there, the Fourier transform takes functions on a group to functions on the dual group. This treatment also allows a general formulation of the convolution theorem, which relates Fourier transforms and convolutions. See also the Pontryagin duality for the generalized underpinnings of the Fourier transform.

### Time-frequency transforms

Time-frequency transforms such as the short-time Fourier transform, wavelet transforms, chirplet transforms, and the fractional Fourier transform try to obtain frequency information from a signal as a function of time (or whatever the independent variable is), although the ability to simultaneously resolve frequency and time is limited by an (mathematical) uncertainty principle.

## Interpretation in terms of time and frequency

In terms of signal processing, the transform takes a time series representation of a signal function and maps it into a frequency spectrum, where ω is angular frequency. That is, it takes a function in the time domain into the frequency domain; it is a decomposition of a function into harmonics of different frequencies.

When the function f is a function of time and represents a physical signal, the transform has a standard interpretation as the frequency spectrum of the signal. The magnitude of the resulting complex-valued function F at frequency ω represents the amplitude of a frequency component whose initial phase is given by: arctan (imaginary part/real part).

However, it is important to realize that Fourier transforms are not limited to functions of time, and temporal frequencies. They can equally be applied to analyze spatial frequencies, and indeed for nearly any function domain.

## Applications in signal processing

In signal processing, Fourier transformation can isolate individual components of a complex signal, concentrating them for easier detection and/or removal. A large family of signal processing techniques consist of Fourier-transforming a signal (such as a clip of audio or an image), manipulating the Fourier-transformed data in a simple way, and reversing the transformation. Some examples include:

Fourier transformation is also useful as a compact representation of a signal. For example, JPEG compression uses Fourier transformation of small square pieces of a digital image. The Fourier components of each square are rounded to lower arithmetic precision, and weak components are eliminated entirely, so that the remaining components can be stored very compactly. In image reconstruction, each Fourier-transformed image square is reassembled from the preserved approximate components, and then inverse-transformed to produce an approximation of the original image.

## 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 $\omega$ 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 $\omega$, not the other way around.

In mathematics and various applied sciences it is often necessary 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 transform depends on.

## References

• Edward W. Kamen, Bonnie S. Heck, "Fundamentals of Signals and Systems Using the Web and Matlab", ISBN 0-13-017293-6
• E. M. Stein, G. Weiss, "Introduction to Fourier Analysis on Euclidean Spaces", Princeton University Press, 1971. ISBN 0-691-08078-X
• A. D. Polyanin and A. V. Manzhirov, Handbook of Integral Equations, CRC Press, Boca Raton, 1998. ISBN 0-8493-2876-4
• Smith, Steven W. The Scientist and Engineer's Guide to Digital Signal Processing, 2nd edition. San Diego: California Technical Publishing, 1999. ISBN 0-9660176-3-3. (also available online: [1])