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# Fourier analysis

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### Fourier analysis

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

In mathematics, Fourier analysis (English pronunciation: ) is the study of the way general functions may be represented or approximated by sums of simpler trigonometric functions. Fourier analysis grew from the study of Fourier series, and is named after Joseph Fourier, who showed that representing a function as a sum of trigonometric functions greatly simplifies the study of heat transfer.

Today, the subject of Fourier analysis encompasses a vast spectrum of mathematics. In the sciences and engineering, the process of decomposing a function into oscillatory components is often called Fourier analysis, while the operation of rebuilding the function from these pieces is known as Fourier synthesis. For example, determining what component frequencies are present in a musical note would involve computing the Fourier transform of a sampled musical note. One could then re-synthesize the same sound by including the frequency components as revealed in the Fourier analysis. In mathematics, the term Fourier analysis often refers to the study of both operations.

The decomposition process itself is called a Fourier transformation. Its output, the Fourier transform, is often given a more specific name, which depends upon the domain and other properties of the function being transformed. 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. Each transform used for analysis (see list of Fourier-related transforms) has a corresponding inverse transform that can be used for synthesis.

## Contents

• Applications 1
• Applications in signal processing 1.1
• Variants of Fourier analysis 2
• (Continuous) Fourier transform 2.1
• Fourier series 2.2
• Discrete-time Fourier transform (DTFT) 2.3
• Discrete Fourier transform (DFT) 2.4
• Summary 2.5
• Fourier transforms on arbitrary locally compact abelian topological groups 2.6
• Time–frequency transforms 2.7
• History 3
• Interpretation in terms of time and frequency 4
• Notes 5
• Citations 7
• References 8

## Applications

Fourier analysis has many scientific applications – in physics, partial differential equations, number theory, combinatorics, signal processing, imaging, probability theory, statistics, option pricing, cryptography, numerical analysis, acoustics, oceanography, sonar, optics, diffraction, geometry, protein structure analysis, and other areas.

This wide applicability stems from many useful properties of the transforms:

Fourier transformation is also useful as a compact representation of a signal. For example, JPEG compression uses a variant of the Fourier transformation (discrete cosine transform) 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 image square is reassembled from the preserved approximate Fourier-transformed components, which are then inverse-transformed to produce an approximation of the original image.

### Applications in signal processing

When processing signals, such as audio, radio waves, light waves, seismic waves, and even images, Fourier analysis can isolate individual components of a compound waveform, concentrating them for easier detection and/or removal. A large family of signal processing techniques consist of Fourier-transforming a signal, manipulating the Fourier-transformed data in a simple way, and reversing the transformation. (Rabiner and Gold, 1975)

Some examples include:

## Variants of Fourier analysis

A Fourier transform and 3 variations caused by periodic sampling (at interval T) and/or periodic summation (at interval P) of the underlying time-domain function. The relative computational ease of the DFT sequence and the insight it gives into S(f) make it a popular analysis tool.

### (Continuous) Fourier transform

Most often, the unqualified term Fourier transform refers to the transform of functions of a continuous real argument, and it produces a continuous function of frequency, known as a frequency distribution. One function is transformed into another, and the operation is reversible. When the domain of the input (initial) function is time (t), and the domain of the output (final) function is ordinary frequency, the transform of function s(t) at frequency ƒ is given by the complex number:

S(f) = \int_{-\infty}^{\infty} s(t) \cdot e^{- i 2\pi f t} dt.

Evaluating this quantity for all values of ƒ produces the frequency-domain function. Then s(t) can be represented as a recombination of complex exponentials of all possible frequencies:

s(t) = \int_{-\infty}^{\infty} S(f) \cdot e^{i 2\pi f t} df,

which is the inverse transform formula. The complex number, S(ƒ), conveys both amplitude and phase of frequency ƒ.

• conventions for amplitude normalization and frequency scaling/units
• transform properties
• tabulated transforms of specific functions
• an extension/generalization for functions of multiple dimensions, such as images.

### Fourier series

The Fourier transform of a periodic function, sP(t), with period P, becomes a Dirac comb function, modulated by a sequence of complex coefficients:

S[k] = \frac{1}{P}\int_{P} s_P(t)\cdot e^{-i 2\pi \frac{k}{P} t}\, dt     for all integer values of k,

and where \scriptstyle \int_P  is the integral over any interval of length P.

The inverse transform, known as Fourier series, is a representation of sP(t) in terms of a summation of a potentially infinite number of harmonically related sinusoids or complex exponential functions, each with an amplitude and phase specified by one of the coefficients:

When sP(t), is expressed as a periodic summation of another function, s(t):

s_P(t)\ \stackrel{\text{def}}{=}\ \sum_{k=-\infty}^\infty s(t-kP),

the coefficients are proportional to samples of S(ƒ) at discrete intervals of 1/P:

S[k] =\frac{1}{P}\cdot S\left(\frac{k}{P}\right).\,[note 1]

A sufficient condition for recovering s(t) (and therefore S(ƒ)) from just these samples is that the non-zero portion of s(t) be confined to a known interval of duration P, which is the frequency domain dual of the Nyquist–Shannon sampling theorem.

### Discrete-time Fourier transform (DTFT)

The DTFT is the mathematical dual of the time-domain Fourier series. Thus, a convergent periodic summation in the frequency domain can be represented by a Fourier series, whose coefficients are samples of a related continuous time function:

S_{1/T}(f)\ \stackrel{\text{def}}{=}\ \underbrace{\sum_{k=-\infty}^{\infty} S\left(f - \frac{k}{T}\right) \equiv \overbrace{\sum_{n=-\infty}^{\infty} s[n] \cdot e^{-i 2\pi f n T}}^{\text{Fourier series (DTFT)}}}_{\text{Poisson summation formula}} = \mathcal{F} \left \{ \sum_{n=-\infty}^{\infty} s[n]\ \delta(t-nT)\right \},\,

which is known as the DTFT. Thus the DTFT of the s[n] sequence is also the Fourier transform of the modulated Dirac comb function.[note 2]

The Fourier series coefficients (and inverse transform), are defined by:

s[n]\ \stackrel{\mathrm{def}}{=} \ T \int_{1/T} S_{1/T}(f)\cdot e^{i 2\pi f nT} df = T \underbrace{\int_{-\infty}^{\infty} S(f)\cdot e^{i 2\pi f nT} df}_{\stackrel{\mathrm{def}}{=} \ s(nT)}\,

Parameter T corresponds to the sampling interval, and this Fourier series can now be recognized as a form of the Poisson summation formula. Thus we have the important result that when a discrete data sequence, s[n], is proportional to samples of an underlying continuous function, s(t), one can observe a periodic summation of the continuous Fourier transform, S(ƒ). That is a cornerstone in the foundation of digital signal processing. Furthermore, under certain idealized conditions one can theoretically recover S(ƒ) and s(t) exactly. A sufficient condition for perfect recovery is that the non-zero portion of S(ƒ) be confined to a known frequency interval of width 1/T. When that interval is [-0.5/T, 0.5/T], the applicable reconstruction formula is the Whittaker–Shannon interpolation formula.

Another reason to be interested in S1/T(ƒ) is that it often provides insight into the amount of aliasing caused by the sampling process.

Applications of the DTFT are not limited to sampled functions. See Discrete-time Fourier transform for more information on this and other topics, including:

• normalized frequency units
• windowing (finite-length sequences)
• transform properties
• tabulated transforms of specific functions

### Discrete Fourier transform (DFT)

The DTFT of a periodic sequence, sN[n], with period N, becomes another Dirac comb function, modulated by the coefficients of a Fourier series.  And the integral formula for the coefficients simplifies to a summation (see DTFT/Periodic data):

S_N[k] =\frac{1}{NT} \underbrace{\sum_N s_N[n]\cdot e^{-i 2\pi \frac{k}{N} n}}_{S_k}\,,     where \scriptstyle \sum_N  is the sum over any n-sequence of length N.

The Sk sequence is what's customarily known as the DFT of sN.  It is also N-periodic, so it is never necessary to compute more than N coefficients.  In terms of Sk, the inverse transform is given by:

s_N[n] = \frac{1}{N} \sum_{N} S_k\cdot e^{i 2\pi \frac{n}{N}k},\,     where \scriptstyle \sum_N  is the sum over any k-sequence of length N.

When sN[n] is expressed as a periodic summation of another function:  s_N[n]\ \stackrel{\text{def}}{=}\ \sum_{k=-\infty}^{\infty} s[n-kN],   and   s[n]\ \stackrel{\text{def}}{=}\ T\cdot s(nT),\,

the coefficients are equivalent to samples of S1/T(ƒ) at discrete intervals of 1/P = 1/NT:   S_k = S_{1/T}(k/P).\,   (see DTFT/Sampling the DTFT)

Conversely, when one wants to compute an arbitrary number (N) of discrete samples of one cycle of a continuous DTFT,  S_{1/T}(f),\,  it can be done by computing the relatively simple DFT of sN[n], as defined above.  In most cases, N is chosen equal to the length of non-zero portion of s[n].  Increasing N, known as zero-padding or interpolation, results in more closely spaced samples of one cycle of  S1/T(ƒ).  Decreasing N, causes overlap (adding) in the time-domain (analogous to aliasing), which corresponds to decimation in the frequency domain. (see Sampling the DTFT) In most cases of practical interest, the s[n] sequence represents a longer sequence that was truncated by the application of a finite-length window function or FIR filter array.

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

• transform properties
• applications
• tabulated transforms of specific functions

### Summary

For periodic functions, both the Fourier transform and the DTFT comprise only a discrete set of frequency components (Fourier series), and the transforms diverge at those frequencies. One common practice (not discussed above) is to handle that divergence via Dirac delta and Dirac comb functions. But the same spectral information can be discerned from just one cycle of the periodic function, since all the other cycles are identical. Similarly, finite-duration functions can be represented as a Fourier series, with no actual loss of information except that the periodicity of the inverse transform is a mere artifact. We also note that none of the formulas here require the duration of s\, to be limited to the period, P or N.  But that is a common situation, in practice.

s(t)\, transforms (continuous-time)
Continuous frequency Discrete frequencies
Transform S(f)\ \stackrel{\text{def}}{=}\ \int_{-\infty}^{\infty} s(t)\ e^{-i 2\pi f t} dt\, \overbrace{\frac{1}{P}\cdot S\left(\frac{k}{P}\right)}^{S[k]}\ \stackrel{\text{def}}{=}\ \frac{1}{P} \int_{-\infty}^{\infty} s(t)\ e^{-i 2\pi \frac{k}{P} t}\,dt \equiv \frac{1}{P} \int_P s_P(t)\ e^{-i 2\pi \frac{k}{P} t} dt\,
Inverse s(t) = \int_{-\infty}^{\infty} S(f)\ e^{ i 2 \pi f t} df\, \underbrace{s_P(t) = \sum_{k=-\infty}^{\infty} S[k] \cdot e^{i 2\pi \frac{k}{P} t}}_{\text{Poisson summation formula (Fourier series)}}\,

In the table below, associating the \scriptstyle \frac{1}{T}  scale factor with function \scriptstyle S_{1/T}(f)  results in some notational simplification without loss of generality.

s(nT)\, transforms (discrete-time)
Continuous frequency Discrete frequencies
Transform \underbrace{\tfrac{1}{T}\ S_{1/T}(f)\ \stackrel{\text{def}}{=}\ \sum_{n=-\infty}^{\infty} s(nT)\cdot e^{-i 2\pi f nT}}_{\text{Poisson summation formula (DTFT)}}\,

\begin{align} \overbrace{\tfrac{1}{T}\ S_{1/T}\left(\frac{k}{NT}\right)}^{S_k}\ &\stackrel{\text{def}}{=}\ \sum_{n=-\infty}^{\infty} s(nT)\cdot e^{-i 2\pi \frac{kn}{N}}\\ &\equiv \underbrace{\sum_{N} s_P(nT)\cdot e^{-i 2\pi \frac{kn}{N}}}_{\text{DFT}}\, \end{align}

Inverse s(nT) = T \int_{1/T} \tfrac{1}{T}\ S_{1/T}(f)\cdot e^{i 2\pi f nT} df\,

\sum_{n=-\infty}^{\infty} s(nT)\cdot \delta(t-nT) = \underbrace{\int_{-\infty}^{\infty} \tfrac{1}{T}\ S_{1/T}(f)\cdot e^{i 2\pi f t}\,df}_{\text{inverse Fourier transform}}\,

\begin{align} s_P(nT) &= \overbrace{\tfrac{1}{N} \sum_{N} S_k\cdot e^{i 2\pi \frac{kn}{N}}}^{\text{inverse DFT}}\\ &= \tfrac{1}{P} \sum_{N} S_{1/T}\left(\frac{k}{P}\right)\cdot e^{i 2\pi \frac{kn}{N}}\, \end{align}

### 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

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.

As alternatives to the Fourier transform, in time–frequency analysis, one uses time–frequency transforms to represent signals in a form that has some time information and some frequency information – by the uncertainty principle, there is a trade-off between these. These can be generalizations of the Fourier transform, such as the short-time Fourier transform, the Gabor transform or fractional Fourier transform (FRFT), 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.

## History

A primitive form of harmonic series dates back to ancient Babylonian mathematics, where they were used to compute ephemerides (tables of astronomical positions).[1] The classical Greek concepts of deferent and epicycle in the Ptolemaic system of astronomy were related to Fourier series (see Deferent and epicycle: Mathematical formalism).

In modern times, variants of the discrete Fourier transform were used by Alexis Clairaut in 1754 to compute an orbit,[2] which has been described as the first formula for the DFT,[3] and in 1759 by Joseph Louis Lagrange, in computing the coefficients of a trigonometric series for a vibrating string.[4] Technically, Clairaut's work was a cosine-only series (a form of discrete cosine transform), while Lagrange's work was a sine-only series (a form of discrete sine transform); a true cosine+sine DFT was used by Gauss in 1805 for trigonometric interpolation of asteroid orbits.[5] Euler and Lagrange both discretized the vibrating string problem, using what would today be called samples.[4]

An early modern development toward Fourier analysis was the 1770 paper Réflexions sur la résolution algébrique des équations by Lagrange, which in the method of Lagrange resolvents used a complex Fourier decomposition to study the solution of a cubic:[6] Lagrange transformed the roots x_1,x_2,x_3 into the resolvents:

\begin{align} r_1 &= x_1 + x_2 + x_3\\ r_2 &= x_1 + \zeta x_2 + \zeta^2 x_3\\ r_3 &= x_1 + \zeta^2 x_2 + \zeta x_3 \end{align}

where ζ is a cubic root of unity, which is the DFT of order 3.

A number of authors, notably Jean le Rond d'Alembert, and Carl Friedrich Gauss used trigonometric series to study the heat equation, but the breakthrough development was the 1807 paper Mémoire sur la propagation de la chaleur dans les corps solides by Joseph Fourier, whose crucial insight was to model all functions by trigonometric series, introducing the Fourier series.

Historians are divided as to how much to credit Lagrange and others for the development of Fourier theory: Daniel Bernoulli and Leonhard Euler had introduced trigonometric representations of functions,[3] and Lagrange had given the Fourier series solution to the wave equation,[3] so Fourier's contribution was mainly the bold claim that an arbitrary function could be represented by a Fourier series.[3]

The subsequent development of the field is known as harmonic analysis, and is also an early instance of representation theory.

The first fast Fourier transform (FFT) algorithm for the DFT was discovered around 1805 by Carl Friedrich Gauss when interpolating measurements of the orbit of the asteroids Juno and Pallas, although that particular FFT algorithm is more often attributed to its modern rediscoverers Cooley and Tukey.[5][7]

## Interpretation in terms of time and frequency

In signal processing, the Fourier transform often takes a time series or a function of continuous time, and maps it into a frequency spectrum. That is, it takes a function from the time domain into the frequency domain; it is a decomposition of a function into sinusoids of different frequencies; in the case of a Fourier series or discrete Fourier transform, the sinusoids are harmonics of the fundamental frequency of the function being analyzed.

When the function ƒ 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 the phase of F.

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. This justifies their use in such diverse branches as image processing, heat conduction, and automatic control.

## Notes

1. ^
\int_{P} \left[\sum_{k=-\infty}^{\infty} s(t-kP)\right] \cdot e^{-i 2\pi \frac{k}{P} t} dt = \underbrace{\int_{-\infty}^{\infty} s(t) \cdot e^{-i 2\pi \frac{k}{P} t} dt}_{\stackrel{\mathrm{def}}{=}\ S(k/P)}
2. ^ We may also note that:   \scriptstyle \sum_{n=-\infty}^{+\infty} T\ s(nT)\ \delta(t-nT)\ =\ \sum_{n=-\infty}^{+\infty} T\ s(t)\ \delta(t-nT)\ =\ s(t)\cdot T \sum_{n=-\infty}^{+\infty} \delta(t-nT).
Consequently, a common practice is to model "sampling" as a multiplication by the Dirac comb function, which of course is only "possible" in a purely mathematical sense.

## Citations

1. ^ , p. 62
, p. 11

2. ^ , p. 30
3. ^ a b c d , p. 4
4. ^ a b , p. 2
5. ^ a b Heideman, M. T., D. H. Johnson, and C. S. Burrus, "Gauss and the history of the fast Fourier transform," IEEE ASSP Magazine, 1, (4), 14–21 (1984)
6. ^ , p. 501
7. ^ , p. 31

## References

• Howell, Kenneth B. (2001). Principles of Fourier Analysis, CRC Press. ISBN 978-0-8493-8275-8
• Kamen, E.W., and B.S. Heck. "Fundamentals of Signals and Systems Using the Web and Matlab". ISBN 0-13-017293-6
• Polyanin, A.D., and A.V. Manzhirov (1998). Handbook of Integral Equations, CRC Press, Boca Raton. ISBN 0-8493-2876-4
• Rabiner, Lawrence R., and Bernard Gold. "Theory and application of digital signal processing." Englewood Cliffs, NJ, Prentice-Hall, Inc., 1975. 777 p. 1 (1975).
• Stein, E.M., and G. Weiss (1971). Introduction to Fourier Analysis on Euclidean Spaces. Princeton University Press. ISBN 0-691-08078-X