The Fourier transform (FT) decomposes a function of time (a…
The Fourier transform (FT) decomposes a function of time (a signal) into its constituent frequencies.
This is similar to the way a musical chord can be expressed in terms of the volumes and frequencies of its constituent notes.
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Harmonic analysis is the systematic study of the relationship between the frequency and time domains, including the kinds of functions or operations that are "simpler" in one or the other, and has deep connections to many areas of modern mathematics.
Linear operations performed in one domain (time or frequency) have corresponding operations in the other domain, which are sometimes easier to perform.
The Fourier transform is not limited to functions of time, but the domain of the original function is commonly referred to as the time domain.
-- and whose argument is the phase offset of the basic sinusoid in that frequency.
This idea makes the spatial Fourier transform very natural in the study of waves, as well as in quantum mechanics, where it is important to be able to represent wave solutions as functions of either position or momentum and sometimes both.
The critical case for this principle is the Gaussian function, of substantial importance in probability theory and statistics as well as in the study of physical phenomena exhibiting normal distribution (e.g., diffusion).
The Fourier transform can be formally defined as an improper Riemann integral, making it an integral transform, although this definition is not suitable for many applications requiring a more sophisticated integration theory.
https://twetch.app/t/74eeb3fe813667d7f467e8203803c03850f98ac784abb3670c1b56d72a099bef
-- the discrete Fourier transform (DFT, group = ℤ mod N) and the Fourier series or circular Fourier transform (group = S1, the unit circle ≈ closed finite interval with endpoints identified).
Functions that are localized in the time domain have Fourier transforms that are spread out across the frequency domain and vice versa, a phenomenon known as the uncertainty principle.
The Fourier transform can also be generalized to functions of several variables on Euclidean space, sending a function of 3-dimensional 'position space' to a function of 3-dimensional momentum (or a function of space and time to a function of 4-momentum).
https://en.wikipedia.org/wiki/Convolution_theorem
The term Fourier transform refers to both the frequency domain representation and the mathematical operation that associates the frequency domain representation to a function of time.
The Fourier transform of a Gaussian function is another Gaussian function. Joseph Fourier introduced the transform in his study of heat transfer, where Gaussian functions appear as solutions of the heat equation.
https://twetch.app/t/a285903f9d3186af886275dd5f7f66bb6f3189dd8e2eddefd3555978edd7f686
Still further generalization is possible to functions on groups, which, besides the original Fourier transform on ℝ or ℝn (viewed as groups under addition), notably includes the discrete-time Fourier transform (DTFT, group = ℤ), --
The Fourier transform of a function of time is itself a complex-valued function of frequency, whose magnitude (modulus) represents the amount of that frequency present in the original function, --
For example, many relatively simple applications use the Dirac delta function, which can be treated formally as if it were a function, but the justification requires a mathematically more sophisticated viewpoint.
The operation of differentiation in the time domain corresponds to multiplication by the frequency, so some differential equations are easier to analyze in the frequency domain.
After performing the desired operations, transformation of the result can be made back to the time domain.
There is also an inverse Fourier transform that mathematically synthesizes the original function from its frequency domain representation.
In general, functions to which Fourier methods are applicable are complex-valued, and possibly vector-valued.
The latter is routinely employed to handle periodic functions. The fast Fourier transform (FFT) is an algorithm for computing the DFT.
Also, convolution in the time domain corresponds to ordinary multiplication in the frequency domain (see Convolution theorem).
https://twetch.app/t/8b9c7a1dd59b9ef13acb26ac2322925502ad356b592448a7534d94ef1a5d7f36
Let me know when you get to power spectral density I need some help