# 1.5 Introduction to fourier analysis

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Lists the four Fourier transforms and when to use them.

## Fourier's daring leap

Fourier postulated around 1807 that any periodic signal (equivalently finite length signal) can be built up as an infinite linear combination of harmonic sinusoidal waves.

i.e. Given the collection

$B={\left\{{e}^{j\frac{2\pi }{T}nt}\right\}}_{n=-\infty }^{\infty }$

any

$f\left(t\right)\in {L}^{2}\left[0,T\right)$

can be approximated arbitrarily closely by

$f\left(t\right)=\sum _{n=-\infty }^{\infty }{C}_{n}\phantom{\rule{0.166667em}{0ex}}{e}^{j\frac{2\pi }{T}nt}.$

Now, The issue of exact convergence did bring Fourier much criticism from the French Academy of Science (Laplace,Lagrange, Monge and LaCroix comprised the review committee) for several years after its presentation on 1807. It was notresolved for also a century, and its resolution is interesting and important to understand from a practical viewpoint. See more in the section on Gibbs Phenomena .

Fourier analysis is fundamental to understanding the behavior of signals and systems. This is a result of the fact thatsinusoids are Eigenfunctions of linear, time-invariant (LTI) systems. This is to say that if we pass any particular sinusoid through aLTI system, we get a scaled version of that same sinusoid on the output. Then, since Fourier analysis allows us to redefine thesignals in terms of sinusoids, all we need to do is determinehow any given system effects all possible sinusoids (its transfer function ) and we have a complete understanding of the system. Furthermore, sincewe are able to define the passage of sinusoids through a system as multiplication of that sinusoid by the transfer function atthe same frequency, we can convert the passage of any signal through a system from convolution (in time) to multiplication (in frequency). These ideas are what give Fourier analysis itspower.

Now, after hopefully having sold you on the value of this method of analysis, we must examine exactly what we mean by Fourieranalysis. The four Fourier transforms that comprise this analysis are the Fourier Series , Continuous-Time Fourier Transform , Discrete-Time Fourier Transform and Discrete Fourier Transform . For this document, we will view the Laplace Transform and Z-Transform as simply extensions of the CTFT and DTFT respectively. All of thesetransforms act essentially the same way, by converting a signal in time to an equivalent signal in frequency (sinusoids).However, depending on the nature of a specific signal i.e. whether it is finite- or infinite-length and whether it is discrete- or continuous-time) there is anappropriate transform to convert the signal into the frequency domain. Below is a table of the four Fourier transforms andwhen each is appropriate. It also includes the relevant convolution for the specified space.

Table of fourier representations
Transform Time Domain Frequency Domain Convolution
Continuous-Time Fourier Series ${L}^{2}(\left[0 , T\right))$ ${l}^{2}(\mathbb{Z})$ Continuous-Time Circular
Continuous-Time Fourier Transform ${L}^{2}(\mathbb{R})$ ${L}^{2}(\mathbb{R})$ Continuous-Time Linear
Discrete-Time Fourier Transform ${l}^{2}(\mathbb{Z})$ ${L}^{2}(\left[0 , 2\pi \right))$ Discrete-Time Linear
Discrete Fourier Transform ${l}^{2}(\left[0 , N-1\right])$ ${l}^{2}(\left[0 , N-1\right])$ Discrete-Time Circular

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salma
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Abhi
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20/(×-6^2)
Salomon
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Salomon
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Salomon
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Salomon
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Abhi
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Kim
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Differences Between Laspeyres and Paasche Indices
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At high concentrations (>0.01 M), the relation between absorptivity coefficient and absorbance is no longer linear. This is due to the electrostatic interactions between the quantum dots in close proximity. If the concentration of the solution is high, another effect that is seen is the scattering of light from the large number of quantum dots. This assumption only works at low concentrations of the analyte. Presence of stray light.
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