# Continuous-time signals  (Page 3/5)

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Although the function to be expanded is defined only over a specific finite region, the series converges to a function that is defined over thereal line and is periodic. It is equal to the original function over the region of definition and is a periodic extension outside of the region.Indeed, one could artificially extend the given function at the outset and then the expansion would converge everywhere.

## A geometric view

It can be very helpful to develop a geometric view of the Fourier series where $x\left(t\right)$ is considered to be a vector and the basis functions are the coordinate or basis vectors. The coefficients become the projections of $x\left(t\right)$ on the coordinates. The ideas of a measure of distance, size, and orthogonality are important and the definition of error is easy topicture. This is done in [link] , [link] , [link] using Hilbert space methods.

## Properties of the fourier series

The properties of the Fourier series are important in applying it to signal analysis and to interpreting it. The main properties are given hereusing the notation that the Fourier series of a real valued function $x\left(t\right)$ over $\left\{0\le t\le T\right\}$ is given by $\mathcal{F}\left\{x\left(t\right)\right\}=c\left(k\right)$ and $\stackrel{˜}{x}\left(t\right)$ denotes the periodic extensions of $x\left(t\right)$ .

1. Linear: $\mathcal{F}\left\{x+y\right\}=\mathcal{F}\left\{x\right\}+\mathcal{F}\left\{y\right\}$
Idea of superposition. Also scalability: $\mathcal{F}\left\{ax\right\}=a\mathcal{F}\left\{x\right\}$
2. Extensions of $x\left(t\right)$ : $\stackrel{˜}{x}\left(t\right)=\stackrel{˜}{x}\left(t+T\right)$
$\stackrel{˜}{x}\left(t\right)$ is periodic.
3. Even and Odd Parts: $x\left(t\right)=u\left(t\right)+jv\left(t\right)$ and $C\left(k\right)=A\left(k\right)+jB\left(k\right)=|C\left(k\right)|\phantom{\rule{0.166667em}{0ex}}{e}^{j\theta \left(k\right)}$
 $u$ $v$ $A$ $B$ $|C|$ $\theta$ even 0 even 0 even 0 odd 0 0 odd even 0 0 even 0 even even $\pi /2$ 0 odd odd 0 even $\pi /2$
4. Convolution: If continuous cyclic convolution is defined by
$y\left(t\right)\phantom{\rule{0.277778em}{0ex}}=\phantom{\rule{0.277778em}{0ex}}h\left(t\right)\circ x\left(t\right)\phantom{\rule{0.277778em}{0ex}}=\phantom{\rule{0.277778em}{0ex}}{\int }_{0}^{T}\stackrel{˜}{h}\left(t-\tau \right)\phantom{\rule{0.166667em}{0ex}}\stackrel{˜}{x}\left(\tau \right)\phantom{\rule{0.166667em}{0ex}}d\tau$

then $\mathcal{F}\left\{h\left(t\right)\circ x\left(t\right)\right\}\phantom{\rule{0.277778em}{0ex}}=\phantom{\rule{0.277778em}{0ex}}\mathcal{F}\left\{h\left(t\right)\right\}\phantom{\rule{0.166667em}{0ex}}\mathcal{F}\left\{x\left(t\right)\right\}$
5. Multiplication: If discrete convolution is defined by
$e\left(n\right)\phantom{\rule{0.277778em}{0ex}}=\phantom{\rule{0.277778em}{0ex}}d\left(n\right)*c\left(n\right)\phantom{\rule{0.277778em}{0ex}}=\phantom{\rule{0.277778em}{0ex}}{\sum }_{m=-\infty }^{\infty }d\left(m\right)\phantom{\rule{0.166667em}{0ex}}c\left(n-m\right)$

then $\mathcal{F}\left\{h\left(t\right)\phantom{\rule{0.166667em}{0ex}}x\left(t\right)\right\}\phantom{\rule{0.277778em}{0ex}}=\phantom{\rule{0.277778em}{0ex}}\mathcal{F}\left\{h\left(t\right)\right\}*\mathcal{F}\left\{x\left(t\right)\right\}$
This property is the inverse of property 4 and vice versa.
6. Parseval: $\frac{1}{T}{\int }_{0}^{T}{|x\left(t\right)|}^{2}dt\phantom{\rule{0.277778em}{0ex}}=\phantom{\rule{0.277778em}{0ex}}{\sum }_{k=-\infty }^{\infty }{|C\left(k\right)|}^{2}$
This property says the energy calculated in the time domain is the same as that calculated in the frequency (or Fourier) domain.
7. Shift: $\mathcal{F}\left\{\stackrel{˜}{x}\left(t-{t}_{0}\right)\right\}\phantom{\rule{0.277778em}{0ex}}=\phantom{\rule{0.277778em}{0ex}}C\left(k\right)\phantom{\rule{0.166667em}{0ex}}{e}^{-j2\pi {t}_{0}k/T}$
A shift in the time domain results in a linear phase shift in the frequency domain.
8. Modulate: $\mathcal{F}\left\{x\left(t\right)\phantom{\rule{0.166667em}{0ex}}{e}^{j2\pi Kt/T}\right\}\phantom{\rule{0.277778em}{0ex}}=\phantom{\rule{0.277778em}{0ex}}C\left(k-K\right)$
Modulation in the time domain results in a shift in the frequency domain. This property is the inverse of property 7.
9. Orthogonality of basis functions:
${\int }_{0}^{T}{e}^{-j2\pi mt/T}\phantom{\rule{0.166667em}{0ex}}{e}^{j2\pi nt/T}\phantom{\rule{0.166667em}{0ex}}dt\phantom{\rule{0.277778em}{0ex}}=\phantom{\rule{0.277778em}{0ex}}T\phantom{\rule{0.277778em}{0ex}}\delta \left(n-m\right)\phantom{\rule{0.277778em}{0ex}}=\phantom{\rule{0.277778em}{0ex}}\left\{\begin{array}{cc}T\hfill & \text{if}\phantom{\rule{4.pt}{0ex}}n=m\hfill \\ 0\hfill & \text{if}\phantom{\rule{4.pt}{0ex}}n\ne m.\hfill \end{array}\right)$
Orthogonality allows the calculation of coefficients using inner products in [link] and [link] . It also allows Parseval's Theorem in property 6 . A relaxed version of orthogonality is called “tight frames" and is importantin over-specified systems, especially in wavelets.

## Examples

• An example of the Fourier series is the expansion of a square wave signal with period $2\pi$ . The expansion is
$x\left(t\right)\phantom{\rule{0.277778em}{0ex}}=\phantom{\rule{0.277778em}{0ex}}\frac{4}{\pi }\left[sin\left(t\right)+\frac{1}{3}sin\left(3t\right)+\frac{1}{5}sin\left(5t\right)\cdots \right].$
Because $x\left(t\right)$ is odd, there are no cosine terms (all $a\left(k\right)=0$ ) and, because of its symmetries, there are no even harmonics (even $k$ terms are zero). The function is well defined and bounded; its derivative is not,therefore, the coefficients drop off as $\frac{1}{k}$ .
• A second example is a triangle wave of period $2\pi$ . This is a continuous function where the square wave was not. The expansion of thetriangle wave is
$x\left(t\right)=\frac{4}{\pi }\left[sin\left(t\right)-\frac{1}{{3}^{2}}sin\left(3t\right)+\frac{1}{{5}^{2}}sin\left(5t\right)+\cdots \right].$
Here the coefficients drop off as $\frac{1}{{k}^{2}}$ since the function and its first derivative exist and are bounded.

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research.net
kanaga
sciencedirect big data base
Ernesto
Introduction about quantum dots in nanotechnology
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nano basically means 10^(-9). nanometer is a unit to measure length.
Bharti
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NANO
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s.
fullerene is a bucky ball aka Carbon 60 molecule. It was name by the architect Fuller. He design the geodesic dome. it resembles a soccer ball.
Tarell
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Damian
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Tarell
what is the Synthesis, properties,and applications of carbon nano chemistry
Mostly, they use nano carbon for electronics and for materials to be strengthened.
Virgil
is Bucky paper clear?
CYNTHIA
carbon nanotubes has various application in fuel cells membrane, current research on cancer drug,and in electronics MEMS and NEMS etc
NANO
so some one know about replacing silicon atom with phosphorous in semiconductors device?
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Harper
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s.
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SUYASH
What is lattice structure?
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Ebrahim
or in general
Ebrahim
in general
s.
Graphene has a hexagonal structure
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what is biological synthesis of nanoparticles
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