# 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.

what is variations in raman spectra for nanomaterials
I only see partial conversation and what's the question here!
what about nanotechnology for water purification
please someone correct me if I'm wrong but I think one can use nanoparticles, specially silver nanoparticles for water treatment.
Damian
yes that's correct
Professor
I think
Professor
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Rafiq
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Damian
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What is STMs full form?
LITNING
scanning tunneling microscope
Sahil
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Santosh
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Rafiq
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Mahi
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Rafiq
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write examples of Nano molecule?
Bob
The nanotechnology is as new science, to scale nanometric
brayan
nanotechnology is the study, desing, synthesis, manipulation and application of materials and functional systems through control of matter at nanoscale
Damian
Is there any normative that regulates the use of silver nanoparticles?
what king of growth are you checking .?
Renato
What fields keep nano created devices from performing or assimulating ? Magnetic fields ? Are do they assimilate ?
why we need to study biomolecules, molecular biology in nanotechnology?
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Kyle
yes I'm doing my masters in nanotechnology, we are being studying all these domains as well..
why?
what school?
Kyle
biomolecules are e building blocks of every organics and inorganic materials.
Joe
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research.net
kanaga
sciencedirect big data base
Ernesto
Introduction about quantum dots in nanotechnology
what does nano mean?
nano basically means 10^(-9). nanometer is a unit to measure length.
Bharti
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absolutely yes
Daniel
how did you get the value of 2000N.What calculations are needed to arrive at it
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