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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 ( t ) is considered to be a vector and the basis functions are the coordinate or basis vectors. The coefficients become the projections of x ( t ) 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 ( t ) over { 0 t T } is given by F { x ( t ) } = c ( k ) and x ˜ ( t ) denotes the periodic extensions of x ( t ) .

  1. Linear: F { x + y } = F { x } + F { y }
    Idea of superposition. Also scalability: F { a x } = a F { x }
  2. Extensions of x ( t ) : x ˜ ( t ) = x ˜ ( t + T )
    x ˜ ( t ) is periodic.
  3. Even and Odd Parts: x ( t ) = u ( t ) + j v ( t ) and C ( k ) = A ( k ) + j B ( k ) = | C ( k ) | e j θ ( k )
    u v A B | C | θ
    even 0 even 0 even 0
    odd 0 0 odd even 0
    0 even 0 even even π / 2
    0 odd odd 0 even π / 2
  4. Convolution: If continuous cyclic convolution is defined by
    y ( t ) = h ( t ) x ( t ) = 0 T h ˜ ( t - τ ) x ˜ ( τ ) d τ

    then F { h ( t ) x ( t ) } = F { h ( t ) } F { x ( t ) }
  5. Multiplication: If discrete convolution is defined by
    e ( n ) = d ( n ) * c ( n ) = m = - d ( m ) c ( n - m )

    then F { h ( t ) x ( t ) } = F { h ( t ) } * F { x ( t ) }
    This property is the inverse of property 4 and vice versa.
  6. Parseval: 1 T 0 T | x ( t ) | 2 d t = k = - | C ( k ) | 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: F { x ˜ ( t - t 0 ) } = C ( k ) e - j 2 π t 0 k / T
    A shift in the time domain results in a linear phase shift in the frequency domain.
  8. Modulate: F { x ( t ) e j 2 π K t / T } = C ( k - K )
    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:
    0 T e - j 2 π m t / T e j 2 π n t / T d t = T δ ( n - m ) = T if n = m 0 if n m .
    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.


  • An example of the Fourier series is the expansion of a square wave signal with period 2 π . The expansion is
    x ( t ) = 4 π [ sin ( t ) + 1 3 sin ( 3 t ) + 1 5 sin ( 5 t ) ] .
    Because x ( t ) is odd, there are no cosine terms (all a ( k ) = 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 1 k .
  • A second example is a triangle wave of period 2 π . This is a continuous function where the square wave was not. The expansion of thetriangle wave is
    x ( t ) = 4 π [ sin ( t ) - 1 3 2 sin ( 3 t ) + 1 5 2 sin ( 5 t ) + ] .
    Here the coefficients drop off as 1 k 2 since the function and its first derivative exist and are bounded.

Questions & Answers

so some one know about replacing silicon atom with phosphorous in semiconductors device?
s. Reply
how to fabricate graphene ink ?
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What is lattice structure?
s. Reply
of graphene you mean?
or in general
in general
Graphene has a hexagonal structure
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what is biological synthesis of nanoparticles
Sanket Reply
what's the easiest and fastest way to the synthesize AgNP?
Damian Reply
types of nano material
abeetha Reply
I start with an easy one. carbon nanotubes woven into a long filament like a string
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I'm interested in nanotube
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Ramkumar Reply
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Sravani Reply
what is system testing?
preparation of nanomaterial
Victor Reply
Yes, Nanotechnology has a very fast field of applications and their is always something new to do with it...
Himanshu Reply
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In this morden time nanotechnology used in many field . 1-Electronics-manufacturad IC ,RAM,MRAM,solar panel etc 2-Helth and Medical-Nanomedicine,Drug Dilivery for cancer treatment etc 3- Atomobile -MEMS, Coating on car etc. and may other field for details you can check at Google
anybody can imagine what will be happen after 100 years from now in nano tech world
after 100 year this will be not nanotechnology maybe this technology name will be change . maybe aftet 100 year . we work on electron lable practically about its properties and behaviour by the different instruments
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silver nanoparticles could handle the job?
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I'm interested in Nanotube
this technology will not going on for the long time , so I'm thinking about femtotechnology 10^-15
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Prasenjit Reply
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.
Ali Reply
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Smarajit Reply
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Source:  OpenStax, Digital signal processing and digital filter design (draft). OpenStax CNX. Nov 17, 2012 Download for free at http://cnx.org/content/col10598/1.6
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