Register allocation: Difference between revisions

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→‎Spilling: verb spill but operation is gerund spilling; section confuses logical registers, hardware registers, and variables; needs a lot more work
en>Dsimic
→‎Introduction: Linked an article, it provides a better description
 
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{{merge to|spectral density estimation|date=September 2013}}
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The '''periodogram''' is an estimate of the [[spectral density]] of a signal.  The term was coined by [[Arthur Schuster]] in 1898<ref name="Perry">[http://onlinelibrary.wiley.com/doi/10.1029/TM003i001p00013/pdf Schuster, A., "On the investigation of hidden periodicities with application to a supposed 26 day period of meteorological phenomena," ''Terrestrial Magnetism'', 3, 13-41, 1898.] </ref> as in the following quote:<ref>{{cite book | url = http://books.google.com/books?id=v-EOAAAAIAAJ&q=schuster+%22convenient+to+have+a+word+for+some+representation+of+a+variable+quantity%22&dq=schuster+%22convenient+to+have+a+word+for+some+representation+of+a+variable+quantity%22&pgis=1 | title = The Annals of Mathematical Statistics | year = 1972 }}</ref>
 
{{cquote|THE PERIODOGRAM. It is convenient to have a word for some representation of a variable quantity which shall correspond to the 'spectrum' of a luminous radiation. I propose the word periodogram, and define it more particularly in the following way:
Let
:::<math>\frac{T}{2}a = \int_{t_1}^{t_1+T}f(t)\cos(kt)dt</math>
:::<math>\frac{T}{2}b = \int_{t_1}^{t_1+T}f(t)\sin(kt)dt</math>
where ''T'' may for convenience be chosen to be equal to some integer multiple of 
:::<math>\frac{2\pi}{k}</math>,
and plot a curve with <math>{2\pi}/k</math> as [[abscissae]] and
:::<math>r = \sqrt{a^2+b^2}</math>
as [[ordinate]]s; this curve, or, better, the space between this curve and the axis of abscissæ, represents the periodogram of f(t).}}
 
Note that the term ''periodogram'' may also be used to describe the quantity <math>r^2</math>,<ref name="Box">Box, George and Jenkins, Gwilym (1970) ''Time series analysis: Forecasting and control'', San Francisco: Holden-Day.</ref> which is its common meaning in astronomy (as in "the modulus-squared of the [[discrete Fourier transform]] of the time series (with the appropriate normalisation)"<ref name="Vaughan">Simon Vaughan and Philip Uttley (2006), "Detecting X-ray QPOs in active galaxies",Advances in Space Research, Volume 38, Issue 7, pp. 1405-1408</ref>). See Scargle (1982) for a detailed discussion in this context.<ref name="Scargle">Scargle (1982), ''Astrophysical Journal'', Part 1, vol. 263, Dec. 15, 1982, p. 835-853</ref>
 
A '''spectral plot''' refers to a smoothed version of the periodogram.<ref>[http://www.itl.nist.gov/div898/handbook/eda/section3/eda33r.htm Spectral Plot], from the [[NIST]] Engineering Statistics Handbook.</ref><ref>[http://www.itl.nist.gov/div898/software/dataplot/refman1/ch2/spectral.pdf Short explanation] of the relation between the spectral plot and the periodogram.</ref> [[Smoothing]] is performed to reduce the effect of measurement noise.
 
In practice, the periodogram is often computed from a finite-length digital sequence using the [[fast Fourier transform]] (FFT). The raw periodogram is not a good spectral estimate because of [[spectral bias]] and the fact that the variance at a given [[frequency]] does not decrease as the number of samples used in the computation increases.  
 
The spectral bias problem arises from a sharp truncation of the sequence, and can be reduced by first multiplying the finite sequence by a [[window function]] which truncates the sequence gradually rather than abruptly.
 
The variance problem can be reduced by smoothing the periodogram. Various techniques to reduce spectral bias and variance are the subject of [[spectral estimation]].
 
One such technique to solve the variance problems is also known as the ''method of averaged periodograms''<ref name="Engelberg">Engelberg, S. (2008), ''Digital Signal Processing: An Experimental Approach'',  Springer, Chap. 7 p. 56</ref> or as [[Bartlett's method]]. The idea behind it is, to divide the set of N samples into L sets of M samples, compute the [[discrete Fourier transform]] (DFT) of each set, square it to get the power spectral density and compute the average of all of them. This leads to a decrease in the standard deviation as <math> \frac{1}{\sqrt{L}}. </math>
 
== See also ==
 
*[[Fourier transform]]
*[[Least-squares spectral analysis]]
*[[Seasonality]]
*[[Spherical harmonics]]
*[[Welch method]]
 
== References ==
{{reflist}}
 
[[Category:Frequency domain analysis]]
[[Category:Fourier analysis]]

Latest revision as of 09:03, 25 October 2014

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