[MD-sorular] Fotograflardaki dogrusal olmayan yapi

[Mustafa Umut Sarac]

Polinomsal egri yerlestirme , galiba aradigim sey bu ! Forier analizden
sonra cikan tepe noktalarina bu yapilacak

On 1/25/10, Mustafa Umut Sarac <mustafaumutsarac at gmail.com> wrote:
>
> Bir arkadas soyle bir asagidaki yaniti gonderdi. Soru yanlizca dogrusal
> olmayan analiz ustuneydi.
> Konu spektral kompozisyon bozumu olarak anlatilmis , Galerkin metodu ile
> yeniden kurma olarak anlatilmis , Galerkin metodu kestirme yontemmis bu
> hesaplamalarda.
> Yazi dahada kestirme algoritmayi bense en basit en temel algoritmayi
> ariyorum
> Tabii ben bolme yapamayan birisi olarak okuyorum bunlari , matematikten hep
> 0 alirdim.
> Linki verilen yaziya ucretsiz ulasabilen var mi ?
>
> Saygilar ,
>
> Mustafa Umut Sarac
>
> Istanbul
>
>  Sounds to me like you need to investigate signal processing algorithms
> commonly used in seismic processing that do spatial spectral decomposition.
> This paper http://portal.acm.org/citation.cfm?id=1461313 discusses some of
> the general theory involved.
>
> The abstract quoted below is pretty self explanatory in its scope:
>
> We present an extension of the generalized spectral decomposition method
> for the resolution of nonlinear stochastic problems. The method consists in
> the construction of a reduced basis approximation of the Galerkin solution
> and is independent of the stochastic discretization selected (polynomial
> chaos, stochastic multi-element or multi-wavelets). Two algorithms are
> proposed for the sequential construction of the successive generalized
> spectral modes. They involve decoupled resolutions of a series of
> deterministic and low-dimensional stochastic problems. Compared to the
> classical Galerkin method, the algorithms allow for significant
> computational savings and require minor adaptations of the deterministic
> codes. The methodology is detailed and tested on two model problems, the
> one-dimensional steady viscous Burgers equation and a two-dimensional
> nonlinear diffusion problem. These examples demonstrate the effectiveness of
> the proposed algorithms which exhibit convergence rates with the number of
> modes essentially dependent on the spectrum of the stochastic solution but
> independent of the dimension of the stochastic approximation space.
>
> There are some public domain signal processing packages that are pretty
> tractable to anyone with basic ability to use unix and some rudimentary
> programming skills to construct some command line tools that will do the
> analysis you need. The software modules are available here:
>
> http://www.freeusp.org/
>
> The only issue is that you will need to write an input module to take the
> place of the standard SEGB input format. The DDS I/O system may be adaptable
> for this purpose.
>
> The manual description for the basic spectral decomposition module is here:
>
> http://www.freeusp.org/RaceCarWebsit...etic/spec.html<http://www.freeusp.org/RaceCarWebsite/TheToolkit/Man_alphabetic/spec.html>
>
> and it is obviously just a simple 2 dimensional analysis. But since the
> source code is available, you could perhaps adapt it for your needs. It
> clearly does not provide the reduced basis Galerkin solution outlined in the
> first article mentioned.
>
>
>
>
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