[MD-sorular] Fotograflardaki dogrusal olmayan yapi

Mustafa Umut Sarac mustafaumutsarac at gmail.com
25 Oca 2010 Pzt 02:48:25 EET

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
Yazi dahada kestirme algoritmayi bense en basit en temel algoritmayi
Tabii ben bolme yapamayan birisi olarak okuyorum bunlari , matematikten hep
0 alirdim.
Linki verilen yaziya ucretsiz ulasabilen var mi ?

Saygilar ,

Mustafa Umut Sarac


 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:


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:


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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