scholarly journals Singular value decomposition with self-modeling applied to determine bacteriorhodopsin intermediate spectra: Analysis of simulated data

1999 ◽  
Vol 96 (8) ◽  
pp. 4408-4413 ◽  
Author(s):  
L. Zimanyi ◽  
A. Kulcsar ◽  
J. K. Lanyi ◽  
D. F. Sears ◽  
J. Saltiel
Keyword(s):  
Singular Value Decomposition ◽  
Simulated Data ◽  
Singular Value ◽  
Spectra Analysis ◽  
Value Decomposition

Indexing of powder diffraction patterns by iterative use of singular value decomposition

2003 ◽  
Vol 36 (1) ◽  
pp. 86-95 ◽  
Author(s):  
A. A. Coelho
Keyword(s):  
Singular Value Decomposition ◽  
Powder Diffraction ◽  
Present Method ◽  
Linear Equations ◽  
Noisy Data ◽  
Simulated Data ◽  
Singular Value ◽  
Fast Method ◽  
Diffraction Patterns ◽  
Value Decomposition

A fast method for indexing powder diffraction patterns has been developed for large and small lattices of all symmetries. The method is relatively insensitive to impurity peaks and missing highd-spacings: on simulated data, little effect in terms of successful indexing has been observed when one in threed-spacings are randomly removed. Comparison with three of the most popular indexing programs, namelyITO,DICVOL91andTREOR90, has shown that the present method as implemented in the programTOPASis more successful at indexing simulated data. Also significant is that the present method performs well on typically noisy data with large diffractometer zero errors. Critical to its success, the present method uses singular value decomposition in an iterative manner for solving linear equations relatinghklvalues tod-spacings.

scholarly journals Using the singular value decomposition to extract 2D correlation functions from scattering patterns

2019 ◽  
Vol 75 (5) ◽  
pp. 766-771 ◽  
Author(s):  
Philipp Bender ◽  
Dominika Zákutná ◽  
Sabrina Disch ◽  
Lourdes Marcano ◽  
Diego Alba Venero ◽  
...  
Keyword(s):  
Singular Value Decomposition ◽  
Small Angle ◽  
Correlation Functions ◽  
Simulated Data ◽  
Colloidal Dispersions ◽  
Singular Value ◽  
Model Free ◽  
X Ray Scattering ◽  
Angle Scattering ◽  
Value Decomposition

The truncated singular value decomposition (TSVD) is applied to extract the underlying 2D correlation functions from small-angle scattering patterns. The approach is tested by transforming the simulated data of ellipsoidal particles and it is shown that also in the case of anisotropic patterns (i.e. aligned ellipsoids) the derived correlation functions correspond to the theoretically predicted profiles. Furthermore, the TSVD is used to analyze the small-angle X-ray scattering patterns of colloidal dispersions of hematite spindles and magnetotactic bacteria in the presence of magnetic fields, to verify that this approach can be applied to extract model-free the scattering profiles of anisotropic scatterers from noisy data.

Improving TF-IDF with Singular Value Decomposition (SVD) for Feature Extraction on Twitter

2017 ◽  
Author(s):  
Ammar Ismael Kadhim ◽  
Yu-N Cheah ◽  
Inaam Abbas Hieder ◽  
Rawaa Ahmed Ali
Keyword(s):  
Feature Extraction ◽  
Singular Value Decomposition ◽  
Singular Value ◽  
Value Decomposition

Multi-scale singular value decomposition polarization image fusion defogging algorithm and experiment

2020 ◽  
Vol 13 (6) ◽  
pp. 1-10
Author(s):  
ZHOU Wen-zhou ◽  
◽  
FAN Chen ◽  
HU Xiao-ping ◽  
HE Xiao-feng ◽  
...  
Keyword(s):  
Singular Value Decomposition ◽  
Image Fusion ◽  
Singular Value ◽  
Multi Scale ◽  
Polarization Image ◽  
Value Decomposition
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