scholarly journals A case of apodization function using singular-value decomposition: Determination of beamforming parameters by optimization

2008 ◽  
Vol 29 (2) ◽  
pp. 185-187 ◽  
Author(s):  
Chikayoshi Sumi
Keyword(s):  
Singular Value Decomposition ◽  
Singular Value ◽  
Value Decomposition ◽  
Apodization Function

scholarly journals Determination of Broadening Functions using the Singular Value Decomposition (SVD) Technique

1999 ◽  
Vol 170 ◽  
pp. 82-90 ◽  
Author(s):  
Slavek Rucinski
Keyword(s):  
Correlation Function ◽  
Singular Value Decomposition ◽  
Cross Correlation ◽  
Singular Value ◽  
Spectral Lines ◽  
Linear Inversion ◽  
Standard Tool ◽  
Contact Binaries ◽  
Value Decomposition

AbstractThe cross-correlation function (CCF) has become the standard tool for extraction of radial-velocity and broadening information from high resolution spectra. It permits integration of information which is common to many spectral lines into one function which is easy to calculate, visualize and interpret. However, the CCF is not the best tool for many applications where it should be replaced by the proper broadening function (BF). Typical applications requiring use of BFs rather than CCFs involve finding locations of star spots, studies of projected shapes of highly distorted stars such as contact binaries (as no assumptions can be made about BF symmetry or even continuity) and [Fe/H] metallicity determinations (good baselines and avoidance of negative lobes are essential). It is stressed that the CCFs are not broadening functions. This note concentrates on the advantages of determining BFs through the process of linear inversion, preferably accomplished using the singular value decomposition (SVD). Some basic examples of numerical operations are given in the IDL programming language.

Determination of the pKa Values of trans-Resveratrol, a Triphenolic Stilbene, by Singular Value Decomposition. Comparison with Theory

2020 ◽  
Vol 124 (31) ◽  
pp. 6294-6302
Author(s):  
László Zimányi ◽  
Shareefa Thekkan ◽  
Brett Eckert ◽  
Alanna R. Condren ◽  
Olga Dmitrenko ◽  
...  
Keyword(s):  
Singular Value Decomposition ◽  
Singular Value ◽  
Pka Values ◽  
Value Decomposition

Application of error-ranked singular value decomposition for the determination of potential-derived atomic-centered point charges

2009 ◽  
Vol 30 (5) ◽  
pp. 733-742 ◽  
Author(s):  
Jeffrey S. Tan ◽  
Stephan X. M. Boerrigter ◽  
Raymond P. Scaringe ◽  
Kenneth R. Morris
Keyword(s):  
Singular Value Decomposition ◽  
Singular Value ◽  
Value Decomposition

scholarly journals INVERSION OF INTERVAL VELOCITIES: APPLICATION TO A GEOLOGICAL MODEL FROM A PRE-SALT AREA

2015 ◽  
Vol 33 (1) ◽  
pp. 19
Author(s):  
Thais Gomes Santana ◽  
Amin Bassrei
Keyword(s):  
Singular Value Decomposition ◽  
Relative Entropy ◽  
A Priori ◽  
Singular Value ◽  
A Priori Information ◽  
Minimum Relative Entropy ◽  
Priori Information ◽  
Interval Velocities ◽  
Value Decomposition

ABSTRACT. Seismic methods study the propagation of elastic wave fields inside the Earth, with the goal to provide subsurface images. In this work, the determination of the time interval velocity distribution is the main information provided. Several synthetic models were used, where one is based in a real situation, a dip section from the pre-salt region, central part of the Santos Basin, Brazil. Themethods used to determine interval velocities were based on the Dix transform, singular value decomposition (SVD) and minimum relative entropy (MRE). Dix transform showed excellent results when used in simple geological models, and was coincident to the other two methods. With the addition of a priori information, the SVD and MRE showed to be good methods for the determination of the interval velocities. When comparing SVD and MRE methods the latter showed the best results. When the a priori information is constant, the SVD and MRE methods give the same velocity estimate given from the direct application of the Dix transform.Keywords: inversion of interval velocities, singular value decomposition, minimum relative entropy, pre-salt.RESUMO. Os métodos sísmicos utilizam o campo de propagação de ondas elásticas no interior da Terra, com o objetivo de fornecer imagens da subsuperfície. Neste trabalho, a determinação do campo de velocidades intervalares é a principal informação a ser fornecida. Foram utilizados modelos sintéticos, sendo um deles baseado em uma situação real, no caso uma sessão dip , na região do pré-sal, parte central da Bacia de Santos. Os métodos usados para determinar as velocidades intervalares foram a transformada de Dix, a decomposição por valores singulares (SVD) e a entropia relativa mínima (MRE). A transformada de Dix, quando usada em modelos geológicos mais simples apresentou excelentes resultados coincidente aos outros dois métodos. Com a adição de estimativas a priori , o SVD e o MRE se mostraram como bons métodos para a determinação das velocidades intervalares, sendo que o MRE apresentou os melhores resultados. Quando a informação a priori é constante, os métodos do SVD e MRE fornecem a mesma estimativa de velocidade que é obtida pela transformada de Dix.Palavras-chave: inversão de velocidades intervalares, decomposição por valores singulares, entropia relativa mínima, pré-sal.

INVESTIGATING CHAOTIC BEHAVIOR IN ECONOMIC SERIES: THE DELAY TIME IN THE GRASSBERGER–PROCACCIA ALGORITHM

1999 ◽  
Vol 02 (04) ◽  
pp. 357-380 ◽  
Author(s):  
TERESA APARICIO ◽  
EDUARDO POZO ◽  
DULCE SAURA
Keyword(s):  
Singular Value Decomposition ◽  
Delay Time ◽  
Chaotic Systems ◽  
Chaotic Behavior ◽  
Singular Value ◽  
Time Parameter ◽  
Practical Application ◽  
Second Stage ◽  
Value Decomposition

In this paper we try to bring forward evidence on the practical application of the Grassberger–Procaccia algorithm, in particular on the determination of the "delay time" parameter. For this purpose, we analyze the results obtained from applying the main methods proposed to calculate this delay time for series simulated from well-known chaotic systems. As the most relevant result we conclude that, in general, all the methods display inadequate behavior, except for that based on the previous filtering of the series according to singular value decomposition. In a second stage we apply the same study to three financial series, with the results appearing to confirm the advantage of this method.

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