SVD analysis of Array transmission and reception and its use for bootstrapping calibration

2016 ◽  
Vol 76 (6) ◽  
pp. 1730-1740 ◽  
Author(s):  
David O. Brunner ◽  
Klaas P. Pruessmann
Keyword(s):  
Svd Analysis

scholarly journals The SVD Analysis of the Tropical MJO Energy Changes in El Nin ∼o Event

2017 ◽  
Vol 04 (03) ◽  
pp. 74-80
Author(s):  
毅 于
Keyword(s):  
Svd Analysis

SVD analysis of a 3D inverse thermal model

2005 ◽  
pp. 142-154 ◽  
Author(s):  
Lykke Gemmer ◽  
Søren B. Nielsen
Keyword(s):  
Thermal Model ◽  
Svd Analysis

Selection of Wavenumbers from SVD Analysis in CSEM 2.5D Modeling

2013 ◽  
Author(s):  
S.M. Abtahi ◽  
L.B. Pedersen ◽  
J. Kamm
Keyword(s):  
Svd Analysis ◽  
Selection Of

SVD analysis of the multi-view scattering operator in 1D inverse problems

2007 ◽  
Author(s):  
Raffaella Barresi ◽  
Giovanni Leone ◽  
Raffaele Solimene
Keyword(s):  
Inverse Problems ◽  
Scattering Operator ◽  
Svd Analysis

The Backus-Gilbert method: SVD analysis and fast implementation

1994 ◽  
Vol 10 (4) ◽  
pp. 895-904 ◽  
Author(s):  
P C Hansen
Keyword(s):  
Fast Implementation ◽  
Svd Analysis

scholarly journals Singular Value Decomposition and Ligand Binding Analysis

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
André Luiz Galo ◽  
Márcio Francisco Colombo
Keyword(s):  
Data Analysis ◽  
Ligand Binding ◽  
Principal Component ◽  
Singular Values ◽  
Analysis Data ◽  
Binding Characteristics ◽  
Nonlinear Fitting ◽  
Svd Analysis ◽  
Binding Analysis ◽  
Value Decomposition

Singular values decomposition (SVD) is one of the most important computations in linear algebra because of its vast application for data analysis. It is particularly useful for resolving problems involving least-squares minimization, the determination of matrix rank, and the solution of certain problems involving Euclidean norms. Such problems arise in the spectral analysis of ligand binding to macromolecule. Here, we present a spectral data analysis method using SVD (SVD analysis) and nonlinear fitting to determine the binding characteristics of intercalating drugs to DNA. This methodology reduces noise and identifies distinct spectral species similar to traditional principal component analysis as well as fitting nonlinear binding parameters. We applied SVD analysis to investigate the interaction of actinomycin D and daunomycin with native DNA. This methodology does not require prior knowledge of ligand molar extinction coefficients (free and bound), which potentially limits binding analysis. Data are acquired simply by reconstructing the experimental data and by adjusting the product of deconvoluted matrices and the matrix of model coefficients determined by the Scatchard and McGee and von Hippel equation.

An EOF and SVD analysis of interannual variability of GPS coordinates, environmental parameters and space gravity data

2013 ◽  
Vol 67 ◽  
pp. 111-124 ◽  
Author(s):  
Susanna Zerbini ◽  
Fabio Raicich ◽  
Maddalena Errico ◽  
Giovanni Cappello
Keyword(s):  
Interannual Variability ◽  
Gravity Data ◽  
Environmental Parameters ◽  
Svd Analysis

scholarly journals Dynamics of Interannual Variability in Summer Precipitation over East Asia*

2011 ◽  
Vol 24 (20) ◽  
pp. 5435-5453 ◽  
Author(s):  
Yu Kosaka ◽  
Shang-Ping Xie ◽  
Hisashi Nakamura
Keyword(s):  
East Asia ◽  
Interannual Variability ◽  
Southern Oscillation ◽  
Wave Train ◽  
Polar Front ◽  
Boreal Summer ◽  
Boreal Winter ◽  
Temperature Advection ◽  
Svd Analysis ◽  
Precipitation Anomalies

Abstract The summertime mei-yu–baiu rainband over East Asia displays considerable interannual variability. A singular value decomposition (SVD) analysis for interannual variability reveals that precipitation anomalies over the mei-yu–baiu region are accompanied by in situ anomalies of midtropospheric horizontal temperature advection. Anomalous warm (cool) advection causes increased (decreased) mei-yu–baiu precipitation locally by inducing adiabatic ascent (descent). The anomalous precipitation acts to reinforce the vertical motion, forming a feedback system. By this mechanism, the remotely forced anomalous atmospheric circulation can induce changes in mei-yu–baiu precipitation. The quasi-stationary precipitation anomalies induced by this mechanism are partially offset by transient eddies. The SVD analysis also reveals the association of mei-yu–baiu precipitation anomalies with several teleconnection patterns, suggesting remote induction mechanisms. The Pacific–Japan (PJ) teleconnection pattern, which is associated with anomalous convection over the tropical western North Pacific, contributes to mei-yu–baiu precipitation variability throughout the boreal summer. The PJ pattern mediates influences of the El Niño–Southern Oscillation in preceding boreal winter on mei-yu–baiu precipitation. In early summer, the leading covariability pattern between precipitation and temperature advection also features the Silk Road pattern—a wave train along the summertime Asian jet—and another wave train pattern to the north along the polar-front jet that often leads to the development of the surface Okhotsk high.

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