Pulsed eddy current signal denoising based on singular value decomposition

2015 ◽  
Vol 21 (1) ◽  
pp. 121-128 ◽  
Author(s):  
Hongyun Zhu ◽  
Changlong Wang ◽  
Hailong Chen ◽  
Jianbin Wang
Keyword(s):  
Singular Value Decomposition ◽  
Eddy Current ◽  
Singular Value ◽  
Signal Denoising ◽  
Current Signal ◽  
Pulsed Eddy Current ◽  
Value Decomposition

scholarly journals Using singular value decomposition for generalized linear autoregression of signals

2018 ◽  
Vol 4 (1) ◽  
pp. 375-378 ◽  
Author(s):  
Thomas Schanze
Keyword(s):  
Singular Value Decomposition ◽  
Least Squares ◽  
Linear Combination ◽  
Singular Value ◽  
Autoregressive Models ◽  
Current Signal ◽  
Linear Superposition ◽  
Biomedical Signals ◽  
Non Linear ◽  
Value Decomposition

AbstractAutoregressive models (AR) are fundamental for analysis, representation, and prediction of signals. AR modelling uses the premise that past signal values influence current ones. This influence is causal and is modelled as a linear superposition, because a weighted addition of past values is used. The calculation of the required linear superposition parameters or weights can be done by the classical Yule-Walker approach or by least squares procedures. Here we show how to use singular value decomposition (SVD) for generalized linear autoregression (GLAR), i.e. using SVD to compute the weights of a linear combination of functions of given signal values and to check or optimize the GLAR model. The GLAR approach opens the possibility to take directly into account non-linear influences from past to current signal values. T he potential of this approach for analysis and representation is presented and demonstrated for simulated signals, i.e. pure and noisy sequences of non-linear recursions, and biomedical signals.

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
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