scholarly journals Reducing of noise structure influence on an accuracy of a desired signal extraction

2018 ◽  
Vol 15 (3) ◽  
pp. 365-370 ◽  
Author(s):  
Vladimir Marchuk
Keyword(s):  
Signal Processing ◽  
Mathematical Expectation ◽  
Random Processes ◽  
Parameters Estimation ◽  
Signal Extraction ◽  
Scientific Contribution ◽  
Noise Component ◽  
Component Structure ◽  
Noise Structure ◽  
Theoretical Results

In the paper, the issues regarding the analysis of the noise component structure are addressed and methods for reducing the error in estimating of the mathematical expectation of the noise component are proposed. The use of the proposed method of ?noise purification? makes possibility to reduce the error introduced by the noise structure when estimating the mathematical expectation and dispersion of the noise component during research. The main scientific contribution in this paper in accuracy increasing of random processes parameters estimation. These theoretical results can be applied in different spheres of data analyzing and signal processing when random processes have some structure.

Fast white light interference signal processing method based on effective signal extraction

2019 ◽  
Vol 48 (10) ◽  
pp. 1013002
Author(s):  
马 龙 Ma Long ◽  
贾 竣 Jia Jun ◽  
裴 昕 Pei Xin ◽  
胡艳敏 Hu Yanmin ◽  
周 航 Zhou Hang ◽  
...  
Keyword(s):  
Signal Processing ◽  
White Light ◽  
Processing Method ◽  
Signal Extraction ◽  
Interference Signal ◽  
Light Interference ◽  
Signal Processing Method ◽  
White Light Interference ◽  
Effective Signal

SURVIVAL COPULA PARAMETERS ESTIMATION FOR ARCHIMEDEAN FAMILY UNDER SINGLY CENSORING

2021 ◽  
Vol 10 (6) ◽  
pp. 2847-2864
Author(s):  
N. Idiou ◽  
F. Benatia
Keyword(s):  
Diabetic Retinopathy ◽  
Simulation Study ◽  
Estimation Method ◽  
Relative Bias ◽  
Parameters Estimation ◽  
Parametric Estimation ◽  
Lifetime Data ◽  
Archimedean Copulas ◽  
Theoretical Results ◽  
Survival Copula

Given $(Z_{i},\delta _{i})=\left\{ \min (T_{i},C_{i}),I_{(T_{i}<C_{i})_{i=1,2}}\right\} ,$ as dependent or independent right-censored variables, general formulas are proven for a semi-parametric estimation of the proposed method. As a logical continuation of results established by N.IDIOU et al 2021 \cite{ref16}, a new estimator of $\tilde{C}$ is proposed by considering that the underlying copula is Archimedean, under singly censoring data. As an application, two Archimedean copulas models have been chosen to illustrate our theoretical results. A simulation study follows, which sheds light on the behavior of the process estimation method shown that the proposed estimator performs well in terms of relative bias and RMSE. The methodology of the proposed estimator is also illustrated by using lifetime data from the Diabetic Retinopathy Study, where its efficiency and robustness are observed.

Correntropy for Random Processes: Properties and Applications in Signal Processing

2010 ◽  
pp. 415-455
Author(s):  
Puskal Pokharel ◽  
Ignacio Santamaria ◽  
Jianwu Xu ◽  
Kyu-hwa Jeong ◽  
Weifeng Liu
Keyword(s):  
Signal Processing ◽  
Random Processes

The Method Based on Pattern Filter for Sonnd & Vibration Signal

2013 ◽  
Vol 333-335 ◽  
pp. 526-530
Author(s):  
Miao Rong Lv ◽  
Bao Jian Wei ◽  
Jian Lu ◽  
Jian Bo Diao
Keyword(s):  
Signal Processing ◽  
Model Building ◽  
Fourier Method ◽  
Vibration Signal ◽  
Complete Separation ◽  
Processing Method ◽  
Signal Extraction ◽  
Filter Method ◽  
Vibration Signals ◽  
Signal Processing Method

The difficulty of the signal processing is not the acquisition of the signals, but how to get the reasonble interpretations from the signals. Since the 1960s, Wavelet Transform, Fast Fourier method and other theoryies have done some works by some innovative processing methods to achieve a breakthrough. But for their limitions, these methods can not achieve a complete separation if the there are two or more signals in one time domain or frequency domain. In this article, a new engineering signal processing method-pattern filter method has is introduced, by which the signal extraction, sepration and noise reduction can be achieved successfully. Experiments show that this method can not only make a reasonable separation of the various vibration signals, but also give the typical signal extractions and model building ways.

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