scholarly journals High-Order Polynomial Fitting Assistance for Fast Double-Peak Finding in Brillouin-Distributed Sensing

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 187
Author(s):  
Marcelo A. Soto ◽  
Alin Jderu ◽  
Dorel Dorobantu ◽  
Marius Enachescu ◽  
Dominik Ziegler
Keyword(s):  
Optical Fibers ◽  
High Order ◽  
Polynomial Fitting ◽  
Gaussian Fitting ◽  
Peak Finding ◽  
Fitting Method ◽  
Fold Reduction ◽  
Order Polynomial ◽  
Fitting In ◽  
Distributed Brillouin Sensor

A high-order polynomial fitting method is proposed to accelerate the computation of double-Gaussian fitting in the retrieval of the Brillouin frequency shifts (BFS) in optical fibers showing two local Brillouin peaks. The method is experimentally validated in a distributed Brillouin sensor under different signal-to noise ratios and realistic spectral scenarios. Results verify that a sixth-order polynomial fitting can provide a reliable initial estimation of the dual local BFS values, which can be subsequently used as initial parameters of a nonlinear double-Gaussian fitting. The method demonstrates a 4.9-fold reduction in the number of iterations required by double-Gaussian fitting and a 3.4-fold improvement in processing time.

Polynomial Moving Fitting Method for Edge Identification

2011 ◽  
Vol 308-310 ◽  
pp. 2560-2564 ◽  
Author(s):  
Xiang Rong Yuan
Keyword(s):  
Edge Detection ◽  
Curve Fitting ◽  
Polynomial Function ◽  
High Order ◽  
Polynomial Fitting ◽  
Fitting Method ◽  
Order Polynomial ◽  
Better Than ◽  
Low Order

A moving fitting method for edge detection is proposed in this work. Polynomial function is used for the curve fitting of the column of pixels near the edge. Proposed method is compared with polynomial fitting method without sub-segment. The comparison shows that even with low order polynomial, the effects of moving fitting are significantly better than that with high order polynomial fitting without sub-segment.

An Improved Iterative Polynomial Fitting Algorithm for Baseline Correction in X-Ray Spectrum

2021 ◽  
Vol 105 ◽  
pp. 90-98
Author(s):  
Xiao Yu Jiang ◽  
Qing Ya Wang ◽  
Mu Qiang Xu ◽  
Jun Hao
Keyword(s):  
Fluorescence Spectrum ◽  
Soil Analysis ◽  
Simulated Data ◽  
Real Data ◽  
Polynomial Fitting ◽  
X Ray ◽  
Iterative Processes ◽  
Fitting Method ◽  
Fitting Algorithm ◽  
Fitting In

An iterative polynomial fitting method is proposed for the estimate of the baseline of the X-ray fluorescence spectrum signal. The new method generates automatic thresholds by comparing the X-ray fluorescence spectrum signal with the calculated signal from polynomial fitting in the iterative processes. The signal peaks are cut out consecutively in the iterative processes so the polynomial fitting will finally give a good estimation of the baseline. Simulated data and real data from the soil analysis spectrum are used to demonstrate the feasibility of the proposed method.

Leveling HEM and aeromagnetic data using differential polynomial fitting

Geophysics ◽  
2010 ◽  
Vol 75 (1) ◽  
pp. L13-L23 ◽  
Author(s):  
Majid Beiki ◽  
Mehrdad Bastani ◽  
Laust B. Pedersen
Keyword(s):  
Differential Polynomial ◽  
Average Height ◽  
Aeromagnetic Data ◽  
Polynomial Fitting ◽  
Data Set ◽  
Sliding Windows ◽  
Fitting Method ◽  
Data Points ◽  
The Difference ◽  
Fitting In

We introduce a new technique to level aerogeophysical data. Our approach is applicable to flight-line data without any need for tie-line measurements. The technique is based on polynomial fitting of data points in 1D and 2D sliding windows. A polynomial is fitted to data points in a 2D circular window that contains at least three flight lines. Then the same procedure is done inside a 1D window placed at the center of the 2D window. The leveling error is the difference between 1D and 2D polynomial fitted data at the center of the windows. To demonstrate the reliability of the method, it was tested on a synthetic aeromagnetic data set contaminated by some linear artifacts. Using the differential polynomial fitting method, we can remove the linear artifacts from the data. The method then was applied to two real airborne data sets collected in Iran. The leveling errors are removed effectively from the aeromagnetic data using the differential polynomial fitting. In the case of helicopter-towed electromagnetic (HEM) data, the polynomial fitting method is used to level the measured real (in-phase) and imaginary (quadrature) components, as well as the calculated apparent resistivity. The HEM data are sensitive to height variations, so we introduce an average-height scaling method to reduce the height effect before leveling in-phase and quadrature components. The method also is effective in recovering some of the attenuated anomalies. After scaling, the differential polynomial fitting method was applied to the data and effectively removed the remaining line-to-line artifacts.

Study on Static Deformation Characteristics of Mental Rubber Material

2010 ◽  
Vol 97-101 ◽  
pp. 826-829
Author(s):  
Dong Wei Li ◽  
Zhong Bo He ◽  
Guo Quan Ren ◽  
Lei Zhang
Keyword(s):  
Empirical Formula ◽  
High Order ◽  
Static Deformation ◽  
Deformation Characteristics ◽  
Polynomial Fitting ◽  
Rubber Material ◽  
Damping Material ◽  
Order Polynomial ◽  
Rubber Component

Mental rubber material is a new kind of high elastic and large damping material. In this paper the static deformation of mental rubber components had been studied from stretch, compression and shearing experiment. By the method of high-order polynomial fitting, the empirical formula of static deformation characters of mental rubber component had been established, and some deformation rules of mental rubber had been obtained.

Performance Model Development for the Adjustable Blades Axial Flow Fan Based on Multivariate Polynomial Fitting

2014 ◽  
Vol 672-674 ◽  
pp. 1584-1587
Author(s):  
Dong Hui Song ◽  
Liu Peng Hua ◽  
Shao Hua Li
Keyword(s):  
Model Development ◽  
Engineering Application ◽  
Axial Flow ◽  
Performance Model ◽  
Axial Flow Fan ◽  
Multivariate Polynomial ◽  
Polynomial Fitting ◽  
Fitting Method ◽  
Order Polynomial ◽  
Performance Surface

To obtain a performance model for the adjustable blades axial flow fan, the performance surface of fan was fitted using binary polynomial fitting method, and the coefficients matrix was solved with QR decomposition. By comparing the fitting results show that the fitting effect is good when the 5 order polynomial was used to fit the performance surface of the fan and the 6 order polynomial was used to fit the boundary of performance surface. The accuracy of the obtained model can meet the requirements of the engineering application and scientific research.

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