Absolute subaperture testing by multiangle averaging and Zernike polynomial fitting method

2013 ◽  
Vol 52 (8) ◽  
pp. 085101 ◽  
Author(s):  
Fengtao Yan ◽  
Bin Fan ◽  
Xi Hou ◽  
Fan Wu
Keyword(s):  
Polynomial Fitting ◽  
Zernike Polynomial ◽  
Fitting Method

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.

scholarly journals Neural network modeling and single-neuron proportional–integral–derivative control for hysteresis in piezoelectric actuators

2019 ◽  
Vol 52 (9-10) ◽  
pp. 1362-1370 ◽  
Author(s):  
Yuen Liang ◽  
Suan Xu ◽  
Kaixing Hong ◽  
Guirong Wang ◽  
Tao Zeng
Keyword(s):  
Neural Network ◽  
Single Neuron ◽  
Piezoelectric Actuators ◽  
Neural Network Modeling ◽  
Proportional Integral Derivative ◽  
Polynomial Fitting ◽  
Proportional Integral ◽  
The Neural Network ◽  
Fitting Method ◽  
One To One

A new polynomial fitting model based on a neural network is presented to characterize the hysteresis in piezoelectric actuators. As hysteresis is multi-valued mapping, and traditional neural networks can only solve one-to-one mapping, a hysteresis mathematical model is proposed to expand the input of the neural network by converting the multi-valued into one-to-one mapping. Experiments were performed under designed excitation with different driven voltage amplitudes to obtain the parameters of the model using the polynomial fitting method. The simulation results were in good accordance with the measured data and demonstrate the precision with which the model can predict the hysteresis. Based on the proposed model, a single-neuron adaptive proportional–integral–derivative controller combined with a feedforward loop is designed to correct the errors induced by the hysteresis in the piezoelectric actuator. The results demonstrate superior tracking performance, which validates the practicability and effectiveness of the presented approach.

Robust polynomial fitting method for regional gravity estimation

Geophysics ◽  
1991 ◽  
Vol 56 (1) ◽  
pp. 80-89 ◽  
Author(s):  
J. F. Beltrão ◽  
J. B. C. Silva ◽  
J. C. Costa
Keyword(s):  
Gravity Data ◽  
Crustal Thickening ◽  
Polynomial Fitting ◽  
Shallow Subsurface ◽  
Residual Field ◽  
Fitting Method ◽  
Definition Of ◽  
Regional Gravity ◽  
Lower Crustal ◽  
Polynomial Surface

Standard polynomial fitting methods are inconsistent in their formulation. The regional field is approximated by a polynomial fitted to the observed field. As a result, in addition to the nonuniqueness in the definition of the regional field, the fitted polynomial is strongly influenced by the residual field (observed field minus regional field). We present a regional‐residual separation method for gravity data which uses a robust procedure to determine the coefficients of a polynomial fitted to the observations. Under the hypothesis that the regional can be modeled correctly by the polynomial surface, the proposed method minimizes the influence of the residual field in the fitted surface. The proposed method was applied to real gravity data from Ceará state, Brazil, and produced information on zones of possible crustal thickening and the occurrence of lower‐crustal granulitic rocks thrust into the shallow subsurface.

scholarly journals Exponential Polynomial Fitting for Fibre Spectrum CCD Profiles

2010 ◽  
Vol 27 (3) ◽  
pp. 290-295 ◽  
Author(s):  
Zhangqin Zhu ◽  
Jia Zhu ◽  
Hanqin Qin ◽  
Chong Wang ◽  
Zhongfu Ye
Keyword(s):  
Least Squares ◽  
Spatial Orientation ◽  
Least Squares Method ◽  
Exponential Polynomial ◽  
Exponential Functions ◽  
Polynomial Fitting ◽  
Gaussian Functions ◽  
Profile Fitting ◽  
Fitting Method

AbstractA fibre spectrum profile fitting method based on the least-squares method is presented in this article. For each spectrum of one fibre in spatial orientation, two exponential functions are employed to approximate the profile. Experiments are performed with both simulated profiles and observed profiles to demonstrate the effectiveness of the algorithm. Specially, the proposed method has a better performance for profiles that are asymmetric or composed of multi-Gaussian functions.

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