scholarly journals Modeling of Chromium, Copper, Zinc, Arsenic and Lead Using Portable X-ray Fluorescence Spectrometer Based on Discrete Wavelet Transform

2017 ◽  
Vol 14 (10) ◽  
pp. 1163 ◽  
Author(s):  
Fang Li ◽  
Anxiang Lu ◽  
Jihua Wang
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Discrete Wavelet ◽  
X Ray ◽  
Copper Zinc ◽  
Fluorescence Spectrometer

Using Iterative Discrete Wavelet Transform to Improve Trace Element Analysis by XRF Spectrometer

2021 ◽  
Vol 105 ◽  
pp. 79-89
Author(s):  
Qian Ma ◽  
Lian Liu ◽  
Fu Sheng Li ◽  
Yan Chun Zhao
Keyword(s):  
Wavelet Transform ◽  
Noise Reduction ◽  
Discrete Wavelet Transform ◽  
Trace Element ◽  
Background Noise ◽  
Limit Of Detection ◽  
Experimental Results ◽  
Discrete Wavelet ◽  
X Ray ◽  
Fluorescence Spectrometer

X-ray fluorescence (XRF) spectrometry has certain difficulties of detecting trace amount material components accurately when measuring material samples composed of variable elements, mainly due to low Signal to Noise Ratio (SNR) issues of the characteristic spectroscopic peaks from the measurement. In this paper, a novel method called background noise reduction using Iterative Discrete Wavelet Transform (IDWT) methodology for trace element material analysis by advanced X-ray fluorescence spectrometer is proposed to improve SNR, thereby decreasing the Limit of Detection (LOD) for elemental qualitative analysis, and then achieve a more accurate quantitative analysis of trace elemental concentration. This paper utilized handheld X-ray fluorescence spectrometer to obtain the content of Sulphur in petroleum and 4 major pollution elements in soil. A total of 81 standard samples were collected and measured. The hardware parameters of the instrument were adjusted to optimize the SNR before background noise reduction. Experimental results illustrate that X-ray tube parameters have great influences on the calibration regression. Different X-ray tube voltages were tested and the optimal results were achieved at 5kV. Furthermore, IDWT algorithm was implemented and the optimal results were achieved by wavelet base “db5” and “sym4” with 7 level decomposition. The calibration regression curves were established for the Sulphur in petroleum. The regression R2 values after IDWT were increased effectively when compared with original data without IDWT. Finally, the experimental results demonstrate a very good linearity between the weight contents of the target material and the XRF spectral characteristic peak intensity, and also it is found the LOD for Sulphur in petroleum can be reduced when combing with the IDWT.

scholarly journals Applications of Discrete Wavelet Transform for Feature Extraction to Increase the Accuracy of Monitoring Systems of Liquid Petroleum Products

Mathematics ◽  
2021 ◽  
Vol 9 (24) ◽  
pp. 3215
Author(s):  
Mohammed Balubaid ◽  
Mohammad Amir Sattari ◽  
Osman Taylan ◽  
Ahmed A. Bakhsh ◽  
Ehsan Nazemi
Keyword(s):  
Feature Extraction ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Volume Ratio ◽  
Petroleum Products ◽  
Discrete Wavelet ◽  
X Ray ◽  
Network Training ◽  
Transport Code ◽  
Pass Through

This paper presents a methodology to monitor the liquid petroleum products which pass through transmission pipes. A simulation setup consisting of an X-ray tube, a detector, and a pipe was established using a Monte Carlo n-particle X-version transport code to investigate a two-by-two mixture of four different petroleum products, namely, ethylene glycol, crude oil, gasoline, and gasoil, in deferent volumetric ratios. After collecting the signals of each simulation, discrete wavelet transform (DWT) was applied as the feature extraction system. Then, the statistical feature, named the standard deviation, was calculated from the approximation of the fifth level, and the details of the second to fifth level provide appropriate inputs for neural network training. Three multilayer perceptron neural networks were utilized to predict the volume ratio of three types of petroleum products, and the volume ratio of the fourth product could easily be obtained from the results of the three presented networks. Finally, a root mean square error of less than 1.77 was obtained in predicting the volume ratio, which was much more accurate than in previous research. This high accuracy was due to the use of DWT for feature extraction.

Denoising X-Ray Image Using Discrete Wavelet Transform and Thresholding

2021 ◽  
pp. 191-198
Author(s):  
P. Rajeswari ◽  
K. K. Thyagharajan ◽  
V. S. Prabhu ◽  
G. Shree Devi
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Discrete Wavelet ◽  
X Ray

scholarly journals Comparative analysis of the methods of watermarking X-ray images

2021 ◽  
Vol 20 ◽  
pp. 189-196
Author(s):  
Weronika Kulbaka ◽  
Paulina Paluch ◽  
Grzegorz Kozieł
Keyword(s):  
Wavelet Transform ◽  
Comparative Analysis ◽  
Singular Value Decomposition ◽  
Discrete Wavelet Transform ◽  
Singular Value ◽  
The Other ◽  
Discrete Wavelet ◽  
X Ray ◽  
And Performance ◽  
Value Decomposition

This paper is devoted to the comparative analysis of watermarking algorithms for X-ray images. The techniques based on discrete wavelet transform (DWT), singular value decomposition (SVD) and DWT-SVD hybrid were compared. Transparency, resistance to graphical transformations, and performance were investigated. The watermarked images were visually evaluated and quality tested. SVD showed the highest resistance to attacks, and the embedded watermarked images were of better quality in the comparison to the other algorithms. The DWT technique was the fastest, but not resistant to graphical transformations. In DWT-SVD labeled images, the watermark is indistinguishable, but the resistance to attacks is low. The SVD was found to be the most suitable method for watermarking of X-ray images.

Improving Space Localization Properties of the Discrete Wavelet Transform

Informatica ◽  
2013 ◽  
Vol 24 (4) ◽  
pp. 657-675
Author(s):  
Jonas Valantinas ◽  
Deividas Kančelkis ◽  
Rokas Valantinas ◽  
Gintarė Viščiūtė
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Discrete Wavelet ◽  
Space Localization
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