scholarly journals Minimization of errors in discrete wavelet filtering of signals during ultrasonic measurements and testing

2021 ◽  
pp. 57-62
Author(s):  
Yuri Taranenko ◽  
Ruslan Mygushchenko ◽  
Olga Kropachek ◽  
Grigoriy Suchkov ◽  
Yuri Plesnetsov
Keyword(s):  
Wavelet Decomposition ◽  
Signal Interference ◽  
Psi Function ◽  
Discrete Wavelet ◽  
Wavelet Filtering ◽  
Threshold Functions ◽  
Discrete Wavelet Decomposition ◽  
Discrete Wavelets ◽  
The Given ◽  
Algorithm Construction

Error minimizing methods for discrete wavelet filtering of ultrasonic meter signals are considered. For this purpose, special model signals containing various measuring pulses are generated. The psi function of the Daubechies 28 wavelet is used to generate the pulses. Noise is added to the generated pulses. A comparative analysis of the two filtering algorithms is performed. The first algorithm is to limit the amount of detail of the wavelet decomposition coefficients in relation to signal interference. The minimum value of the root mean square error of wavelet decomposition signal deviation which is restored at each level from the initial signal without noise is determined. The second algorithm uses a separate threshold for each level of wavelet decomposition to limit the magnitude of the detail coefficients that are proportional to the standard deviation. Like in the first algorithm, the task is to determine the level of wavelet decomposition at which the minimum standard error is achieved. A feature of both algorithms is an expanded base of discrete wavelets ‒ families of Biorthogonal, Coiflet, Daubechies, Discrete Meyer, Haar, Reverse Biorthogonal, Symlets (106 in total) and threshold functions garotte, garrote, greater, hard, less, soft (6 in total). The model function uses random variables in both algorithms, so the averaging base is used to obtain stable results. Given features of algorithm construction allowed to reveal efficiency of ultrasonic signal filtering on the first algorithm presented in the form of oscilloscopic images. The use of a separate threshold for limiting the number of detail coefficients for each level of discrete wavelet decomposition using the given wavelet base and threshold functions has reduced the filtering error.

Thailand tourism forecasting based on a hybrid of discrete wavelet decomposition and NARX neural network

2016 ◽  
Vol 116 (6) ◽  
pp. 1242-1258 ◽  
Author(s):  
Ratree Kummong ◽  
Siriporn Supratid
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Wavelet Decomposition ◽  
Tourism Industry ◽  
Relative Price ◽  
Discrete Wavelet ◽  
Content Type ◽  
Input Factor ◽  
Discrete Wavelet Decomposition ◽  
Forecasting Performance

Purpose – Accurate forecast of tourist arrivals is crucial for Thailand since the tourism industry is a major economic factor of the country. However, a nonstationarity, normally consisted in nonlinear tourism time series can seriously ruin the forecasting computation. The purpose of this paper is to propose a hybrid forecasting method, namely discrete wavelet decomposition (DWD)-NARX, which combines DWD and the nonlinear autoregressive neural network with exogenous input (NARX) to cope with such nonstationarity, as a consequence, improve the effectiveness of the demand-side management activities. Design/methodology/approach – According to DWD-NARX, wavelet decomposition is executed for efficiently extracting the hidden significant, temporal features contained in the nonstationary time series. Then, each extracted feature set at a particular resolution level along with a relative price as an exogenous input factor are fed into NARX for further forecasting. Finally, the forecasting results are reconstructed. Forecasting performance measures rely on mean absolute percentage error, mean absolute error as well as mean square error. Model overfitting avoidance is also considered. Findings – The results indicate the superiority of the DWD-NARX over other efficient related neural forecasters in the cases of high forecasting performance rate as well as competently coping with model overfitting. Research limitations/implications – The scope of this study is confined to Thailand tourist arrivals forecast based on short-term projection. To resolve such limitations, future research should aim to apply the generalization capability of DWD-NARX on other domains of managerial time series forecast under long-term projection environment. However, the exogenous input factor is to be empirically revised on domain-by-domain basis. Originality/value – Few works have been implemented either to handle the nonstationarity, consisted in nonlinear, unpredictable time series, or to achieve great success on finding an appropriate and effective exogenous forecasting input. This study applies DWD to attain efficient feature extraction; then, utilizes the competent forecaster, NARX. This would comprehensively and specifically deal with the nonstationarity difficulties at once. In addition, this study finds the effectiveness of simply using a relative price, generated based on six top-ranked original tourist countries as an exogenous forecasting input.

Clamping diodes failure identification based on the discrete wavelet decomposition of the magnetic near‐field

2019 ◽  
Vol 12 (5) ◽  
pp. 1141-1148 ◽  
Author(s):  
Ali Lahouar ◽  
Mahmoud Hamouda ◽  
Ibtissem Abari ◽  
Jaleleddine Ben Hadj Slama
Keyword(s):  
Wavelet Decomposition ◽  
Near Field ◽  
Discrete Wavelet ◽  
Discrete Wavelet Decomposition ◽  
Failure Identification

scholarly journals An Approach to Detect Image Forgery by Discrete Wavelet Decomposition

2019 ◽  
Vol 15 (2) ◽  
pp. 79-86
Author(s):  
Dibakar Raj Pant
Keyword(s):  
Wavelet Decomposition ◽  
Color Space ◽  
Discrete Wavelet ◽  
Multimedia Technology ◽  
Image Forgery ◽  
Ycbcr Color Space ◽  
Discrete Wavelet Decomposition ◽  
Gradient Technique ◽  
Rgb Color Space ◽  
Sensitivity Specificity

 Image forgery or manipulation by using the multimedia technology is becoming a challenging issue. The most common type of image forgery is copy-move forgery where some part of one image is copied and spliced in the other image. In this article, first the images in RGB color space is converted into YCbCr color space and the four-level discrete wavelet transform (DWT) is implemented to detect image forgery. The output of the DWT is further processed by using the image gradient technique for the edge detection of spliced objects. Morphological operation and Wiener filtering are applied for locating the tempered region in the forged image. Sensitivity, specificity and accuracy calculated for spliced images of CASIA datasets are obtained 89%, 86% and 88% respectively.  

Coding of Biosignals Using the Discrete Wavelet Decomposition

2010 ◽  
pp. 144-151 ◽  
Author(s):  
Ramon Reig-Bolaño ◽  
Pere Marti-Puig ◽  
Jordi Solé-Casals ◽  
Vladimir Zaiats ◽  
Vicenç Parisi
Keyword(s):  
Wavelet Decomposition ◽  
Discrete Wavelet ◽  
Discrete Wavelet Decomposition
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