Research on Denoising of GPS Data Based on Nonlinear Wavelet Transform Threshold Method

2012 ◽  
Vol 446-449 ◽  
pp. 926-936
Author(s):  
De Bao Yuan ◽  
Xi Min Cui ◽  
Guo Wang ◽  
Jing Jing Jin ◽  
Wan Yang Xu
Keyword(s):  
Wavelet Transform ◽  
Gps Data ◽  
Threshold Method

Research on Denoising of GPS Data Based on Nonlinear Wavelet Transform Threshold Method

2012 ◽  
Vol 446-449 ◽  
pp. 926-936
Author(s):  
De Bao Yuan ◽  
Xi Min Cui ◽  
Guo Wang ◽  
Jing Jing Jin ◽  
Wan Yang Xu
Keyword(s):  
Signal Processing ◽  
Wavelet Transform ◽  
Wavelet Analysis ◽  
Wavelet Denoising ◽  
Systematic Research ◽  
Gps Data ◽  
Signal Denoising ◽  
Threshold Method ◽  
Denoising Method

Signal denoising is one of the classic problems in the field of signal processing. As a new kind of signal processing tool, the good denoising performance of wavelet analysis has caused public growing concern and attention. The paper does systematic research on nonlinear wavelet threshold denoising method. And the wavelet denoising method has been used on GPS signal, and good results have been achieved.

scholarly journals LSTM with Wavelet Transform Based Data Preprocessing for Stock Price Prediction

2019 ◽  
Vol 2019 ◽  
pp. 1-8 ◽  
Author(s):  
Xiaodan Liang ◽  
Zhaodi Ge ◽  
Liling Sun ◽  
Maowei He ◽  
Hanning Chen
Keyword(s):  
Wavelet Transform ◽  
Stock Price ◽  
Signal Reconstruction ◽  
Data Preprocessing ◽  
Original Data ◽  
Training Data ◽  
Threshold Method ◽  
Denoising Method ◽  
Stock Price Prediction ◽  
Price Prediction

For profit maximization, the model-based stock price prediction can give valuable guidance to the investors. However, due to the existence of the high noise in financial data, it is inevitable that the deep neural networks trained by the original data fail to accurately predict the stock price. To address the problem, the wavelet threshold-denoising method, which has been widely applied in signal denoising, is adopted to preprocess the training data. The data preprocessing with the soft/hard threshold method can obviously restrain noise, and a new multioptimal combination wavelet transform (MOCWT) method is proposed. In this method, a novel threshold-denoising function is presented to reduce the degree of distortion in signal reconstruction. The experimental results clearly showed that the proposed MOCWT outperforms the traditional methods in the term of prediction accuracy.

The efficiency of using wavelet transforms for filtering noise in the signals of measuring transducers

2021 ◽  
pp. 16-21
Author(s):  
Yuriy K. Taranenko
Keyword(s):  
Wavelet Transform ◽  
Wavelet Transforms ◽  
Threshold Value ◽  
Wavelet Function ◽  
Threshold Function ◽  
Discrete Wavelet ◽  
Threshold Method ◽  
Wavelet Filtering ◽  
Vanishing Moments ◽  
Measuring Signal

Methods of wavelet filtering of noise in signals of measuring transducers using the threshold method of discrete wavelet transform are considered. To study the methods of wavelet filtering of noise, special model signals were used to estimate the filtering errors. A method has been developed for determining the parameters of wavelet filtering of noise with a threshold for all levels of decomposition, which makes it possible to determine the wavelet function, threshold function and filtering threshold of the detailing coefficients of the discrete wavelet decomposition. The influence of the parameters of the noise distribution, the noise level, the number of vanishing moments of the Daubechies wavelet function, the nature of the threshold function and the threshold value on the filtering error caused by the noises of non-stationary measuring signals has been investigated by the method of a computational experiment. The results of the study of six threshold functions are given with the addition of noise to the measuring signal with nonstationary amplitude, frequency and duty cycle of rectangular pulses. The signal of the Doppler sensors is investigated, the wavelet filtering parameters are calculated, which provide the minimum error. The obtained parameters are used to construct graphs of signals before and after filtering directly in the time domain using the inverse wavelet transform.

Wavelet-Based Filtering Method for Sleep EEG Signal

2011 ◽  
Vol 130-134 ◽  
pp. 2160-2165
Author(s):  
Hua Qiang ◽  
Rui Yang ◽  
Guo Dong Zhang
Keyword(s):  
Wavelet Transform ◽  
Wavelet Decomposition ◽  
Sleep Eeg ◽  
Signal Interference ◽  
Reconstruction Method ◽  
Threshold Method ◽  
Baseline Drift ◽  
Modulus Maximum ◽  
Common Signal ◽  
Wavelet Modulus Maximum

In this paper, in accordance with several common signal interference in sleep EEG detection, it is processed by wavelet transform. It mainly includes: ①.remove white noise from EEG using wavelet threshold method; ②.remove baseline drift from EEG using wavelet decomposition and reconstruction method; ③.remove sharp pulse interference using wavelet modulus maximum algorithm; ④.remove EMG from EEG using wavelet decomposition and reconstruction as well as modulus maximum method. The results of simulation study show that: it can filter a variety of common interference in EEG detection preferably by wavelet transform.

scholarly journals The Discrete Wavelet Transform and Its Application for Noise Removal in Localized Corrosion Measurements

2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
Rogelio Ramos ◽  
Benjamin Valdez-Salas ◽  
Roumen Zlatev ◽  
Michael Schorr Wiener ◽  
Jose María Bastidas Rull
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Electrical Potential ◽  
Digital Processing ◽  
Noise Removal ◽  
Localized Corrosion ◽  
Electrical Noise ◽  
Discrete Wavelet ◽  
Electrical Measurements ◽  
Threshold Method

The present work discusses the problem of induced external electrical noise as well as its removal from the electrical potential obtained from Scanning Vibrating Electrode Technique (SVET) in the pitting corrosion process of aluminum alloy A96061 in 3.5% NaCl. An accessible and efficient solution of this problem is presented with the use of virtual instrumentation (VI), embedded systems, and the discrete wavelet transform (DWT). The DWT is a computational algorithm for digital processing that allows obtaining electrical noise with Signal to Noise Ratio (SNR) superior to those obtained with Lock-In Amplifier equipment. The results show that DWT and the threshold method are efficient and powerful alternatives to carry out electrical measurements of potential signals from localized corrosion processes measured by SVET.

IMAGE DENOISING BASED ON THE DYADIC WAVELET TRANSFORM AND IMPROVED THRESHOLD

2009 ◽  
Vol 07 (03) ◽  
pp. 269-280 ◽  
Author(s):  
ZHENGHONG HUANG ◽  
BIN FANG ◽  
XIPING HE ◽  
LI XIA
Keyword(s):  
Wavelet Transform ◽  
Theoretical Analysis ◽  
Image Denoising ◽  
The Self ◽  
Threshold Function ◽  
Wavelet Coefficients ◽  
Threshold Method ◽  
Dyadic Wavelet ◽  
Dyadic Wavelet Transform ◽  
Noise Experiment

Wavelet transform has shown a strong capability to filter out noise that existed in images. The main problem to improve denoising performance lies in the fact that it is difficult to select suitable wavelet transform and related thresholds. In this paper, we completely explore characteristics of dyadic wavelet transform and its possible use to image denoising. An improved threshold method is elaborately designed to establish the self-adjusted layer threshold function and wavelet reconstruction. Theoretical analysis shows that denoising precision can be improved by way of adopting different thresholds according to the different scales of the wavelet coefficients of image and noise. Experiment results demonstrated that our method is obviously superior to that of fixed threshold regarding the effect of image denoising.

Study on White Noise Suppression Using Complex Wavelet Threshold Algorithm

2014 ◽  
Vol 521 ◽  
pp. 347-351 ◽  
Author(s):  
Shu Qi Zhang ◽  
Jin Zhong Li ◽  
Rui Guo ◽  
Hao Tang ◽  
Tao Zhao ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
White Noise ◽  
Noise Suppression ◽  
Threshold Selection ◽  
Complex Wavelet Transform ◽  
Threshold Method ◽  
Modulus Maximum ◽  
Complex Wavelet ◽  
Wavelet Transform Modulus Maximum ◽  
Selection Of

The complex wavelet transform modulus maximum of the PD signal increases with scale, while the complex wavelet transform modulus maximum of white noise decreases with scale. According to the characteristics, a study on white noise suppression using the effective complex wavelet coefficient (ECWC) threshold method is launched in this paper and a comparison is conducted with the wavelet threshold denoising method of threshold selection of Stein unbiased risk estimate theory and threshold selection of minimax theory. The PD signal denoising results show that ECWC threshold method is more effective and the distortion of the extract PD signal is lower compared with the other method.

scholarly journals Extraction method of weak underwater acoustic signal based on the combination of wavelet transform and empirical mode decomposition

2021 ◽  
Vol 12 ◽  
pp. 7
Author(s):  
Junbing Shi ◽  
Yingmin Wang ◽  
Xiaoyong Zhang ◽  
Libo Yang
Keyword(s):  
Wavelet Transform ◽  
Empirical Mode Decomposition ◽  
Signal To Noise Ratio ◽  
Intrinsic Mode Functions ◽  
Threshold Method ◽  
Underwater Acoustic ◽  
Baseline Drift ◽  
Mode Decomposition ◽  
Empirical Mode Decomposition Method ◽  
Combined Algorithm

When studying underwater acoustic exploration, tracking and positioning, the target signals collected by hydrophones are often submerged in strong intermittent noise and environmental noise. In this paper, an algorithm that combines empirical mode decomposition and wavelet transform is proposed to achieve the efficient extraction of target signals in the environment with strong noise. First the calibration of baseline drift is performed on the algorithm, and then it is decomposed into different intrinsic mode functions via empirical mode. The wavelet threshold processing is conducted according to the correlation coefficient of each mode component and the original signal, and finally the signals are reconstructed. The simulation and experiment results show that compared with the conventional empirical mode decomposition method and wavelet threshold method, when the signal-to-noise ratio is low and there exist high-frequency intermittent jamming and baseline drift, the combined algorithm can better extract the target signal, laying the foundation for direction-of-arrival estimation and target positioning in the next step.

scholarly journals REMOVAL OF ELECTRODE MOTION ARTIFACT IN ECG SIGNALS USING WAVELET BASED THRESHOLD METHODS WITH GREY INCIDENCE DEGREE

2013 ◽  
pp. 11-18
Author(s):  
G. UMAMAHESWARA REDDY ◽  
M. MURALIDHAR
Keyword(s):  
Wavelet Transform ◽  
Signal To Noise Ratio ◽  
Modern Society ◽  
Motion Artifact ◽  
Experimental Result ◽  
Discrete Wavelet ◽  
Grey Theory ◽  
Wavelet Thresholding ◽  
Threshold Method ◽  
Ecg Signals

Cardiovascular diseases are one of the most frequent and dangerous problems in modern society in nowadays. Unfortunately electrocardiograms (ECG) signals, during their acquisition process, are affected by various types of noise and artifacts due to the movement, or breathing of the patient, electrode contact, power-line interferences, etc. The aim of this study was to develop an algorithm to remove electrode motion artifact in ECG signals. Donoho and Johnstone proposed Wavelet thresholding de-noising method based on discrete wavelet transform (DWT) is suitable for non-stationary signals. The wavelet transform coefficient is processed by using grey relation analysis of the grey theory, and a new wavelet threshold method namely wavelet threshold method with grey incidence degree (GID) (or the GID threshold method) based is introduced. It shows that the signal smoothness and similarity of the two signal criteria have been greatly improved by the GID threshold method compared with existing threshold methods. According to the characteristics of different ECG signals, GID threshold method gets better results than it can adaptively deal with noise separation and details remaining of the two opposing signal problems, so as to provide a better choice for wavelet threshold methods of signal processing. Performance analysis was performed by evaluating Mean Square Error (MSE), Signal-to-noise ratio (SNR) and visual inspection over the denoised signal from each algorithm. The experimental result shows that GID hard shrinkage method with sub-band or level dependent thresholding gives the best denoising performance on ECG signal. The result shows that soft threshold not always gives better denoising performance; it depends on which wavelet thresholding algorithm was chosen.

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