Research on De-noising of Downhole Engineering Parameters by Wavelet based on Improved Threshold Function

Acoustic emission (AE) detection, as a non-electrical detection method, is very suitable for effective fault detection of power equipment with a strong electromagnetic field. However, the AE signal collected at industrial sites often contains a lot of interference noise, affecting the analysis and prediction of faults. In this study, a wavelet denoising method based on a new threshold function is proposed, to achieve a noise reduction in the low signal-to-noise ratio (SNR) signals. Simulation experiment results show that the proposed threshold function not only overcomes the shortcomings of the discontinuous hard threshold function, but also solves the constant deviation of the soft threshold function. What’s more, the proposed function achieves a good adaptability. When SNR = 10 dB: The SNR of the new threshold is 20.6622, and the RMSE is 0.0026. The SNR of the hard threshold is 20.2246 and the RMSE is 0.0027, compared with the traditional hard threshold method, the SNR of the new threshold is increased by 2.16% and the root mean square error (RMSE) is reduced by 3.7%; the SNR of the soft threshold is 15.5656, and the RMSE is 0.0047, compared with the traditional soft threshold method, the new threshold has a 32.74% increase in SNR and a 40.43% reduction in RMSE. When SNR = -10 dB: The SNR of the new threshold is 4.2602, and the RMSE is 0.0172. The SNR of the hard threshold is 3.8558 and the RMSE is 0.0182, compared with the traditional hard threshold method, the SNR of the new threshold is increased by 10.49% and the RMSE is reduced by 5.49%; the SNR of the soft threshold is 2.1625, and the RMSE is 0.0212, compared with the soft threshold method, SNR is improved by 97% and RMSE is reduced by 18.87%. Performance analyses have proved that the improved wavelet denoising method can obtain a good noise reduction effect. It is very helpful for AE signal analysis with the generally low SNR, which can improve the accuracy of failure identification in subsequent acts.

This paper presents a new denoising method called EMD-ITF that was based on Empirical Mode Decomposition (EMD) and the Improved Thresholding Function (ITF) algorithms. EMD was applied to decompose adaptively a noisy signal into intrinsic mode functions (IMFs). Then, all the noisy IMFs were thresholded by applying the improved thresholding function to suppress noise and improve the signal to noise ratio (SNR). The method was tested on simulated and real data and the results were compared to the EMD-Based signal denoising methods using the soft thresholding. The results showed the superior performance of the new EMD-ITF denoising over the traditional approach. The performance were evaluated in terms of SNR in dB, and Mean Square Error (MSE).

In order to improve the effects of denoising, this paper introduces the basic principles of wavelet threshold denoising and traditional structures threshold functions. Meanwhile, it proposes wavelet threshold function and fixed threshold formula which are both improved here. First, this paper studies the problems existing in the traditional wavelet threshold functions and introduces the adjustment factors to construct the new threshold function basis on soft threshold function. Then, it studies the fixed threshold and introduces the logarithmic function of layer number of wavelet decomposition to design the new fixed threshold formula. Finally, this paper uses hard threshold, soft threshold, Garrote threshold, and improved threshold function to denoise different signals. And the paper also calculates signal-to-noise (SNR) and mean square errors (MSE) of the hard threshold functions, soft thresholding functions, Garrote threshold functions, and the improved threshold function after denoising. Theoretical analysis and experimental results showed that the proposed approach could improve soft threshold functions with constant deviation and hard threshold with discontinuous function problems. The proposed approach could improve the different decomposition scales that adopt the same threshold value to deal with the noise problems, also effectively filter the noise in the signals, and improve the SNR and reduce the MSE of output signals.

In the process of running-in vibration signal denoising for remanufacturing engine based on wavelet transform, there are relatively large differences in denoising results when different wavelet bases are used, so the selection of wavelet bases affect the consequent of de-noising processing. On the base of analyzing the characteristics of the commonly used wavelet bases, the paper has compared the impact of the wavelet base on signal denoising. Using SNR(Signal to Noise Ratio) and RMSE(Root Mean Square Error) as criteria, and combined with the characteristics of the running-in vibration signal of the remanufacturing engine, the wavelet base coif4 has been selected as the optimistic base, which has relatively better denoising effect, and improves the SNR and resolution, so it can be used in actual practice.

In laser Doppler velocimeter (LDV), calculation precision of Doppler shift is affected by noise contained in Doppler signal. In order to restrain the noise interference and improve the precision of signal processing, wavelet packet threshold denoising methods are proposed. Based on the analysis of Doppler signal, appropriate threshold function and decomposition layer number are selected. Heursure, sqtwolog, rigrsure, and minimaxi rules are adopted to get the thresholds. Processing results indicate that signal-to-noise ratio (SNR) and root mean square error (RMSE) of simulated signals with original SNR of 0 dB, 5 dB, and 10 dB in both low- and high-frequency ranges are significantly improved by wavelet packet threshold denoising. A double-beam and double-scattering LDV system is built in our laboratory. For measured signals obtained from the experimental system, the minimum relative error of denoised signal is only 0.079% (using minimaxi rule). The denoised waveforms of simulated and experimental signals are much more smooth and clear than that of original signals. Generally speaking, denoising effects of minimaxi and saqtwolog rules are better than those of heursure and rigrsure rules. As shown in the processing and analysis of simulated and experimental signals, denoising methods based on wavelet packet threshold have ability to depress the noise in laser Doppler signal and improve the precision of signal processing. Owing to its effectiveness and practicability, wavelet packet threshold denoising is a practical method for LDV signal processing.

Broiler sound signal filtering method based on improved wavelet denoising and effective pulse extraction

The aim of the present research is to calculate the Relative Accuracy (R.A) and Root Mean Square Error (RMSE) for the location of Technical Institute, Mosul in Nineveh Governorate, Iraq. at the northern part of Mosul city by using surveying Instruments (GPS 1230 , Total Station 06 & Level NAK2), to calculate the coordinates and elevations of (16) Ground Control Points which covered the study area. The field data, remote sensing techniques and GIS software (Arc Map GIS10.3) are used to check the digital map of study area that helped the administrator to take the correct decision, determining positions of the future expansionist for buildings, roads, measuring lengths, areas and there coordinates, directions as well as the total costs estimation needed for any development from the final map. The gradient percentage between the highest and lowest points is calculated to be (9.954%). Relative accuracy for the based measured points (R.A) = 1:60000), and Root Mean Square error in Easting coordinates (RMSE) = ± 0.102m, in Northing coordinates (RMSN) = ± 0.096m, the Root Mean Square for the resultant (RMSR) = ± 0.099m, and Root Mean Square for Elevations ( RMSRL)= ± 0.005m. We Matched and correlated (10) measured points from study area with their graphic data positions on the aerial photo, determining the offsets for position and elevations as follows: (RMSE = ± 0.076m), (RMSN = ± 0.125m), (RMSR = ± 0.088m), and (Root Mean Square for Elevations = RMSRL= ± 0.045m). This research enable us after checking the results to produce new maps from Aerial photograph or Digital Image to use it for land uses and future extensions in whole study area.

With little or no human intervention, almost every object in Internet of Things (IoT) has ability to communicate, sense, and process information to make everything connected. Noise in complex environment has a great impact on machine-to-machine (M2M) interaction in IoT. Adaptive wavelet threshold function (AWTF)-based M2M Gaussian Noise Removal Method is proposed in this article. First, a bilateral enhanced wavelet threshold function is derived based on the adjustable zeroing window. Further, threshold is used to construct a bilateral enhanced wavelet threshold function, which can eliminate the oscillations in the existing wavelet threshold function. This ensures that there is no break in wavelet coefficients during the reconstruction process and that stable wavelet decomposition and reconstruction can be achieved. The signal-to-noise ratio (SNR) of a signal is estimated based on the variance of the noise-containing signal, and the zeroing window parameters are adjusted adaptively according to the SNR value to eliminate the noisy wavelet coefficients and improve the denoising performance. In addition, when the wavelet coefficients are reconstructed, the proposed algorithm can select a suitable threshold function according to a particular threshold value, which improves the robustness of the algorithm. “Doppler” and “Bumps” standard test signals are used as interactive signals to simulate the proposed algorithms. For “Doppler” signals, the SNR, root mean square error (RMSE), and noise suppression ratio (NSR) of AWTF increased by 8.48%, 42.59%, and 1.69%, respectively, compared with the GDES+ABC algorithm. For “Bumps” signal, the SNR, RMSE, and NSR of AWTF are improved by 11.67%, 24.46%, and 2.99%, respectively, compared with GDES+ABC algorithm. In addition, we also use different modulated signals to carry out real field experiments in Qingdao cruise ship home port, which proves the effectiveness of the proposed algorithm.

Heart health becomes more important to monitor, which is analyzed using Electrocardiography (ECG) signal. ECG signal can be corrupted by different forms of noises, when it is acquired. These noises mislead the ECG analysis and reduce the chance of accurate identification of the ECG signal features. To remove these noises, various adaptive and non adaptive filter techniques are used. This paper proposes a methodology to remove power line interference noise, using various adaptive techniques, like least mean square (LMS) algorithm, normalized least mean square (NLMS) algorithm and Kalman least mean square (KLMS) algorithm. All three filters results are compared based on evaluation metrics like signal to noise ratio (SNR), Root mean square error and convergence. KLMS algorithm is analyzed, as it overcomes the drawback of LMS and NLMS algorithm. And the advantage of KLMS algorithm is that; it is step size independent, and has least root mean square error (RMSE) and converges quickly. The results have been concluded with the MIT-BIH arrhythmia data base and simulated using MATLAB software. The algorithms are implemented in TMS320C6713 DSP kit using Code composer studio v3. TMS320C6713 is use Very long instruction word (VLIW), which helps to compute numerically complex algorithms.

<span lang="EN-US">This paper introduces a new thresholding function that mixes the soft thresholding functions and Smoothly Clipped Absolute Deviation (SCAD) for denoising images using the decimated wavelet transform technique, which is widely popular in various applications. The proposed method is applied to denoise noisy images contaminated with additive white Gaussian noise, employing the Top rule. The efficiency of this new thresholding function is also evaluated within the context of the Translation Invariant method. The results are compared with existing methods such as SCAD, soft thresholding, and the Wiener filter-based denoising approach. Parameters such as Root Mean Square Error (RMSE) and Peak Signal Noise Ratio (PSNR) are employed to assess the quality of denoising.</span>

The concept of orthometric heights system determination plays a major key role in geodesy, and it has broad applications in various fields and activities. In geodesy, one significant quantity is the orthometric height, the height above or below the geoid along the gravity plumbline. Conventionally, the orthometric height is determined by gravimetry and levelling techniques. However, the aforementioned techniques has its own demerits. Thus, the error is accumulated with the increase of the propagation measurement line, it is difficult to convert two separated points which is located in two continents or islands separated by sea. These techniques are tedious, time consuming and expensive. In order to resolve this challenge, many researchers resort to various techniques and approaches of obtaining orthometric heights for an area using various mathematical models. It is in this quest that, this study seek to estimate orthometric heights of a mine by utilizing plausible alternative techniques based on artificial neural networks (ANN), multivariate adaptive regression splines (MARS), polynomial regression models and multiple linear regression (MLR). The working efficiency and performance of each model has been assessed based on statistical indicators of Mean (M), Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Bias Error (MBE), Mean Absolute Error (MAE), Standard Deviation (SD), Correlation coefficient (R), Correction of determination (R2), and Signal to Noise Ratio (SNR). The statistical findings reveal that all the models produce satisfactory results in estimating the orthometric heights in the mine. MARS and ANN models compare to the MLR and polynomial models achieved higher results in terms of accuracy with mean and standard deviation of -0.000001888 m, +2.24736 m, and +0.005835 m and 0.095063 m respectively. This study will create the opportunity for geospatial practitioners to recognize the significant of ANN, MARS, MLR, and Polynomial model in solving some of the problems in geoscientific community.

In order to solve the problem of noise interference in the collected magneto mechanical signals, a new wavelet shrinkage threshold based on adaptive estimation is proposed. Based on the shortcomings of the traditional threshold function, an improved threshold function is proposed, and the parameters of the threshold function are solved by the improved genetic algorithm, and the optimal denoising effect is finally obtained. The new threshold function can not only make up the defects of each threshold function, ensure the continuity of threshold function, but also flexibly adjust the threshold to adapt to different noise conditions, and solve the deviation caused by inherent threshold function, and protect the useful information with noise signals.

Single User, Transmission Control Protocol Downstream Throughput (TCPDST) probability models in an IEEE802.11b WLAN have been developed, validated and evaluated for performance. Measurement of single user TCPDST were taken using Tamosoft throughput test while that of signal to noise ratio (SNR) were taken using inSSIDer 2.1. The Tamosoft throughput tests were conducted using different quality of service (QoS) traffic. These QoS traffic (which were sent through an infrastructure based network) correspond to different wireless multimedia tags. Measurements were taken in free space, small offices and open corridor environments. By assuming a normal distribution, single user TCPDST Cumulative distribution function (CDF) probability models were developed for different signal categories namely: (i) all the SNR considered, (ii) strong signals only, (iii) grey signals only and (iv) weak signals only. The models were validated and their performances evaluated using root mean square (RMS) errors. RMS errors were computed by comparing model predicted values with validation data. The RMS errors for single user CDF all signals model was 0.1466%. RMS errors for strong signals models, grey signals model and weak signals model respectively were 0.1466%, 0.6756% and 0.1233% indicating acceptable performances. All signals, strong signals, grey signals and weak signals CDF probability models predicted probabilities of obtaining TCPDST values greater than 5Mbps as 74.79%, 90.55%, 13.00% and 4.77% respectively while probabilities of obtaining TCPDST values less than 2Mbps were predicted as 4.91%, 0.00%, 18.98% and 52.41% respectively. These probability models will provide additional useful information needed to design efficient distributed data networks.
 Keywords: Throughput, TCP, WLAN, probability models

OPINION article Front. Physiol., 22 January 2014Sec. Integrative Physiology Volume 4 - 2013 | https://doi.org/10.3389/fphys.2013.00403

Coal rock rupture microseismic signal is characterized by time-varying, nonstationary, unpredictability, and transient property. Wavelet transform is an important method in microseismic signals processing. However, different wavelet bases yield different results when analyzing the same signal. To study the comparability of different wavelet bases in analyzing microseismic signals, the current paper uses the microseismic signals released from coal rock bursting as the research subject. Through the analysis of the properties of commonly used wavelet basis functions and the characteristics of coal rock microseismic signals, the current study found that Coiflet and Symlet wavelets are suitable for analyzing coal rock microseismic signals. Sym 8 and Coif 2 wavelets were found to be suitable for analyzing and denoising coal rock microseismic signals. After Sym 8 wavelet denoising, signal-to-noise ratio (SNR) and the root mean square error were 30.4184 and 1.3109 E–07, respectively. After Coif 2 wavelet denoising, the SNR and the root mean square error values were 35.2176 and 1.0312 E–07, respectively. The results will aid in the analysis and extraction of coal rock microseismic signals.