Research on fiber optic gyro signal de-noising based on wavelet packet soft-threshold

Gyro＇s drift is not only the main drift error which influences gyro＇s precision but also the primary factor that affects gyro＇s reliability. Reducing zero drift and random drift is a key problem to the output of a gyro signal. A three-layer de-nosing threshold algorithm is proposed based on the wavelet decomposition to dispose the signal which is collected from a running fiber optic gyro （FOG）. The coefficients are obtained from the three-layer wavelet packet decomposition. By setting the high frequency part which is greater than wavelet packet threshold as zero, then reconstructing the nodes which have been filtered out noise and interruption, the soft threshold function is constructed by the coefficients of the third nodes. Compared wavelet packet de-noise with forced de-noising method, the proposed method is more effective. Simulation results show that the random drift compensation is enhanced by 13.1%, and reduces zero drift by 0.052 6°/h.

A New Image Denoising Scheme Using Soft-Thresholding

The VisuShrink is one of the important image denoising methods. It however does not provide good quality of image due to removing too many coefficients especially using soft-thresholding technique. This paper proposes a new image denoising scheme using wavelet transformation. In this paper, we modify the coefficients using soft-thresholding method to enhance the visual quality of noisy image. The experimental results show that our proposed scheme has better performance than the VisuShrink in terms of peak signal-to-noise ratio (PSNR) i.e., visual quality of the image.

Application of S-transform threshold filtering in Anhui experiment airgun sounding data de-noising

As a relatively new method of processing non-stationary signal with high time-frequency resolution, S transform can be used to analyze the time-frequency characteristics of seismic signals. It has the following characteristics: its time-frequency resolution corresponding to the signal frequency, reversible inverse transform, basic wavelet that does not have to meet the permit conditions. We combined the threshold method, proposed the S-transform threshold filtering on the basis of S transform timefrequency filtering, and processed airgun seismic records from temporary stations in "Yangtze Program"(the Anhui experiment). Compared with the results of the bandpass filtering, the S transform threshold filtering can improve the signal to noise ratio(SNR) of seismic waves and provide effective help for first arrival pickup and accurate travel time. The first arrival wave seismic phase can be traced farther continuously, and the Pm seismic phase in the subsequent zone is also highlighted.

Reduction of ultrasonic echo noise based on improved wavelet threshold de-noising algorithm for friction welding

In the ultrasonic detection of defects in friction welded joints, it is difficult to exactly detect some weak bonding defects because of the noise pollution. This paper proposed an improved threshold function based on the multi-resolution analysis wavelet threshold de-noising method which was put forward by Donoho and Johnstone, and applied this method in the de-noising of the defective signals. This threshold function overcomes the discontinuous shortcoming of the hard-threshold function and the disadvantage of soft threshold function which causes an invariable deviation between the estimated wavelet coeffwients and the decomposed wavelet coefficients. The improved threshold function is of simple expression and convenient for calculation. The actual test results of defect noise signal show that this improved method can get less mean square error （ MSE ） and higher signal-to-noise ratio of reconstructed signals than those calculated from hard threshold and soft threshold methods. The improved threshold function has excellent de-noising effect.

The Effect of a High-Frequency-Hearing-Threshold Weighted Value on the Diagnosis of Occupational-Noise-Induced Deafness

Objective: In order to provide a theoretical basis for the revision of the current diagnostic criteria for occupational noise-induced deafness (ONID), we evaluated the degree of ONID by analyzing different high-frequency-hearing- threshold-weighted values (HFTWVs). Methods: A retrospective study was conducted to evaluate the diagnosis of patients with ONID from January 2016 to January 2017 in Guangdong province, China. Based on 3 hearing tests (each interval between the tests was greater than 3 days), the minimum threshold value of each frequency was obtained using the 2007 edition’s diagnostic criteria for ONID. The speech frequency and the HFTWVs were analyzed based on age, noise exposure, and diagnostic classi-fication using SPSS21.0. Results: 168 patients in total were involved in this study, 154 males and 14 females, and the average age was 41.18 ± 6.07. The diagnosis rate was increased by the weighted value of the high frequencies and was more than the mean value of the pure speech frequency (MVPSF). The diagnosis rate for the weighted 4 kHz frequency level increased by 13.69% (χ2 = 9.880, P = 0.002), the weighted 6 kHz level increased by 15.47% (χ2 = 9.985, P = 0.002), and the weighted 4 kHz + 6 kHz level increased by 15.47% (χ2 = 9.985, P = 0.002). The differences were all statistically significant. The diagnostic rate of the different thresholds showed no obvious difference between the genders. The age groups were divided into less than or equal to 40 years old (group A) and 40 - 50 years old (group B). There were several groups with a high frequency: high frequency weighted 4 kHz ( group A χ2 = 3.380, P = 0.050;group B χ2 = 4.054, P = 0.032), high frequency weighted 6 kHz (group A χ2 = 6.362, P = 0.012;group B χ2 = 4.054, P = 0.032), weighted 4 kHz + 6 kHz (group A χ2 = 6.362 P = 0.012;B χ2 = 4.054, P = 0.032) than those of MVPSF in the same group on ONID diagnosis rate. The differences between the groups were statistically significant. There was no significant difference between the age groups (χ2 = 2.265, P = 0.944). The better ear’s (the smaller hearing threshold weighted value) MVPSF and the weighted values for the different high frequencies were examined in light of the number of working years;the group that was exposed to noise for more than 10 years had significantly higher values than those of the average thresholds of each frequency band in the groups with 3 - 5 years of exposure (F = 2.271, P = 0.001) and 6 - 10 years of exposure (F = 1.563, P = 0.046). The differences were statistically significant. The different HFTWVs were higher than those of the MVPSF values, and the high frequency weighted 4 kHz + 6 kHz level showed the greatest difference, with an average increase of 2.83 dB. The diagnostic rate that included the weighted high frequency values was higher for the mild, moderate, and severe cases than those patients who were only screened with the pure frequency tests. The results of the comparisons of the diagnosis rates for mild ONID were as follows: the weighted 3 kHz high frequency level (χ2 = 3.117, P = 0.077) had no significant difference, but the weighted 4 kHz level (χ2 = 10.835, P = 0.001), 6 kHz level (χ2 = 9.985, P = 0.002), 3 kHz + 4 kHz level (χ2 = 6.315, P = 0.012), 3 kHz + 6 kHz level (χ2 = 6.315, P = 0.012), 4 kHz + 6 kHz level (χ2 = 9.985, P = 0.002), and 3 kHz + 4 kHz + 6 kHz level (χ2 = 7.667, P = 0.002) were significantly higher than the diagnosis rate of the mean value of the PSF. There was no significant difference between the 2 groups for the moderate and severe grades (P > 0.05). Conclusion: Different HFTWVs increase the diagnostic rate of ONID. The weighted 4 kHz, 6 kHz, and 4 kHz + 6 kHz high frequency values greatly affected the diagnostic results, and the weighted 4 kHz + 6 kHz high frequency hearing threshold value has the maximum the effect on the ONID diagnosis results.

Adaptive Threshold Median Filter for Multiple-Impulse Noise

Attenuating the noises plays an essential role in the image processing. Almost all the traditional median filters concern the removal of impulse noise having a single layer, whose noise gray level value is constant. In this paper, a new adaptive median filter is proposed to handle those images corrupted not only by single layer noise. The adaptive threshold median filter （ATMF） has been developed by combining the adaptive median filter （AMF） and two dynamic thresholds. Because of the dynamic threshold being used, the ATMF is able to balance the removal of the multiple-impulse noise and the quality of image. Comparison of the proposed method with traditional median filters is provided. Some visual examples are given to demonstrate the performance of the proposed filter.

An Improved Image Denoising Method Based on Wavelet Thresholding

VisuShrink, ModineighShrink and NeighShrink are efficient image denoising algorithms based on the discrete wavelet transform (DWT). These methods have disadvantage of using a suboptimal universal threshold and identical neighbouring window size in all wavelet subbands. In this paper, an improved method is proposed, that determines a threshold as well as neighbouring window size for every subband using its lengths. Our experimental results illustrate that the proposed approach is better than the existing ones, i.e., NeighShrink, ModineighShrink and VisuShrink in terms of peak signal-to-noise ratio (PSNR) i.e. visual quality of the image.

Denoising of Medical Images Using Multiwavelet Transforms and Various Thresholding Techniques

The problem of estimating an image corrupted by additive white Gaussian noise has been of interest for practical reasons. Non-linear denoising methods based on wavelets, have become popular but Multiwavelets outperform wavelets in image denoising. Multiwavelets are wavelets with several scaling and wavelet functions, offer simultaneously Orthogonality, Symmetry, Short support and Vanishing moments, which is not possible with ordinary (scalar) wavelets. These properties make Multiwavelets promising for image processing applications, such as image denoising. The aim of this paper is to apply various non-linear thresholding techniques such as hard, soft, universal, modified universal, fixed and multivariate thresholding in Multiwavelet transform domain such as Discrete Multiwavelet Transform, Symmetric Asymmetric (SA4), Chui Lian (CL), and Bi-Hermite (Bih52S) for different Multiwavelets at different levels, to denoise an image and determine the best one out of it. The performance of denoising algorithms and various thresholding are measured using quantitative performance measures such as, Mean Square Error (MSE), and Root Mean Square Error (RMSE), Signal-to-Noise Ratio (SNR), Peak Signal-to-Noise Ratio (PSNR). It is found that CL Multiwavelet transform in combination with modified universal thresholding has given best results.

Denoising of an Image Using Discrete Stationary Wavelet Transform and Various Thresholding Techniques

Image denoising has remained a fundamental problem in the field of image processing. With Wavelet transforms, various algorithms for denoising in wavelet domain were introduced. Wavelets gave a superior performance in image denoising due to its properties such as multi-resolution. The problem of estimating an image that is corrupted by Additive White Gaussian Noise has been of interest for practical and theoretical reasons. Non-linear methods especially those based on wavelets have become popular due to its advantages over linear methods. Here I applied non-linear thresholding techniques in wavelet domain such as hard and soft thresholding, wavelet shrinkages such as Visu-shrink (non-adaptive) and SURE, Bayes and Normal Shrink (adaptive), using Discrete Stationary Wavelet Transform (DSWT) for different wavelets, at different levels, to denoise an image and determine the best one out of them. Performance of denoising algorithm is measured using quantitative performance measures such as Signal-to-Noise Ratio (SNR) and Mean Square Error (MSE) for various thresholding techniques.

Seismic data denoising based on data-driven tight frame dictionary learning method

Because of various complicated factors in seismic data collection,the random noise of seismic data is too difficult to avoid.This random noise reduces the quality of seismic data and increases the difficulty of seismic data processing and interpretation.Improving the denoising technology is significant.In order to improve seismic data denoising result,a novel method named data-driven tight frame(DDTF)is introduced in this paper.First,we get the sparse coefficients of seismic data with noise by DDTF.Then we remove the smaller sparse coefficient by using the hard threshold function.Finally,we get the denoised seismic data by inverse transform.Furthermore,the DDTF is compared with curvelet transform in the stimulation and practical seismic data experiments to validate its performance.DDTF can raise the signal-to-noise ratio of seismic data denoising and protect the effective signal well.

Implementation of GPR Signals De-Noising Based on DSP

An important issue of ground-penetrating radar （GPR） signals analysis is de-noising thai is the guarantee of acquiring good detecting effect. The paper illustrates a successful application of digital single processor （DSP） based on wavelet shrinkage algorithm. In order to realize real-time GPP, signals analysis, some key issues are discussed such as the realization of fast wavelet transformation, the selection of CPU chip and the optimization of data movement. Experimenial results show that the DSP based application not only basically meets the real-time requirement of GPP, signals analysis, but also assures the quality of the GPR signals analysis.

An iterative curvelet thresholding algorithm for seismic random noise attenuation

In this paper,we explore the use of iterative curvelet thresholding for seismic random noise attenuation.A new method for combining the curvelet transform with iterative thresholding to suppress random noise is demonstrated and the issue is described as a linear inverse optimal problem using the L1 norm.Random noise suppression in seismic data is transformed into an L1 norm optimization problem based on the curvelet sparsity transform. Compared to the conventional methods such as median filter algorithm,FX deconvolution, and wavelet thresholding,the results of synthetic and field data processing show that the iterative curvelet thresholding proposed in this paper can sufficiently improve signal to noise radio（SNR） and give higher signal fidelity at the same time.Furthermore,to make better use of the curvelet transform such as multiple scales and multiple directions,we control the curvelet direction of the result after iterative curvelet thresholding to further improve the SNR.

Threshold分红策略下带干扰的两类索赔风险模型的Geber-Shiu函数(英文)

本文研究了在threshold分红策略下带干扰的两类索赔风险模型的Geber-Shiu函数.这里假设两个索赔计数过程为独立的更新过程,其中一个为Poisson过程另一个为时间间隔服从广义Erlang(2)分布的更新过程.本文得到了threshold分红策略下Gerber-Shiu函数所满足的积分-微分方程及其边界条件.最后,本文指出threshold分红策略下Gerber-Shiu函数可以由不分红(即:6=∞)时所对应的Geber-Shiu函数和一个齐次积分-微分方程的线性独立解表示出来.

基于EEMD-WPT的温室环境数据优化处理研究

【目的】解决温室系统中的数据采集传感器容易受到多种环境因素的干扰,从而导致数据中存在噪声的问题。【方法】提出一种集合经验模态分解(Ensemble empirical mode decomposition,EEMD)与小波包自适应阈值(Wavelet packet adaptive threshold,WPT)算法联合的数据降噪处理方法,并采用卡尔曼滤波与自适应加权平均算法对降噪后的数据进行融合。【结果】将EEMD-WPT算法应用于含噪温、湿度数据的降噪处理,相较于降噪前的数据,信噪比提升了73.08%。该算法相较于传统WPT算法具有更好的降噪效果,处理后的数据信噪比提升了40.31%,均方根误差降低了84.75%。【结论】该算法能解决数据跳动、冗余和丢失等问题,并为温室控制系统提供了有效的参数,具有较大的实际应用价值。

CEEMDAN-SE-WT降噪方法在航空发动机燃油流量信号中的应用

燃油流量信号是反映发动机状态和计算飞机排放物排放量的重要信号,但飞机飞行过程中传感器采集信号时不可避免地会受到外界环境以及内部因素干扰。提出一种结合样本熵(sample entropy,SE)的完全自适应噪声集合经验模态分解(complete ensemble empirical mode decomposition with adaptive noise,CEEMDAN)与小波变换(wavelet transform,WT)的联合降噪方法。首先使用CEEMDAN对燃油流量信号进行分解得到本征模态分量,利用样本熵筛选含噪分量,并用相关系数与方差贡献率进行复核。对于含噪分量使用小波阈值降噪进行处理。最后将未处理的模态分量和完成降噪的模态分量重构得到最终燃油流量信号。通过与其他方法比较,CEEMDAN-SE-WT方法拥有最高信噪比为85.287,降噪后燃油消耗总量与飞机总重变化最为接近,可以认为该方法较大程度保留了燃油流量信号中的有效特征,为后续计算民机排放物排放总量提供了良好的数据支持。

Subjective annoyance caused by indoor low-level and low frequency noise and control method

The influence of low_level noise has not been widely noticed. This paper discovered that low_level and low frequency noise(A_weighted equivalent level L eq <45 dB) causes higher probability of subjective annoyance. The fuzzy mathematic principle was applied to deal with the threshold level of subjective annoyance from noise in this study; there is preferable relationship between the indoor noise and noise annoyance at low frequency noise level. Study indicated at the same centered noise level, the change of annoyance probability is mainly caused by the change of the frequency spectrum characteristic of the indoor noise. Under low noise level environment, without change of the medium_low frequency noise, the slight increase of medium_high frequency noise level with the help of noise sheltering effect can significantly reduce the noise annoyance. This discovery brings a new resolution on how to improve the environmental quality of working or living places. A noise control model is given in this study according to the acoustic analysis.

Low noise,continuous-wave single-frequency 1.5-μm laser generated by a singly resonant optical parametric oscillator

We report a low noise continuous-wave （CW） single-frequency 1.5-μm laser source obtained by a singly resonant optical parametric oscillator （SRO） based on periodically poled lithium niobate （PPLN）. The SRO was pumped by a CW single-frequency Nd：YVO4 laser at 1.06μm. The 1.02 W of CW single-frequency signal laser at 1.5 μm was obtained at pump power of 6 W. At the output power of around 0.75 W, the power stability was better than ±l.5% and no mode-hopping was observed in 30 min and frequency stability was better than 8.5 MHz in 1 min. The signal wavelength could be tuned from 1.57 to 1.59 μm by varying the PPLN temperature. The 1.5-μm laser exhibits low noise characteristics, the intensity noise of the laser reaches the shot noise limit （SNL） at an analysis frequency of 4 MHz and the phase noise is less than 1 dB above the SNL at analysis frequencies above 10 MHz.

PTEN inhibitor bisperoxovanadium protects against noise-induced hearing loss

Studies have shown that phosphatase and tensin homolog deleted on chromosome ten(PTEN)participates in the regulation of cochlear hair cell survival.Bisperoxovanadium protects against neurodegeneration by inhibiting PTEN expression.However,whether bisperoxovanadium can protect against noise-induced hearing loss and the underlying mechanism remains unclear.In this study,we established a mouse model of noise-induced hearing loss by exposure to 105 dB sound for 2 hours.We found that PTEN expression was increased in the organ of Corti,including outer hair cells,inner hair cells,and lateral wall tissues.Intraperitoneal administration of bisperoxovanadium decreased the auditory threshold and the loss of cochlear hair cells and inner hair cell ribbons.In addition,noise exposure decreased p-PI3K and p-Akt levels.Bisperoxovanadium preconditioning or PTEN knockdown upregulated the activity of PI3K-Akt.Bisperoxovanadium also prevented H_(2)O_(2)-induced hair cell death by reducing mitochondrial reactive oxygen species generation in cochlear explants.These findings suggest that bisperoxovanadium reduces noise-induced hearing injury and reduces cochlear hair cell loss.

WAVELET TRANSFORM THRESHOLD NOISE REDUCTION METHODS IN THE OIL PIPELINE LEAKAGE MONITORING AND POSITIONING SYSTEM

This letter investigates the wavelet transform, as well as the principle and the method of the noise reduction based on wavelet transform, it chooses the threshold noise reduction, and discusses in detail the principles, features and design steps of the threshold method. Rigrsure, heursure, sqtwolog and minimization four kinds of threshold selection method are compared qualitatively, and quantitatively. The wavelet analysis toolbox of MATLAB helps to realize the computer simulation of the signal noise reduction. The graphics and calculated standard deviation of the various threshold noise reductions show that, when dealing with the actual pressure signal of the oil pipeline leakage, sqtwolog threshold selection method can effectively remove the noise. Aiming to the pressure signal of the oil pipeline leakage, the best choice is the wavelet threshold noise reduction with sqtwolog threshold. The leakage point is close to the actual position, with the relative error of less than 1%.

Comparative Methodical Assessment of Established MOSFET Threshold Voltage Extraction Methods at 10-nm Technology Node

Threshold voltage (VTH) is the most evocative aspect of MOSFET operation. It is the crucial device constraint to model on-off transition characteristics. Precise VTH value of the device is extracted and evaluated by several estimation techniques. However, these assessed values of VTH diverge from the exact values due to various short channel effects (SCEs) and non-idealities present in the device. Numerous prevalent VTH extraction methods are discussed. All the results are verified by extensive 2-D TCAD simulation and confirmed through analytical results at 10-nm technology node. Aim of this research paper is to explore and present a comparative study of largely applied threshold extraction methods for bulk driven nano-MOSFETs especially at 10-nm technology node along with various sub 45-nm technology nodes. Application of the threshold extraction methods to implement noise analysis is briefly presented to infer the most appropriate extraction method at nanometer technology nodes.