Statistical Modeling with a Hidden Markov Tree and High-resolution Interpolation for Spaceborne Radar Reflectivity in the Wavelet Domain

With the increasing availability of precipitation radar data from space,enhancement of the resolution of spaceborne precipitation observations is important,particularly for hazard prediction and climate modeling at local scales relevant to extreme precipitation intensities and gradients.In this paper,the statistical characteristics of radar precipitation reflectivity data are studied and modeled using a hidden Markov tree(HMT)in the wavelet domain.Then,a high-resolution interpolation algorithm is proposed for spaceborne radar reflectivity using the HMT model as prior information.Owing to the small and transient storm elements embedded in the larger and slowly varying elements,the radar precipitation data exhibit distinct multiscale statistical properties,including a non-Gaussian structure and scale-to-scale dependency.An HMT model can capture well the statistical properties of radar precipitation,where the wavelet coefficients in each sub-band are characterized as a Gaussian mixture model(GMM),and the wavelet coefficients from the coarse scale to fine scale are described using a multiscale Markov process.The state probabilities of the GMM are determined using the expectation maximization method,and other parameters,for instance,the variance decay parameters in the HMT model are learned and estimated from high-resolution ground radar reflectivity images.Using the prior model,the wavelet coefficients at finer scales are estimated using local Wiener filtering.The interpolation algorithm is validated using data from the precipitation radar onboard the Tropical Rainfall Measurement Mission satellite,and the reconstructed results are found to be able to enhance the spatial resolution while optimally reproducing the local extremes and gradients.

Electrocardiogram Feature Extraction Technique Based on Wavelet Domain Lorentz Differential Deconvolution

In order to extract the cardiac characteristics in electrocardiogram (ECG), a feature extraction technique was developed based on wavelet domain Lorentz differential deconvolution. During the feature extraction of QRS complex, baseline drifts were firstly removed from raw ECG records by a mathematical morphology method and the feature sub-band of QRS complex was separated by using wavelet transform. Then an evolving Lorentz differential deconvolution technique was applied to estimating the local features of QRS complex from this sub-band. During the feature extraction of P and T waves, the above steps were similarly employed and, before wavelet transform, QRS complex was eliminated through locating their positions to avoid relevant disturbance. The proposed technique achieved a recognition of 99.37% for QRS recognition and a detection rate of 98.62% for P waves detection when tested with the MIT/BIH Database. And validated with the QT Database, the results of QT intervals detection also showed an obvious improvement (85.26% when target domain less than 14 ms and 95.34% when target domain less than 28 ms separately on average).

Determination of regional earthquake source parameters in wavelet domain

Investigating source parameters of small and moderate earthquakes plays an important role in seismology research. For small and moderate earthquakes, the mechanisms are usually obtained by first motion of P-Wave, surface wave spectra method in frequency-domain or the waveform inversion in time-domain, based on the regional waveform records. We applied the wavelet domain inversion method to determine mechanism of regional earthquake. Using the wavelet coefficients of different scales can give more information to constrain the inversion. We determined the mechanisms of three earthquakes occurred in California, the United States. They are consistent with the previous results (Harvard Centroid Moment Tensor and United States Geological Service). This proves that the wavelet domain inversion method is an efficient method to determine the source parameters of small and moderate earthquakes, especially the strong aftershocks after a large, disastrous earthquake.

Domain Decomposition for Wavelet Single Layer on Geometries with Patches

We focus on the single layer formulation which provides an integral equation of the first kind that is very badly conditioned. The condition number of the unpreconditioned system increases exponentially with the multiscale levels. A remedy utilizing overlapping domain decompositions applied to the Boundary Element Method by means of wavelets is examined. The width of the overlapping of the subdomains plays an important role in the estimation of the eigenvalues as well as the condition number of the additive domain decomposition operator. We examine the convergence analysis of the domain decomposition method which depends on the wavelet levels and on the size of the subdomain overlaps. Our theoretical results related to the additive Schwarz method are corroborated by numerical outputs.

Power System Harmonic Detection Using Frequency-Domain Interpolation Wavelet Transform

Aiming at harmonic detection, fast Fourier transform can only detect integer harmonics precisely, short time Fourier transform can detect non-integer harmonics with low resolution, and some former wavelet based methods have no aliasing-reduction scheme which result in low measurement precision and poor robustness. A frequency-domain interpolation algorithm to detect harmonics is proposed by choosing Shannon wavelet. Shannon wavelet is an orthogonal wavelet possessing best ideal frequency domain localization ability, it can restrict wavelet abasing but bring about Gibbs oscillation phenomenon simultaneously. An interpolation algorithm is developed to overcome this problem. Simulation reveals that the proposed method can effectively cancel aliasing, spectral leakage and Gibbs phenomenon, so it provides an effective means for power system harmonic analysis.

A NEW APPROACH FOR UNSUPERVISED RESTORING IMAGES BASED ON WAVELET-DOMAIN PROJECTION PURSUIT LEARNING NETWORK

The Wavelet-Domain Projection Pursuit Learning Network (WDPPLN) is proposedfor restoring degraded image. The new network combines the advantages of both projectionpursuit and wavelet shrinkage. Restoring image is very difficult when little is known about apriori knowledge for multisource degraded factors. WDPPLN successfully resolves this problemby separately processing wavelet coefficients and scale coefficients. Parameters in WDPPLN,which are used to simulate degraded factors, are estimated via WDPPLN training, using scalecoefficients. Also, WDPPLN uses soft-threshold of wavelet shrinkage technique to suppress noisein three high frequency subbands. The new method is compared with the traditional methodsand the Projection Pursuit Learning Network (PPLN) method. Experimental results demonstratethat it is an effective method for unsupervised restoring degraded image.

Analysis of terahertz waveguide modes in continuous wavelet domain

Conventional analytical methods in the wavelet domain are used to present an analysis of terahertz （THz） waveguide modes.To obtain THz radiation pulses passing through a waveguide,we build a simple experimental system with a 5-mm-long,230-μm-inner-diameter stainless steel waveguide.The single-mode guided signal from the experiments and the multi-mode signal of a similar THz waveguide reported in the literature are analyzed using the continuous wavelet transform （CWT）.The results demonstrate that analyzing THz waveguide modes in the wavelet domain not only possesses all the functionality of the traditional THz time-domain spectroscopy （TDS） data processing but also has the ability to unscramble quantitatively and intuitively detailed information about the target samples,such as mode type,cut-off frequency,amplitude distinction,and group velocity.

基于小波域威布尔分布模型的电机滚动轴承故障诊断分析

阐述小波域威布尔分布模型的原理,探讨基于小波域威布尔分布模型的电机滚动轴承故障诊断方法,包括数据采集和预处理、小波变换和威布尔分布模型的应用、故障特征提取和诊断准确性评估。

Conformal Wavelet Finite-difference Time-domain Algorithm for Analysis of Milimeter Wave Attenuation on Coplanar Waveguide

Addressed is the calculation of millimeter wave attenuation on coplanar waveguide(CPW). A novel conformal wavelet finite-difference time-domain(CWFDTD) algorithm is proposed with emphasis on its application in calculation of millimeter wave attenuation on CPW, which is the combination of conformal algorithm dealing with the deformed cell with Wavelet-FDTD using multi-resolution analysis(MRA). Derived is the difference formulation for multi-resolution time domain(MRTD) based on Daubechies wavelets, and also given is the stability conditions for wavelet-FDTD algorithm. To validate its accuracy and efficiency, this novel method is applied to calculate the millimeter wave attenuation on lithium niobate CPW. Numerical results demonstrate that this new CWFDTD algorithm has the same accuracy with the conformal finite-difference time-domain(CFDTD) and conformal finite-difference time-domain based on alternating-direction implicit method(ADI-CFDTD), but saves computational time and computer memory.

基于PSO-SVM的Φ-OTDR系统模式识别研究

针对相位敏感光时域反射仪(phase sensitive optical time domain reflectometer,Φ-OTDR)系统中误报率高的问题,提出一种多域特征提取与粒子群算法优化支持向量机(particle swarm optimization-support vector machine,PSO-SVM)相结合的模式识别算法。首先,对原始信号进行差分处理后提取时域特征,并利用小波包分解方法,通过验证不同分解层数下的事件分类准确率,设定最优分解层数为6层,提取差分信号的能量特征。然后以SVM分类器为基础,利用PSO算法优化SVM分类器参数,提高光纤振动信号识别准确率。最后利用Φ-OTDR事件数据集进行验证,实验结果表明,该模式识别算法达到了95.6%的振动事件分类准确率。

机体表面振动信号影响因素关联性分析

为了研究机体表面振动信号各影响因素的影响规律,在不同转速、转矩、润滑油温度及配缸间隙下,对比分析振动信号的峰值、方差、标准差及均方根等4个时域参数以及小波包分解得到的各频段能量的变化趋势。结果表明:随着转速的升高,燃烧压力峰值出现波动,在活塞惯性力的主导作用下,振动信号时域特征参数及频段5以上频率成分的能量呈增加的趋势;燃烧压力峰值随着转矩的增加而增大,振动信号时域特征参数及各频段能量均呈增加的趋势;随着润滑油温度的升高,在润滑油阻尼及燃烧压力的双重作用下,振动信号时域特征参数幅值整体呈降低的趋势,频段12以上频率成分的能量呈增加的趋势;随着配缸间隙的增加,密封性降低导致燃烧压力峰值减小,但由于活塞二次运动加剧,导致振动信号时域特征参数及频段5以上频率成分的能量呈现增加的趋势。

直埋热水供热管道泄漏定位检测实验研究

选用傅里叶滤波法和小波阈值法进行了管道泄漏信号降噪和泄漏定位的对比研究。在DN300管路的实验直埋热水供热系统上进行了泄漏检测实验,采用小管径支管和调节阀模拟泄漏点、加速度传感器测量声波振动信号,依次选用2种滤波方法对直埋热水供热管道泄漏的声波振动信号进行数据降噪处理,讨论了泄漏信号降噪前后的信号频域特征和定位精度。在北京市某小区直埋供热管道进行了声波降噪泄漏检测工程验证,定位偏差不超过1.0 m。研究表明,傅里叶滤波法和小波阈值法对直埋热水供热管道泄漏定位的声波振动信号降噪处理效果显著,小波阈值法保持了全频段的信号特征,可获得更高的定位精度。

小波分频自注意力Transformer图像去雨网络

针对视觉Transformer对高频信息捕捉能力弱以及目前许多图像去雨方法易丢失细节的问题,提出小波分频自注意力Transformer图像去雨网络(WFDST-Net)。小波分频自注意力Transformer(WFDST)作为WFDST-Net的主要模块,其利用不可分提升小波变换获取特征图的低频分量和高频分量,分别在低频和高频中进行自注意力交互,使模块从低频中学习恢复全局结构的能力,在高频中强化捕捉雨纹等线条细节的能力,增强对不同频域特征的建模能力。WFDST-Net采用U形架构并通过不可分提升小波变换获取多尺度特征,可在捕获不同形状高频雨纹的同时保证信息的完整性。相比其他图像去雨相关的Transformer,WFDST-Net具有更低的参数量。此外,提出VOCRain250数据集用于联合图像去雨和语义分割任务,该数据集比目前广泛使用的BDD150更具优势。实验表明,所提方法增强了视觉Transformer对不同频域信息的捕获能力,并在合成和真实数据集以及VOCRain250中的表现优于目前先进的去雨方法,能有效去除复杂雨纹并保留更多细节特征。

基于改进小波变换的OTDR事件检测方法

针对信号中噪声影响的问题,提出了基于改进小波变换的光时域反射仪(OTDR)事件检测方法。首先通过对小波去噪算法进行改进,获得新的阈值选取方式和阈值判断函数,然后结合小波模极大值法和斜率法定位测量光纤发生事件位置和光纤始末端位置,计算事件衰减损耗的同时对事件进行分隔和误判消除。仿真和实验结果表明:所提方法的定位精准度较高,反射事件和非反射事件的计算位置偏差分别小于5 m和15 m;测量光纤的事件检测分析结果与光纤连接位置及光纤总长度相符。

平抑风电波动的混合储能容量配置策略

风电波动特性会削弱电力系统安全稳定性,阻碍风电上网与消纳。因此,平抑风电波动是实现风电价值的重要手段与途径,对市场化的电力运营具有重要的意义。首先,基于风电输出特性,提出一种基于小波去噪(wavelet domain denoising,WDD)联合模型预测控制(model predictive control,MPC)优化风电输出的短期平抑策略;其次,以综合价格为混合储能容量配置指标,风储联合运行为限制条件,实现风电功率波动平抑及混合储能的最优分配;最后,对混合储能进行荷电状态优化控制,延长储能使用生命周期。结果表明,采用小波去噪和MPC方法具有最低的综合评判指数,说明此策略有助于减少弃风,可实现更有效的储能分配。

低压直流配电网强弱电弧特征及检测算法

直流电弧没有过零点特性,难以自行熄灭,易引发电气火灾,威胁直流配用电安全。通过搭建电弧模拟发生实验平台,采集不同电压下电弧信号,研究电弧电阻、电压等特性,分析了不同电压下电弧特征的差异性。以GB/T 35727—2017中低压直流配网标称电压优选值为依据,研究220 V、750 V、1 500 V3种不同电压等级电弧检测特征,对比傅里叶、小波、奇异值分解等频域特性,最大值、最小值、平均值、标准差等时域特性,得到不同电压等级下电弧检测的优质判据,证明了不同时频域特征适用于不同的电压等级。结合神经网络模型,设计了适用于750 V系统的电弧检测算法,算法检测精度为99.12%。

用于地下金属浅层探测的便携式频域电磁探测系统降噪方法研究

浅地表电磁探测的接收信号是十分微弱的,其受宇宙辐射电磁噪声、电源频率、谐波干扰以及工业电器噪声等环境因素影响,采用电磁感应法时不可避免地会受到线圈耦合的影响,会导致频域电磁系统信噪比低、一次场干扰严重且接收信号很容易淹没在噪声中。因此本文通过对差分模型的互感建模分析,提出了一种收-发线圈水平共轴差分式组合设计方案来去除一次场干扰;在系统采集模块提出了一种二次场数字正交矢量解调算法,用来提取电磁信号中的幅值和相位;在数据处理模块提出了一种小波阈值去噪技术,利用信噪比、均方根误差对实测数据与小波变换的去噪效果对比分析,最后基于上述技术设计相应的便携式频率域电磁地下金属探测原理样机系统。实验结果表明,水平共轴差分方案可以有效抑制一次场的干扰,数字正交矢量解调算法可以有效地从噪声信号中提取出有用信号的幅值和相位,通过对比分析小波变换,将系统信噪比提高了27.32 dB。本文研究成果对频率域电磁法探测系统提取二次场信号和对采集信号进行噪声去除具有实际应用价值。

Recognizing Breast Cancer Using Edge-Weighted Texture Features of Histopathology Images

Around one in eight women will be diagnosed with breast cancer at some time.Improved patient outcomes necessitate both early detection and an accurate diagnosis.Histological images are routinely utilized in the process of diagnosing breast cancer.Methods proposed in recent research only focus on classifying breast cancer on specific magnification levels.No study has focused on using a combined dataset with multiple magnification levels to classify breast cancer.A strategy for detecting breast cancer is provided in the context of this investigation.Histopathology image texture data is used with the wavelet transform in this technique.The proposed method comprises converting histopathological images from Red Green Blue(RGB)to Chrominance of Blue and Chrominance of Red(YCBCR),utilizing a wavelet transform to extract texture information,and classifying the images with Extreme Gradient Boosting(XGBOOST).Furthermore,SMOTE has been used for resampling as the dataset has imbalanced samples.The suggested method is evaluated using 10-fold cross-validation and achieves an accuracy of 99.27%on the BreakHis 1.040X dataset,98.95%on the BreakHis 1.0100X dataset,98.92%on the BreakHis 1.0200X dataset,98.78%on the BreakHis 1.0400X dataset,and 98.80%on the combined dataset.The findings of this study imply that improved breast cancer detection rates and patient outcomes can be achieved by combining wavelet transformation with textural signals to detect breast cancer in histopathology images.

基于双频域特征聚合的低照度图像增强

针对低照度图像质量较差、噪声多、纹理模糊等问题,提出一种基于双频域特征聚合的低照度增强网络(dual frequency-domain feature aggregation network,DF-DFANet)。首先,构建频谱光照估计模块(frequency domain illumination estimation module,FDIEM)实现跨域特征提取,通过共轭对称约束调整频域特征图抑制噪声信号,并采用逐层融合方式提高多尺度融合效率以扩大特征图感受野范围。其次,设计多谱双注意力模块(multiple spectral attention module,MSAM)聚焦图像局部频率特征,通过小波域空间、通道注意力机制关注图像细节信息。最后,提出双域特征聚合模块(dual domain feature aggregation module,DDFAM)融合傅里叶域和小波域特征信息,利用激活函数计算自适应调整权重实现像素级图像增强,并结合傅里叶域全局信息提高融合效果。实验结果表明,在LOL数据集上所提网络的PSNR达到24.3714,SSIM达到0.8937。与对比网络相比,所提网络增强效果更具自然性。