Spectroscopic Leaf Level Detection of Powdery Mildew for Winter Wheat Using Continuous Wavelet Analysis

Powdery mildew （Blumeria graminis） is one of the most destructive crop diseases infecting winter wheat plants, and has devastated millions of hectares of farmlands in China. The objective of this study is to detect the disease damage of powdery mildew on leaf level by means of the hyperspectral measurements, particularly using the continuous wavelet analysis. In May 2010, the reflectance spectra and the biochemical properties were measured for 114 leaf samples with various disease severity degrees. A hyperspectral imaging system was also employed for obtaining detailed hyperspectral information of the normal and the pustule areas within one diseased leaf. Based on these spectra data, a continuous wavelet analysis （CWA） was carried out in conjunction with a correlation analysis, which generated a so-called correlation scalogram that summarizes the correlations between disease severity and the wavelet power at different wavelengths and decomposition scales. By using a thresholding approach, seven wavelet features were isolated for developing models in determining disease severity. In addition, 22 conventional spectral features （SFs） were also tested and compared with wavelet features for their efficiency in estimating disease severity. The multivariate linear regression （MLR） analysis and the partial least square regression （PLSR） analysis were adopted as training methods in model mildew on leaf level were found to be closely related with the development. The spectral characteristics of the powdery spectral characteristics of the pustule area and the content of chlorophyll. The wavelet features performed better than the conventional SFs in capturing this spectral change. Moreover, the regression model composed by seven wavelet features outperformed （R2=0.77, relative root mean square error RRMSE=0.28） the model composed by 14 optimal conventional SFs （R2---0.69, RRMSE--0.32） in estimating the disease severity. The PLSR method yielded a higher accuracy than the MLR method. A combination of CWA and PLSR was found to be promising in providing relatively accurate estimates of disease severity of powdery mildew on leaf level.

Possibility for Recognition of Psychic Brain Activity with Continuous Wavelet Analysis of EEG

The brain is a unique organization in nature, possessing the ability for psychic activity, which manifests itself in thoughts, feelings and emotions. In present days, numbers of mathematical methods for analysis of electroencephalogram (EEG) were developed with continuous wavelet transform being one of the most successive approaches for studying of brain activity. This paper is aimed to develop methods for investigation of psychic brain activity with help of continuous wavelet analysis of EEG. Ability of human to realize semantic content of the image presented on the screen was tested. Experiment was accompanied with simultaneous EEG recording, which was held with developed software and PC-based experimental setup. The information capabilities of continuous wavelet transform-based method for EEG analysis were improved for the recognition of specific patterns in human brain activity. Comparative wavelet analysis was carried out for EEG recordings at the moment of awareness of the semantic content of the image and for EEG recordings in the absence of conscious (subjective) perception of the semantic content of the image. Significant differences were shown in the alpha rhythm of EEG for the moments of awareness of the semantic content of the image and for the moments of absence of conscious perception. Continuous wavelet analysis of EEG showed that the alpha rhythm is the main EEG rhythm, which can be used to estimate the presence of subjective perception of the visual image. Significant differences were shown in the alpha rhythm of EEG for the moments of awareness of the semantic content of the image and for the moments of absence of conscious perception. Conducted studies allow to conclude that revealing of brain activity related to visual image awareness is possible through analysis of EEG.

A novel method for gamma spectrum analysis of low-level and intermediate-level radioactive waste

The uncertainty of nuclide libraries in the analysis of the gamma spectra of low-and intermediate-level radioactive waste(LILW)using existing methods produces unstable results.To address this problem,a novel spectral analysis method is proposed in this study.In this method,overlapping peaks are located using a continuous wavelet transform.An improved quadratic convolution method is proposed to calculate the widths of the peaks and establish a fourth-order filter model to estimate the Compton edge baseline with the overlapping peaks.Combined with the adaptive sensitive nonlinear iterative peak,this method can effectively subtracts the background.Finally,a function describing the peak shape as a filter is used to deconvolve the energy spectrum to achieve accurate qualitative and quantitative analyses of the nuclide without the aid of a nuclide library.Gamma spectrum acquisition experiments for standard point sources of Cs-137 and Eu-152,a segmented gamma scanning experiment for a 200 L standard drum,and a Monte Carlo simulation experiment for triple overlapping peaks using the closest energy of three typical LILW nuclides(Sb-125,Sb-124,and Cs-134)are conducted.The results of the experiments indicate that(1)the novel method and gamma vision(GV)with an accurate nuclide library have the same spectral analysis capability,and the peak area calculation error is less than 4%;(2)compared with the GV,the analysis results of the novel method are more stable;(3)the novel method can be applied to the activity measurement of LILW,and the error of the activity reconstruction at the equivalent radius is 2.4%;and(4)The proposed novel method can quantitatively analyze all nuclides in LILW without a nuclide library.This novel method can improve the accuracy and precision of LILW measurements,provide key technical support for the reasonable disposal of LILW,and ensure the safety of humans and the environment.

The continuous wavelet projections algorithm: A practical spectral-feature-mining approach for crop detection

Spectroscopy can be used for detecting crop characteristics. A goal of crop spectrum analysis is to extract effective features from spectral data for establishing a detection model. An ideal spectral feature set should have high sensitivity to target parameters but low information redundancy among features.However, feature-selection methods that satisfy both requirements are lacking. To address this issue,in this study, a novel method, the continuous wavelet projections algorithm(CWPA), was developed,which has advantages of both continuous wavelet analysis(CWA) and the successive projections algorithm(SPA) for generating optimal spectral feature set for crop detection. Three datasets collected for crop stress detection and retrieval of biochemical properties were used to validate the CWPA under both classification and regression scenarios. The CWPA generated a feature set with fewer features yet achieving accuracy comparable to or even higher than those of CWA and SPA. With only two to three features identified by CWPA, an overall accuracy of 98% in classifying tea plant stresses was achieved, and high coefficients of determination were obtained in retrieving corn leaf chlorophyll content(R^(2)= 0.8521)and equivalent water thickness(R^(2)= 0.9508). The mechanism of the CWPA ensures that the novel algorithm discovers the most sensitive features while retaining complementarity among features. Its ability to reduce the data dimension suggests its potential for crop monitoring and phenotyping with hyperspectral data.

Rolling Element Bearing Diagnostics by Combination of Envelope Analysis and Wavelet Transform

Rolling element-bearing diagnostics has been studied over the last thirty years, and envelope analysis is widely recognized as being the best approach for detection and diagnosis of rolling element bearing incipient failure. But one of the on-going difficulties with envelope technique is to determine the best frequency band to envelop. Here, wavelet transform technique is introduced into envelope analysis to solve the problem by capturing bearing defects-sensory scales (i.e. frequency bands). A modulated Gaussian function is chosen to be the analytical wavelet because it coincides well with bearing defect-induced vibration signal patterns. Vibration signals measured from railway bearing tests were studied by the proposed method. Cases of bearings with single and multiple defects on inner and outer race under different testing conditions are presented. Experimental results showed that the proposed method allowed a more accurate local description and separation of transient signal part, which were caused by impacts between defects and the mating surfaces in the bearing. The combination method provides an effective signal detection technique for rolling element-bearing diagnostics.

Complexity analysis of precipitation in changing environment in Chien River Basin,China

The hydrological processes influenced by the multiple factors of climate, geography, vegetation, and human activities are becoming more and more complex, which is an important characteristic of hydrological systems. The different complexity distributions of precipitation processes of the Chien River Basin （a sub-basin of the Minjiang Basin） in two periods （from 1952 to 1980, and from 1981 to 2009） are illustrated using the fractal based on the continuous wavelet transform （CWT）. The results show that （1） at the basin scale the precipitation process in the latter period is more complex than in the former period; （2） the maximum value of the complexity distribution moved from the east to the middle; and （3） through analysis of the time-information and space-information concealed in this complexity change, the precipitation characteristics in the changing environment in the basin can be illuminated. This study could provide a reference for research on disaster pre-warning in changing environments and for integrated water resources management in the local basin.

CEEMD-FastICA-CWT联合瞬态响应阶次的电驱总成噪声源识别

以某增程式电驱动总成为研究对象,提出基于联合算法的噪声分离识别模型。首先,采用互补集合经验模态分解(complementary ensemble empirical mode decomposition,CEEMD)联合快速独立分量分析(fast independent component analysis,FastICA)方法提取纯电模式稳态工况下单一通道噪声信号特征,利用复Morlet小波变换及FFT对各分量信号时频特性进行识别。其次,采用阶次分析法和声能叠加法对稳态分量信号对应的各瞬态响应阶次能量进行对比分析,并结合皮尔逊积矩相关系数(Pearson product moment correlation coefficient,PPMCC)相似性识别确定不同噪声激励源贡献度。结果表明:减速齿副啮合噪声对该增程式电驱总成纯电模式运行噪声整体贡献度最大。

Quantitative identification of crop disease and nitrogen-water stress in winter wheat using continuous wavelet analysis

It is necessary to quantitatively identify different diseases and nitrogen-water stress for the guidance in spraying specific fungicides and fertilizer applications.The winter wheat diseases,in combination with nitrogen-water stress,are therefore common causes of yield loss in winter wheat in China.Powdery mildew(Blumeria graminis)and stripe rust(Puccinia striiformis f.sp.Tritici)are two of the most prevalent winter wheat diseases in China.This study investigated the potential of continuous wavelet analysis to identify the powdery mildew,stripe rust and nitrogen-water stress using canopy hyperspectral data.The spectral normalization process was applied prior to the analysis.Independent t-tests were used to determine the sensitivity of the spectral bands and vegetation index.In order to reduce the number of wavelet regions,correlation analysis and the independent t-test were used in conjunction to select the features of greatest importance.Based on the selected spectral bands,vegetation indices and wavelet features,the discriminate models were established using Fisher’s linear discrimination analysis(FLDA)and support vector machine(SVM).The results indicated that wavelet features were superior to spectral bands and vegetation indices in classifying different stresses,with overall accuracies of 0.91,0.72,and 0.72 respectively for powdery mildew,stripe rust and nitrogen-water by using FLDA,and 0.79,0.67 and 0.65 respectively by using SVM.FLDA was more suitable for differentiating stresses in winter wheat,with respective accuracies of 78.1%,95.6%and 95.7%for powdery mildew,stripe rust,and nitrogen-water stress.Further analysis was performed whereby the wavelet features were then split into high-scale and low-scale feature subsets for identification.The accuracies of high-scale and low-scale features with an overall accuracy(OA)of 0.61 and 0.73 respectively were lower than those of all wavelet features with an OA of 0.88.The detection of the severity of stripe rust using this method showed an enhanced reliability(R^(2)=0.828).

基于CWT和CNN-BiLSTM的散绕同步电机定子绕组短路故障检测方法

近年来,基于脉冲频率响应法(impulse frequency response analysis,IFRA)的神经网络模型已被证实能够有效检测定子绕组故障。然而,这些模型普遍具有鲁棒性不强、抗噪能力差等特点,究其原因是大多数的模型采用简单的神经网络架构且常规的IFRA普遍采用快速傅里叶变换(fast Fourier transform,FFT)对暂态信号进行时频变换,而FFT并不适合处理暂态突变的非平稳信号。文中以散绕结构的同步电机定子绕组为检测对象,采用连续小波变换(continual wavelet transform,CWT)代替FFT处理IFRA的暂态信号,并基于一维卷积神经网络(convolutional neural networks,CNN)和双向长短时记忆网络(bi-directional long short-term memory,BiLSTM)构建CNN-BiLSTM模型对采用CWT变换之后的信号进行故障检测。实验结果表明:采用CWT处理后的频域序列作为该模型的输入,相较于其它结构单一的模型,其平均准确率最优且高达99.01%。噪声对比实验表明:采用CWT变换后的数据能使故障诊断模型的鲁棒性及泛化性更强。

Application of multi-dimensional wavelet transform to fluid mechanics

This paper first reviews the application research works of wavelet transform on the fluid mechanics. Then the theories of continuous wavelet transform and multi-dimensional orthogonal(discrete) wavelet transform, including wavelet multiresolution analysis, are introduced. At last the applications of wavelet transform on 2 D and 3 D turbulent wakes and turbulent boundary layer flows are described based on the hot-wire, 2 D particle image velocimetry(PIV) and 3 D tomographic PIV.

利用叶片高光谱反射率预测棉花叶绿素含量

为提高棉花叶绿素含量预测的准确性,利用连续小波分析和传统光谱变换对棉花叶片原始光谱进行分解和变换,以特征小波系数和光谱特征波段为自变量,并利用单变量、逐步回归和偏最小二乘法建立反演棉花叶片叶绿素含量的数学模型。结果显示,不同的光谱处理方法使得棉花叶片叶绿素和光谱反射率的相关性都有不同程度的提升,对于传统光谱变换,倒数对数一阶微分lg(1/R′)对棉花叶片叶绿素相关性提高了0.41。结果表明,连续小波分析在信息降噪和挖掘特征信息方面优于传统光谱模型,建立的模型RPD>2,具有很好的稳定性,对样本数据都具很好的预测能力。

Application of wavelet packet in gravity anomaly processing

Based on the advantages of the wavelet to separate regional field and local anomalies in MATLAB environment,a high-precision regional-residual separation was finally realized. Analytical continuation and trend surface analysis are conventional methods for gravity anomaly separation. But the wavelet packet analysis in analyzing gravity data can make the gravity anomaly to be computed at a higher precision. In this paper,wavelet packet method is used to process gravity anomaly data obtained in Laos,and the separation result is good. Daubechies wavelet series has a higher precision in the wavelet packet.

基于连续交叉小波相干分析和自适应CYCBD的轴承故障诊断

最大二阶循环平稳指标盲解卷积(maximum second-order cyclostationarity blind deconvolution,CYCBD)能从强背景噪声信号中恢复周期脉冲,是轴承故障诊断的有效方法。故障特征频率是CYCBD的关键参数,由于滚动轴承存在制造误差、滚子滑移等现象,导致真实的故障特征频率与理论值存在偏差,降低了CYCBD的有效性。同时,故障轴承测试信号中含有大量噪声和谐波干扰,也降低了CYCBD的故障特征提取能力。对此,提出了一种基于连续交叉小波相干分析和自适应CYCBD的轴承故障诊断方法,首先,利用正常轴承、故障轴承测试信号的交叉小波相干分析获取轴承故障共振频带。其次,基于3种归一化的周期检测指标提出一种新的周期检测技术以获取真实的轴承故障特征频率。最后,基于轴承故障共振频带信号和真实轴承故障特征频率进行CYCBD滤波,并针对滤波信号进行Teager能量算子解调分析得到能量频谱,从而进行轴承故障诊断。仿真信号和高速列车牵引电机轴承试验信号的分析结果表明,该方法能够有效识别轴承故障特征,且优于传统的CYCBD方法。

基于混合时频域特征的卷积神经网络心律失常分类方法的研究

心律失常是常见的心血管疾病之一,目前很多方法通过计算机辅助系统对心电图进行分析以识别心律失常,但由于大多数心律失常数据样本较少,计算机辅助系统识别心律失常效果不佳.本文提出了一种基于混合时频域分析特征提取的卷积神经网络方法,该方法提取心电图的RR间期时域特征、希尔伯特-黄变换提取的频域特征和连续小波变换提取的时频域联合特征,经过特征融合后输入卷积神经网络训练分类模型,并采用Focal Loss作为网路的损失函数,实现对心律失常的分类.本文使用MIT-BIH(Massachusetts Institute of Technology-Boston’s Beth Israel Hospital)心律失常数据库验证本文提出方法对4类心电数据分类的结果,实验结果表明,与现有的分类算法相比,本文所提出的混合时频域特征方法能有效提升心律失常分类的准确性.

基于不同叶位日光诱导叶绿素荧光信息的水稻叶瘟病早期监测

[目的/意义]基于遥感手段的稻叶瘟(Rice Leaf Blast,RLB)无损早期监测对于抗性育种和植保防控具有重要作用。目前对稻瘟病的研究多使用反射光谱在其显症阶段进行监测,针对稻叶瘟早期侵染阶段的日光诱导叶绿素荧光(Solar-Induced Chlorophyll Fluorescence,SIF)光谱监测研究尚未见报道。本研究的目的是基于不同叶位的日光诱导叶绿素荧光信息,实现水稻叶瘟病早期阶段感病叶片的准确识别。[方法]基于一年的温室接种试验和大田采样实验,配合使用主动光源、ASD(Analytical Spectral Devices)地物光谱仪和FluoWat叶片夹,获取了拔节期和抽穗期水稻植株顶1至顶4叶位的叶片SIF光谱,并人工标注了被测样本的发病等级。研究基于连续小波分析(Continue Wavelet Analysis,CWA)提取对稻叶瘟敏感的小波特征,比较了不同叶位敏感特征及其感病叶片识别精度,最后基于线性判别分析(Linear Discriminant Analysis,LDA)算法构建了稻叶瘟识别模型。[结果和讨论]各叶位感病叶片远红光区域的上行和下行SIF均显著高于健康叶片;基于SIF小波特征的感病叶片识别精度显著高于原始SIF波段,顶1叶的稻瘟病识别精度显著高于其他三个叶位,其识别精度最高可达70%;提取的适用于多叶位的共性敏感小波特征↑WF832,3和↓WF809,3在顶1至顶4叶的精度分别达到69.45%、62.19%、60.35%、63.00%和69.98%、62.78%、60.51%、61.30%。[结论]本研究揭示了稻瘟病胁迫下水稻叶片SIF光谱响应规律,提取了对稻叶瘟敏感的SIF小波特征,结果证明了连续小波分析和SIF技术用于诊断稻叶瘟的潜力,为实现稻瘟病的田间早期、快速、原位诊断提供了重要参考与技术支撑。

变速工况下基于WSST的地铁钢轨波磨检测

地铁列车由于制动和启动较为频繁会出现钢轨波磨,对列车运行造成安全隐患,有效地检测变速工况下钢轨波磨的产生有助于提升地铁轨道车辆的安全性和运营舒适性。利用多体动力学仿真软件SIMPACK构建地铁车辆动力学分析模型,并将钢轨波磨仿真信号输入轨道,以获得变速工况下的轴箱加速度数据。针对变速工况下波磨产生的非平稳调频信号,采用优化的同步压缩连续小波变换(wavelet synchrosqueezing transform,WSST)的方法对该数据进行时频分析。通过使用钢轨波磨仿真信号进行分析验证,结果表明:所提出的诊断方法可以有效对牵引及制动工况下的钢轨波磨进行诊断,实现故障频率的准确定位,且具有较高的频率分辨率以及抗干扰性能,具有一定的普适应用价值。

塔里木盆地巴麦地区石炭系卡拉沙依组年代标尺及地层剥蚀厚度精细计算

古地貌的精确恢复对于寻找优质储层及烃源岩优势相带起着十分重要的作用,而剥蚀厚度计算又是古地貌恢复研究中的核心步骤。基于自然伽马曲线数据,利用频谱分析、连续小波变换和经验模态分解等技术手段,建立了塔里木盆地巴麦地区石炭系卡拉沙依组具有相对时间概念的“浮动”天文年代标尺和高精度的地层层序格架,并进一步精确计算了石炭系卡拉沙依组剥蚀厚度。结果显示:①巴麦地区石炭系卡拉沙依组沉积时受天文轨道周期的控制,保存有完整的米兰科维奇旋回;②经验模态分解方法得出的固有模态分量imf_(3)与长偏心率(e_(1))控制下的地层旋回个数基本一致,据此建立了研究区具有相对时间概念的“浮动”天文年代标尺和高精度的地层层序格架;③基于经验模态分解方法计算的结果,结合不同地区钻井缺失旋回数量和平均旋回厚度之间的关系,精确计算了研究区内石炭系卡拉沙依组剥蚀厚度;④研究区内石炭系卡拉沙依组剥蚀厚度在0~390 m,剥蚀强度整体表现为“西强东弱”的特征,中部BT5井附近斜坡区域,易形成良好的储集体,是下一步有利的勘探区域。研究提出的思路和方法为类似地区高频地层层序格架的构建、“浮动”天文年代标尺的建立及地层剥蚀厚度的精细计算等提供了参考。

基于理化参数和图谱特征的稻曲病病害高光谱识别

基于水稻冠层高光谱数据和样点植株理化数据,首先利用连续小波分析、灰度共生矩阵等方法提取高光谱图像的图谱特征,并对主要理化指标和病害程度做相关性分析,然后通过Relief F算法对多特征进行优选,最后建立不同核的支持向量机分类模型。结果表明:理化特征参与下的分类精度较高,通过Relief F算法筛选得到的最优特征中四项理化特征均在内,最后精度达到0.95。研究证实了理化参数对稻曲病识别的重要作用,可以为大田水稻病害监测提供新思路。