Analysis and comparison of safety and fatigue comfort of postal bicycles based on finite element method and sEMG

The safety and the fatigue comfort were compared between a domestic and a Japanese postal bicycle. Firstly, the fatigue comfort of these two kinds of bicycles was evaluated by surface electromyographic signal (sEMG) experiment, in which human lower limb muscle groups were research objects, and the average EMG (AEMG) index and median frequency (MF) were chosen as the evaluation indexes. Secondly, the safety of these two kinds of bicycle frames was analyzed and compared by using the finite element analysis. The results show that the riding fatigue comfort of the Japanese postal bicycle is better, and the Japanese postal bicycle frame is more safe and reasonable although both the postal bicycles meet the requirement for strength. Finally, based on the above analysis, the frame structure and related parameters of the domestic postal bicycle were improved with reference to the Japanese postal bicycle and biomechanics theory.     

Multifractal analysis of surface EMG signals for assessing muscle fatigue during static contractions

This study is aimed at assessing muscle fatigue during a static contraction using multifractal analysis and found that the surface electromyographic （SEMG） signals characterized multiffactality during a static contraction. By applying the method of direct determination ofthef（a） singularity spectrum, the area of the multifractal spectrum of the SEMG signals was computed. The results showed that the spectrum area significantly increased during muscle fatigue. Therefore the area could be used as an assessor of muscle fatigue. Compared with the median frequency （MDF）--the most popular indicator of muscle fatigue, the spectrum area presented here showed higher sensitivity during a static contraction. So the singularity spectrum area is considered to be a more effective indicator than the MDF for estimating muscle fatigue.     

基于Curvelet变换和均值滤波的图像去噪方法

针对图像中的高斯噪声干扰,提出一种改进的图像去噪方法．首先利用Curvelet变换将含噪声图像分解成多个子频带,再根据子带系数的高斯分布特性,利用阈值去噪和加权平均滤波相结合的方法对高频子带进行去噪处理,最后利用Curvelet反变换得到去噪后的图像．为了验证该方法的有效性,与传统的硬阈值、软阈值、基于小波变换的方法相比较,实验结果表明,该方法能够获得较好的峰值信噪比和视觉特性,保留较多的细节信息．同时也说明了Curvelet变换比小波变换能够得到更好的去噪效果．     

基于小波变换的图像降噪

根据小波变换和噪声信号的能量分布特性,提出了一种先用小波变换对含噪图像进行多尺度分解,求出各尺度小波变换高频系数的噪声方差和阈值,利用各尺度的阈值对高频系数进行处理,然后利用小波变换系数重构图像,实现图像降噪的方法;实验结果说明该方法可以有效地降低噪声,又可以较好地保持图像细节。     

Fault diagnosis of valve clearance in diesel engine based on BP neural network and support vector machine

Based on wavelet packet transformation(WPT), genetic algorithm(GA), back propagation neural network(BPNN)and support vector machine(SVM), a fault diagnosis method of diesel engine valve clearance is presented. With power spectral density analysis, the characteristic frequency related to the engine running conditions can be extracted from vibration signals. The biggest singular values(BSV)of wavelet coefficients and root mean square (RMS)values of vibration in characteristic frequency sub-bands are extracted at the end of third level decomposition of vibration signals, and they are used as input vectors of BPNN or SVM. To avoid being trapped in local minima, GA is adopted. The normal and fault vibration signals measured in different valve clearance conditions are analyzed. BPNN, GA back propagation neural network (GA-BPNN), SVM and GA-SVM are applied to the training and testing for the extraction of different features, and the classification accuracies and training time are compared to determine the optimum fault classifier and feature selection. Experimental results demonstrate that the proposed features and classification algorithms give classification accuracy of 100%.     

基于小波变换及子带阈值的彩色图像降噪

提出了一种对彩色图像降噪的新方法,该方法先把彩色图像分解为红、绿、蓝3个基色图像,然后利用小波变换分别对3个基色图像进行多尺度分解,求出其各个尺度三个方向高频系数的子带阈值,并组成1个子带阈值矩阵,对单基色图像的各个尺度3个方向的高频系数进行阈值处理,实现对单基色图像的降噪,最后把降噪后的3个基色图像融合为一幅彩色图像。实验结果表明该方法可以很好地消除噪声,又可以较好地保持图像细节。     

基于小波变换的低对比度图像增强改进算法

提出一种基于小坡变换的图像增强算法.利用小波变换的时频局域性和方向性特点,对分解后的高频系数进行子树划分,以子树为单位实现图像的对比度增强.在VC++环境下实现了该方法,实验结果表明该算法能得到整体视觉好的图像,是一种有效的图像增强算法.     

小波变换应用于Cu~(2+)离子催化振荡化学体系的频率分析

利用连续小波变换的多分辨特性分析了Cu2+催化振荡体系的频率特征,得出了其特征频率值.由此反映出了该振荡体系具有多频特性.通过其正、负反馈反应机理分析了体系中多频特性形成的原因.     

谐波干扰下的异步电机故障诊断研究

推导出在高次谐波干扰下,PARK矢量模平方函数的数学表达式,消除直接进行定子电流分析时基波频率对特征频率的影响。但是新增的频率成分很容易被直流分量、2f分量和4f分量淹没,对PARK矢量模平方函数进行傅立叶变换效果很差。采用多分辨率小波分析,对其各层系数进行分解与重构,其中第7层高频系数和第8层高频系数重构后的波形在发生故障时幅值明显大于无故障时的波形,对电机故障有很高的辨识度,是一种有效的方法。     

Image Coding Using Wavelet Transform and EntropyConstrained Vector Quantization with Quadtree Structure Vectors

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短基线GPS应用中多径信号提取方法的对比分析(英文)

为了消除静态GPS短基线应用中的多径误差,引入了一种基于奇异谱分析的去噪方法(称为DSSA)提取多径信号.首先,从双差分残差中提取出多径误差,然后,利用相邻历元间多径误差的相关性对双差分观测数据进行多径误差校正.为了验证DSSA算法的有效性,基于仿真信号对DSSA、离散小波变换(DWT)和平稳小波变换(SWT)的去噪方法进行对比分析.采用真实的GPS基线误差对不同的多径提取方法进行了分析,多径抑制结果表明,DSSA校正后基线误差的均方根(RMS)较SWT改善48.6%,运算时间减少了75.3%.     

Motion classification of EMG signals based on wavelet packet transform and LS-SVMs ensemble

This paper presents an effective method for motion classification using the surface electromyographic (sEMG) signal collected from the forearm. Given the nonlinear and time-varying nature of EMG signal, the wavelet packet transform (WPT) is introduced to extract time-frequency joint information. Then the multi-class classifier based on the least squares support vector machine (LS-SVM) is constructed and verified in the various motion classification tasks. The results of contrastive experiments show that different motions can be identified with high accuracy by the presented method. Furthermore, compared with other classifiers with different features, the performance indicates the potential of the SVM techniques combined with WPT in motion classification.     

基于人的视觉系统的小波性能评价

We have constructed a compactly supported biorthogonal wavelet that approximates the modulation transfer function(MTF) of human visual system in the frequency domain.In this paper,we evaluate performance of the constructed wavelet,and compare it with the widely used Daubechies9-7,Daubechies 9-3 and GBCW-9-7 wavelets.The result shows that coding performance of the constructed wavelet is better than Daubechies9-3,and is competitive with Daubechies 9-7 and GBCW-9-7 wavelets.Like Dauechies 9-3 wavelet,the filter coefficients of the constructed waveklet are all dyadic fractions,and the tap is less than Daubechies 9-7 and GBOW 9-7,It has an attractive feature in the realization of discrete wavelet transform.     

一种改进的基于小波变换的语音基频检测

传统的小波变换基频检测通过比较相邻尺度上的小波系数极值点来进行检测．该方法往往存在着伪极值点的误判,降低了检测的精确度,而且增加了运算量．针对小波系数的伪极值点误判情况做了分析,进行了最佳尺度的判定,以及极值点搜索方法的改进,提出了一种基于最佳尺度的小波变换基频检测算法,提高了算法的实时性和准确性,并通过实验证明了该算法的有效性．     

Application of formant instantaneous characteristics to speech recognition and speaker identification

This paper proposes a new phase feature derived from the formant instantaneous characteristics for speech recognition (SR) and speaker identification (SI) systems. Using Hilbert transform (HT), the formant characteristics can be represented by instantaneous frequency (IF) and instantaneous bandwidth, namely formant instantaneous characteristics (FIC). In order to explore the importance of FIC both in SR and SI, this paper proposes different features from FIC used for SR and SI systems. When combing these new features with conventional parameters, higher identification rate can be achieved than that of using Mel-frequency cepstral coefficients (MFCC) parameters only. The experiment results show that the new features are effective characteristic parameters and can be treated as the compensation of conventional parameters for SR and SI.     

基于小波变换和中值滤波相结合的SAR图像相干斑的抑制

合成孔径雷达图像中相干斑噪声的存在降低了图像的质量,影响了图像的解译和后续处理。分析了图像相干斑噪声产生的机理,介绍了基于空域和频域的滤波方法的研究现状,并在阐述小波阈值去噪声方法的基础上考虑根据小波系数子带分布特征用定向窗口中值滤波代替阈值去噪声,提出了结合中值滤波的小波域SAR相干斑抑制方法,并通过和已有的方法比较,说明该方法非常有效。     

Multiple descriptions based wavelet image coding

INTRODUCTIONJPEG2 0 0 0 (LawsonandZhu ,2 0 0 2 )isanewlypublishedstillimage codinginternationalstandard .Itsintroductionmeantthatforthefirsttime,thediscretewavelettransform (DWT)wouldbeusedforthedecompositionandrecon structionofimagestogetherwithanefficientcod ingscheme.IntheJPEG2 0 0 0coder,beforeanywaveletdecompositionisperformed ,theimageispartitionedintonon overlappingtiles.Thesetilesareofequalsizeexceptpossiblyforthoseadjacenttotheimageboundary .Thenwavelettransformisappliedtotr…     

基于小波变换的图像融合算法及性能评价

在阐述小波图像融合算法的基础上,针对小波分解后各频域融合算子和融合规则的选择,提出一种新的基于FPGA动态可重构的图像融合算法。该方法对小波分解后的图像低频子带采用平均融合算子处理,在高频子带的融合中依据小波系数树状结构特点提出了一种新的自适应融合方法,最后经过小波逆变换得到融合图像。核心算法集成到一片FPGA中实现,提高算法的实时性,降低系统的实际功耗,有效地减少融合图像的失真。对多组图像进行实验,实验结果表明,该方法是有效的。     

基于SVM的小波图像去噪

在小波分析的基础上,运用支持向量机(SVM)方法来对噪声和非噪声数据进行分类。首先,把一带有噪声的信号进行多尺度小波分解;然后通过试验检测出小波分解系数中部分噪声信号和非噪声信号,得到样本数据来训练SVM;最后对所有的小波系数用训练后的SVM来进行分类得到非噪声信号,并且对这部分非噪声信号进行小波重构即达到了去噪的目的。