frwt_8876

说明：  真正的3 d双树离散小波变换(DWT)的一个数据集x是使用四个严格采样实现可分3 d DWTs在平行。然后subbands四DWTs结合适当。 (The real the 3 d Doubletree discrete wavelet transform (DWT) data set x four to strict sampling separable 3 d the DWTs In parallel. The then the subbands four DWTs combined appropriately.)

ECG feature wavelet transform

小波分解在图像处理和电能质量分析中应用广泛，本文应用小波分解的方法将高频的暂态信号提取出来，获取暂态信号的特征量，就能够有效地计算暂态信号。(Wavelet decomposition in image processing and power quality analysis is widely used, this application of wavelet decomposition method to extract high-frequency transient signals, to obtain the amount of transient characteristics of the signal, we can effectively calculate the transient signal.)

小波包和EMD方法对信号分析后的频谱图比较，可以对EMD的运行参数进行修改。(Spectrum comparison of Wavelet package and EMD)

心电图(ECG)是临床上诊断心血管疾病的重要方法。 数字滤波作为数字信号处理技术的重要组成部分，广泛应用于诸如信号分离、恢复、整形等多种场合中。为了使信号不失真及在可编程逻辑器件中容易实现，采用具有严格线性相位特性的FIR滤波器进行低通滤波（0-150Hz），用整系数梳状滤波器来去工频干扰及基线漂移,从而达到对心电信号进行预处理的目的。(Electrocardiogram (ECG) is an important method for clinical diagnosis of cardiovascular diseases. As an important part of digital signal processing technology, digital filtering is widely applied to many occasions, such as signal separation, recovery, shaping and so on. In order to make the signal undistorted and easy to be realized in programmable logic device, the FIR filter with strict linear phase characteristics is used for low pass filtering (0-150Hz), and the entire coefficient comb filter is used to get the interference of the power frequency and the baseline drift, thus the purpose of preprocessing the ECG signal is achieved.)

描述电磁波（电场或磁场）在自由空间传播的Matlab程序。(Description of electromagnetic waves (electric or magnetic) in free-space propagation of Matlab procedures.)

高斯滤波.这段代码主要用于以为信号的高斯滤波。(Gaussian Filter.Gaussian Filter.Gaussian Filter.Gaussian Filter.Gaussian Filter.Gaussian Filter.Gaussian Filter.Gaussian Filter.Gaussian Filter.Gaussian Filter.Gaussian Filter.Gaussian Filter.Gaussian Filter.Gaussian Filter.Gaussian Filter)

本文研究的是利用奇异性指数实现对设备状态监视和故障预警功能。首先用LabVIEW 采集传感器电信号，然后对采集到的电信 号进行小波变换得到信号包络奇异点的奇异性指数，即设备运行状态的特征值(In this research project, we use LabVIEW to do wavelet-transformation to the signal collected by sensors in order to get singularity index from the singular points of the particular signal’s envelope. Through comparing this eigenvalue to the figures in a standard database, we can know the functional status of the instruments. Not only the diagnoses are continuously displayed, but alarms about signs of dysfunction will also be raised in a proper way. In other words, both the supervision of the instrument and warning of dysfunction can be achieved)

利用小波包变换对信号进行分解及重构，对于初学者是一个很好的学习材料。(Wavelet packet transform signal decomposition and reconstruction, is a good learning materials for beginners.)

使用小波能量差提取信号，PCA降维，SVM对电能质量扰动分类。(Extract the signal using the wavelet energy difference between the PCA dimensionality reduction, SVM classification of power quality disturbances.)