tuxiangshuyan

本程序应用小波变换进行电力系统的在线监测。电力系统在线监测中的检测信号含有大量的背景噪声，不便于检测。应用小波变换解决这一问题。程序中应用db3小波对信号进行5层分解，并得出了各层的细节信号与逼近信号。(This procedure based on wavelet transform power-line monitoring system. Online monitoring of power system test signal contains a lot of background noise, not easy to detect. Application of wavelet transform to resolve this problem. Application procedures for db3 wavelet signal decomposition layer 5, and arrive at different levels of detail signal and approximation signal.)

对入射光进行相位调控，再经过傅里叶变换后进行输出，显示各个截面的光场分布(The incident light is regulated by phase, and then the output is carried out after Fourier transform, and the distribution of the light field of each section is displayed)

Le Gall 5/3 小波变换及其反变换，可用于JPEG2000无损压缩中(Le Gall 5/3 wavelet transform and its inverse transform，which can be used for lossless compression in JPEG2000)

这篇关于从心电信号提取呼吸信号的调查，一共介绍了三种方法，即：频率提取，滤波器滤波，小波变换，三种方法。(This extract on the respiratory signal from ECG investigation, presented a total of three methods, namely: the frequency extraction filter filtering, wavelet transform, three ways.)

简单的方向滤波器， 带测试图的， 可以直接运行(Simple directional filter, with a test chart, you can directly run)

说明：  可以将目标矩阵进行线性褶积运算，用于数字信号处理(The target matrix can be linearly convoluted for digital signal processing)

心电信号的预处理。高通滤波器，低通滤波器，50Hz带阻滤波器。(The preprocessing of the ECG signal. High-pass filter, low pass filter, 50Hz band-stop filter....)

心电信号分析 （1）设计滤波器，实现对心电信号的噪声抑制和基线纠漂。 （2）时域分析：R波检测算法与实现. （3）功率谱分析：对消噪后的信号进行功率谱分析。要求计算信号的功率谱，功率谱峰值，峰值频率。 (ECG Analysis (1) design filters to achieve noise suppression of ECG and baseline drift correction. (2) time-domain analysis: R-wave detection algorithm and implementation (3) The power spectrum analysis: noise cancellation signal after the power spectrum analysis. Require calculation of the signal power spectrum, peak power spectrum, peak frequency.)

用于提取瑞雷面波频散曲线（两层递增模型效果最好）(Extraction of Rayleigh wave dispersion curve of Rayleigh wave)

小波变换应用，小波软阈值及硬阈值去噪算法及对此算法的改进算法。(Application of Wavelet Transform, wavelet soft threshold and hard threshold denoising algorithm and improved algorithm for this method.)