Ship-detection

说明：  用MATLAB编程实现实现滤波，与数字信号处理相关(Implementing filtering)

去除在心电信号采集过程中混入的肌电干扰、工频干扰、基线漂移等噪声信信号，避免噪声对心电信号特征点的识别与提取造成误判漏判 已通过测试。 (Remove the EMG interference mixed with the signal acquisition process in mind, frequency interference, baseline drift and noise channel signal to avoid noise caused by the misjudgment of the Missing has been tested on ECG feature identification and extraction.)

编写MATLAB程序，产生协方差函数为C(τ)=9??^(?10|??| )的零均值平稳高斯过程，产生一条样本函数.测量所产生样本的时间自相关函数，将结果与理论值比较。(Procedures for the preparation of MATLAB produced C covariance function (tau) =9^ (- 10||) zero mean stationary Gauss process to produce a sample function. Measuring the resulting sample time autocorrelation function, the results will be compared with the theoretical value.)

小波变换是一种非常重要的时频分析方法，研究小波变换对于地震处理研究有着非常重要的作用(Wavelet transform is a important tool for time-frequency,which plays an important part in the seismic processing.)

说明：  使用小波变换技术实现图像分割，利用matlab编程实现(Using wavelet transform technology to achieve image segmentation, matlab programming)

说明：  利用matalab中的simulink的仿真做的pm的调制与解调 非常的有用(Matalab use of simulation in simulink to do the modulation and demodulation pm very useful)

说明：  图像小波变换，小波变换（wavelet transform，WT）是一种新的变换分析方法，它继承和发展了短时傅立叶变换局部化的思想，同时又克服了窗口大小不随频率变化等缺点，能够提供一个随频率改变的“时间-频率”窗口，是进行信号时频分析和处理的理想工具。它的主要特点是通过变换能够充分突出问题某些方面的特征，能对时间（空间）频率的局部化分析，通过伸缩平移运算对信号（函数)逐步进行多尺度细化，最终达到高频处时间细分，低频处频率细分，能自动适应时频信号分析的要求，从而可聚焦到信号的任意细节，解决了Fourier变换的困难问题，成为继Fourier变换以来在科学方法上的重大突破。(Image Wavelet Transform)

此程序用DWT(离散小波变换)实现二维小波变换(discrete wavelet transform )

小波神经网络在变压器故障诊断中的应用，介绍一种小波神经网络诊断变压器故障的新方法 好文章大家一起分享(Wavelet neural network in fault diagnosis of transformer applications, introduced a wavelet neural network of the new transformer fault diagnosis method of a good article to share)

通过设计Visual C程序源码对简单易懂的一维信号在加上了高斯白噪声之后进行Daubechies小波、Morlet小波与Haar小波变换，从而的到小波分解系数；再通过改变分解的到的各层高频系数数进行信号的小波重构达到消噪噪的目的。在这一程序源码实现的过程中能直观地理解信号小波分解重构的过程与在信号消噪中的重要作用，和在对各层高频系数进行权重处理时系数的选取对信号消噪效果的影响。 可直接 (Design Visual C program source code on a simple one-dimensional signal with a Gaussian white noise Daubechies wavelet, Morlet wavelet and Haar wavelet transform, and thus to the wavelet coefficients and then change the decomposition to each storey wavelet reconstruction frequency coefficient of the number of signal to noise canceling noise. In the process of realization of this program source code can be intuitively understood the signal wavelet decomposition and reconstruction process and an important role in signal denoising, and the selection coefficient in the high frequency coefficients of the layers of the weight of processing noise cancellation signal impact. Can be directly)