CH3MATLAB

互相关求时延差的一个程序，通过各种不同方法实现的(Poor cross-correlation order to delay a program through a variety of different methods to achieve)

This is code about Photovoltaic (PV) in Matlab

给出卫星适当的初始位置和速度，利用其运动微分方程，求卫星运行的轨道（航天航空）( Appropriate given the initial satellite position and velocity, using the equations of motion, find the satellite orbit)

实现最小二乘法的拟合，适用于曲线的拟合，其广泛运用于当今科研等实际工作中(Implement least squares fitting curve fitting for its widely used today in practical work and scientific research)

这三个小程序都是matlab的.m文件，分别实现用灰度变换的方法增强图像的对比度和直方图均匀化，模拟图像受高斯白噪声和椒盐噪声的影响。(The three procedures are Matlab. M documents, were used to achieve gray transform method to enhance the contrast of the image and histogram equalization. Simulation images by white Gaussian noise and the noise of Salt and Pepper.)

教你如何用编写上传文件的功能，里面包含编码所需要的包，和API文档。(Teach you how to write the uploaded file, which contains the package code and API documentation.)

双站交叉定位误差展示程序 产生观测数据 假设被定位对象匀速地从A点(dx1,dy1)运动到B点(dx2,dy2)，单位：km 使用速度输入V(km/h)，则dx2=V*t，t为观测时长(h) dy1=dy2=R，目标距离 假设采样时间间隔为1h(Bistatic cross-positioning errors show program)

利用单应点进行透视反变换，matlab实现(Perspective inverse transform, matlab achieve)

此乃离散系统频率响应和零极点分布实验的编程，其中有系统频响的幅度响应和相位响应图，还有零极点分布图(This is the discrete system frequency response and pole-zero distribution of experimental programming, of which the amplitude response of the system frequency response and phase response plots, and pole-zero distribution)

ROC curves illustrate performance on a binary classification problem where classification is based on simply thresholding a set of scores at varying levels. Lenient thresholds give high sensitivity but low specificity, strict thresholds give high specificity but low sensitivity the ROC curve plots this trade-off over a range of thresholds (usually with sens vs 1-spec, but I prefer sens vs spec this code gives you the option). It is theoretically possible to operate anywhere on the convex hull of an ROC curve, so this is plotted too. The area under the curve (AUC) for a ROC plot is a measure of overall accuracy, and the area under the ROCCH is a kind of upper bound on what might be achievable with a weighted combination of differently thresholded results from the given classifier