Classic-Progress-Display

一个简单的计算hurst指数的matlab程序(A simple calculation of Hurst index of matlab program)

trubo code with sova algoritm in matlab

Visual C#显示隐藏鼠标的一个例子，隐藏鼠标后，使用Tab键移动光标，从代码中可发现，隐藏鼠标只需将ShowCursor(false);即可，显示时就这样：ShowCursor(true);面向C#初学者。下面是核心的代码： 　　//重写API函数 　　[System.Runtime.InteropServices.DllImport("user32.dll", EntryPoint = "ShowCursor")] 　　public extern static bool ShowCursor(bool bShow); 　　private void btnHide_Click(object sender, EventArgs e) 　　{ 　　 ShowCursor(false);//鼠标隐藏 　　} 　　private void btnShow_Click(object sender, EventArgs e) 　　{ 　　 ShowCursor(true);//鼠标显示 　　}

用Eviews对时间序列进行平稳性检验并建模(Using Eviews to verify and model time series)

电子地图服务器,搭建自己的地图服务，与Openlayers一起使用-Electronic map servers, to build their own mapping service, used together with Openlayers

1dCNN 用于分类，使用一维的CNN，和相应的一维数据输入和卷积核，对一维信号数据进行处理(use 1dCNN for fault classification)

STM32F103RCT6矩阵键盘，4×4键盘，按键串口打印(STM32F103RCT6 Matrix Keyboard, 4*4 Keyboard, Key Serial Port Printing)

说明：  这是关于LFM信号利用WHT变换估计其参数的仿真代码。程序基于时频分析包，但对其中相应部分做了修改。程序中有算法的详细设计内容。(This is on the LFM signal using WHT transform to estimate the parameters of the simulation code. Procedures based on time-frequency analysis package, but part of which had been amended accordingly. Procedures in the detailed design of the contents of algorithm.)

AWGN信道下的极化码实现，有参考文献，代码有中文注释，N=2048(Polar Code Constructions for the AWGN Channel)

常用的盲分离算法有二阶统计量方法、高阶累积量方法、信息最大化（ Infomax ）以及独 立成分分析（ ICA ）等。这些方法取得最佳性能的条件总是与源信号的概率密度函数假设有关， 一旦假设的概率密度与实际信号的密度函数相差甚远，分离性能将大大降低。本文提出采用 核函数密度估计的方法进行任意信号源的盲分离，并通过典型算例与几种盲分离算法进行了 性能比较，验证了方法的可行性。(The commonly used blind separation algorithms include two order statistics, higher order cumulants, and maximization of information Infomax) and independent component analysis (ICA). The conditions for obtaining the best performance of these methods are always related to the hypothesis of the probability density function of the source signal. The separation performance will be greatly reduced when the assumed probability density is very different from the density function of the actual signal. In this paper, a method of kernel function density estimation for blind separation of arbitrary signal sources is proposed. The performance of several blind separation algorithms is compared with some typical examples, and the feasibility of the method is verified.)