PCNN---2D-OTSU

说明：  电机定子绕组故障，它可以准确诊断定子绕组故障(Stator winding fault, it can be accurate diagnosis of stator winding fault)

为了抑制两电平逆变器输出中产生的共模电压，本算法采用在代价函数中加入相应的权重系数使得共模电压得到抑制(In order to suppress the common-mode voltage generated in the output of two-level inverters, the algorithm uses the corresponding weight coefficient in the cost function to suppress the common-mode voltage.)

说明：  光通信中断概率的判决，用于判断在保证一定误码率情况下的最低发射功率(Optical communication interruption probability judgments for the judge in the case of guaranteed minimum bit error rate of transmission power)

显示图像，利用按键切换图像，可批量查看。MATLAB环境。(Display images, use keys to switch images, available in volume view. MATLAB environment.)

matlab遗传算法经典语句，可以让我们学到在别处学不到的东西，为我们在写程序的时候提供帮助。(matlab genetic algorithm classic statement, you can let us learn things you can not learn elsewhere, provided help for us in the writing program.)

线性化法仿真三个参考节点定位一个定位节点，随机噪声均方差随机分布在[4,10]，多径效应，权重分布在[0.85，1]。(Linearization method simulation of three reference nodes positioning a positioning node, the variance of random noise are randomly distributed in [4,10], multi-path effects, the weight distribution in [0.85,1].)

关于求解光在光纤中传播的非线性薛定谔方程的一些资料(Some information about solving the nonlinear Schrodinger equation of light propagation in the fiber)

在matlab编译环境下，下实现迪杰斯特拉算法。 程序鲁棒，速度较快。(the implementation of Dijstla algorithm)

地震信号数据处理 显示各道信号波形 地震成像(Data processing seismic signal waveforms showing the Road, seismic imaging)

这个zip文件中包含的原则和双方的扩展卡尔曼滤波（EKF）和全球定位系统（GPS）算法简要说明。它的主要目的是提供一个相对容易实现的扩展卡尔曼滤波，在我们使用的输入函数的形式，而不是禁止函数处理符号表达式。它也可作为一个简要介绍了卡尔曼滤波算法GPS和学习它的意思。在为EKF的例子，我们提供的原始资料，同时使用的定位扩展卡尔曼滤波和最小二乘法的任务和解决方案。 有四个M文件和两个数据文件。 Extended_KF.m包含EKF的函数体。另一些功能和GPS的示例数据文件。(This zip file contains a brief illustration of principles and algorithms of both the Extended Kalman Filtering (EKF) and the Global Position System (GPS). It is designed primarily to provide a relatively easy-to-implement EKF, in which we use symbol expressions as the form of input functions rather than the forbidding function handles. It also serves as a brief introduction to the Kalman Filtering algorithms for GPS and a mean of learning it. In the example for the EKF, we provide the raw data and solution for the positioning task using both EKF and the Least Square method. There are four m files and two data files. Extended_KF.m contains the function body of the EKF. Others are functions and data files for the GPS example.)