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Calculate Trunking Efficiency with MatLab

关于matlab学习的PPT，里面介绍了matlab的一些基本知识，对于初学者非常有用。(PPT on matlab learning, which introduced some basic knowledge of matlab is very useful for beginners. )

数字 图像处理特征提取 非常详细，推荐学习下载(Feature point extraction with matlab performance)

matlab 编写的源程序交互多模算法，用于目标多机动蒙特卡罗法仿真跟踪滤波器 本人十分关注 机动目标。这个是利用交互多模算法，用于目标多机动假设运动情况下的蒙特卡罗法仿真跟踪滤波器。matlab 编写的源程序。 但是有一点小错误，如果您能修改，不胜感激。(matlab source code prepared by the interactive multi-mode algorithm, used to target many motor simulation Monte Carlo method I am very concerned about tracking filter for maneuvering target. This is the use of interactive multi-mode algorithm, used to target many of motor sport where the assumption of the Monte Carlo method simulation tracking filter. matlab source code prepared. But there is a small error, if you can modify, greatly appreciated.)

光纤光栅法布里—珀罗腔（FBG F-P）的matlab仿真程序。利用光纤光栅的矩阵传输特性。(Fiber Bragg Grating Fabry- Perot cavity (FBG FP) of matlab simulation program. Optical fiber grating transmission characteristics of the matrix.)

matlab的核密度工具箱，非常实用，简单易用。(nuclear density matlab toolbox, very practical, easy to use.)

本程序实现多模态故障检测与故障诊断，用的方法是LPP_GMM，GMM是用的FJ算法，效果很好(This procedure realize multimodal, fault detection and fault diagnosis method is LPP_GMM, GMM FJ algorithm is used, the effect is very good )

相控阵雷达的MIMO的仿真,对于雷达中的仿真(MIMO simulation of phased array radar, radar simulation)

雷达高斯分布相干视频噪声和高斯分布中频噪声仿真(Coherent Radar Gaussian noise and Gaussian frequency video noise simulation)

聚类算法，不是分类算法。分类算法是给一个数据，然后判断这个数据属于已分好的类中的具体哪一类。聚类算法是给一大堆原始数据，然后通过算法将其中具有相似特征的数据聚为一类。这里的k-means聚类，是事先给出原始数据所含的类数，然后将含有相似特征的数据聚为一个类中。所有资料中还是Andrew Ng介绍的明白。首先给出原始数据{x1,x2,...,xn}，这些数据没有被标记的。初始化k个随机数据u1,u2,...,uk。这些xn和uk都是向量。根据下面两个公式迭代就能求出最终所有的u,这些u就是最终所有类的中心位置。(Clustering algorithm, not a classification algorithm. Classification algorithm is to give a figure, and then determine the data belonging to a specific class of good which category. Clustering algorithm is to give a lot of raw data, and then through the algorithm which has similar characteristics data together as a class. Here k-means clustering, is given in advance the number of classes contained in the raw data, then the data contain similar characteristics together as a class. All information presented in or Andrew Ng understand. Firstly, raw data {x1, x2, ..., xn}, the data is not labeled. K random initialization data u1, u2, ..., uk. These are the vectors xn and uk. According to the following two formulas can be obtained final iteration all u, u is the ultimate all these classes the center position.)