matlab-Variance-analysis

用matlab做的有关边缘检测的一个代码，该代码可以进行多种的边缘检测(Using matlab to do the edge detection of a code that can be a variety of edge detection of)

这是一个802.11a物理层OFDM技术的仿真与研究，可以观察各示波器输出波形(This is a simulation and research of 802.11a s OFDM physical layer, which can see its wave form.)

说明：  运用光反馈测距的部分程序，运用此程序可实现运用。希望大家参考，并且适当的修改。(The use of optical feedback ranging part of the process, use can be achieved using this procedure. Hope your reference, and the appropriate changes.)

实现瑞利衰落的仿真，利用改进的jakes模型来产生单径的平坦型瑞利衰落信道。(Realize the simulation of Rayleigh fading, improved jakes model is used to produce a single type of flat Rayleigh fading channel.)

实现雷达信号处理的跟踪滤波功能，亲测可用，效果很好(Tracking radar signal processing filtering, pro-test can be used with good results)

坐标系转变，通过运行，很好的代码，哈哈哈(Coordinate system transformation, by running, a good code)

用于双基地MIMO雷达波离角和波达角估计的二维ESPRIT算法的matlab程序(a 2 dimensional ESPRIT Algorithm based on bistatic mimo radar for joint dod & doa estimation ，realized on matlab)

简单的使用*.ffe文件返回值计算一维距离像的小程序(Simple to use* .ffe file returns the value calculated the one-dimensional image of the applet)

本研究基于MATLAB，可以实现对鸡蛋新鲜度的无损检测(In this study, based on MATLAB, can be achieved on the freshness of eggs, non-destructive testing)

统计模式识别工具箱（Statistical Pattern Recognition Toolbox）包含： 1，Analysis of linear discriminant function 2，Feature extraction: Linear Discriminant Analysis 3，Probability distribution estimation and clustering 4，Support Vector and other Kernel Machines( This section should give the reader a quick overview of the methods implemented in STPRtool. • Analysis of linear discriminant function: Perceptron algorithm and multiclass modification. Kozinec’s algorithm. Fisher Linear Discriminant. A collection of known algorithms solving the Generalized Anderson’s Task. • Feature extraction: Linear Discriminant Analysis. Principal Component Analysis (PCA). Kernel PCA. Greedy Kernel PCA. Generalized Discriminant Analysis. • Probability distribution estimation and clustering: Gaussian Mixture Models. Expectation-Maximization algorithm. Minimax probability estimation. K-means clustering. • Support Vector and other Kernel Machines: Sequential Minimal Optimizer (SMO). Matlab Optimization toolbox based algorithms. Interface to the SVMlight software. Decomposition approaches to train the Multi-class SVM classifiers. Multi-class BSVM formulation trained by Kozinec’s algorithm, Mitchell- Demyanov-Molozenov algorithm )