randinterval

说明：  应用无迹卡尔曼滤波较好的实现单目标跟踪。(Unscented Kalman filter applied to achieve good single-target tracking.)

说明：  matlab使用详解的书籍源码，对学习matlab非常有用！！(This is the source code of the book---Matlab shiyong xiangjie，it is very useful for matlab learners！！)

用matlab对lenna图像进行处理，方法是自适应加权中值滤波，自己写的。(Lenna with matlab image processing of the method is adaptive weighted median filter, write your own.)

在matlab的开发环境中，对图像进行RGB到RLab的转换(The development environment in matlab for image conversion of RGB to RLab)

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 )

说明：  miusic 方法实现线性均匀阵列的doa估计方法，帮助学习阵列信号的相关知识(miusic method to achieve uniform linear array of doa estimation method to help study and knowledge of the array signal)

基于matlab的字母识别，基于模板匹配，因为采用区域生长法，速度可能有些慢！(Base on matlab!)

An important aspect of an ANN model is whether it needs guidance in learning or not. Based on the way they learn, all artificial neural networks can be divided into two learning categories - supervised and unsupervised. • In supervised learning, a desired output result for each input vector is required when the network is trained. An ANN of the supervised learning type, such as the multi-layer perceptron, uses the target result to guide the formation of the neural parameters. It is thus possible to make the neural network learn the behavior of the process under study. • In unsupervised learning, the training of the network is entirely data-driven and no target results for the input data vectors are provided. An ANN of the unsupervised learning type, such as the self-organizing map, can be used for clustering the input data and find features inherent to the problem.

lda face recgnition...............matlab

由切比雪夫权值矢量及阵元间距画波束方向图：有x输入，计算该方向的值,否则计算所有方向的值，画出方向图(By the weight vector and the array element spacing beam pattern draw: there is x input, calculate the value of the direction, otherwise the value calculated in all directions, draw the direction of graph)