poik

sltoolbox (Statistical Learning Toolbox) organizes a comprehensive set of matlab codes in statistical learning, pattern recognition and computer vision. It includes 256 m-files in 24 categories, which are from low-level computational routines to high-level frameworks and algorithms

matlab gui, 包括了4个很好的例子： 1.具有多个坐标轴的GUI，主要学习控制哪一个坐标轴是绘图命令的对象和用编辑文本框读入输入的数。 2.列表框目录阅读器，主要学习获取路径，加载，打开文件 3.从列表框中存取工作区变量，学习把存在于基本工作区的变量名放进列表框，选择多项等 4.地址簿阅读器，这个比较综合，用得技术也比较多。包括打开，保存，修改mat文件，利用handles结构保存和重新调用共享数据。(matlab gui, including four very good example: 1. with multiple axes of GUI, the main learning control which axis is the object of drawing commands and edit the text boxes with the number of read input. 2. The list box directory reader, the main study to obtain the path, load, open the file 3. From the variable list box to access the work area, learning the basic work area exists in the variable names into the list box, select a number of other 4 . address book readers, the more integrated, more technology used. Including open, save, modify mat file, save and re-use handles structure called shared data.)

Face recognition is still a challenging task due to the different problems such as pose, illumination, expression and occlusions. Due to these problems, it needs an extensive research. Various face recognition techniques exists but they suffer one or the other limitations.

ART and ARTMAP neural networks

Saving Matlab variables to R toolbox Format. Useful for R users.

watershed segmentation code

说明：  将时域信号进行处理得到加速度频域幅值谱或振级谱(Acceleration amplitude spectrum or magnitude spectrum in frequency domain can be obtained by processing time domain signals.)

基于块状导频的信道估计，包含MMSE等算法。(Based on the pilot block channel estimation, including the MMSE and other algorithms.)

现代通信系统第10章 matlab程序源码(Modern communications systems Chapter 10 proceedings matlab source)

本代码为牛顿下山法求解非线性方程组。其调用格式为[r,m]=mulDNewton(F,x0,eps) 其中F:非线性方程组，x0：初始解，eps：解的精度；r：求得的一组解，n：迭代步数。(The code for Newton' s method for solving nonlinear equations. Its call format [r, m] = mulDNewton (F, x0, eps) where F: non-linear equations, x0: initial solution, eps: solution accuracy r: obtain a set of solution, n : the number of iterative steps.)