kalman

一本学习matlab7.0的好书，不过是英文版，需要耐心看。(a study matlab7.0 books, but in English, requires patience to see.)

reed solomn codes with error correcting capability

implementation particle swarm optimation in C++

《卡尔曼滤波与组合导航》，PDF版本电子书籍(" Kalman filtering and navigation" , PDF versions of e-books)

这个也是关于空间谱的matlab 挺好的 可以(This is on the spatial spectrum of matlab can be quite good)

NSGA_II最原始的英文文献，详细介绍了算法的过程及参数选择，适合初学者学习该算法(NSGA_II the most primitive English literature, introduces in detail the process and parameter selection algorithm, suitable for beginners to learn the algorithm)

这是一个matlab人脸识别程序代码，有GUI界面(This is a face recognition matlab code, there is a GUI interface)

信噪比从0～13 dB，每隔1 dB仿真得到二进制双极性码的误码率(The bit error rate of binary bipolar codes is simulated every 1 dB from 0 to 13 dB.)

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 )

机器人的全部目标是产生一个实验室的引导路线。实验室里的各种不同方向坐标已经存储，并且机器人在不同方向移动的非常成功。在某一方向，它可以给出该方向的位置。在不同方位的移动，机器人需要几个阶段，首先定位在某一位置，接着计算下一个方向的路程。(The overall goal of the robot is to conduct a guided tour of the laboratory. Coordinates of various stations in the room are stored, and the robot moves successively from station to station. At each station, it gives an explanation of that station. To move between stations, the robot make several steps, localizing itself at each step and recalculating the way to the next station)