MATLAB-command

i trie to implement the research paper "Robust and Efficient Algorithm for Eye Detection on eye" but i need to verify my detected eyes

武汉大学 遥感信息工程学院 2014-2015年 第一学期 matlab 第一次实习 学霸代码(Remote Sensing Information Engineering, Wuhan University first semester of 2014-2015 school matlab first internship， Pa code)

一个9输入4输出的矩阵，利用BP神经网络训练并预测。通过不断的调试获取最可靠地输出。(A 9-input 4-output matrix, using BP neural network training and prediction. Continue to debug access to the most reliable output.)

说明：  计算掘进机横摆工况截割头合力，并绘制合力曲线(Calculated yaw conditions TBM cutting head together)

simple IDMA TYPE RANDOM INTERLEAVER

已知空间三个点的坐标，求通过三个点的圆的圆心和半径，输入为三个点(Known spatial coordinates of three points, three points of the circle which passes through the center and radius, enter three points)

用Matlab实现了机器人最优路径规划中的全局路径规划，算法采用的是基于方向权的二叉树算法。(Using Matlab to achieve the optimal robot path planning in the global path, planning algorithm uses a binary tree algorithm based on the direction. )

说明：  数字信号处理的一些算法等，包括扩散，衰减，补偿等，对于DSP专业的有一定帮助(err)

非线性滤波算法中的粒子滤波，用于目标定位与跟踪。(Nonlinear filtering algorithm in the particle filter for target tracking.)

Rosenbrock函数优化属于无约束函数优化问题，其全局极小值位于一条平滑而狭长的抛物线形状的山谷底部，且为优化算法提供的信息很少，因此找到其全局极小值就显得很困难。根据Rosenbrock函数的这种特性，专门提出了一种改进的PSO算法，该算法引入三角函数因子，利用三角函数具有的周期振荡性，使每个粒子获得较强的振荡性，扩大每个粒子的搜索空间，引导粒子向全局极小值附近靠近，避免算法过早地收敛，陷入局部最优，从而找到Rosenbrock函数的全局极小值。大量实验结果表明，该算法具有很好的优化性能，为某些领域某些特定的类似于Rosenbrock函数的优化问题提供了一种新的思路。(Rosenbrock function optimization functions are unconstrained optimization problems, its global minimum value at a smooth parabolic shape and narrow mountain valley Department, and for the optimization algorithm provides little information, and therefore find its global minimum value becomes very difficult. According to this feature Rosenbrock function, specifically mentioning Out an improved PSO algorithm (PSO-R), the algorithm introduced trigonometric factor, using trigonometric has periodic oscillations, so that each particle to obtain more Strong oscillations of expanding the search space of each particle, the particle is close to nearby guide global minimum, avoiding premature convergence algorithm, into a local optimum, thus Finding the global minimum Rosenbrock function. Experimental results show that the algorithm has a good optimizing performance, similar to some certain areas Rosenbrock function in the optimization problem provides a new way of thinking.)