psbio

以某个用户的权限运行程序，相当于windows下的run as(Permissions to a user running the program, which is equivalent to run as under windows)

非常不错的书籍，《微粒群算法》供8章和2个附录，依次上传第3章绪论(Very good books, Particle Swarm Optimization for 8 chapters and two appendices, followed by From Chapter 3 Introduction)

TwIST：两步迭代的图像分割matlab源码，包括图象压缩／重建／增强／去噪等应用(TwIST: two-step iterative image segmentation matlab source, including image compression/reconstruction/enhancer/de-noising applications such as)

使用案例讲解了matlab中的随机数生成，有源代码程序(Generating random Numbers)

规划与lingo入门教程，这些资料可以对读者有一定的帮助。请读者不要乱。(Planning and lingo introductory tutorial, these data will certainly help the reader. Readers are cautioned not chaos.)

利用matlab仿真GPS坐标转换，即知道一点的GPS坐标利用测出来的距离俯仰方位信息计算另外一点的GPS坐标。(GPS coordinates conversion using matlab simulation, that is to know that the GPS coordinates use the distance measured from the pitching position information to calculate the GPS coordinates of another point.)

图像Sift特征提取,由MATLAB和C混合编程完成。(Sift feature extraction, image performed by MATLAB and C programming. )

机组组合问题的粒子群算例，不错的算例，大家一起学习(Unit Commitment particle swarm example, a good example, we will study together)

Servo motors and industrial

本文论述并设计实现了一个脱机自由手写体数字识别系统。文中首先对待识别数字的预处理进行了介绍，包括二值化、平滑滤波、规范化、细化等图像处理方法；其次，探讨了如何提取数字字符的结构特征和笔划特征，并详细地描述了知识库的构造方法；最后采用了以知识库为基础的模板匹配识别方法,并以MATLAB作为编程工具实现了具有友好的图形用户界面的自由手写体数字识别系统。实验结果表明，本方法具有较高的识别率，并具有较好的抗噪性能。(In this paper, and design implementation of a freely Offline handwritten numeral recognition system. First, identify the figure of the pre-processing treatment were introduced, including binarization, smoothing filtering, standardization, refinement, such as image processing method Secondly, to explore how to extract the structural features of characters and stroke characteristics, and described in detail Knowledge of the construction method Finally the use of a Knowledge-based template matching to identify methods and MATLAB as a programming tool to achieve with a friendly graphical user interface of freely handwritten numeral recognition system. The experimental results show that this method has higher recognition rate, and has better anti-noise performance.)