ShadowDetection

说明：  机器人PID控制仿真 控制律设计 以及收敛性分析(robot PID control )

包括各种EOF的程序包，有普通EOF和复EOF等。(Including all kinds of EOF program bag, the common EOF and complex EOF, etc. )

说明：  对于经典np问题矩形排样问题通过遗传算法进行优化，从而原料利用率，降低废料面积，提升厂家利益(For the classical NP rectangular layout problem, the genetic algorithm is used to optimize, so as to reduce the utilization rate of raw materials, reduce the waste area, and improve the interests of manufacturers)

使用TOA的方法來計算位置，相當好用的程式(Using the TOA method to calculate the location, very easy to use program)

this is leach protocol implemented in wsn

sift源代码，以及对sift原理的详细解说，附带low的sift原始源代码(the sift the source code, as well as a detailed explanation of the SIFT principle with low sift the original source code)

基于harris技术图像角点的检测，标准的算法，可以改变其中参数，来改变显示效果(Detection based on the the Harris technical image angle point, the standard algorithm, where the parameters can be changed, to change the display effect)

exercice for matlab code in machine learning

有限元分析优化，主要基于水平集方法，comsol的二次matlab开发(Finite element analysis and optimization, mainly based on the level set method, comsol matlab development of secondary)

说明：  遗传算法是计算数学中用于解决最佳化的搜索算法，是进化算法的一种。进化算法最初是借鉴了进化生物学中的一些现象而发展起来的，这些现象包括遗传、突变、自然选择以及杂交等。遗传算法通常实现方式为一种计算机模拟。对于一个最优化问题，一定数量的候选解（称为个体）的抽象表示（称为染色体）的种群向更好的解进化。传统上，解用二进制表示（即0和1的串），但也可以用其他表示方法。进化从完全随机个体的种群开始，之后一代一代发生。在每一代中，整个种群的适应度被评价，从当前种群中随机地选择多个个体（基于它们的适应度），通过自然选择和突变产生新的生命种群，该种群在算法的下一次迭代中成为当前种群。(Genetic algorithm (GA) is a search algorithm used to solve optimization in computational mathematics. It is one of evolutionary algorithms. Evolutionary algorithms were originally developed by borrowing phenomena in evolutionary biology, such as heredity, mutation, natural selection and hybridization. Genetic algorithms are usually implemented as a computer simulation. For an optimization problem, an abstract representation of a certain number of candidate solutions (called individuals) (called chromosomes) evolves towards a better solution. Traditionally, solutions are represented in binary (that is, strings of zeros and ones), but other representations are possible. Evolution begins with a population of completely random individuals, and it happens from generation to generation.)