scale

circle tracking with the image as the input using Matlab

Face detection with facial Expression Analyze, Feature points stored in excel sheet,GUI module also used.

离散点的极值寻找。一组离散数据中的极大值或者极小值点，不同于fmax函数(Discrete extremum looking. Maxima or minima of a set of discrete data points, unlike fmax function)

MATLAB code with the creation of analog communication modulation techniques

generation of pctc in matlab...simulation code

哈里斯算法特征点，基础的算法，在机器上试验成功，把文件名称换一下就行了(Harris algorithm feature point-based algorithms, successfully tested on the machine, the file name on the line for what)

利用matlab模拟菲涅尔全息何用傅里叶变换实现单射光衍射(The use of the matlab simulation Fresnel holographic What Fourier transform single shot-ray diffraction)

数据联合检测与信道估计源码，此为博士论文相关程序，对信道及信号检测有详尽的描述和仿真(Joint data detection and channel estimation source, this doctoral thesis program, a detailed description of the channel and the signal detection and simulation)

bootstrap自助法,实现小样本的扩充，并且的达到估计均值区间的作用。(Self bootstrap method to achieve the expansion of small samples, and the estimated mean interval to the role.)

测试可以跑，根据自己情况修改下函数即可. NSGA-III 首先定义一组参考点。然后随机生成含有 N 个（原文献说最好与参考点个数相同）个体的初始种群，其中 N 是种群大小。接下来，算法进行迭代直至终止条件满足。在第 t 代，算法在当前种群 Pt的基础上，通过随机选择，模拟两点交叉（Simulated Binary Crossover，SBX）和多项式变异 产生子代种群 Qt。Pt和 Qt的大小均为 N。因此，两个种群 Pt和 Qt合并会形成种群大小为 2N 的新的种群 Rt=Pt∪Qt。 为了从种群 Rt中选择最好的 N 个解进入下一代，首先利用基于Pareto支配的非支配排序将 Rt分为若干不同的非支配层（F1，F2等等）。然后，算法构建一个新的种群St，构建方法是从 F1开始，逐次将各非支配层的解加入到 St，直至 St的大小等于 N，或首次大于 N。假设最后可以接受的非支配层是 L层，那么在 L+ 1 层以及之后的那些解就被丢弃掉了，且 St FL中的解已经确定被选择作为 Pt+1中的解。Pt+1中余下的个体需要从 FL中选取，选择的依据是要使种群在目标空间中具有理想的多样性。(The test can run and modify the function according to its own situation. NSGA-III first defines a set of reference points. Then the initial population containing N individuals (preferably the same number of reference points as the original literature) was randomly generated, where N was the size of the population. Next, the algorithm is iterated until the termination condition is satisfied. On the basis of current population Pt, the algorithm simulates two-point crossover (SBX) and polynomial mutation to produce offspring population Qt by random selection.)