Desk224top

说明：  两个用MATLAB语言编写的相机标定程序，一个是Tsai的一个是Zhang的(The two languages used MATLAB camera calibration procedure, one is a Tsai is Zhang)

稀疏表示分类算法，用于样本分类的数学算法(Sparse that classification algorithms, mathematical algorithms for sample classification)

this is primer for matlab, this website is irritating

说明：  资料为基于BP基于BP神经网络的人脸识别源码（matlab），是课程设计做的一个课题，效果还不错，配备有测试显示界面，比较直观！(The data is the source code of face recognition based on BP neural network (matlab), which is a subject of curriculum design. The effect is good, equipped with test display interface, which is more intuitive!)

lena 图像的线性插值 对图像处理的理解很有帮助(lena bilinear)

这是一个matlab编写的电离层误差图的程序。里边分别利用广播星历和模型修正进行了编程。(It is written in a matlab program ionospheric error plots. Inside were using broadcast ephemeris and model updating for the programming.)

The objective is to measure the differences between two images, and measurement of image quality.

配电网中IEEE标准节点的一个传统算例.(IEEE standard distribution grid nodes a conventional example.)

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.)

多传感器数据融合：matlab仿真程序实现多传感器数据融合算法(Multi-sensor data fusion: matlab simulation program to achieve multi-sensor data fusion algorithm)