PCs

logical operation in matlab for image manipulationg

提出了一种基于小波变换和模糊集的图像融合算法.其基本思想是: 首先对图像进行小波变换,获得图 像的低频和高频分量 随后在融合过程中, 对低频和高频分量采取不同的融合策略, 即对低频分量采用平均能 量法进行融合,对高频成分利用图像的模糊集, 寻求一个模糊隶属函数作为融合算子进行融合 最后再对融合 后的图像进行小波反变换,重构出融合后的图像.实验结果证明了方法的有效性.(Proposed image fusion algorithm based on wavelet transform and fuzzy sets. The basic idea is: First the image wavelet transform to obtain the low and high frequency components of the image followed by low and high frequency components in the fusion process to take a different the integration of strategy, integration of the low frequency components using the average energy method, the use of high frequency components of the image fuzzy sets, look for a fuzzy membership function as a fusion operator for fusion image fusion wavelet transform and re- structure of fused images. experimental results demonstrate the effectiveness of the method.)

使用神经网络实现了异或的功能，代码为matlab代码，输入为二维数组，输出为一个数，训练样本为异或的四种情况，结果误差为1e-010,(Using a neural network to achieve the XOR functions, code for matlab code, enter a two-dimensional array, the output is a number of training samples for exclusive or four cases, the result error is 1e-010,)

fiber optics communication

可以求出复杂网络中两节点间的距离以及平均路径长度，各节点的度及度的分布曲线，以及节点的聚类系数。(You can find out the distance and average path length between the two nodes in complex networks .you can also find out degree and degree distribution curve of each node, and the node clustering coefficient.)

APM飞控程序，Ardupilot3.2.1(Flight control program of the APM, Ardupilot3.2.1 )

ACDC简单仿真，单相桥式结构，适合新手了解用。(ACDC simple simulation, single-phase bridge structures, suitable for a novice to understand.)

说明：  高尚版本的粒子群算法源代码，用MATLAB编写(High version of Particle Swarm algorithm source code written with MATLAB)

Adaptive Filter Theory

说明：  近年来，基于启发式的多目标优化技术得到了很大的发展，研究表明该技术比经典方法更实用和高效。有代表性的多目标优化算法主要有NSGA、NSGA-II、SPEA、SPEA2、PAES和PESA等。粒子群优化(PSO)算法是一种模拟社会行为的、基于群体智能的进化技术，以其独特的搜索机理、出色的收敛性能、方便的计算机实现，在工程优化领域得到了广泛的应用，多目标PSO(MOPSO)算法应用到了不同的优化领域[9~11]，但存在计算复杂度高、通用性低、收敛性不好等缺点。 多目标粒子群（MOPSO）算法是由CarlosA. Coello Coello等在2004年提出来的(In recent years, heuristic-based multi-objective optimization technology has been greatly developed, and research shows that this technology is more practical and efficient than classical methods. Representative multi-objective optimization algorithms mainly include NSGA, NSGA-II, SPEA, SPEA2, PAES and PESA. Particle Swarm Optimization (PSO) algorithm is an evolutionary technology based on swarm intelligence that simulates social behavior. With its unique search mechanism, excellent convergence performance, and convenient computer implementation, it has been widely used in the field of engineering optimization. The objective PSO (MOPSO) algorithm is applied to different optimization fields [9~11], but it has shortcomings such as high computational complexity, low versatility, and poor convergence. The multi-objective particle swarm optimization (MOPSO) algorithm was proposed by Carlos A. Coello Coello et al. in 2004)