PoEdges

SFN MFN BER with Matlab

图像配准算法SIFT，matlab源程序，可运行(Image registration algorithm the SIFT, matlab source code can be run)

how to deploy nodes for energy efficient protocol based on hierarchical routing

阵列信号处理，采用遗传算法对阵列进行稀疏化处理，对于研究阵列天线的学者有帮助(Array signal processing and sparse processing with genetic algorithm are helpful to scholars who study array antenna.)

基于雷达系统所做的极化对消仿真，给出了简单明了的具体程序，并对结果进行画图。(Polarization radar system based on consumption simulation done, given the simplicity of the specific procedures and the results were drawing.)

说明：  基于EEMD的回转支承故障多尺度ICA方法 检测与诊断(EEMD-based multiscale ICA method for slewing bearing fault detection and diagnosis)

This is IP optimization method examples with nonequality constraints

耦合影响格子序列仿真，随机性，平衡性，级多瓣分治混沌序列在cnma中的应用(Simulation of the impact of lattice coupling sequence, randomness, balance, class of chaotic sequence多瓣divided the application in cnma)

电池全寿命加速老化试验的采集数据。在不同温度下的充电和放电下完成。数据格式为matlab的mat，文件里面有详细的实验数据说明，希望对研究电池寿命预测和诊断的同志有帮助。(Battery life-accelerated aging test data collection. At different temperatures to complete the charging and discharging. The data format is the matlab mat, which has detailed documentation of the experimental data shows that the hope of battery life prediction and diagnosis to help comrades.)

说明：  遗传算法（Genetic Algorithm，GA）最早是由美国的 John holland于20世纪70年代提出,该算法是根据大自然中生物体进化规律而设计提出的。是模拟达尔文生物进化论的自然选择和遗传学机理的生物进化过程的计算模型，是一种通过模拟自然进化过程搜索最优解的方法。该算法通过数学的方式,利用计算机仿真运算,将问题的求解过程转换成类似生物进化中的染色体基因的交叉、变异等过程。在求解较为复杂的组合优化问题时,相对一些常规的优化算法,通常能够较快地获得较好的优化结果。遗传算法已被人们广泛地应用于组合优化、机器学习、信号处理、自适应控制和人工生命等领域。(Genetic algorithm (GA) was first proposed by John Holland of the United States in the 1970s. It is designed and proposed according to the evolution law of organisms in nature. It is a computational model of biological evolution process simulating natural selection and genetic mechanism of Darwin's biological evolution theory. It is a method to search the optimal solution by simulating natural evolution process. The algorithm transforms the solving process of the problem into a process similar to the crossover and mutation of chromosome genes in biological evolution by means of mathematical method and computer simulation. When solving complex combinatorial optimization problems, compared with some conventional optimization algorithms, it usually can get better optimization results quickly. Genetic algorithm has been widely used in combinatorial optimization, machine learning, signal processing, adaptive control and artificial life.)