modulation_mapping

在CR网络中，能量检测是最基础，也是最重要的一个环节，该程序实现了能量检测算法。(Energy detection of CR network)

对摄像的标定进行模拟，来分析误差对结果的影响，来确定标定的对最后结果的影响。(To simulate camera calibration to analyze the impact of the error on the results to determine the impact on the final outcome of the calibration.)

用不同的函数求得阈值进行消噪，希望对大家有用(The threshold denoising obtained with the different functions the hope that useful)

training Takagi-Sugeno fuzzy systems using the Levenberg-Marquardt method

说明：  利用MATLAB，编写的混凝土随机骨料投放程序代码(Concrete random aggregate delivery program code)

说明：  platEMO是Ye Tian等学者写的一款基于MATLAB的多目标优化工具，为新入门的同学或者开发者提供了很大的便利，其今年已经发布到PlatEMO 2.0版本了。这款工具主要具有以下的几个特点： 1.完全由MATLAB开发，不需要任何其它库。 2.包括现有的90个流行的MOEAs，包括遗传算法、差分进化、粒子群优化、模因算法、分布估计算法和基于代理模型的算法。其中大多数是2010年以后在顶级期刊上发表的代表性算法。 3.用户可以显示各种图形，包括结果的pareto front，真实的pareto front等等。 4.强大友好的GUI，可以不用编辑任何代码。 5.可以直接生成Excel或者LaTex。(PlaEMO is a multi-objective optimization tool based on MATLAB written by Ye Tian and other scholars. It is a great convenience for beginners or developers. It has been released to PlatEMO version 2.0 this year. This tool has the following characteristics: 1. Developed entirely by MATLAB without any other libraries. 2. Including 90 popular MOEAs, including genetic algorithm, differential evolution, particle swarm optimization, meme algorithm, distribution estimation algorithm and proxy model-based algorithm. Most of them are representative algorithms published in top journals after 2010. 3. Users can display various graphics, including Pareto front, real Pareto front and so on. 4. Powerful and friendly GUI, you can edit no code. 5. Excel or LaTex can be generated directly.)

BER simulation of BPSK OFDM with a rectangular filter

gauss and horner numerical simulation

说明：  这是matlab中进行矩阵LU分解的代码。是一个包，集合了模拟LU分解所需的函数文件。(A function for matrix LU factorization.This is the first function.There are another three function named "bslashtx","forward","backsubs".)

谱分析，气象、海洋专业程序，用于分析时间序列的显著周期。(Spectral analysis, meteorological, oceanographic professional program for the analysis of a significant period of time series.)