Battery

STEGANALYSIS ALGORITHMS FOR DETECTING THE HIDDEN INFORMATION IN IMAGE, AUDIO AND VIDEO COVER MEDIA

以短时傅里叶变换为基础，进行信号的时频分析，可以在做出三维瀑布图。(In short time Fourier transform as the foundation, carries on the signal of time-frequency analysis, we can make 3 d waterfall diagram )

利用遗传算法解决电力系统最优潮流问题。程序opf1为最优潮流的模型建立部分，gaopf是主函数，求解最小燃料费用。(By using the genetic algorithm to solve the power system optimal power flow problem.Program opf1 for optimal power flow model is established in this part, gaopf is the main function, and solving the minimum fuel costs.)

Long window algorithm source code

matlab fem meshing code vsefulery u

matlab粗糙集算法，主要使用于神经网络和支持向量机等算法的预处理，可以有效地筛选数据(matlab rough set algorithm, mainly used in the pre-processing of the neural network and support vector machine algorithm can effectively filter the data)

说明：  做自适应去噪时用到的三篇很有价值的文献，，并有实现程序，(Adaptive denoising used to do three valuable literature, and have the program)

EA名称：套利精灵 EA类型：对冲套利 一、功能介绍 本EA遵行顺大势，逆小势策略入场交易； 二、核心优势 多货币套利，对冲风险获取盈利，每月有一次机会单局最大利润达到30% 三、注意事项 （1）如适用周期：该EA只须在欧美一分钟图表上，就能运行多对货币。 （2）资金要求：建议3000美金，最少入金不少于1000美金 （3）时间段：全时段，正常情况无需人工干预，在盈利的情况下，可视情况自行平仓;(EA Name: Arbitrage Elf EA Type: Hedging Arbitrage I. Introduction of Functions This EA follows the general trend and counter-minor strategy to enter the market. II. Core Advantages Multi-currency arbitrage, hedging risk for profit, once a month, the maximum profit of a single board reached 30%. Notes (1) If applicable: the EA can run multiple pairs of currencies only on a one-minute chart in Europe and the United States. (2) Capital requirements: Recommend $3000, at least $1000 (3) Time period: Full-time period, normal conditions without manual intervention, in the case of profit, according to the circumstances of self-liquidation;)

MATLAB对带噪声的信号进行最小均方误差估计，得到去噪声的信号。(MATLAB is used to estimate the minimum mean square error of the signal with noise, and the noise is obtained.)

论文 Least Squares Loss for Multi-class (task) Learning via the sparse group Lasso penalty 的算法源代码，是sparse+l1+lq,1的稀疏属性选择算法。( Least Squares Loss for Multi-class (task) Learning via the sparse group Lasso penalty Problem min 1/2 || A x- y||^2+ lambda_1* |x|_1+ lambda_2*|x|_{q,1} q=2, infty)