noiseModel.Designer

VC++键盘HOOK监测键位变化，运行本程序后，当用户在敲击键盘的时候，按动键盘上的任意键，程序都会监测到，并弹出用户所敲击键的字符，如示例截图所示，敲击了Y键。本程序退出时需要通过任务管理器强制关闭。

说明：  实现遗传算法,选用python3作为平台开发，直接导入即可运行(Implementation of genetic algorithm, using Python 3 as a platform for development, direct import can run)

：好风格应该成为一种习惯。如果你在开始写代码时就关心风格问 题，如果你花时间去审视和改进它，你将会逐渐养成一种好的编程习惯。一旦这种习惯变成 自动的东西，你的潜意识就会帮你照料许多细节问题，甚至你在工作压力下写出的代码也会 更好。-: Good style should become a habit. If you write code at the beginning when the issue of concern for style, if you take the time to examine and improve it, you will gradually develop a good programming practice. Once this habit becomes automatic things, your subconscious will help you take care of many details, and even under the pressure of your work will be better to write code.

说明：  遗传算法波束形成，有效地在一定的迭代次数下降低旁瓣电平，非常好用(Genetic algorithm beamforming, effectively reducing the sidelobe level under a certain number of iterations, very easy to use)

说明：  粘滞声波频率域有限差分数值模拟 可修改模型(2-D viscoacoustic wave simulations)

左右展开的竖条状flash图片导航菜单，鼠标放在任意条状图片上，该条状会在当前页重点显示，其它条目则向两端滑去，隐藏在两端，整体效果十分动态唯美，希望大家喜欢这款Flash菜单哦。

说明：  Answer to a question

机器学习领域五本经典书籍（英文原版）电子版(Bayesian Reasoning and Machine Learning deep learning INTRODUCTION TO MACHINE LEARNING Reinforcement Learning: An Introduction Understanding Machine Learning: From Theory to Algorithms)

算法实现题 最长公共子序列问题 给定两个序列 X = { x1 , x2 , ... , xm } Y = { y1 , y2 , ... , yn } 求X和Y的一个最长公共子序列 举例 X = { a , b , c , b , d , a , b } Y = { b , d , c , a , b , a } 最长公共子序列为 LSC = { b , c , b , a }-Algorithm to achieve the longest common sub-title sequence of the problem given two sequences X = (x1, x2, ..., xm) Y = (y1, y2, ..., yn) for X and Y, a maximum common substructure sequence, for example X = (a, b, c, b, d, a, b) Y = (b, d, c, a, b, a) the longest common sub-sequence for LSC = (b, c, b, a )

研究利用RLS算法自适应均衡器纠正存在加性白噪声的信道的畸变。讨论特征值扩散度 对学习曲线的影响。 比较RLS算法和LMS算法在不同信噪比情况下的学习曲线。 -RLS algorithm using adaptive equalizer to correct the existence of additive white noise distortion channel. Eigenvalue discussion diffusivity impact on the learning curve. Comparison of RLS algorithm and LMS algorithm for different signal to noise ratio in the case of the learning curve.