wxWakeOnLan-1.0.tar

文档，教你如何循序渐进使用WINPCAP-Document, teach you how to use step-by-step WINPCAP ..

国外黑客800例，比较全面的讲解黑客技术，能够让你快速的找到需要的技术，进行研读(800 cases of foreign hackers, a more comprehensive explanation hacker technology that allows you to quickly find the needed technology, the study)

这是一个比较完整的引擎源码，安装组件后就能编译运行(This is a relatively complete engine source code, after the installation of components can be compiled and run)

含有可執行文件,用bmp來卸的,非常漂亮的表面(Contains executable files, unloaded with bmp, very nice surface)

说明：  Probabilistic Programming and Bayesian Methods for Hackers是一本写给开发者的关于贝叶斯方法和概率问题的免费开源书。贝叶斯方法的用途十分广泛，在经济学上能找出一堆的例子。而在IT行业，机器学习是非常典型的一个应用。而机器学习也是本书作者写本书的一个重要的理由。 本书选择了Python作为编程语言，这一点都不奇怪，Python在科研和数据分析上的应用是非常方便和普遍的，比如大名鼎鼎的Numpy等。作者在本书中使用另一个库PyMC，它依赖于Numpy和SciPy(可选)。 如果你正从事数据分析和机器学习的研究，这是一本非常棒的书籍。它不仅介绍了贝叶斯的相关理论，告诉了我们一些这个伟大的理论没告诉我们的东西，更重要的是，他的文档基于ipython-notebook完成，这是一种输入代码实时在网页显示各种统计图的工具。图文并茂，十分生动。唯一的要求可能是需要有一定的数学功力和英语水平了。(The Bayesian method is the natural approach to inference, yet it is hidden from readers behind chapters of slow, mathematical analysis. The typical text on Bayesian inference involves two to three chapters on probability theory, then enters what Bayesian inference is. Unfortunately, due to mathematical intractability of most Bayesian models, the reader is only shown simple, artificial examples. This can leave the user with a so-what feeling about Bayesian inference. In fact, this was the author's own prior opinion.)

一种新的蜂群算法，优化能力超强！！！，还可以用于其他应用，例如特征选择当中(a new colony algorithm optimization capabilities super! ! ! Can also be used for other applications, such as the choice of which)

该算法明确描述了量子遗传算法的实现流程，通过运行，该程序是正确的。(The algorithm clearly describes the realization of the quantum genetic algorithm process, by running the program is correct.)

一个网络后门程序。演示了通过Http通道的功能穿透安全级别非常高的防火墙，甚至硬件防火墙。()

基于rtp的实时语音聊天，采用adpcm压缩/* comm.h - Communication input/output modules * * This program is free software; you can redistribute it and/or modify it * under the terms of the GNU General Public License as published by the Free * Software Foundation; either version 2 of the License, or (at your option) * any later version. * * This program is distributed in the hope that it will be * useful, but WITHOUT ANY WARRANTY; without even the implied * warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR * PURPOSE.  See the GNU General Public License for more * details (available in COPYING).

The proposed approach is based on three stages which (1) use neural networks for constructing a response function model of a dynamic multiresponse system, (2) use exponential desirability functions for evaluating overall performance of a specific factor combination, and (3) use a genetic algorithm to optimize parameter design. Effectiveness of the proposed approach is illustrated with a simulated example. Analysis results reveal that the approach has higher performance than the traditional experimental design.