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  • java语言开发的P2P流媒体系统

    java语言开发的P2P流媒体系统,“Stream-2-Stream implements multicast+, a next generation streaming protocol. Multicast+ is more efficient and requires less bandwidth than direct streaming (e.g. shoutcast/icecast). Stream-2-Stream (abbreviated "s2s" or "S2S") stations Have no user limit stations can be set up without paying a fortune for bandwidth. Stream-2-Stream saves bandwidth by passing streams from one peer to another through multicast and unicast p2p, rather than everyone getting a stream from one central server (Shoutcast/Icecast). ”更多:http://s2s.sourceforge.net/about.php

    标签: java P2P 语言 流媒体系统

    上传时间: 2016-01-09

    上传用户:ikemada

  • This forced me to write about more interesting and comprehensive sorting methods, the result of whic

    This forced me to write about more interesting and comprehensive sorting methods, the result of which is this one. Through this writing I Have tried to give in-depth coverage of the entire sort algorithm I hope Peter wouldn t mind reading it. This article assumes that you really don t know about the iterations, looping, and so forth hence, it explains these in detail first.

    标签: comprehensive interesting methods sorting

    上传时间: 2016-01-10

    上传用户:athjac

  • * first open client.cpp and search for that USER_MSG_INTERCEPT(TeamInfo) over it u add this

    * first open client.cpp and search for that USER_MSG_INTERCEPT(TeamInfo) over it u add this Code: USER_MSG_INTERCEPT(Health) { BEGIN_READ(pbuf,iSize) me.iHealth = READ_BYTE() return USER_MSG_CALL(Health) } * then we search for int HookUserMsg (char *szMsgName, pfnUserMsgHook pfn) and add this Code: REDIRECT_MESSAGE( Health ) *k now we Have the health registered and can read it out i stop this hear know cuz i must thanks panzer and w00t.nl that they helped me with it first time! *ok now we go to int HUD_Redraw (float x, int y) and packing this draw code in it Code:

    标签: USER_MSG_INTERCEPT TeamInfo client search

    上传时间: 2016-01-22

    上传用户:ynzfm

  • This directory contains the Genetic Algorithm Optimization Toolbox for Matlab To use this, if you

    This directory contains the Genetic Algorithm Optimization Toolbox for Matlab To use this, if you are local to NCSU and Have AFS access to this directory, simply extend the matlab path using the following command. You can also place this command in a file called startup.m. Everytime you start Matlab in the directory containing this file, the path will always be extended.

    标签: Optimization Algorithm directory contains

    上传时间: 2014-01-18

    上传用户:songnanhua

  • Mod_python is an Apache module that embeds the Python interpreter within the server. With mod_python

    Mod_python is an Apache module that embeds the Python interpreter within the server. With mod_python you can write web-based applications in Python that will run many times faster than traditional CGI and will Have access to advanced features such as ability to retain database connections and other data between hits and access to Apache internals. A more detailed description of what mod_python can do is available in this O Reilly article.

    标签: interpreter Mod_python mod_python the

    上传时间: 2016-01-25

    上传用户:yd19890720

  • This a collection of MATLAB functions for extended Kalman filtering, unscented Kalman filtering, par

    This a collection of MATLAB functions for extended Kalman filtering, unscented Kalman filtering, particle filtering, and miscellaneous other things. These utilities are designed for reuse and I Have found them very useful in many projects. The code has been vectorised for speed and is stable and fast.

    标签: filtering Kalman collection functions

    上传时间: 2013-12-23

    上传用户:ljmwh2000

  • KEPware Automation DLL installation instructions: To use the KEPware Automation DLL in your Visua

    KEPware Automation DLL installation instructions: To use the KEPware Automation DLL in your Visual Basic project: 1) In the VB IDE, Select "Project/References". 2) If you Have been using another vender s automation dll, uncheck it. For example, uncheck "OPC Automation 2.0" for the OPC Foundation s automation dll. 3) Check "KEPware OPC Automation 2.0". 4) Recompile your VB project. If you Have been using the OPC Fondation s automation dll, you should not need to make any changes to the VB source code.

    标签: Automation KEPware installation instructions

    上传时间: 2013-12-28

    上传用户:电子世界

  • PCA and PLS aims:to get some insight into the bilinear factor models Principal Component Analysis

    PCA and PLS aims:to get some insight into the bilinear factor models Principal Component Analysis (PCA) and Partial Least Squares (PLS) regression, focusing on the mathematics and numerical aspects rather than how s and why s of data analysis practice. For the latter part it is assumed (but not absolutely necessary) that the reader is already familiar with these methods. It also assumes you Have had some preliminary experience with linear/matrix algebra.

    标签: Component Principal Analysis bilinear

    上传时间: 2016-02-07

    上传用户:zuozuo1215

  • Thinking in C++ patiently and methodically explores the issues of when and how to use inlines, refer

    Thinking in C++ patiently and methodically explores the issues of when and how to use inlines, references, operator overloading, inheritance and dynamic objects, as well as advanced topics such as the proper use of templates, exceptions and multiple inheritance. The entire effort is woven in a fabric that includes Eckel’s own philosophy of object and program design. A must for every C++ developer’s bookshelf, Thinking in C++ is the one C++ book you must Have if you’re doing serious development with C++.

    标签: methodically and patiently Thinking

    上传时间: 2014-01-03

    上传用户:it男一枚

  • The algorm of viterbi. You talk to your friend three days in a row and discover that on the first da

    The algorm of viterbi. You talk to your friend three days in a row and discover that on the first day he went for a walk, on the second day he went shopping, and on the third day he cleaned his apartment. You Have two questions: What is the overall probability of this sequence of observations? And what is the most likely sequence of rainy/sunny days that would explain these observations? The first question is answered by the forward algorithm the second question is answered by the Viterbi algorithm. These two algorithms are structurally so similar (in fact, they are both instances of the same abstract algorithm) that they can be implemented in a single function:

    标签: discover viterbi algorm friend

    上传时间: 2016-02-16

    上传用户:xc216