<tfoot id="q0eqc"></tfoot> JAVA得到網卡物理地址(windows和Linux) ,當時覺得挺好,后來正好項目里有需要,就用了它,但好像有點問題.因為它是采用固定字符串搜索(if (line.indexOf("Physical Address") != -1) )獲得MAC 地址的,后來在應用時出了問題,因為沒有"Physical Address"這一項.后來在外網在查查了一下,后來發現老外有寫一個這樣的類,可能那樣的方式更加可靠一點.算是做個標記
標簽： Physical windows indexOf Linux
上傳時間： 2016-01-24
上傳用戶：腳趾頭
TOOL (Tiny Object Oriented Language) is an easily-embedded, object-oriented, C++-like-language interpreter. The language, and indeed a significant part of the core of the TOOL engine, is based on the BOB project, a work that was originally developed by David Betz covered in previously published issues of Dr. Dobb s Journal.
標簽： easily-embedded object-oriented like-language Language
上傳時間： 2016-01-30
上傳用戶：ainimao
The line echo canceller (LEC) is designed to provide the maximum attainable transparent voice quality for de-echoing of a PSTN or POTS connection in voice-over-LAN systems with internal delays, or on a codec end of a telecom switch,基于TI 54X/55X平臺
標簽： transparent attainable canceller designed
上傳時間： 2014-01-17
上傳用戶：qoovoop
An object-oriented C++ implementation of Davidson method for finding a few selected extreme eigenpairs of a large, sparse, real, symmetric matrix
標簽： object-oriented implementation Davidson eigenpai
上傳時間： 2014-01-09
上傳用戶：TRIFCT
It is siemens plc program,it is our work-shop of an assembly line turning out cars program.
標簽： program work-shop assembly siemens
上傳時間： 2016-03-15
上傳用戶：yph853211
dsPIC DSC Line Echo Cancellation Library
標簽： Cancellation Library dsPIC Line
上傳時間： 2013-12-31
上傳用戶：王小奇
samples for pipe line source code
上傳時間： 2013-12-12
上傳用戶：comua
候捷 inside object Oriented C
上傳時間： 2016-04-02
上傳用戶：ardager
Demonstrates how to open a line and get general information
標簽： Demonstrates information general open
上傳時間： 2016-04-04
上傳用戶：playboys0
On-Line MCMC Bayesian Model Selection This demo demonstrates how to use the sequential Monte Carlo algorithm with reversible jump MCMC steps to perform model selection in neural networks. We treat both the model dimension (number of neurons) and model parameters as unknowns. The derivation and details are presented in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Sequential Bayesian Estimation and Model Selection Applied to Neural Networks . Technical report CUED/F-INFENG/TR 341, Cambridge University Department of Engineering, June 1999. After downloading the file, type "tar -xf version2.tar" to uncompress it. This creates the directory version2 containing the required m files. Go to this directory, load matlab5 and type "smcdemo1". In the header of the demo file, one can select to monitor the simulation progress (with par.doPlot=1) and modify the simulation parameters.
標簽： demonstrates sequential Selection Bayesian
上傳時間： 2016-04-07
上傳用戶：lindor
