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  • KeePass for J2ME is a J2ME port of KeePass Password Safe, a free, open source, light-weight and easy

    KeePass for J2ME is a J2ME port of KeePass Password Safe, a free, open source, light-weight and easy-to-use password manager. You can store passwords in a highly-encrypted database on a mobile phone, and view them on the go.

    標簽: KeePass J2ME light-weight Password

    上傳時間: 2016-01-25

    上傳用戶:er1219

  • This the third edition of the Writing Device Drivers articles. The first article helped to simply ge

    This the third edition of the Writing Device Drivers articles. The first article helped to simply get you acquainted with device drivers and a simple framework for developing a device driver for NT. The second tutorial attempted to show to use IOCTLs and display what the memory layout of Windows NT is. In this edition, we will go into the idea of contexts and pools. The driver we write today will also be a little more interesting as it will allow two user mode applications to communicate with each other in a simple manner. We will call this the “poor man’s pipes” implementation.

    標簽: the articles Drivers edition

    上傳時間: 2014-01-16

    上傳用戶:ommshaggar

  • 編程珠璣

    編程珠璣,程序員的警世恒言,讓您Good reading、Happy coding

    標簽: 編程 珠璣

    上傳時間: 2013-12-18

    上傳用戶:firstbyte

  • 前段時間一直忙著學習

    前段時間一直忙著學習,最近有空就用c言編的FFT算法,請大家指教。

    標簽:

    上傳時間: 2014-01-02

    上傳用戶:maizezhen

  • VBScript Help file

    VBScript Help file,方便查詢VB語法,可直接開啟使用。

    標簽: VBScript Help file

    上傳時間: 2014-03-11

    上傳用戶:jackgao

  • 圖的廣度優先遍歷算法

    圖的廣度優先遍歷算法,c 語 言實現

    標簽: 算法

    上傳時間: 2016-03-20

    上傳用戶:jjj0202

  • Just what is a regular expression, anyway? Take the tutorial to get the long answer. The short answ

    Just what is a regular expression, anyway? Take the tutorial to get the long answer. The short answer is that a regular expression is a compact way of describing complex patterns in texts. You can use them to search for patterns and, once found, to modify the patterns in complex ways. You can also use them to launch programmatic actions that depend on patterns. A tongue-in-cheek comment by programmers is worth thinking about: "Sometimes you have a programming problem and it seems like the best solution is to use regular expressions now you have two problems." Regular expressions are amazingly powerful and deeply expressive. That is the very reason writing them is just as error-prone as writing any other complex programming code. It is always better to solve a genuinely simple problem in a simple way when you go beyond simple, think about regular expressions. Tutorial: Using regular expressions

    標簽: expression the tutorial regular

    上傳時間: 2013-12-19

    上傳用戶:sardinescn

  • 為了提高使用精度

    為了提高使用精度,研究了某型號MEMS陀螺儀的隨機漂移模型。采用游程檢驗法分析了 該陀螺儀隨機漂移數據的平穩性,并根據該漂移為均值非平穩、方差平穩的隨機過程的結論, 采用梯度徑向基(RBF)神經網絡對漂移數據進行了建模。實驗結果表明:相比經典RBF網絡模 型而言,這種方法建立的模型能更好地描述MEMS陀螺儀的漂移特;相對于季節時間序列模型而 言,其補償效果提高了大約15%。

    標簽: 精度

    上傳時間: 2016-04-01

    上傳用戶:weixiao99

  • n this demo, we show how to use Rao-Blackwellised particle filtering to exploit the conditional inde

    n this demo, we show how to use Rao-Blackwellised particle filtering to exploit the conditional independence structure of a simple DBN. The derivation and details are presented in A Simple Tutorial on Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks. This detailed discussion of the ABC network should complement the UAI2000 paper by Arnaud Doucet, Nando de Freitas, Kevin Murphy and Stuart Russell. After downloading the file, type "tar -xf demorbpfdbn.tar" to uncompress it. This creates the directory webalgorithm containing the required m files. Go to this directory, load matlab5 and type "dbnrbpf" for the demo.

    標簽: Rao-Blackwellised conditional filtering particle

    上傳時間: 2013-12-17

    上傳用戶:zhaiyanzhong

  • On-Line MCMC Bayesian Model Selection This demo demonstrates how to use the sequential Monte Carl

    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

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