A New Optimization Model for Dynamic Power and Sub-Carrier
標簽： Optimization Sub-Carrier Dynamic Model
上傳時間： 2014-01-01
上傳用戶：jiahao131
Verilog based simluation model for MXIC SPI Flash.
標簽： simluation Verilog Flash based
上傳時間： 2014-11-04
上傳用戶：咔樂塢
模型-視圖-控制器（Model-View-Controller，MVC）模式在面向對象應用程序的軟件工程中相當有用。本文將討論如何將其應用到靜態或動態調用的 Web 服務中去。
標簽： Model-View-Controller 模型 控制器
上傳時間： 2016-03-24
上傳用戶：jhksyghr
在我們的開發項目中使用MVC（Model-View-Control）模式的益處是，可以完全降低業務層和應用表示層的相互影響。此外，我們會有完全獨立的對象來操作表示層。MVC在我們項目中提供的這種對象和層之間的獨立，將使我們的維護變得更簡單使我們的代碼重用變得很容易（下面你將看到）。
標簽： Model-View-Control MVC 項目 模式
上傳時間： 2013-12-22
上傳用戶：問題問題
The fluid editor is a prototype source code editor supporting a "bring from" exploration model.With the fluid editor programmers can avoid explicit navigation between source documents by incorporating related code into the document under examination
標簽： editor exploration supporting prototype
上傳時間： 2013-12-07
上傳用戶：1079836864
you can use this matlab code to change image into different color system such as luv, rgb, yuv and lab
標簽： different matlab change system
上傳時間： 2016-03-27
上傳用戶：小眼睛LSL
Data compression using a word-based model and revised arithmetic coding method.
標簽： compression arithmetic word-based revised
上傳時間： 2013-12-24
上傳用戶：zsjinju
applet to servlet use to change from applet to servlet
上傳時間： 2014-01-03
上傳用戶：jeffery
ESS-Model is a powerful, reverse engine, UML-tool for Delphi/Kylix and Java-files.
標簽： Java-files ESS-Model UML-tool powerful
上傳時間： 2013-12-14
上傳用戶：zsjinju
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
