The present paper deals with the problem of calculating mean delays in polling systems
with either exhaustive or gated service. We Develop a mean value analysis (MVA) to
compute these delay figures. The merits of MVA are in its intrinsic simplicity and its
intuitively appealing derivation. As a consequence, MVA may be applied, both in an
exact and approximate manner, to a large variety of models.
二維SIMPLEC程序，包含三個project:二維穩(wěn)態(tài)傳熱，常粘度系數(shù)湍流的動態(tài)模擬，湍流中被動標(biāo)量擴(kuò)散（基于時均的 k –ε方程．
同時壓縮包中還有詳細(xì)的說明文檔．
注意：本程序只限于學(xué)習(xí)用途，切勿作商業(yè)用途，作者享有對此代碼及使用目的的最終解釋權(quán)！
Cccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccc
Cccc This computer program was copied from the graduate student course program
Cccc of the University of Minnesota. Part of it was re-formulated to meet the
Cccc personal computer environment. Some inappropriate expressions were also
Cccc corrected. The program is used only for teaching purpose. No part of it
Cccc may be published. You may use it as a frame to re-Develop your own code
Cccc for research purpose. XJTU Instructor, 1995.11
Cccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccc
In this article, we present an overview of methods for sequential simulation from posterior distributions.
These methods are of particular interest in Bayesian filtering for discrete time dynamic models
that are typically nonlinear and non-Gaussian. A general importance sampling framework is Developed
that unifies many of the methods which have been proposed over the last few decades in several
different scientific disciplines. Novel extensions to the existing methods are also proposed.We showin
particular how to incorporate local linearisation methods similar to those which have previously been
employed in the deterministic filtering literature these lead to very effective importance distributions.
Furthermore we describe a method which uses Rao-Blackwellisation in order to take advantage of
the analytic structure present in some important classes of state-space models. In a final section we
Develop algorithms for prediction, smoothing and evaluation of the likelihood in dynamic models.
We propose a novel approach for head tracking, which combines particle filters with Isomap. The particle filter works on the low-dimensional embedding of training images. It indexes into the Isomap with its state variables to find the closest template for each particle. The most weighted particle approximates the location of head. We Develop a synthetic video sequence to test our technique. The results we get show that the tracker tracks the head which changes position, poses and lighting conditions.
Introduction
Some times it is required that we build a shared library (DLL) from an m-file. M-files are functions that are written in Matlab editor and can be used from Matlab command prompt. In m-files, we employ Matlab built-in functions or toolbox functions to compute something. In my past articles, I showed you some ways to use Matlab engine (vis. API, C++ class or Matlab engine API) for employing Matlab built-in functions, but what about functions that we Develop? How can we use them in VC? Is there any interface? This article shows you an idea to employ your own Matlab functions.
Session 1: common and useful built-in Mathematica functions variable assignment and function definition
the Front End and the Kernel Notebooks.
Session 2: organisation of data in Mathematica lists and expressions simple programming functions
nesting.
Session 3: the opportunity to Develop your proficiency as a Mathematica user through work on an extended
problem Case Studies Mathematica resources on the Internet.
Advanced_modelling_in_finance_using_Excel_and_VBA（pdf） The book adopts a step-by-step approach to understanding the more sophisticated aspects of Excel macros and VBA programming, showing how these programming techniques can be used to model and manipulate financial data, as applied to equities, bonds and options. The book is essential for financial practitioners who need to Develop their financial modelling skill sets as there is an increase in the need to analyse and Develop ever more complex what if scenarios.
Motoko it s a 2D library to handle the graphical user interface of the game. It supports the basic controls of the windows GUI look like: PictureBox, TextBox, ListBox, LabelBox, ControlListBox, ComboBox, Button, CheckButton, Dialog, Panel, HScrollBar and VScrollBar. It uses the library CRM32Pro, so the CRM32Pro devkit will be needed to Develop any application with Motoko, and it s contained in the Motoko devkit. The Motoko library is under the LGPL license, so read first the license if you want to make any change to the library.
A .zip file contains a series of scripts that were used in the MathWorks webinar "Using MATLAB to Develop Portfolio Optimization Models." The scripts generate 3D efficient frontiers for a universe of 44 stocks with time as the third axis. Additional scripts perform various ex-ante and ex-post analyses. Results are generated with and without market adjustments in the data. A readme.txt. file in the .zip folder describes each script and how to use it
IBM® Rational® Application Developer V6.0 is the full function Eclipse 3.0 based
Development platform for Developing Java™ 2 Platform Standard Edition (J2SE)
and Java 2 Platform Enterprise Edition (J2EE) applications with a focus on
applications to be deployed to IBM WebSphere® Application Server and IBM
WebSphere Portal. Rational Application Developer provides integrated
Development tools for all Development roles, including Web Developers, Java
Developers, business analysts, architects, and enterprise programmers.
This IBM Redbook is a programming guide that highlights the features and
tooling included with IBM Rational Application Developer V6.0. Many of the
chapters provide working examples that demonstrate how to use the tooling to
Develop applications as well as achieve the benefits of visual and rapid Web
Development.
