Visual Basic for Windows下多媒体程序的设计

本文介绍了在Ｗｉｎｄｏｗｓ３．ｘ下利用ＶｉｓｕａｌＢａｓｉｃ（ＶＢ）４．０高级程序设计语言开发多媒体程序的几种方法，首先从ＭＣＩ指令入手，阐述了其基本含义，然后依次介绍了利用ＶＢ本身的ＭＤＩ．ＶＢＸ，动态连接库ＭＭＳＹＳＴＥＭ．ＤＬＬ，ＡＡＰＬＡＹ．ＤＬＬ以及ＳＯＵＮＤ．ＤＲＶ来开发多媒体程序的方法，本文对于利用ＶＢ开发多媒体程序具有重要的指导意义。     

基于OLE/COM的“119”多媒体模拟演练创作平台的设计与实现

采用面向对象技术,以“１１９”模拟演练应用为目标,构造一个模拟演练课件模型,研究并实现１个基于ＯＬＥ／ＣＯＭ的可视化多媒体创作平台ＶＭＰＤＰ（ＶｉｓｕａｌＭｕｌｔｉｍｅｄｉａＰｒｅｓｅｎｔａｔｉｏｎＤｅｖｅｌｏｐｉｎｇＰｌａｔｆｏｒｍ）。     

基于语义贴近度的模糊数据依赖

本文基于语义贴近度的概念给出了模糊函数依赖（ＦＦＤ）、模糊多值依赖（ＦＭＶＤ）和模型联接依赖（ＦＪＤ）的定义。本文得到一组类似于Ａｒｍｓｔｒｏｎｇ公理的正确、完备的ＦＦＤ和ＦＭＶＤ的推理规则。进一步，我们证明了经典数据依赖满足模糊数据依赖的定义，ＦＦＤ是ＦＭＶＤ的特例，而ＦＭＶＤ又是ＦＪＤ的特例。     

自已动手架设UBB讨论区

安装ＡＣＴＩＶＥ ＰＥＲＬ ｌ由于 ＵＬＴＩＭＡＴＥ ＢＨＬＬＥＴＩＮＢＯＡＲＤ ＭＭ．Ｍ＊ｕ ＣＧＩ撰写的讨论区程序,因此如果要在ＷＩＮＤＯＷＳ环境下执行,就必须安装ＰＥＲＬ执行程序。所以首先请到这里下载免费的ＡＣＴＩＶＥＰＥＲＬ程序：ＨＴＴＰ：／／ＷＷＷ．ＡＣＴＩＶＥＳＴＡＴＥ．ＣＯＭ／ＤＯＷＮ－ＬＯＡＤ／ＡＣＴＩＶＥＰＥＲＬ／ＷＩＮＤＯＷＳ／５．６／ＡＣＴＩＶＥＰＥＲＬ－５．６．０．６２３－ＭＳＷＩＮ３２－Ｘ８６－ＭＵＬＴＩ—ＴＨＲＥＡＤ．ＭＳＩ。 下载完后,请直接执行ＡＣＴＩＶＥＰＥＲＬ－５．…     

一种基于VRML的三维逼真地形生成方法

本文首先对ＤＥＭ数据进行处理,得到ＬＯＤ模型;然后采用ＶＲＭＬ和Ｊａｖａ语言相结合的方法,在Ｃｌｉｅｎｔ／Ｓｅｒｖｅｒ上组成具有ＬＯＤ功能的ＶＲＭＬ文件;最后在Ｗｅｂ上实现了三维地形场景的交互浏览。     

NVIDIA GeForce256显卡价格对比

ＨＺ（现在的标准是１６６ＭＨｚ）,带有３００ＭＨｚ　ＲＡＭＤＡＣ（标准是３５０ＭＨ２的ＲＡＭＤＡＣ）．没有ＴＶ－ｏｕｔ输出接口。这种ＳＥ版本一定是流到市场上去的 ＯＥＭ显卡,它看起来更像那些ＴＮＴ２ Ｍ６４和 Ｖａｎｔａ显卡的地位．ＴＮＴ２Ｍ６４和ＶＡＮＴＡ也是标准ＴＮＴ２的廉价版。 第二个令人惊奇的是．创通的 Ａｎｎｉｈｉｌａｔｏｒ ＳＤＲ依然是最便宜的ＳＤＲ显卡（国内可不是这样的情况）．甚至比３Ｄ Ｐｒｏｐｈｅｔ　ＳＥ显卡的价格还要低,除了不太知名的ＶｉｓｉｏｎＴｅｋ显卡,其他的ＳＤＲ版本星卡价格…     

一个实用的并行/分布程序设计工具PJVM

ＰＪＶＭ（ＰａｒａｌｅｌＪａｖａＶｉｒｔｕａｌＭａｃｈｉｎｅ）采用扩充Ｊａｖａ语言功能的方法，提供了基于分布式共享存储器的编程接口ＤＳＭ（ＤｉｓｔｒｉｂｕｔｅｄＳｈａｒｉｎｇＭｅｍｏｒｙ）和基于消息传递的编程接口ＭＰＩ（ＭｅｓｓａｇｅＰａｓｉｎｇＩｎｔｅｒｆａｃｅ），用户可以利用Ｊａｖａ语言和扩充的两个编程接口，方便地设计在异构环境下运行的并行／分布程序。     

话说GVC SUPER5 MODEM

一款好的ＭＯＤＥＭ,不但能为你节省时间,而且还能节省宝贵的金钱。综观现时的ＭＯＤＥＭ市场,厂商品牌多至几十种,又有内置、外置、软猫等之别,速率更从３３．６Ｋ到５６Ｋ,令人眼花缭乱,难于抉择。 日前,我试用了一款型号为“ＳＵＰＥＲ５”（中译速霸）的内置ＭＯＤＥＭ,这是ＧＶＣ（致福）公司新出品的一款速率为５６Ｋ的中档产品,目前市场售价大约为２１０元。该款ＭＯＤＥＭ属于内置卡式结构,ＰＣＩ接口,采用了型号为“ＲＳ５６／ＳＰ－ＰＣＩ”的ＲＯＣＫＷＥＬＬ双频芯片组,能在 ＩＴＵ－ＴＶ．９０和 Ｋ５６ＦＬＥＸ…     

基于CATV的NVOD系统结构分析与关键技术研究

作为视频点播（ＶＩＤＥＯＯＮＤＥＭＡＮＤ）的一种替代品一准视频点播（ＮＥＡＲＶＩＤＥＯＯＮＤＥＭＡＮＤ）随着有关领域的发展已逐渐成为研究的热点。本文对基于ＣＡＴＶ的ＮＶＯＤ系统结构进行分析,并对其中的关键技术进行了讨论。     

语义决策过程支撑环境及其语义表示方法

针对决策支持系统缺乏有效的决策过程控制手段的问题,提出了语义决策及其过程支撑环境（ＤＰＳＥ）的概念．ＤＰＳＥ 利用决策问题语义指导系统方案,以决策过程中行为为应激式调度,实现决策支持系统运行全过程中语义形式化．ＤＰＳＥ 将决策环境分为需求组织、资源分配和行为规划３个方面,并定义了语义决策过程中动态语义的表示方法,提供了语义封装的方法及其过程支撑相关的算法．最后给出了系统原型,并通过实验表明ＤＰＳＥ 的有效性．     

Security of autoregressive speech watermarking model under guessing attack

The security of the "autoregressive (AR) watermark in AR host" signal model is investigated. It is demonstrated through analysis and Monte Carlo simulation that the AR watermarking model is asymptotically as secure as the "white watermark in white host" model under the guessing attack.     

Nonlinear modelling and control of helicopters

This paper presents the development of a nonlinear model and of a nonlinear control strategy for a VARIO scale model helicopter. Our global interest is a 7-DOF (degree-of-freedom) general model to be used for the autonomous forward-flight of helicopter drones. However, in this paper we focus on the particular case of a reduced-order model (3-DOF) representing the scale model helicopter mounted on an experimental platform. Both cases represent underactuated systems ( for the 7-DOF model and for the 3-DOF model studied in this paper). The proposed nonlinear model possesses quite specific features which make its study an interesting challenge, even in the 3-DOF case. In particular aerodynamical forces result in input signals and matrices which significantly differ from what is usually considered in the literature on mechanical systems control. Numerical results and experiments on a scale model helicopter illustrate the theoretical developments, and robustness with respect to parameter uncertainties is studied.     

Linearly Combining Density Estimators via Stacking

This paper presents experimental results with both real and artificial data combining unsupervised learning algorithms using stacking. Specifically, stacking is used to form a linear combination of finite mixture model and kernel density estimators for non-parametric multivariate density estimation. The method outperforms other strategies such as choosing the single best model based on cross-validation, combining with uniform weights, and even using the single best model chosen by Cheating and examining the test set. We also investigate (1) how the utility of stacking changes when one of the models being combined is the model that generated the data, (2) how the stacking coefficients of the models compare to the relative frequencies with which cross-validation chooses among the models, (3) visualization of combined effective kernels, and (4) the sensitivity of stacking to overfitting as model complexity increases.     

Porting a global ocean model onto a shared-memory multiprocessor: Observations and guidelines

A three-dimensional global ocean circulation model has been modified to run on the BBN TC2000 multiple instruction stream/multiple data stream (MIMD) parallel computer. Two shared-memory parallel programming models have been used to implement the global ocean model on the TC2000: the TCF (TC2000 Fortran) fork-join model and the PFP (Parallel Fortran Preprocessor) split-join model. The method chosen for the parallelization of this global ocean model on a shared-memory MIMD machine is discussed. The performance of each version of the code has been measured by varying the processor count for a fixed-resolution test case. The statically scheduled PFP version of the code achieves a higher parallel computing efficiency than does the dynamically scheduled TCF version of the code. The observed differences in the performance of the TCF and PFP versions of the code are discussed. The parallel computing performance of the shared-memory implementation of the global ocean model is limited by several factors, most notably load imbalance and network contention. The experience gained while porting this large, real world application onto a shared-memory multiprocessor is also presented to provide insight to the reader who may be contemplating such an undertaking.     

A comparison of linear and neural network ARX models applied to a prediction of the indoor temperature of a building

A neural network auto regressive with exogenous input (NNARX) model is used to predict the indoor temperature of a residential building. Firstly, the optimal regressor of a linear ARX model is identified by minimising Akaikes final prediction error (FPE). This regressor is then used as the input vector of a fully connected feedforward neural network with one hidden layer of ten units and one output unit. Results show that the NNARX model outperforms the linear model considerably: the sum of the squared error (SSE) is 15.0479 with the ARX model and 2.0632 with the NNARX model. The optimal network topology is subsequently determined by pruning the fully connected network according to the optimal brain surgeon (OBS) strategy. With this procedure near 73% of connections were removed and, as a result, the performance of the network has been improved: the SSE is equal to 0.9060.     

Sporadic model building for efficiency enhancement of the hierarchical BOA

Efficiency enhancement techniques—such as parallelization and hybridization—are among the most important ingredients of practical applications of genetic and evolutionary algorithms and that is why this research area represents an important niche of evolutionary computation. This paper describes and analyzes sporadic model building, which can be used to enhance the efficiency of the hierarchical Bayesian optimization algorithm (hBOA) and other estimation of distribution algorithms (EDAs) that use complex multivariate probabilistic models. With sporadic model building, the structure of the probabilistic model is updated once in every few iterations (generations), whereas in the remaining iterations, only model parameters (conditional and marginal probabilities) are updated. Since the time complexity of updating model parameters is much lower than the time complexity of learning the model structure, sporadic model building decreases the overall time complexity of model building. The paper shows that for boundedly difficult nearly decomposable and hierarchical optimization problems, sporadic model building leads to a significant model-building speedup, which decreases the asymptotic time complexity of model building in hBOA by a factor of to where n is the problem size. On the other hand, sporadic model building also increases the number of evaluations until convergence; nonetheless, if model building is the bottleneck, the evaluation slowdown is insignificant compared to the gains in the asymptotic complexity of model building. The paper also presents a dimensional model to provide a heuristic for scaling the structure-building period, which is the only parameter of the proposed sporadic model-building approach. The paper then tests the proposed method and the rule for setting the structure-building period on the problem of finding ground states of 2D and 3D Ising spin glasses.