在Visual C＋＋中设计多媒体应用程序的方法

本文旨在介绍如何用VC实现多媒体应用,使读者初步掌握VC设计多媒体应用程序的方法.由于VC不直接支持多媒体应用,所以据笔者经验,可通过三种方法实现对声音、图形、图象、动画、视频文件等多媒体信息的播放和控制.一、利用MCI函数实现多媒体应用MCI(Media Control Interface)是MS-Windows定义的多媒体接口标准,应用程序通过该接口可以控制音频、视频、激     

VisualC＋＋多媒体开发方法研究

针对使用ＶｉｓｕａｌＣ＋＋４．０以上的开发平台设计多媒体应用程序这一较新的课题，文本试图在方法上进行一些研究探讨，阐述了使用ＯＬＥ、ＯＣＸ、ＤＬＬ、多媒体类、ＭＦＣ类、Ｗｉｎｄｏｗｓ类、ＡＰＩ等多种方法进行多媒体应用程序设计的思想。     

C＋＋B＋uilder下的视频捕促技术

本文详细说明了在C＋＋B uilder环境下进行视频捕捉的方法。介绍了利用Windows操作系统中的Video for Windows函数库进行视频捕捉的框架，并给出了相应的编程示例。     

如何在Visual C＋＋6.0中播放多媒体动画

本文介绍了利用Ｖｉｓｕａｌ Ｃ＋＋６．０播放多媒体动画的四种不同的方法。     

WIN32下C＋＋与VFP间通信

介绍了ＶｉｓｕａｌＦｏｘｐｒｏ与Ｃ＋＋程序间的动态数据交换方法,并引用了具体实例详细介绍了ＶＦＰ与Ｃ＋＋程序之间的数据交换方法。     

VC＋＋中如何制作多媒体封面

利用ＶＣ＋＋软件包提供的一些程序设计工具以及ＭＦＣ基本类库,能够开发范围广泛的多媒体应用软件。如果给你的应用软件制作一个图文声像并茂的多媒体封面,定会使你的软件增色不少。笔者在程序开发过程中总结出三种方法,可在ＶＣ＋＋中实现应用程序的封面设计。１利用内部组件ＶＣ＋＋中有许多组件和控件可以被直接用来添加到项目文件中,封面就是组件中的一个。假设已经建立了一个Ｐｌａｙｅｒ项目,通过向该项目添加组件ＳｐｌａｓｈＳｃｒｅｅｎ,一个已设计好的固定的封面便被自动的插入到程序中。下边我们来看看添加过程：１）打开…     

基于C＋＋的COM实现

随着计算机网络技术和分布式技术的发展,COM技术越来越被重视。文中对COM的基本机制进行了深入地剖析,对COM技术的底层实现进行了详细的讨论,并在此基础上,用C＋＋实现了一个进程内服务器,通过实例详细讨论了C＋＋在实现COM时所涉及的技术。     

MicrosoftC／C＋＋和BorlandC＋＋编程环境比较

本文从ＭｉｃｒｏｓｏｆｔＣ／Ｃ＋＋和ＢｏｒｌａｎｄＣ＋＋在编程环境，编程工具，内存模式，关键字，浮点数结构等方面的详细比较，旨在为程序员提供一些资料，帮助编程环境转换。     

VC＋＋下利用DirectShow播放多媒体文件

本文介绍了DirectShow的体系结构,用C＋＋类对其基本接口进行了封装,并结合实例讨论了多媒体文件播放的实现细节。     

用VC＋＋和Photoshop制作敏感性按钮的方法技巧

在多媒体应用程序编制过程中,将功能强大的编程软件VC＋＋和性能优越的图形处理软件Photoshop结合起来以制作敏感性按钮,会给应用程序增加不少亮色。本文介绍了采用这种方法制作敏感性按钮的步骤与技巧。     

An efficient machine tool control instruction compression method for networked numerical control systems

With the development of computer and information technology, function modules of computer numerical control (CNC) systems are moving up to network environment to simplify local machine tool controller architecture and increase function reusability. In this context, network-based CNC system generates machine control instructions (MCI) in network environment and send MCI to local machines. Compared with building a remote real-time communication channel between networked CNC and local machines, applying offline interpolator is more feasible under state-of-the-art hardware and software. However, the size of MCI generated by offline interpolator is large. Transmitting MCI without compression will be inefficient and increase lead time. Currently there has been few researches on the features and compression of offline MCI. In this paper, a simple and effective compression method for MCI named multichannel differential run-length encoding (MDRL) is proposed. Utilizing the limited variation and high repetition features of MCI, MDRL algorithm first calculates the differences of MCI, then encodes the amplitudes and repetition of difference values using fixed length codes. Compression performance discrepancy among different channels is controlled using compressed data chunks. The decompression process of MDRL only needs a few hundred of bytes of space and can process in real-time. Compression performance of MDRL is tested using three common manufacturing tasks under different system configurations. The results show that the mean compression ratio of MDRL is 3.41%-15.86%. The advantages of MDRL are further validated by comparing with three other data compression algorithms. By using MDRL, MCI transmission efficiency can be significantly increased for networked CNC systems to increase function reusability and shorten lead time.     

Diagnose the mild cognitive impairment by constructing Bayesian network with missing data

Mild Cognitive Impairment (MCI) is thought to be the prodromal phase to Alzheimer’s disease (AD), which is the most common form of dementia and leads to irreversible neurogenerative damage of the brain. In order to further improve the diagnostic quality of the MCI, we developed a MCI expert system to address MCI’s prediction and inference question, consequently, assist the diagnosis of doctor. In this system, we mainly deal with following problems: (1) Estimate missing data in the experiment by utilizing mutual information and Newton interpolation. (2) Make certain the prior feature ordering in constructing Bayesian network. (3) Construct the Bayesian network (We term the algorithm as MNBN). The experimental results indicate that MNBN algorithm achieved better results than some existing methods in most instances. The mean square error comes to 0.0173 in the MCI experiment. Our results shed light on the potential application in MCI diagnosis.     

Identification of differential brain regions in MCI progression via clustering-evolutionary weighted SVM ensemble algorithm

Mild cognitive impairment (MCI) as the potential sign of serious cognitive decline could be divided into two stages, i.e., late MCI (LMCI) and early MCI (EMCI). Although the different cognitive states in the MCI progression have been clinically defined, effective and accurate identification of differences in neuroimaging data between these stages still needs to be further studied. In this paper, a new method of clustering-evolutionary weighted support vector machine ensemble (CEWSVME) is presented to investigate the alterations from cognitively normal (CN) to EMCI to LMCI. The CEWSVME mainly includes two steps. The first step is to build multiple SVM classifiers by randomly selecting samples and features. The second step is to introduce the idea of clustering evolution to eliminate inefficient and highly similar SVMs, thereby improving the final classification performances. Additionally, we extracted the optimal features to detect the differential brain regions in MCI progression, and confirmed that these differential brain regions changed dynamically with the development of MCI. More exactly, this study found that some brain regions only have durative effects on MCI progression, such as parahippocampal gyrus, posterior cingulate gyrus and amygdala, while the superior temporal gyrus and the middle temporal gyrus have periodic effects on the progression. Our work contributes to understanding the pathogenesis of MCI and provide the guidance for its timely diagnosis.     

Communications consolidation after an era of no restraints

AT&T recently accepted an offer to be acquired by another company. So did MCI, which is what is left of MCI Worldcom. Both will become part of large local telephone companies, SBC and Verizon. Qwest also would like to buy MCI, but MCI had rejected its bid, as of this writing.     

用MCI控制Authorware中的MPEG文件播放

介绍了MCI技术在Authorware中的应用，并讨论了在Authorware中用MCI播放MPEG文件的方法。     

Ontology driven decision support for the diagnosis of mild cognitive impairment

In recent years, mild cognitive impairment (MCI) has attracted significant attention as an indicator of high risk for Alzheimer's disease (AD), and the diagnosis of MCI can alert patient to carry out appropriate strategies to prevent AD. To avoid subjectivity in diagnosis, we propose an ontology driven decision support method which is an automated procedure for diagnosing MCI through magnetic resonance imaging (MRI). In this approach, we encode specialized MRI knowledge into an ontology and construct a rule set using machine learning algorithms. Then we apply these two parts in conjunction with reasoning engine to automatically distinguish MCI patients from normal controls (NC). The rule set is trained by MRI data of 187 MCI patients and 177 normal controls selected from Alzheimer's Disease Neuroimaging Initiative (ADNI) using C4.5 algorithm. By using a 10-fold cross validation, we prove that the performance of C4.5 with 80.2% sensitivity is better than other algorithms, such as support vector machine (SVM), Bayesian network (BN) and back propagation (BP) neural networks, and C4.5 is suitable for the construction of reasoning rules. Meanwhile, the evaluation results suggest that our approach would be useful to assist physicians efficiently in real clinical diagnosis for the disease of MCI.     

基于联合估计的Wyner-Ziv系统边信息重建

在不显著增加解码复杂度的前提下,为提高Wyner-Ziv视频编码系统的边信息(SI)重建质量,提出一种基于SI信度的整像素/半像素联合估计算法.该算法引入一个SI质量判定因子判断边信息的重建质量,根据SI置信度是否超过设定的判别阈值进行整像素和半像素边信息重建方法的自适应切换.实验结果表明,在保持甚至超过传统亚像素边信...     

使用深度学习和不同频率维度的脑功能性连接对轻微认知障碍的诊断

准确诊断轻微认知障碍（MCI）对于阿尔兹海默症（AD）的预防和治疗十分关键,目前常使用深度学习和静息态功能核磁共振成像（rs-fMRI）对MCI进行辅助诊断。皮尔逊（Pearson）相关法和加窗的皮尔逊（WP）相关法能在时间维度上表示脑功能性连接（FC）,但不能将不同频率维度上的信息进行分解表示。针对这一问题,提出将不同频率维度的FC系数作为现有深度学习的特征输入的方法,以提高MCI分类准确率。首先将被试的数据进行拼接后进行多通道经验模态分解（MEMD）,然后通过切割求得不同频率维度上的FC系数,最后使用VGG16和长短期记忆（LSTM）网络进行测试。实验结果表明,使用所提出的FC系数时,MCI的分类准确率最高可达84.33%,相较使用传统FC系数时的准确率提高了18.33~21.00个百分点;而且不同频率维度的FC系数对MCI有着不同的分辨率。     

Spectral and complexity analysis of scalp EEG characteristics for mild cognitive impairment and early Alzheimer's disease

Amnestic mild cognitive impairment (aMCI) often is an early stage of Alzheimer's disease (AD). MCI is characterized by cognitive decline departing from normal cognitive aging but that does not significantly interfere with daily activities. This study explores the potential of scalp EEG for early detection of alterations from cognitively normal status of older adults signifying MCI and AD. Resting 32-channel EEG records from 48 age-matched participants (mean age 75.7 years)—15 normal controls (NC), 16 early MCI, and 17 early stage AD—are examined. Regional spectral and complexity features are computed and used in a support vector machine model to discriminate between groups. Analyses based on three-way classifications demonstrate overall discrimination accuracies of 83.3%, 85.4%, and 79.2% for resting eyes open, counting eyes closed, and resting eyes closed protocols, respectively. These results demonstrate the great promise for scalp EEG spectral and complexity features as noninvasive biomarkers for detection of MCI and early AD.     

基于AT91RM9200的SD卡主控制器的设计

通过对于AT91RM9200的研究,提出了一种应用于RFID手持阅读器的SD卡主控制器的设计方案,该设计采用工业级ARM9控制器AT91RM9200作为主控制器,采用MCI总线实现了对于SD卡的读写操作。详细介绍了SD卡以及AT91RM9200芯片中的MCI接口的结构和工作原理,讨论了相关的设置、硬件连接和软件设计。