构筑中控系统网络安全体系探析

中控系统网络化给广播电台提供了更灵活、功能更强大的中控传输系统,使得安全备份系统完善可靠,节目通道任意调配,状态信息实时掌握和控制处理手段多样灵活。本文着重阐述了中控系统网络化的基础框架、系统网络化的技术实现及功能特点。     

理论力学演示型多媒体课件素材的选取与制作

本文主要论述了理论力学演示型多媒体课件素材选取的基本原则、素材制作和集成方法。     

基于网络的多媒体课件制作的设计思索

本文论述了网络多媒体教学的特点,提出了多媒体课件制作应该遵循的原则,以及优秀多媒体课件应具备的标准。     

多路数字混音方案设计

混音技术是多媒体会议系统中的一个重要环节,极大影响了各用户之间的相互交流.通过对现有算法的总结分析,得出了参与混音的路数和各路语音强度大小是影响混音效果的主要因素.针对上述两个主要问题,提出了一种新的混音模型,在硬件设计上采用多路分组并行处理方式,限制最大混音的路数,并通过自动增益控制电路,控制输入语音信号强度;在软件设计上采用自动门限检测技术和改进的自对齐算法,减少了噪声的引入,避免了溢出检测问题,混音效果自然流畅.可以广泛应用于大规模多媒体会议系统中.     

基于认知负荷理论的多媒体课件设计研究

多媒体课件的信息加工主要包括信息的选择、组织和整合策略,它直接影响学习者对学习内容的选择、组织和整合。目前高校的多媒体课件比较注重体现多媒体技术形象直观、信息量大、效率高等特点,却往往忽视过量信息加工和学习者认知资源有限之间的矛盾。依据认知负荷理论,合理设计多媒体课件中文字、图片、声音、动画和视频等的呈现方式,可以降低认知负荷,最大限度提高学习者的信息加工水平,以达到提高教学效果的目的,同时对指导当前多媒体辅助教学实践也有重大意义。     

多媒体定时器方法中MFC跨线程传递窗口类消息的实现

为实现高频数据采集,采用多媒体定时器的方法实现上位机精确定时,同时研究实现了MFC中跨线程窗口类消息传递问题的解决方案。实验表明,定时精度高,达到10毫秒精确定时,不会出现数据丢失,运行良好。     

多媒体社会事件分析综述

由于网络技术的飞速发展,自媒体、微博、论坛等基于互联网的多种交流渠道日渐完善,人们能够方便地在线生成和共享丰富的社会多媒体内容。社会事件数据具有跨平台、多模态、大规模、噪声大等特点,基于多媒体社会事件的分析研究非常具有挑战性。因此,如何对社会媒体数据进行处理,研究社会事件分析方法、设计有效的社会事件分析模型成为社会事件分析研究的关键问题。文中对近年来多媒体社会事件分析的相关研究展开了综述,重点回顾了多媒体社会事件表示方法及其在虚假新闻检测、多媒体热点事件检测跟踪及演化分析、社交媒体危机事件响应等领域的应用,并对不同应用涉及的数据集进行了详细介绍。最后对多媒体社会事件分析方面未来可能的研究课题进行了展望。     

Resource-aware hybrid scheduling algorithm in heterogeneous distributed computing

Today, almost everyone is connected to the Internet and uses different Cloud solutions to store, deliver and process data. Cloud computing assembles large networks of virtualized services such as hardware and software resources. The new era in which ICT penetrated almost all domains (healthcare, aged-care, social assistance, surveillance, education, etc.) creates the need of new multimedia content-driven applications. These applications generate huge amount of data, require gathering, processing and then aggregation in a fault-tolerant, reliable and secure heterogeneous distributed system created by a mixture of Cloud systems (public/private), mobile devices networks, desktop-based clusters, etc. In this context dynamic resource provisioning for Big Data application scheduling became a challenge in modern systems. We proposed a resource-aware hybrid scheduling algorithm for different types of application: batch jobs and workflows. The proposed algorithm considers hierarchical clustering of the available resources into groups in the allocation phase. Task execution is performed in two phases: in the first, tasks are assigned to groups of resources and in the second phase, a classical scheduling algorithm is used for each group of resources. The proposed algorithm is suitable for Heterogeneous Distributed Computing, especially for modern High-Performance Computing (HPC) systems in which applications are modeled with various requirements (both IO and computational intensive), with accent on data from multimedia applications. We evaluate their performance in a realistic setting of CloudSim tool with respect to load-balancing, cost savings, dependency assurance for workflows and computational efficiency, and investigate the computing methods of these performance metrics at runtime.     

汽车构造实验网络教学辅助系统设计

为切实提高汽车专业教学水平,建立全方位教学体系,设计基于网络的汽车构造实验教学辅助系统。系统充分利用学校现有计算机网络平台,利用多媒体和网络开发技术,开发或整合多媒体资料,开辟学习交流平台,依据课程学习规律和认知特点,建立起方便快捷、生动有趣的实验教学辅助系统。该系统基于现有教材,又高于教学内容,不仅对于传授汽车zai新技术和剖析专业难点,调动学习积极性,具有重要补充和推动作用,而且对于教学示范中心创建工作,也具有重要意义。     

Key-dependent 3D model hashing for authentication using heat kernel signature

Multimedia-based hashing is considered an important technique for achieving authentication and copy detection in digital contents. However, 3D model hashing has not been as widely used as image or video hashing. In this study, we develop a robust 3D mesh-model hashing scheme based on a heat kernel signature (HKS) that can describe a multi-scale shape curve and is robust against isometric modifications. We further discuss the robustness, uniqueness, security, and spaciousness of the method for 3D model hashing. In the proposed hashing scheme, we calculate the local and global HKS coefficients of vertices through time scales and 2D cell coefficients by clustering HKS coefficients with variable bin sizes based on an estimated L² risk function, and generate the binary hash through binarization of the intermediate hash values by combining the cell values and the random values. In addition, we use two parameters, bin center points and cell amplitudes, which are obtained through an iterative refinement process, to improve the robustness, uniqueness, security, and spaciousness further, and combine them in a hash with a key. By evaluating the robustness, uniqueness, and spaciousness experimentally, and through a security analysis based on the differential entropy, we verify that our hashing scheme outperforms conventional hashing schemes.