基于不对称加密的网络信息安全对策研究

本文对SSL VPN的原理与连接过程及优势作了详细的分析,通过SSL VPN客户端经过安全的身份验证和加密,从而有效保障了网络资源的安全性,并使用了一个实例H3C SSL VPN。     

基于Nios Ⅱ的CCD采集系统的设计

设计了一种在NiosⅡ处理器上的CCD数据采集系统。电荷耦合器件（Charge—Coupled Device,CCD）采集到的信号经过前端的差分运放处理后再进行A／D转换,转换后的数据存储于外部SDRAM中,被读取后显示在LCD上。本文重点介绍了各器件的电路设计和驱动程序的编写。实践表明,该设计成本低,实时性较高。     

基于等级保护的网络改造方法及异常情况分析与处理

本文介绍了在电力企业信息网中基于等级保护的网络安全防护改造方法。在具体案例中,针对网络改造过程中遇到的网络异常情况,技术人员通过逐一分析、排查,最终恢复正常网络,达到企业信息网络二级域改造的目的。网络安全防护改造方法以及异常情况分析排查案例对同类型企业在网络改造及异常排查工作中具有一定借鉴和指导意义。     

基于MeeGo的电容式触摸屏驱动设计

随着人们对操控要求的不断提高,电容触摸屏因为能支持多点触摸而得到广泛使用。本文基于Nokia和Intel公司合作开发的开源操作系统MeeGo,采用基于内核对象的Linux输入子系统来设计触摸屏的驱动。该方案极大地方便了触摸屏的驱动开发,可应用在车载娱乐、上网本、智能手机等电子产品上。     

存储系统细粒度加密安全设计

存储系统会有成百上千的存储设备和大量的数据。其中有相当部分敏感数据需要加密存储,然而现有的存储系统只能对整个文件进行加密,而不能对文件某一部分进行加密。尽管其实现方法比较简单,但它会导致系统额外的开销并且极大的影响系统性能。本文提出了存储系统的一种文件细粒度加密方案,它可以充分利用分布式存储内在的特性,有效地利用元数据信息对文件内多个任意大小的数据块进行加密。用户还可以根据数据块的保密级别对不同的数据块定义不同的加密算法以满足需要。对于广泛存在的视频、地图、档案等大文件实现不同粒度加密策略,可减少文件中不必要的数据加密操作,极大提高存储系统的性能。     

石英晶体振荡器的微处理器温度补偿技术研究

为改善石英晶体振荡器的频率特性,减小温度影响,通过对石英晶体振荡器的频率-温度特性的研究,提出了以微处理器（STC89C52RC）为核心的基于AT切晶体谐振器的温度补偿技术,并介绍了系统结构、补偿原理及硬件电路,给出了补偿结果。本设计具有结构简单、功耗低等优点。     

Adjustable prediction-based reversible data hiding

Reversible data hiding has received considerable interest recently in the field of information hiding. However, most of difference expansion (DE-based) schemes suffer from the problem that image cannot be restored into its original one free from location map. Reversible data hiding scheme with location map cannot effectively raise hiding capacity; in addition, it also incurs the computational cost during embedding and extracting. This study presents a reversible data hiding scheme that is free of location map and thus the computational cost, associated with embedding and extracting, is lower than that of most DE-based schemes. Furthermore, our scheme solves the problem of the low embedding rate of Tseng and Hsieh?s scheme because our scheme takes advantage of the bulk of relatively high small prediction-errors to raise embedding capacity. Experimental results demonstrate that the proposed scheme achieves the larger payload and the better image quality than some existing schemes by an adjustable control factor. The performance of the proposed scheme is more stable for the different images.     

Morse set classification and hierarchical refinement using Conley index

Morse decomposition provides a numerically stable topological representation of vector fields that is crucial for their rigorous interpretation. However, Morse decomposition is not unique, and its granularity directly impacts its computational cost. In this paper, we propose an automatic refinement scheme to construct the Morse Connection Graph (MCG) of a given vector field in a hierarchical fashion. Our framework allows a Morse set to be refined through a local update of the flow combinatorialization graph, as well as the connection regions between Morse sets. The computation is fast because the most expensive computation is concentrated on a small portion of the domain. Furthermore, the present work allows the generation of a topologically consistent hierarchy of MCGs, which cannot be obtained using a global method. The classification of the extracted Morse sets is a crucial step for the construction of the MCG, for which the Poincare′ index is inadequate. We make use of an upper bound for the Conley index, provided by the Betti numbers of an index pair for a translation along the flow, to classify the Morse sets. This upper bound is sufficiently accurate for Morse set classification and provides supportive information for the automatic refinement process. An improved visualization technique for MCG is developed to incorporate the Conley indices. Finally, we apply the proposed techniques to a number of synthetic and realworld simulation data to demonstrate their utility.     

Ambiguity-free edge-bundling for interactive graph visualization

Graph visualization has been widely used to understand and present both global structural and local adjacency information in relational data sets (e.g., transportation networks, citation networks, or social networks). Graphs with dense edges, however, are difficult to visualize because fast layout and good clarity are not always easily achieved. When the number of edges is large, edge bundling can be used to improve the clarity, but in many cases, the edges could be still too cluttered to permit correct interpretation of the relations between nodes. In this paper, we present an ambiguity-free edge-bundling method especially for improving local detailed view of a complex graph. Our method makes more efficient use of display space and supports detail-on-demand viewing through an interactive interface. We demonstrate the effectiveness of our method with public coauthorship network data.     

Scalable influence maximization for independent cascade model in large-scale social networks

Influence maximization, defined by Kempe et al. (SIGKDD 2003), is the problem of finding a small set of seed nodes in a social network that maximizes the spread of influence under certain influence cascade models. The scalability of influence maximization is a key factor for enabling prevalent viral marketing in large-scale online social networks. Prior solutions, such as the greedy algorithm of Kempe et al. (SIGKDD 2003) and its improvements are slow and not scalable, while other heuristic algorithms do not provide consistently good performance on influence spreads. In this article, we design a new heuristic algorithm that is easily scalable to millions of nodes and edges in our experiments. Our algorithm has a simple tunable parameter for users to control the balance between the running time and the influence spread of the algorithm. Our results from extensive simulations on several real-world and synthetic networks demonstrate that our algorithm is currently the best scalable solution to the influence maximization problem: (a) our algorithm scales beyond million-sized graphs where the greedy algorithm becomes infeasible, and (b) in all size ranges, our algorithm performs consistently well in influence spread—it is always among the best algorithms, and in most cases it significantly outperforms all other scalable heuristics to as much as 100–260% increase in influence spread.