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.     

Scaling effects on flow hydrodynamics of confined microdroplets induced by Rayleigh surface acoustic wave

This paper reports an experimental and numerical investigation on the scaling effects in the flow hydrodynamics for confined microdroplets induced by a surface acoustic wave (SAW). The characteristic parameters of the flow hydrodynamics were studied as a function of the separation height, H, between the LiNbO₃ substrate and a top glass plate, for various droplets volumes and radio-frequency powers. The ratio of the gap height to attenuation length of the SAW, H/l _SAW, is shown to be an important parameter affecting the streaming flow induced in this confined regime. The reported numerical and experimental results are in good agreement over the range examined in this study and demonstrate that, at a lower gap heights of H?≤?100?μm, a significant decrease in streaming velocity or Reynolds number is induced, with the velocity approaching zero when the gap height is decreased to ~50?μm. An increase in the gap height results in an increased streaming velocity; however, if the gap height exceeds 70?% of the SAW attenuation length, any further increase in the gap height induces a drop in the streaming velocity.     

Data preprocessing using hybrid general regression neural networks and particle swarm optimization for remote terminal units

Data corruption in SCADA systems refers to errors that occur during acquisition, processing, or transmission, introducing unintended changes to the original data. In SCADA-based power systems, the data gathered by remote terminal units (RTUs) is subject to data corruption due to noise interference or lack of calibration. In this study, an effective approach based on the fusion of the general regression neural network (GRNN) and the particle swarm optimization (PSO) technique is employed to deal with errors in RTU data. The proposed hybrid model, denoted as GRNN-PSO, is able to handle noisy data in a fast speed, which makes it feasible for practical applications. Experimental results show the GRNN-PSO model has better performance in removing the unintended changes to the original data compared with existing methods.     

Superconducting properties of Pb/Ag multilayers

The electrical resistivity, ac susceptibility, critical current density, and Josephson tunneling of Pb/Ag multilayers have been measured in wide temperature ranges in order to study the proximity effect. A resistivity drop was found to occur atT _c of Pb in versusT measurement and, at low temperature, the multilayers show proximity effect. The I-V characteristic of the multilayer shows voltage steps, indicating a resistive state has occurred in the sample. The superconducting properties of the multilayer were analyzed with the bilayer theory of the proximity effect.