Gdel语言编译时类型处理技术研究

Gdel是一种发展中的逻辑程序设计语言,目前还没有开发出编译器。Gdel语言的类型系统独特于通常的逻辑程序设计语言,语义上已初步形成了相应的描述方法,建立了严格的理论基础。按照传统的方法,逻辑程序的运行机制和Gdel语言的多态性决定了一些变量的类型和语义只能在运行时才能确定,把这类变量的类型检查放到程序运行中处理,势必增加许多额外的计算。提出了一种处理Gdel语言类型系统的编译技术。针对语言的类型,对其相关语法进行适当修改,通过类型推断,把变量类型的检查工作提前到编译阶段完成,可以较好地处理其变量类型的语义问题,同时减少了运行时类型检查工作,从而提高程序的运行效率。     

基于PCI Express的闪存存储系统设计

通过采用PCI Express接口,应用并行和流水线技术,提高存储系统的读写速率。设计闪存映射层算法,在不降低系统读写性能的基础上易于管理数据。该方案在Virtex 5 FPGA上实现,并对其性能进行测试,结果表明,数据连续写速率达到400 MB/s,读速率达到500 MB/s,能满足高端应用的需求。     

基于聚类的快速Wi-Fi定位算法

Wi-Fi定位中用户移动终端计算能力差,提供的能量有限。为此,提出一种基于信号强度聚类的快速定位算法,依据接收信号强度的相似度对参考点分组再定位,从而减少定位计算量,提高Wi-Fi定位精度。在校园和市内2种不同室外环境下进行现场实验,数据显示定位均方根误差从16.71 m减小到9.68 m,定位时间从96.3 ms减小到5.2 ms,验证了该算法的高效性和有效性。     

改进Dijkstra算法在PGIS中的应用

传统Dijkstra算法用于路径诱导会使路网节点的数量增多、搜索范围扩大,从而耗费大量时间和空间,降低停车诱导信息系统(PGIS)的运行效率和实时性。针对城市路网的特定环境和路径诱导需求,根据2点之间直线最短的原理,在Dijkstra算法的基础上,提出一种应用于PGIS、基于矩形搜索范围的改进Dijkstra算法,设计并实现城市路网模型中单行、禁行、交叉点时间延误等问题的解决方案。实验结果表明,改进Dijkstra算法可以减少路网节点搜索范围和计算复杂度,提高用户搜索路径的实时性。     

HCAA:一种哈希冲突过度的动态解决算法

HASH表作为一种快速查询的数据结构,在防火墙等网络安全应用中得到了广泛的应用。然而,攻击者可能通过一些手段对这些应用发动HASH攻击使其失去响应,从而使某些恶意的数据流能够逃脱网络安全应用的管理和控制。提出一种动态的哈希冲突过度的解决算法—HCAA(Hash Collision-Acceptable Algorithm)算法,该算法在哈希冲突过于集中时通过动态申请HASH表并使用不同哈希函数来对冲突数据流进行处理,使冲突在可接受的范围内。实验结果表明,与已有方法相比,HCAA算法能在使用更少HASH表项的情况下获得更均衡的HASH效果,从而能对数据流进行更快的HASH操作。     

Analysis of an unobservable queue using arrival and departure times

Suppose one wants to assess the efficiency of a queueing system but is unable to observe directly its internal operations. This situation might arise if one works with a restricted set of historical data or because secrecy restricts access to the queueing facility. One might still be able to observe, from outside the system, the exact arrival and departure times of each customer. Using observations of arrival and departure times and knowledge of whether the service discipline is either first-come-first-served or last-come-first-served, one can exactly reconstruct the unobserved queue delays and service times of any sequence of arrivals during busy periods when the number of customers is greater than the number of servers. If the number of servers is also unknown, it too can be estimated. In this paper, we propose optimization models which determine the unknown number of servers.     

On-demand minimum cost benchmarking for intermediate dataset storage in scientific cloud workflow systems

Many scientific workflows are data intensive: large volumes of intermediate datasets are generated during their execution. Some valuable intermediate datasets need to be stored for sharing or reuse. Traditionally, they are selectively stored according to the system storage capacity, determined manually. As doing science on clouds has become popular nowadays, more intermediate datasets in scientific cloud workflows can be stored by different storage strategies based on a pay-as-you-go model. In this paper, we build an intermediate data dependency graph (IDG) from the data provenances in scientific workflows. With the IDG, deleted intermediate datasets can be regenerated, and as such we develop a novel algorithm that can find a minimum cost storage strategy for the intermediate datasets in scientific cloud workflow systems. The strategy achieves the best trade-off of computation cost and storage cost by automatically storing the most appropriate intermediate datasets in the cloud storage. This strategy can be utilised on demand as a minimum cost benchmark for all other intermediate dataset storage strategies in the cloud. We utilise Amazon clouds’ cost model and apply the algorithm to general random as well as specific astrophysics pulsar searching scientific workflows for evaluation. The results show that benchmarking effectively demonstrates the cost effectiveness over other representative storage strategies.     

Stability and synchronization for Markovian jump neural networks with partly unknown transition probabilities

This paper addresses the problems of stability and synchronization for a class of Markovian jump neural networks with partly unknown transition probabilities. We first study the stability analysis problem for a single neural network and present a sufficient condition guaranteeing the mean square asymptotic stability. Then based on the Lyapunov functional method and the Kronecker product technique, the chaos synchronization problem of an array of coupled networks is considered. Both the stability and the synchronization conditions are delay-dependent, which are expressed in terms of linear matrix inequalities. The effectiveness of the developed methods is shown by simulation examples.     

Multi-enzyme layer-by-layer assembly for dual amplified ultrasensitive electronic detection of cancer biomarkers

The preparation and use of multi-enzyme layer-by-layer (LBL) assembled single wall carbon nanotube (SWCNT) composite labels for amplified ultrasensitive electrochemical detection of a cancer biomarker is described. The target protein, carcinoembryonic antigen (CEA), is sandwiched between an electrode surface-confined capture anti-CEA antibody and the secondary signal anti-CEA/enzyme-LBL/SWCNT bioconjugate. The dual biocatalytic signal amplification for CEA monitoring is achieved through both the numerous enzymes loaded on the CNTs and redox-recycling of the enzymatic products in the presence of the secondary enzyme and the corresponding substrate. Our novel dramatic signal amplification strategy, with a detection limit of 0.04 pg mL⁻¹, shows about 2-4 orders of magnitude improvement in sensitivity for CEA detection compared with other universal signal amplified assays, which makes our signal amplification approach hold great potential applications in detection of ultra-trace protein biomarkers.     

基于语义相似度的领域本体概念更新方法研究

随着信息的快速更新,知识本体经常不能满足用户的需求,提出了一个能把新关键词插入到相应本体中的领域本体更新方法。该方法用LSA(潜在语义分析)来获取关键词的语义特征,然后把LSA矩阵转换成基于Web文档的高维空间。通过比较高维空间中关键词和相应本体概念来获取关键词和概念之间的相似值,如果这个值低于某个设定值,这个关键词就插入到领域本体概念节点中。初步的试验结果表明了这个方法的有效性。