Improved delay‐dependent absolute stability and robust stability for a class of nonlinear systems with a time‐varying delay

This paper investigates the problem of the absolute stability of Lur'e systems with a time‐varying delay. By considering the relationships among the time‐varying delay, its upper bound, and the difference between them, less conservative delay‐dependent stability criteria are obtained and formulated in terms of linear matrix inequalities, without ignoring any useful terms in the derivative of a Lyapunov–Krasovskii functional. Numerical example shows that the results obtained in this paper are better than the previous results. Copyright © 2009 John Wiley & Sons, Ltd.     

Modeling Massive RFID Data Sets: A Gateway-Based Movement Graph Approach

Massive Radio Frequency Identification (RFID) data sets are expected to become commonplace in supply chain management systems. Warehousing and mining this data is an essential problem with great potential benefits for inventory management, object tracking, and product procurement processes. Since RFID tags can be used to identify each individual item, enormous amounts of location-tracking data are generated. With such data, object movements can be modeled by movement graphs, where nodes correspond to locations and edges record the history of item transitions between locations. In this study, we develop a movement graph model as a compact representation of RFID data sets. Since spatiotemporal as well as item information can be associated with the objects in such a model, the movement graph can be huge, complex, and multidimensional in nature. We show that such a graph can be better organized around gateway nodes, which serve as bridges connecting different regions of the movement graph. A graph-based object movement cube can be constructed by merging and collapsing nodes and edges according to an application-oriented topological structure. Moreover, we propose an efficient cubing algorithm that performs simultaneous aggregation of both spatiotemporal and item dimensions on a partitioned movement graph, guided by such a topological structure.     

Efficient evaluation of generalized tree-pattern queries on XML streams

The streaming evaluation is a popular way of evaluating queries on XML documents. Besides its many advantages, it is also the only option for a number of important XML applications. Unfortunately, existing algorithms focus almost exclusively on tree-pattern queries (TPQs). Requirements for flexible querying of XML data have motivated recently the introduction of query languages that are more general and flexible than TPQs. These languages are not supported by existing algorithms. In this paper, we consider a partial tree-pattern query (PTPQ) language which generalizes and strictly contains TPQs. PTPQs can express a fragment of XPath which comprises reverse axes and the node identity equality (is) operator, in addition to forward axes, wildcards and predicates. They constitute an important subclass of XPath, which is very useful in practice. Unfortunately, previous streaming algorithms for TPQs cannot be applied to PTPQs. PTPQs can be represented as dags enhanced with constraints. We explore this representation to design an original polynomial time streaming algorithm for PTPQs. Our algorithm aggressively filters incoming data that is irrelevant to the query and wisely avoids processing redundant query matches (i.e., matches of the query dag that do not contribute to new solutions). Our algorithm is the first one to support the streaming evaluation of such a broad fragment of XPath. We provide an analysis of it, and conduct an extensive experimental evaluation of its performance and scalability. Compared to the only known streaming algorithm that supports TPQs extended with reverse axes, our algorithm performs better by orders of magnitude while consuming a much smaller fraction of memory space. Current streaming applications have stringent requirements on query response time and memory consumption because of the large (possibly unbounded) size of data they handle. In order to keep memory usage and CPU consumption low for the PTPQ streaming evaluation, we design another streaming algorithm called Eager PSX for PTPQs. Its key feature is that it applies an eager evaluation strategy to quickly determine when node matches should be returned as solutions to the user and also to proactively detect redundant matches. We theoretically analyze Eager PSX, and experimentally test its time and space performance and scalability. We compare it with PSX. Our results show that Eager PSX not only achieves better space performance without compromising time performance, but it also greatly improves query response time for both simple and complex queries, in many cases, by orders of magnitude.     

Effects of Perceived Interactivity,Perceived Web Assurance and Disposition to Trust on Initial Online Trust

We developed an interdisciplinary model to examine how online consumers' perceived interactivity, perceived Web assurance and disposition to trust affect their initial online trust. Perceived interactivity is theorized as an interpersonal‐based antecedent, disposition to trust as a personality‐based antecedent, and perceived Web assurance as an institution‐based antecedent to initial online trust. Results indicate that both consumers' perceived interactivity and perceived Web assurance positively influence their initial online trust. Therefore, it is of importance that e‐vendors make efforts to enhance online consumers' perceived interactivity of their Web sites as well as addressing consumers' major trust concerns by adopting third‐party Web assurance seals.     

AdaBoost-based face detection for embedded systems

Face detection is a widely studied topic in computer vision, and recent advances in algorithms, low cost processing, and CMOS imagers make it practical for embedded consumer applications. As with graphics, the best cost-performance ratio is achieved with dedicated hardware. In this paper, we design an embedded face detection system for handheld digital cameras or camera phones. The challenges of face detection in embedded environments include an efficient pipeline design, bandwidth constraints set by low cost memory, a need to find parallelism, and how to utilize the available hardware resources efficiently. In addition, consumer applications require reliability which calls for a hard real-time approach to guarantee that processing deadlines are met. Specifically, the main contributions of the paper include: (1) incorporation of a Genetic Algorithm in the AdaBoost training to optimize the detection performance given the number of Haar features; (2) a complexity control scheme to meet hard real-time deadlines; (3) a hardware pipeline design for Haar-like feature calculation and a system design exploiting several levels of parallelism. The proposed architecture is verified by synthesis to Altera’s low cost Cyclone II FPGA. Simulation results show the system can achieve about 75–80% detection rate for group portraits.     

High-Dynamic-Range Texture Compression for Rendering Systems of Different Capacities

In this paper, we propose a novel approach for high-dynamic-range (HDR) texture compression (TC) suitable for rendering systems of different capacities. Based on the previously proposed DHTC scheme, we first work out an improved joint-channel compression framework, which is robust and flexible enough to provide compressed HDR textures at different bit rates. Then, two compressed HDR texture formats based on the proposed framework are developed. The 8 bpp format is of near lossless visual quality, improving upon known state-of-the-art algorithms. And, to our knowledge, the 4 bpp format is the first workable 4 bpp solution with good quality. We also show that HDR textures in the proposed 4 bpp and 8 bpp formats can compose a layered architecture in the texture consumption pipeline, to significantly save the memory bandwidth and storage in real-time rendering. In addition, the 8 bpp format can also be used to handle traditional low dynamic range (LDR) RGBA textures. Our scheme exhibits a practical solution for compressing HDR textures at different rates and LDR textures with alpha maps.     

Personalisation in web computing and informatics: Theories, techniques, applications, and future research

Recently, personalised search engines and recommendation systems have been widely adopted by users who require assistance in searching, classifying, and filtering information. This paper presents an overview of the field of personalisation systems and describes current state-of-the-art methods and techniques. It reviews approaches for (1) user profiling, including behaviour, preference, and intention modelling; (2) content modelling, comprising content representation, analysis, and classification; and (3) filtering methods for recommendation, classified into four main categories: rule-based, content-based, collaborative, and hybrid filtering. The paper also discusses personalisation systems in different domains, and various techniques and their limitations. Finally, it identifies several issues and possible directions for further research that can improve recommendation capabilities and enhance personalised systems.     

Performance analysis for hierarchical resource allocation in multiplexed mobile packet data networks

The general packet radio service (GPRS) can be thought of as an overlay network above a Global Systems for Mobile (GSM) network. In cooperation with the GSM system, GPRS supports both voice and packet data services. Obviously, combining voice and data traffic implies the nature of contending with finite radio resources. The paper focuses on voice/data integrated GPRS downlink traffic and proposes two hierarchical resource allocation strategies by considering time slots and radio blocks simultaneously with two different TDMA frame configurations. The radio block based resource allocation can characterize the multiplexing scheme of the practical GPRS operation. The performance of the proposed strategies were evaluated analytically by multidimensional Markov chain and verified through intensive computer simulations. Based on both the analytical and simulation results, it showed that the proposed allocation strategies can offer high utilization of transmission resource, as well as low blocking probability to GPRS users. The results of the study provide not only a practical thinking for implementation but also a guideline for analysis.     

Evaluation Techniques for Generalized Path Pattern Queries on XML Data

Finding the occurrences of structural patterns in XML data is a key operation in XML query processing. Existing algorithms for this operation focus almost exclusively on path patterns or tree patterns. Current applications of XML require querying of data whose structure is complex or is not fully known to the user, or integrating XML data sources with different structures. These applications have motivated recently the introduction of query languages that allow a partial specification of path patterns in a query. In this paper, we consider partial path queries, a generalization of path pattern queries, and we focus on their efficient evaluation under the indexed streaming evaluation model. Our approach explicitly deals with repeated labels (that is, multiple occurrences of the same label in a query). We show that partial path queries can be represented as rooted dags for which a topological ordering of the nodes exists. We present three algorithms for the efficient evaluation of these queries. The first one exploits a structural summary of data to generate a set of path patterns that together are equivalent to a partial path query. To evaluate these path patterns, we extend a previous algorithm for path-pattern queries so that it can work on path patterns with repeated labels. The second one extracts a spanning tree from the query dag, uses a stack-based algorithm to find the matches of the root-to-leaf paths in the tree, and merge-joins the matches to compute the answer. Finally, the third one exploits multiple pointers of stack entries and a topological ordering of the query dag to apply a stack-based holistic technique. We analyze our algorithms and perform extensive experimental evaluations. Our experimental results show that the holistic algorithm outperforms the other ones. Our approaches are the first ones to efficiently evaluate this class of queries in the indexed streaming model.