16023484
Asymptotically convergent modified recursive least-squares with data-dependent updating and forgetting factor
Continual updating of estimates required by most recursive estimation schemes often involves redundant usage of information and may result in system instabilities in the presence of bounded output disturbances. This paper investigates an algorithm which has the capability of eliminating these difficulties. Based on a set theoretic assumption, the algorithm yields modified least-squares estimates with a forgetting factor. It updates the estimates selectively depending on whether the observed data contain sufficient information. The information evaluation required at each step involves very simple computations. In addition, the parameter estimates are shown to converge asymptotically to a region around the true parameter at an exponential rate.
764405
Quantization based on statistical moments for signal detection: design and analysis
This paper addresses a signal detection problem based on a class of quantizers. The known moments of the underlying noise distribution are used to index a set of quantization points that have been predetermined under an assumed noise model. The fact that lower order moments are easy to obtain and that they are required in the implementation and analysis of most threshold detectors makes this approach quite appealing. The performance of the resulting quantizers is shown to be relatively insensitive to variations in the underlying noise distribution and to small deviations of the presumed moments. The detector's performance in the finite-sample-size case is investigated, and the distribution resulting in the highest false alarm rate is described for both symmetric and asymmetric noise cases. By setting the test threshold according to the worst distribution, a lower bound on the detector's performance is guaranteed. >
5254746
Low-power techniques for network security processors
In this paper, we present several techniques for low-power design, including a descriptor-based low-power scheduling algorithm, design of dynamic voltage generator, and dual threshold voltage assignments, for network security processors. The experiments show that the proposed methods and designs provide the opportunity for network security processors to achieve the goals of both high performance and low power.
1605324
Using contextual information to improve retrieval performance
In this paper, we propose a contextual retrieval framework which incorporates the user and global contextual information into the probabilistic retrieval model. We investigate different techniques of using contextual information to improve information retrieval performance in details. In particular, (1) we use the related text contextual information for query expansion; (2) we use the granularity information to construct the document level index and paragraph level index; (3) we use the geographic information for filtering. In addition, a new term weighting function BM5O is proposed based on the global context information. This framework is adaptable and extensible. If there is a new context category, we can extend the existing search system to accommodate it. Finally, we report our experimental findings on TREC data sets.
10038411
On personal data license design and negotiation
Web applications often require users to provide personal information for customization, identification, or completing a transaction. However, the way we disclose our information in current cyberspace does not allow us to control the usage, release and circulation of the information, violating the basic principle of personal information privacy. Online personal data licensing (OPDL) is a framework to bring the control of personal information back to individuals by requiring application and service providers to obtain a license from a person before collecting, processing, and using his personal information. In this paper we present the license design and automation of the license negotiation process.
16574049
Feature Selection with Rough Sets for Web Page Classification
Web page classification is the problem of assigning predefined categories to web pages. A challenge in web page classification is how to deal with the high dimensionality of the feature space. We present a feature reduction method based on the rough set theory and investigate the effectiveness of the rough set feature selection method on web page classification. Our experiments indicate that rough set feature selection can improve the predictive performance when the original feature set for representing web pages is large.
12850870
Supporting Mobile Multimedia Services with Intermittently Available Grid Resources
Advances in high quality digital wireless networks and differentiated services have enabled the development of mobile multimedia applications that can execute in global infrastructures. In this paper, we introduce a novel approach to supporting mobile multimedia services by effectively exploiting the intermittently available idle computing, storage and communication resources in a Grid infrastructure. Specifically, we develop efficient resource discovery policies that can ensure continuous access to information sources and maintain application Quality-of-Service (QoS) requirements, e.g. required network transmission bandwidth on the mobile clients. Our performance studies indicate that mobility patterns obtained via tracking or user-supplied itineraries assist in optimizing resource allocation. The proposed policies are also resilient to dynamic changes in the availability of grid resources.