A sophisticated ad hoc cloud computing environment (SpACCE) providing calculation capacity of PCs is proposed to facilitate distributed collaboration. Distributed collaboration is now indispensable in daily work and m...
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ISBN:
(纸本)9781467308670
A sophisticated ad hoc cloud computing environment (SpACCE) providing calculation capacity of PCs is proposed to facilitate distributed collaboration. Distributed collaboration is now indispensable in daily work and mainly occurs ad hoc in offices and laboratories. However, computer resources in offices and laboratories are under-utilized, while conventional cloud computing environments composed of dedicated servers are not suited to flexibly deploying applications ad hoc. A SpACCE can be built according to the needs that occur at any given time on a set of personal, i.e., non-dedicated, PCs and dynamically migrate a server for application sharing to another PC. CollaboTray, an application-sharing system, indispensable to share any application without modification, is employed to realize the migration of a server. By migrating a server, the redundant calculation capacity of PCs used for individual work can be utilized to produce a sophisticated ad hoc cloud computing environment, where the response time of the application shared among the users is improved. The level of calculation capacity required to execute the migration of a server and the effectiveness of the migration were clarified by building a SpACCE in a university research room.
We propose an approach for dependence tree structure learning via copula. A nonparametric algorithm for copula estimation is presented. Then a Chow-Liu like method based on dependence measure via copula is proposed to...
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We propose an approach for dependence tree structure learning via copula. A nonparametric algorithm for copula estimation is presented. Then a Chow-Liu like method based on dependence measure via copula is proposed to estimate maximum spanning bivariate copula associated with bivariate dependence relations. The main advantage of the approach is that learning with empirical copula focuses on dependence relations among random variables, without the need to know the properties of individual variables as well as without the requirement to specify parametric family of entire underlying distribution for individual variables. Experiments on two real-application data sets show the effectiveness of the proposed method.
In general, due to some limitations of nonlinear control methods, it is difficult to analyze control performance for nonlinear multi-agent network. The T-S fuzzy model-based approach is often introduced to help solve ...
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Previously, weighted kernel regression (WKR) for solving small sample problems has been reported. The proposed WKR has been successfully employed to solve rational functions with very few samples. The design and devel...
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This paper investigates the problem of robust H_∞ filtering for uncertain two-dimensional (2-D) discrete systems in the Fornasini-Marchesini local state-space (FM LSS) model with polytopic uncertain parameters. The g...
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ISBN:
(纸本)9781467320658
This paper investigates the problem of robust H_∞ filtering for uncertain two-dimensional (2-D) discrete systems in the Fornasini-Marchesini local state-space (FM LSS) model with polytopic uncertain parameters. The goal of the paper is to design filters such that the finite frequency (FF) H_∞ norm of the filtering error system has a specified upper bound for all uncertainties. A generalized bounded real lemma (BRL) is first derived for FF H_∞ performance analysis of nominal 2-D FM LSS systems, and then a method, in terms of solving optimization problems with LMI constraints, is presented for robust FF H_∞ filter analysis and design. An illustrative example is given to show the improvements of the proposed filter design methods.
One of the main expectations of Web surfers is the inclusion of accurate and fresh result set in search engines' outcomes. Moreover, regarding the limited time and patience of nowadays Web users, they need to be a...
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One of the main expectations of Web surfers is the inclusion of accurate and fresh result set in search engines' outcomes. Moreover, regarding the limited time and patience of nowadays Web users, they need to be advised on the significance of the Web domain they entered. Due to the exponentially growing and dynamic nature of the World Wide Web, considering these demands for Web crawlers with the existing Web page ranking and importance calculation methods are unachievable, expensive or time-consuming. Therefore, proposing novel heuristics in order to empower the search answer set is the concern of many researchers in this area. In this paper, we employ our previously proposed novel Web page importance metric of LogRank in order to calculate Web site significance value and to include the newly uploaded or infant Web pages into search result set to meet the mentioned expectations of Web surfers. We append our experiment on above matters through analyzing UTM University server log file and over UTM University Web domain.
Information extraction of the Web and more precise ranking methods of Web pages are among the open issues in search engines' research area due to the ever-growing and dynamic nature of the World Wide Web. Therefor...
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Information extraction of the Web and more precise ranking methods of Web pages are among the open issues in search engines' research area due to the ever-growing and dynamic nature of the World Wide Web. Therefore, proposing novel approaches or performing any enhancement to the existed algorithms is the concern of many researchers in this field. Since the performance of any Web crawler is highly dependent to the applied Web page importance metric and regarding the obstacles of existed link-dependent or context-based metrics, the innovative heuristics that guarantees the accuracy of search results and better employment of resources is highly on demand. This paper introduces a novel link independent clickstream-based Web page importance metric, illustrates the metric's effectiveness through experimentally testing it over the UTM University Web domain and evaluates the results with information retrieval evaluation measures.
When used for function approximation purposes, neural networks belong to a class of models whose parameters can be separated into linear and nonlinear, according to their influence in the model output. This concept of...
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We introduce 2011's progress and achievements of an internet based crossover robot remote control competition between Japan and China, Thailand, and Taiwan respectively. The competition project has two subjects: t...
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The objective of this work is to devise a new way to embed a watermark into digital audio signal. In conventional methods, it is required that the watermark is embedded without noise perception. However, it is difficu...
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The objective of this work is to devise a new way to embed a watermark into digital audio signal. In conventional methods, it is required that the watermark is embedded without noise perception. However, it is difficult to embed the watermark without noise perception. Hence, we propose an embedding method so as to permit a perception of an embedded watermark signal. For watermarked audio signal, high sound quality is needed even if noises are perceived. Therefore, we estimate notes into music data and decide their timbre by musical instrument identification. Furthermore, for improving the sound quality, we embed one bit of watermark into one tone using the corresponding sampled sound for each tone.
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