45556411
On Implementation of a Scalable Wallet-Size Cluster Computing System for Multimedia Applications
In order to obtain a low-cost, low-power consumption and small sized digital home processing center, especially for multimedia applications, we propose a scalable wallet-size cluster computing system in this paper. Such system uses low-cost, low-power consumption and small sized microprocessors as computing nodes to form a cluster system. 
 
The main reason why we are interested to investigate and build such system is to increase the computing power and to accelerate the H.264 video processing. We analyzed the H.264 decoder scheme and distributed the most heavy-load computations in the decoder scheme to each computing node (or microprocessor) of the cluster system for the processing of signals, in parallel. To run multimedia applications in this cluster system, an efficient real-time video processing is implemented.
32464432
An Enhanced Parallel Loop Self-Scheduling Scheme for Cluster Environments
In this paper, a parallel loop self-scheduling scheme for heterogeneous PC cluster systems is proposed. Though the proposed scheme does allow users to choose parameters before the execution initialization phase, there are still weaknesses that motivate us to go further with new improvements in that scheme. For instance, a decision on a fixed and monotonous parameter can easily lead to invalid schedule by using previous input information. Thus, it is proposed in this paper a new scheme, where the scheduling parameter can be adjusted dynamically and fit into most widely available computer systems, in order to provide higher overall performance.
5072535
A Workflow-based Computational Resource Broker with Information Monitoring in Grids
As grid computing becomes a reality, a resource broker is needed to manage and monitor available resources. This work presents a workflow-based computational resource broker whose main function is to match available resources with user requests and consider network information status during matchmaking. The resource broker provides a uniform interface for accessing available and the appropriate resources via user credentials. We utilize the NWS tool to monitor the network-related information and resources status. Also, we constructed a grid platform using Globus Toolkit that integrates the distributed resources of five universities in Taichung, Taiwan, where the resource broker is developed. As a result, the proposed broker provides secure and updated information about available resources and serves as a link to the diverse systems available in the grid
5672930
An e-Learning Platform Based on Grid Architecture
The overall popularity of the Internet has helped e-learning become a hot method for learning in recent years. Over the Internet, learners can freely absorb new knowledge without restrictions on time or place. Many companies have adopted e-learning to train their employees. An e-learning system can make an enterprise more competitive by increasing the knowledge of its employees. E-learning has been shown to have impressive potential in e-commerce. At present, most e-learning environment architectures use single computers or servers as their structural foundations. As soon as their work loads increase, their software and hardware must be updated or renewed. This is a big burden on organizations that lack sufficient funds. Thus, in this study we employ a kind of Grid Computing technology, called the ”Data Grid” to integrate idle computer resources in enterprises into e-learning platforms, thus eliminating the need to purchase costly high-level servers and other equipment.
12951016
A shareable e-learning platform using data grid technology
Over the past decade, speed in the transmission of knowledge is no longer the way it used to be. Due to this influence, conventional educational methodology has also followed this trend and has gone through a momentous transformation. An excellent example is e-learning which has achieved and broken through the restrictions of time and space. In recent years it has become one of the most popular teaching methods. SCORM (Sharable Course Object Reference Model) the most popular e-learning standard has been adapted from many differing e-learning platforms. Normally, courseware meeting SCORM standards is sharable with other e-learning platforms. Most e-learning platforms however are unable to share courseware directly on the Internet. With this in mind, we have employed one type of grid computing technology called "data grid" in order to achieve this. This technology allows the integration of different SCORM platforms into a huge e-learning environment.
36980746
Decision tree construction for data mining on grid computing
Decision tree is one of the frequently used methods in data mining for searching prediction information. Due to its characteristics which are suitable for parallelism, it has been widely adopted in high performance field and developed into various parallel decision tree algorithms to deal with huge data and complex computation. Following the development of other technology fields, grid computing is regarded as the extension of PC cluster and therefore it future research development is highly valued. This new wave of Internet application is the 3rd generation of Internet applications following the traditional Internet and Web application. We have presented a grid-based decision tree architecture, and hope it can be applied on both parallel and sequential algorithms for the decision tree applications. Also, based on the scope and model of data mining applied in grid environment as well as user equivalent perspective, grid roles can be categorized into three types. We are hoping that through these definitions, software developers can define clear system processes and differentiate the application scope for software applications. To fulfil our architecture, we first apply an existing parallel decision tree algorithm-SPRINT algorithm in the grid environment. The performance and differences in many other areas are compared using different sizes of dataset. The experimental results are used for future reference and further development.
968890
Measuring similarity of semi-structured documents with context weights
In this work, we study similarity measures for text-centric XML documents based on an extended vector space model, which considers both document content and structure. Experimental results based on a benchmark showed superior performance of the proposed measure over the baseline which ignores structural knowledge of XML documents.
6411174
SMILAuthor: An Authoring System for SMIL-Based Multimedia Presentations
In this paper, an authoring tool named SMILAuthor for SMIL-based multimedia presentations is proposed. SMILAuthor adopts standard SMIL language as the format of the presentation to generate reusable and easily accessible presentations. Moreover, powerful editing functions such as cut, copy, and paste are supported by the system in a timeline-based manner. In order to support timeline-based editing functions, the playback duration of each object in the input SMIL script is first calculated by the parsing process of the system. The parsing process extracts and converts the temporal relationship of the input script to Real-Time Synchronization Model (RTSM), and the playback duration of each object in the script is then computed by traversing the RTSM. Editing results are converted to the SMIL format and saved in the output file. Language structure of SMIL is hidden by the system and the temporal information is visualized in the timeline manner to provide users an easy way to understand and control the timing of each object. Implementation of the system provides a friendly WYSIWYG environment and multiple views/windows are provided by the system to help authors compose multimedia presentations efficiently.
17968973
Authoring Cases from Free-Text Maintenance Data
Automatically authoring or acquiring cases in the case-based reasoning (CBR) systems is recognized as a bottleneck issue that can determine whether a CBR system will be successful or not. In order to reduce human effort required for authoring the cases, we propose a framework for authoring the case from the unstructured, free-text, historic maintenance data by applying natural language processing technology. This paper provides an overview of the proposed framework, and outlines its implementation, an automated case creation system for the Integrated Diagnostic System. Some experimental results for testing the framework are also presented.
2566456
Interval constraint networks for tolerance analysis and synthesis
This paper proposes interval constraint networks and interval propagation techniques for automatic tolerance design. To develop an intelligent automatic tolerance design mechanism, the following interrelated problems are addressed: (1) representing the relationships between the tolerances of the entities, attributes, and the functional requirements of a design component by a constraint network, (2) developing tolerance propagation techniques in the network that will satisfy the constraints between the entities, attributes and functional requirement. For the purpose of tolerance design, we propose new definitions for consistency of constraints and consistency of a constraint network. New forward and backward propagation techniques achieve worst-case tolerance analysis and synthesis, respectively. A tank design problem illustrates the use of the procedures for tolerance analysis and synthesis. The discussion and examples compare the new constraint network and propagation techniques with previous works.
7211602
Improved fast gauss transform and efficient kernel density estimation
Evaluating sums of multivariate Gaussians is a common computational task in computer vision and pattern recognition, including in the general and powerful kernel density estimation technique. The quadratic computational complexity of the summation is a significant barrier to the scalability of this algorithm to practical applications. The fast Gauss transform (FGT) has successfully accelerated the kernel density estimation to linear running time for low-dimensional problems. Unfortunately, the cost of a direct extension of the FGT to higher-dimensional problems grows exponentially with dimension, making it impractical for dimensions above 3. We develop an improved fast Gauss transform to efficiently estimate sums of Gaussians in higher dimensions, where a new multivariate expansion scheme and an adaptive space subdivision technique dramatically improve the performance. The improved FGT has been applied to the mean shift algorithm achieving linear computational complexity. Experimental results demonstrate the efficiency and effectiveness of our algorithm.