Event-driven,pattern-based methodology for cost-effective development of standardized personal health devices

Experiences applying standards in personal health devices (PHDs) show an inherent trade-off between interoperability and costs (in terms of processing load and development time). Therefore, reducing hardware and software costs as well as time-to-market is crucial for standards adoption. The ISO/IEEE11073 PHD family of standards (also referred to as X73PHD) provides interoperable communication between PHDs and aggregators. Nevertheless, the responsibility of achieving inexpensive implementations of X73PHD in limited resource microcontrollers falls directly on the developer. Hence, the authors previously presented a methodology based on patterns to implement X73-compliant PHDs into devices with low-voltage low-power constraints. That version was based on multitasking, which required additional features and resources. This paper therefore presents an event-driven evolution of the patterns-based methodology for cost-effective development of standardized PHDs. The results of comparing between the two versions showed that the mean values of decrease in memory consumption and cycles of latency are 11.59% and 45.95%, respectively. In addition, several enhancements in terms of cost-effectiveness and development time can be derived from the new version of the methodology. Therefore, the new approach could help in producing cost-effective X73-compliant PHDs, which in turn could foster the adoption of standards.     

基于扩散距离和MDS的非刚性模型相似性分析

在非刚性模型相似性分析方法中, 基于测地距离的等距嵌入方法对模型拓扑变化非常敏感, 对于有局部拓扑变化的完全相似的模型也无法得到正确结果。为了弥补这一不足, 提高非刚性模型相似性分析的准确性, 将扩散距离与多维尺度分析(MDS)相结合, 计算非刚性模型的等距嵌入模型, 再利用迭代最近点(ICP)算法通过计算嵌入模型的相似性来实现原始非刚性模型的相似性分析。实例证明该方法对含有拓扑变化的非刚性模型可以得到理想的相似性分析结果。     

基于对象存储文件系统研究综述

网络数据信息爆炸性的增长,使网络存储技术变得越来越重要,已成为Interne汲其相关行业进一步发展的关键。近年来,存储技术的发展日新月异,已经由后台走到了前台,从而引发了数字技术的第三次浪潮。本文结合网络存储研究现状,将介绍一些热点网络存储技术及其新进展。     

A hybrid clustering and graph based algorithm for tagSNP selection

TagSNP selection, which aims to select a small subset of informative single nucleotide polymorphisms (SNPs) to represent the whole large SNP set, has played an important role in current genomic research. Not only can this cut down the cost of genotyping by filtering a large number of redundant SNPs, but also it can accelerate the study of genome-wide disease association. In this paper, we propose a new hybrid method called CMDStagger that combines the ideas of the clustering and the graph algorithm, to find the minimum set of tagSNPs. The proposed algorithm uses the information of the linkage disequilibrium association and the haplotype diversity to reduce the information loss in tagSNP selection, and has no limit of block partition. The approach is tested on eight benchmark datasets from Hapmap and chromosome 5q31. Experimental results show that the algorithm in this paper can reduce the selection time and obtain less tagSNPs with high prediction accuracy. It indicates that this method has better performance than previous ones.     

Power efficient scheme for self localization in wireless sensor networks

With the tremendous applications of the wireless sensor network, self-localization has become one of the challenging subject matter that has gained attention of many researchers in the field of wireless sensor network. Localization is the process of assigning or computing the location of the sensor nodes in a sensor network. As the sensor nodes are deployed randomly, we do not have any knowledge about their location in advance. As a result, this becomes very important that they localize themselves as manual deployment of sensor node is not feasible. Also, in WSN the main problem is the power as the sensor nodes have very limited power source. This paper provides a novel solution for localizing the sensor nodes using controlled power of the beacon nodes such that we will have longer life of the beacon nodes which plays a vital role in the process of localization as it is the only special nodes that has the information about its location when they are deployed such that the remaining ordinary nodes can localize themselves in accordance with these beacon node. We develop a novel model that first finds the distance of the sensor nodes then it finds the location of the unknown sensor nodes in power efficient manner. Our simulation results show the effectiveness of the proposed methodology in terms of controlled and reduced power.     

Landmark MDS ensemble

Landmark multidimensional scaling (LMDS) uses a subset of data (landmark points) to solve classical multidimensional scaling (MDS), where the scalability is increased but the approximation is noise-sensitive. In this paper we present an LMDS ensemble where we use a portion of the input in a piecewise manner to solve classical MDS, combining individual LMDS solutions which operate on different partitions of the input. Ground control points (GCPs) that are shared by partitions considered in the ensemble, allow us to align individual LMDS solutions in a common coordinate system through affine transformations. We incorporate priors into combining multiple LMDS solutions such that the weighted averaging by priors improves the noise-robustness of our method. Our LMDS ensemble is much less noise-sensitive while maintaining the scalability and the speed of LMDS. Experiments on synthetic data (noisy grid) and real-world data (similar image retrieval) confirm the high performance of the proposed LMDS ensemble.     

RankVisu: Mapping from the neighborhood network

Most multidimensional scaling methods focus on the preservation of dissimilarities to map high dimensional items in a low-dimensional space. However, the mapping function usually does not consider the preservation of small dissimilarities as important, since the cost is small with respect to the preservation of large dissimilarities. As a consequence, an item's neighborhoods may be sacrificed for the benefit of the overall mapping. We have subsequently designed a mapping method devoted to the preservation of neighborhood ranks rather than their dissimilarities: RankVisu. A mapping of data is obtained in which neighborhood ranks are as close as possible according to the original space.A comparison with both metric and non-metric MDS highlights the pros (in particular, cluster enhancement) and cons of RankVisu.     

Information Ordering with an Event‐Enriched Vector Space Model for Multi‐Document News Summarization

Information ordering is a nontrivial task in multi‐document summarization (MDS), which typically relies on the traditional vector space model (VSM) notorious for semantic deficiency. In this article, we propose a novel event‐enriched VSM to alleviate the problem by building event semantics into sentence representations. The mediation of event information between sentence and term, especially in the news domain, has an intuitive appeal as well as technical advantage in common sentence‐level operations such as sentence similarity computation. Inspired by the block‐style writing by humans, we base the sentence ordering algorithm on sentence clustering. To accommodate the complexity introduced by event information, we adopt a soft‐to‐hard clustering strategy on the event and sentence levels, using expectation–maximization clustering and K‐means, respectively. For the purpose of cluster‐based sentence ordering, the event‐enriched VSM enables us to design an ordering algorithm to enhance event coherence computed between sentence and sentence–context pairs. Drawing on the findings of earlier research, we also incorporate topic continuity measures and time information into the scheme. We evaluate the performance of the model and its variants automatically and manually, with experimental results showing clear advantage of the event‐based model over baseline and non‐event‐based models in information ordering for multi‐document news summarization. We are confident that the event‐enriched VSM has even greater potential in summarization and beyond, which awaits further research. © 2014 Wiley Periodicals, Inc.     

A resource broker with an efficient network information model on grid environments

This paper describes a resource broker whose main function is to match available resources to user needs. The resource broker provides a uniform interface for accessing available and appropriate resources via user credentials. We also focus on providing approximate measurement models for network-related information using NWS for future scheduling and benchmarking. We first propose a network measurement model for gathering network-related information (including bandwidth, latency, forecasting, error rates, etc.) without generating excessive system overhead. Second, we constructed a grid platform using Globus Toolkit that integrates the resources of five schools in Taichung integrated grid environment resources (TIGER). The resource broker runs on top of TIGER. Therefore, it provides security and current information about available resources and serves as a link to the diverse systems available in the Grid.

网格监控系统研究

The monitoring system is very critical for complex and large high-performance distributed system such as a Grid. With the help of a monitoring system the users and developers of a Grid can perform tasks such as fault-detec-tion and diagnosis, performance analysis and tuning and performance prediction. And a good monitoring system can give useful data to the scheduling module of the Grid. This paper gives a detailed presentation and analysis of the key techniques of a grid monitoring system, and points out the research directions of the grid monitoring system based on current research around the world.