In spite of the rising demands for reusable information systems, current designs are still insufficient in providing efficient reusable mechanisms for system design. One of the major problems hindering the development...
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This paper made an in-depth research on information resources and personalized service and research status of system implementation techniques, combined with the practical work of graduate design, the use of Ajax, dat...
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This paper presents the third part a new approach for embedded systems courses appropriate for both high school and undergraduate classrooms, that has been conceived and designed to accomplish these goals, while motiv...
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As a knowledge sharing platform, Community Question Answering (CQA) services have attracted much attention from both academic and industry. This paper studies the problem of mining evolutionary community structures in...
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Many modern recommender systems are not suitable for recommending infrequently purchased products such as cars due to lack of user rating data to infrequently purchased products. A big challenge for recommending infre...
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With the development of collaborative electronic government system and the development trend of organizational collaboration in e-government. More and more e-government systems and safety platforms are developed and d...
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Biomedical Sensor Networks are event driven systems that rely on collective efforts of several sensor nodes. These nodes are used for acquisition of physiological information to monitor health status and physical well...
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ISBN:
(纸本)9783642148248
Biomedical Sensor Networks are event driven systems that rely on collective efforts of several sensor nodes. These nodes are used for acquisition of physiological information to monitor health status and physical wellbeing of an individual specifically suffering from chronic diseases. The reliable event detection for such networks is based on multi-sensor data fusion. The physiological signals are multi-dimensional and multi-parametric in nature. The major objective of this paper is to discuss challenges and opportunities for developing an ambulatory patient monitoring system. The proposed prototype model addresses design and development issues required to report any severe condition related to cardiovascular malfunctioning without compromising mobility and convenience of the patient. The analysis of fused cardio-respiratory time series data requires dimensionality reduction before drawing detection decisions related to cardiac arrhythmia. This paper briefs about a simulation model of arrhythmia detection for ambulatory patient monitoring. The early detection of cardiovascular risk factors can reduce expected cost pressure on healthcare and enhance social security issues.
Replication service in Distributed systems can reduce access latency and bandwidth consumption. When different nodes hold replicas accessed, there will be a significant benefit by selecting the best replica. Most of t...
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ISBN:
(纸本)9780769540207
Replication service in Distributed systems can reduce access latency and bandwidth consumption. When different nodes hold replicas accessed, there will be a significant benefit by selecting the best replica. Most of the existed replication strategies deal with the prediction of the response time. However, these strategies do not take fully into account the network dynamic and access locality. To solve this problem, a dynamic replica selection strategy using improved Kernel Density Estimation (KDE) is presented. Firstly, it distinguishes old replicas from new ones. Then, it predicts the network load and available bandwidth to choose the best replica. The improved KDE can select accurately the best accessed replica with only a little history data, which is very useful in a dynamic network. Simulation results demonstrate the efficiency and effectiveness of improved KDE in comparison with other approaches.
Huge amounts of data are available in large-scale networks of autonomous data sources dispersed over a wide area. data mining is an essential technology for obtaining hidden and valuable knowledge from these networked...
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ISBN:
(纸本)9780769540207
Huge amounts of data are available in large-scale networks of autonomous data sources dispersed over a wide area. data mining is an essential technology for obtaining hidden and valuable knowledge from these networked data sources. In this paper, we investigate clustering, one of the most important data mining tasks, in one of such networked computing environments, i.e., Peer-to-Peer (P2P) network. The lack of a central control and the sheer large size of P2P systems make the existing clustering techniques not applicable here. We propose a hybrid clustering algorithm, called P2PKMM. In each node, the K-medoids algorithm is used. Thus, the local noise can be avoided greatly. Meanwhile, the K-means method is used between different nodes, which can be calculated easily over distributed environment. The proposed algorithm takes a completely decentralized approach, where peers (nodes) only synchronize with their immediate topological neighbors in the underlying communication network. Furthermore, this algorithm can easily adapt to dynamic P2P network where existing nodes drop out and new nodes join in during the execution of the algorithm and the data in network changes. Experimental results show P2PKMM can not only produce highly accurate clustering results, but also with high scalability.
Robot learning is an efficient strategy to achieve control task without explicit programming. In this work, we present a learning framework in which the robot is taught how to perform desired behaviors through a demon...
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ISBN:
(纸本)9781424477081
Robot learning is an efficient strategy to achieve control task without explicit programming. In this work, we present a learning framework in which the robot is taught how to perform desired behaviors through a demonstration procedure. During the demonstration, the behavior sequences are recorded and transferred to time series data, and intelligentcomputing methods are proposed to learn the behaviors from the data. To assess the performance of the proposed framework, different sets of experiments have been conducted and the results show that our framework can be used to learn robot behaviors efficiently and successfully.
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