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學術出版;;Publications

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收集所有交通大學研究人員與學生個人或團體所發表之各項學術著作，涵蓋期刊論文、教師專書、學位論文等項。;;Collections of Publications of NCTU Researchers and Students Like Journal Papers and Thesis.

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🏛️國立陽明交通大學機構典藏

Access type: Open accessSubject: search.filters.subject.Cloud Computing

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Now showing 1 - 3 of 3
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    Item type:Item,
    Pervasive brain monitoring and data sharing based on multi-tier distributed computing and linked data technology
    (2014-06-03)
    Zao, John K.
    ;
    Gan, Tchin-Tze
    ;
    You, Chun-Kai
    ;
    Chung, Cheng-En
    ;
    Wang, Yu-Te
    EEG-based Brain-computer interfaces (BCI) are facing basic challenges in real-world applications. The technical difficulties in developing truly wearable BCI systems that are capable of making reliable real-time prediction of users' cognitive states in dynamic real-life situations may seem almost insurmountable at times. Fortunately, recent advances in miniature sensors, wireless communication and distributed computing technologies offered promising ways to bridge these chasms. In this paper, we report an attempt to develop a pervasive on-line EEG-BCI system using state-of-art technologies including multi-tier Fog and Cloud Computing, semantic Linked Data search, and adaptive prediction/classification models. To verify our approach, we implement a pilot system by employing wireless dry-electrode EEG headsets and MEMS motion sensors as the front-end devices, Android mobile phones as the personal user interfaces, compact personal computers as the near-end Fog Servers and the computer clusters hosted by the Taiwan National Center for High-performance Computing (NCHC) as the far-end Cloud Servers. We succeeded in conducting synchronous multi-modal global data streaming in March and then running a multi-player on-line EEG-BCI game in September, 2013. We are currently working with the ARL Translational Neuroscience Branch to use our system in real-life personal stress monitoring and the UCSD Movement Disorder Center to conduct in-home Parkinson's disease patient monitoring experiments. We shall proceed to develop the necessary BCI ontology and introduce automatic semantic annotation and progressive model refinement capability to our system.
  • Thumbnail Image
    Item type:Item,
    A Hadoop based Web log Analysis System
    (2014-01-01)
    Wang, Chen-Hau
    ;
    Tsai, Ching-Tsorng
    ;
    Fan, Chia-Chen
    ;
    Yuan, Shyan-Ming
    ;
    資訊工程學系
    In recent years, cloud computing has been an important issue in the Field of research. Cloud computing employs distributed storage and distributed computing technology to achieve a large number of stored data, as well as fast data analysis and processing. As the rapid development of Internet technology, digital data showing explosive growth, the face of massive data processing, the traditional text software and relational database technology has been facing a bottleneck, presented the results are not very satisfactory. For this problem, the concept of cloud computing is a more appropriate choice. In this paper, based on the architecture of Hadoop with HDFS (Hadoop Distributed File System) and Hadoop NlapRecluce software framework and Pig Latin language, we design and implement an enterprise Web log analysis system. Experimental results, by analyzing daily Web log records, we get Application Server traffic trends, performance of program statistical reports, and performance reports of different intervals and different actions of program by user request. The main purpose of this system is to assist system administrators to quickly capture and analyze data hidden in the massive potential value, thus providing an important basis for business decisions.
  • Thumbnail Image
    Item type:Item,
    Pervasive brain monitoring and data sharing based on multi-tier distributed computing and linked data technology
    (2014-06-03)
    Zao, John K.
    ;
    Gan, Tchin-Tze
    ;
    You, Chun-Kai
    ;
    Chung, Cheng-En
    ;
    Wang, Yu-Te
    EEG-based Brain-computer interfaces (BCI) are facing basic challenges in real-world applications. The technical difficulties in developing truly wearable BCI systems that are capable of making reliable real-time prediction of users\' cognitive states in dynamic real-life situations may seem almost insurmountable at times. Fortunately, recent advances in miniature sensors, wireless communication and distributed computing technologies offered promising ways to bridge these chasms. In this paper, we report an attempt to develop a pervasive on-line EEG-BCI system using state-of-art technologies including multi-tier Fog and Cloud Computing, semantic Linked Data search, and adaptive prediction/classification models. To verify our approach, we implement a pilot system by employing wireless dry-electrode EEG headsets and MEMS motion sensors as the front-end devices, Android mobile phones as the personal user interfaces, compact personal computers as the near-end Fog Servers and the computer clusters hosted by the Taiwan National Center for High-performance Computing (NCHC) as the far-end Cloud Servers. We succeeded in conducting synchronous multi-modal global data streaming in March and then running a multi-player on-line EEG-BCI game in September, 2013. We are currently working with the ARL Translational Neuroscience Branch to use our system in real-life personal stress monitoring and the UCSD Movement Disorder Center to conduct in-home Parkinson\'s disease patient monitoring experiments. We shall proceed to develop the necessary BCI ontology and introduce automatic semantic annotation and progressive model refinement capability to our system.