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    宁兆龙

    • 副教授     硕士生导师
    • 主要任职:无
    • 性别:男
    • 毕业院校:东北大学
    • 学位:博士
    • 在职信息:在职
    • 所在单位:软件学院
    • 学科:软件工程 通信与信息系统
    • 联系方式:zhaolongning@dlut.edu.cn
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    On-Chip Hardware Accelerator for Automated Diagnosis Through Human-Machine Interactions in Healthcare Delivery

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    论文类型:期刊论文

    第一作者:Hou, Weigang

    合写作者:Ning, Zhaolong,Hu, Xiping,Guo, Lei,Deng, Xiaolan,Yang, Yan,Kwok, Ricky Y. K.

    发表时间:2019-01-01

    发表刊物:IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING

    收录刊物:SCIE、Scopus

    卷号:16

    期号:1

    页面范围:206-217

    ISSN号:1545-5955

    关键字:Automated cyber-physical system (ACPS); automated diagnosis; human-machine interactions; optical network-on-chip (ONoC)

    摘要:The automated diagnosis helps us better understand the complex landscape of diseases, leading to more effective, early and reliable medical diagnosis and therapy. The human-machine interactions in healthcare delivery relying on automated cyber-physical systems (ACPSs) play an important role in the automated diagnosis. Currently, the multicore accelerator used for ACPS has utilized the network-on-chip (NoC) for personalized healthcare. However, the discrete cores based on NoC are affected by limited computation speed, since the data have to pass through an electrical interconnect. In this paper, we propose a novel optical NoC (ONoC) solution of designing discrete cores to quickly understand biomarkers for early detecting abnormal pathophysiology, such as the deviation from the protein's native state. We analyze the performance of our ONoC-based ACPS accelerator for personalized healthcare by virtue of the tested proteins widely adopted in the lattice protein model. Our mathematical analysis and simulation results demonstrate that: 1) the chip area becomes smaller than a traditional design, which makes the personalized healthcare product more convenient; 2) the computation speed is promoted, resulting in the rapid understanding of biomarkers; and 3) we improve the data transmission reliability through accurately capturing the photonic effect so that desirable human-machine interactions can be guaranteed.
       Note to Practitioners-We design an on-chip hardware accelerator for automated diagnosis and personalized healthcare by predicting biological protein folding. The simulation results based on the lattice protein model can well guide the practitioners to design a more convenient and reliable product quickly detecting the biomarker, such as the deviation from the protein's native state.