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Award ID contains: 1647175

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  1. ; ; ; ; (, 2021 IEEE 22nd International Conference on Information Reuse and Integration for Data Science (IRI))
    The conventional ground penetrating radar (GPR) data analysis methods, which use piecemeal approaches in processing the GPR data formulated in variant formats such as A-Scan, B-Scan, and C-Scan, fail to provide a global view of underground objects on the fly to adapt the operations of GPR systems in the field. To bridge the gap, in this paper, we propose a novel GPR data analysis approach termed “ScanCloud” which is focused on the whole in situ GPR dataset rather than on individual A-Scans, B-Scans or C-Scans. We also study the integration of ScanCloud and a deep reinforcement learning method called deep deterministic policy gradient (DDPG) to adapt the operation of GPR system. The proposed method is evaluated using GPR modeling software called GprMax. Simulation results show the efficacy of ScanCloud and the adaptive GPR system enabled by the integration of ScanCluod and DDPG. 
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  2. ; ; ; ; ; (, IEEE Internet of Things Journal)
    null (Ed.)
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  3. ; ; ; (, IEEE International Symposium on Networks, Computers and Communications)
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  4. ; ; (, 2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC))
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  5. ; ; ; (, 2020 International Symposium on Networks, Computers and Communications (ISNCC))
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  6. ; ; ; ; ; (, MOBIMEDIA 2018)
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  7. ; ; ; (, 2020 International Symposium on Networks, Computers and Communications (ISNCC))
    Accepted Manuscript Full Text Available
  8. ; ; ; ; ; (, 2019 IEEE International Conference on Industrial Internet (ICII))
    null (Ed.)
    Full Text Available 
  9. ; ; ; ; ; (, IEEE International Conference on Industrial Internet (ICII) 2019)
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    Accepted Manuscript Full Text Available
  10. ; ; ; (, ISSAT International Conference on Data Science and Intelligent Systems)
    null (Ed.)
    Accepted Manuscript Full Text Available