Radio Frequency Sensing Systems for Artificial Intelligence of Things
Metadata Field | Value | Language |
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dc.contributor.advisor | Mao, Shiwen | |
dc.contributor.author | Yang, Chao | |
dc.date.accessioned | 2022-05-02T15:45:35Z | |
dc.date.available | 2022-05-02T15:45:35Z | |
dc.date.issued | 2022-05-02 | |
dc.identifier.uri | https://etd.auburn.edu//handle/10415/8198 | |
dc.description.abstract | With the rapid development of artificial intelligence, the Internet of Things (IoT) has evolved into artificial intelligence (AIoT). The development of an effective and low-cost human health detection system has attracted intensive research interest from both academia and industrial areas, such as vital sign monitoring, indoor localization, and. To achieve low cost and high accuracy for smart health systems, Radio Frequency Identification (RFID) based techniques have been utilized for human vital signs measurement. In addition, the RFID system could be used for effective indoor localization and human pose estimation. Compared with a vital sign signal, a human pose signal, as a complicated 3-Dimensional signal, could be more challenging. With the foundation of developing these systems with multiple RF devices, we also propose a technology-agnostic RF sensing system for human activity recognition, which could be performed on multiple RF platforms. The dissertation includes all Radio Frequency(RF) sensing systems we have developed during Ph.D. study. | en_US |
dc.subject | Electrical and Computer Engineering | en_US |
dc.title | Radio Frequency Sensing Systems for Artificial Intelligence of Things | en_US |
dc.type | PhD Dissertation | en_US |
dc.embargo.status | NOT_EMBARGOED | en_US |
dc.embargo.enddate | 2022-05-02 | en_US |
dc.contributor.committee | Roppel, Thaddeus | |
dc.contributor.committee | Nelms, Mark | |
dc.contributor.committee | Gong, Xiaowen |