Xueyuan Yang

ORCID: 0000-0003-2972-5382
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About
Contact & Profiles
Research Areas
  • Antenna Design and Analysis
  • Indoor and Outdoor Localization Technologies
  • Energy Harvesting in Wireless Networks
  • Physical Unclonable Functions (PUFs) and Hardware Security
  • Industrial Gas Emission Control
  • Embedded Systems and FPGA Design
  • Lignin and Wood Chemistry
  • Neural Networks and Reservoir Computing
  • Recycling and Waste Management Techniques
  • Advanced Radiotherapy Techniques
  • Optical Network Technologies
  • Nanoplatforms for cancer theranostics
  • Advanced Memory and Neural Computing
  • Dyeing and Modifying Textile Fibers
  • RFID technology advancements
  • Wireless Signal Modulation Classification
  • Radiation Therapy and Dosimetry

Hong Kong Polytechnic University
2022-2024

University of Georgia
2021

nLIGHT (United States)
2009

Iodine has shown promise in enhancing radiotherapy. However, conventional iodine compounds show fast clearance and low retention inside cancer cells, limiting their application as a radiosensitizer. Herein, we synthesize poly(maleic anhydride-alt-1-octadecene) coated KI nanoparticles (PMAO-KI NPs) evaluate potential for Owing to the polymer coating, core of PMAO-KI NPs is not instantly dissolved aqueous solutions but slowly degraded, allowing controlled release iodide (I–). I– transported...

10.1021/acsnano.1c01435 article EN ACS Nano 2021-10-25

Physical-layer identification aims to identify wireless devices during RF communication by exploiting the imperfections of their radio circuitry, i.e., hardware fingerprint. Previous work proposed several fingerprints for RFIDs (e.g., TIE, ABD, PSD, etc). However, these suffer from either unscalability or acquisition inefficiency. This presents RF-DNA, a new fingerprint composed millions Dual Natural Attributes (DNA) organized in helical structure, where pair DNA represents tag's intrinsic...

10.1145/3495243.3517028 article EN Proceedings of the 28th Annual International Conference on Mobile Computing And Networking 2022-10-14

Deep learning excels in advanced inference tasks using electronic neural networks (ENN), but faces energy consumption and limited computation speed challenges. To mitigate this, optical (ONNs) were developed, utilizing light for computations. However, their high manufacturing costs accessibility. In this work, we first introduce the binary network (BONN) - a streamlined ONN variant with binarized weights, which significantly reduces fabrication complexities costs. Specifically, address (i)...

10.1145/3636534.3649384 article EN Proceedings of the 28th Annual International Conference on Mobile Computing And Networking 2024-05-29

10.1109/infocom53939.2023.10229106 article EN IEEE INFOCOM 2022 - IEEE Conference on Computer Communications 2023-05-17

Although billions of battery-free backscatter devices (e.g., RFID tags) are intensively deployed nowadays, they still unsatisfying in the two major performance limitations (i.e., short reading range and high miss rate) resulting from current harvesting inefficiency. The classic beamforming technique is regarded as most promising solution to address issue. However, applying it systems meets deadlock start problem, i.e., without enough power, cannot wake up provide channel parameters; but,...

10.1109/tmc.2023.3318741 article EN IEEE Transactions on Mobile Computing 2023-09-25
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