Welcome to my page!
I am a PhD student researching Machine Learning for WiFi technologies, with applications for indoor positioning.
Supervised by Dr Khuong Nguyen, I joined the University of Brighton after completing my BSc degree with an 85% GPA at Zhejiang University. I'm a Utility Model Patent owner of a sensor design published in my junior year of undergraduate studies.
- Aug 2023 -
- Journal paper “WiFi round-trip time (RTT) fingerprinting: an analysis of the properties and the performance in non-line-of-sight environments” published by Taylors & Francis.
- Won the Best Papers IEEE J-ISPIN Award organised by the IPIN 2023 committee, with submitted research paper being one of the top 30 papers.
- Jul 2023 -
- Research article “A dynamic model switching algorithm for WiFi fingerprinting indoor positioning” accepted by IPIN 2023, the biggest conference on indoor positioning and indoor navigation, as a regular paper and to be presented on 26th Sept, Nuremberg, Germany.
- Successfully completed project conclusion and final report submission for Student Research Experience Scheme 2022-23.
- May 2023 - Published three brand new publicly available WiFi datasets, collected on three new complicated scenarios to facilitate research within model selection and indoor localisation domains.
- Mar 2023 -
- Published the proceedings paper " A Multi-Scale Feature Selection Framework for WiFi Access Points Line-of-sight Identification", presented at the WCNC 2023, a leading conference in Wireless Communications.
- Won Project Lead award for Student Research Experience Scheme 2022-23, Funded by Santander Universities and Enhancing Research Culture Fund.
- Dec 2022 - Successfully completed project conclusion and final report submission for Brighton BRITE programme in collaboration with Naurt (UK).
- Nov 2022 -
- Journal article "WiFi Access Points Line-of-Sight Detection for Indoor Positioning Using the Signal Round Trip Time", published on Remote Sensing (Impact Factor 5.0) by MDPI.
- Senior Research Support Assistant, developing performance analysis experiments to evaluate the outdoor-indoor tracking SDK in numerous challenging everyday scenarios. Part of the Brighton BRITE programme. In collaboration with Naurt (UK).
- Sept 2022 - Published the proceedings paper "An analysis of the properties and the performance of WiFi RTT for indoor positioning in non-line-of-sight environments", presented at the LBS 2022
- Mar 2022 - Published three publicly available WiFi Round-Trip-Time and Received Signal Strength datasets, collected on three challenging, complex indoor environments, to facilitate research in WiFi-based indoor positioning and contribute to the broader body of knowledge.
- Aug 2022 - Managed on-site operations and coordination at COPA 2022.
- Sept 2021 - Journal paper “A survey of deep learning approaches for WiFi-based indoor positioning” based on more than 150 research papers published by Taylors & Francis, cited by 38 (by Sept 2023).
- Jan 2021 - Joined the University of Brighton as a PhD student.
- Aug 2020 - Awarded BSc in Engineering with an 85% GPA, thesis titled " Research on dynamic obstacle detection on urban roads based on vehicular millimetre-wave radar".
How about navigating indoors with the same ease as outdoors, using just your smartphone?
With the proliferation of WiFi-enabled devices and the growing need for precise indoor navigation, my research interest centres on leveraging machine learning and feature selection methods to enhance WiFi-based indoor positioning systems. Traditional indoor positioning methods often fall short in delivering consistent accuracy due to environmental factors and signal interference. By integrating state-of-the-art WiFi Round-Trip-Time technology and machine learning algorithms, we can model and predict positioning with improved precision. Furthermore, the incorporation of feature selection techniques ensures that the most relevant signal attributes are considered, reducing computational complexity and enhancing the system's efficiency. This synergy of machine learning and feature selection holds the promise of great improvement in indoor navigation, delivering a seamless and accurate user experience.
Keywords: Indoor Positioning · WiFi Fingerprinting · WiFI Round-Trip-Time · Model Switching · Feature Selection · Machine Learning