Xuyu Wang

Assistant Professor

Florida International University

United States

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Dr. Xuyu Wang is an Assistant Professor at Florida International University, USA. His research areas encompass indoor localization, quantum machine learning, and artificial intelligence-driven wireless sensing, particularly in health management and gesture recognition. He has contributed significantly to recent advancements in human activity recognition and federated learning frameworks.

Articles (18)

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2025

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2025

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2024

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2024

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2023

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2023

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2023

Collaborators (1)

Qiang Ni

University of Lancaster

UNITED KINGDOM

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Xuyu Wang

Research Interests

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Articles (18)

Large-Scale Indoor Localization via Outdoor Crowdsourcing Trajectories on Ride-Hailing Platform

Quantum contrastive learning for human activity recognition

Explanation-Guided Backdoor Attacks Against Model-Agnostic RF Fingerprinting Systems

Artificial Intelligence-Driven Wireless Sensing for Health Management

Evasion Attacks and Countermeasures in Deep Learning-Based Wi-Fi Gesture Recognition

FDALLM+: A Functional Data Analysis-Driven Large Language Model Framework for Network Traffic Prediction

Wireless Sensing in Artificial Intelligence of Things: A General Quantum Machine Learning Framework

Introduction to the Special Section on Contact-free Smart Sensing in AIoT

APC: Contactless healthy sitting posture monitoring with microphone array

Contactless wheat foreign material monitoring and localization with passive RFID tag arrays

Contactless wheat foreign material monitoring and localization with passive RFID tag arrays

Inter-Temporal Reward Strategies in the Presence of Strategic Ethical Hackers

Real-world Large-scale Cellular Localization for Pickup Position Recommendation at Black-hole

TagSense: Robust Wheat Moisture and Temperature Sensing Using RFID

SARS: A Personalized Federated Learning Framework Towards Fairness and Robustness against Backdoor Attacks

Respiratory biofeedback using acoustic sensing with smartphones

Respiratory biofeedback using acoustic sensing with smartphones

Hierarchical Clustering-based Personalized Federated Learning for Robust and Fair Human Activity Recognition

Collaborators (1)

Qiang Ni

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