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Bo Wei
Researcher at Northumbria University
Publications - 60
Citations - 1178
Bo Wei is an academic researcher from Northumbria University. The author has contributed to research in topics: Computer science & Wireless sensor network. The author has an hindex of 14, co-authored 50 publications receiving 757 citations. Previous affiliations of Bo Wei include Northeastern University (China) & University of New South Wales.
Papers
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Proceedings ArticleDOI
WiFi-ID: Human Identification Using WiFi Signal
TL;DR: For the first time WiFi signals can also be used to uniquely identify people and a system called WiFi-ID is proposed that analyses the channel state information to extract unique features that are representative of the walking style of that individual and thus allow for uniquely identify that person.
Journal ArticleDOI
Data Augmentation and Dense-LSTM for Human Activity Recognition Using WiFi Signal
TL;DR: A WiFi-based human activity recognition system that synthesizes variant activities data through eight channel state information (CSI) transformation methods to mitigate the impact of activity inconsistency and subject-specific issues is proposed and a novel deep-learning model is designed that caters to the small-size WiFi activity data.
Proceedings ArticleDOI
Radio-based device-free activity recognition with radio frequency interference
TL;DR: This paper investigates the impact of RFI on device-free CSI-based location-oriented activity recognition and proposes a number of counter measures to mitigate the impact on the CSI vectors and improve the location- oriented activity recognition performance.
Proceedings ArticleDOI
Efficient background subtraction for real-time tracking in embedded camera networks
TL;DR: This paper proposes a new background subtraction method which is both accurate and computational efficient and demonstrates the feasibility of the proposed method by the implementation and evaluation of an end-to-end real-time embedded camera network target tracking application.
Journal ArticleDOI
A Deep-Learning-Driven Light-Weight Phishing Detection Sensor.
TL;DR: A light-weight deep learning algorithm to detect the malicious URLs and enable a real-time and energy-saving phishing detection sensor is proposed by exploring deep learning techniques.