Y
Yudi Dong
Researcher at Stevens Institute of Technology
Publications - 9
Citations - 188
Yudi Dong is an academic researcher from Stevens Institute of Technology. The author has contributed to research in topics: Communications system & Deep learning. The author has an hindex of 4, co-authored 9 publications receiving 51 citations.
Papers
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Journal ArticleDOI
IoT Platform for COVID-19 Prevention and Control: A Survey
Yudi Dong,Yu-Dong Yao +1 more
TL;DR: A potential fog-cloud combined IoT platform that can be used in the systematic and intelligent COVID-19 prevention and control, which involves five interventions including CO VID-19 Symptom Diagnosis, Quarantine Monitoring, Contact Tracing & Social Distancing, COVID -19 Outbreak Forecasting, and SARS-CoV-2 Mutation Tracking is demonstrated.
Journal ArticleDOI
Secure mmWave-Radar-Based Speaker Verification for IoT Smart Home
Yudi Dong,Yu-Dong Yao +1 more
TL;DR: This article proposes a secure method for speaker verification in IoT smart homes using millimeter-wave (mmWave) radar, which utilizes the radar to capture both vocal cord vibration and lip motion as multimodal biometrics for identifying speakers.
Journal ArticleDOI
Channel Estimation for One-Bit Multiuser Massive MIMO Using Conditional GAN
TL;DR: In this article, a conditional generative adversarial networks (cGAN) is developed to predict more realistic channels by adversarially training two DL networks, which not only learn the mapping from quantized observations to real channels but also learn an adaptive loss function to correctly train the networks.
Proceedings ArticleDOI
Continuous User Verification via Respiratory Biometrics
TL;DR: A continuous user verification system, which re-uses the widely deployed WiFi infrastructure to capture the unique physiological characteristics rooted in user’s respiratory motions and derives the user-specific respiratory features based on the waveform morphology analysis and fuzzy wavelet transformation of the respiration signals.
Proceedings ArticleDOI
Leveraging Breathing for Continuous User Authentication
TL;DR: A respiration-based user authentication scheme is developed to accurately identify users and reject spoofers and can achieve a high authentication success rate of over 93% and robustly defend against various types of attacks.