Bin Chen

ORCID: 0009-0008-0807-3112
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About
Contact & Profiles
Research Areas
  • Seismic Waves and Analysis
  • EEG and Brain-Computer Interfaces
  • Geophysics and Sensor Technology
  • Epilepsy research and treatment
  • Seismology and Earthquake Studies
  • Biometric Identification and Security
  • Advanced Image and Video Retrieval Techniques
  • ECG Monitoring and Analysis
  • Face recognition and analysis
  • Human Pose and Action Recognition
  • Video Analysis and Summarization
  • Neonatal and fetal brain pathology
  • Face and Expression Recognition

Sichuan University
2024

West China Second University Hospital of Sichuan University
2024

City University of Macau
2024

Hangzhou City University
2024

Harbin Institute of Technology
2023

Self-Supervised Video Hashing (SSVH) models learn to generate short binary representations for videos without ground-truth supervision, facilitating large-scale video retrieval efficiency and attracting increasing research attention. The success of SSVH lies in the understanding content ability capture semantic relation among unlabeled videos. Typically, state-of-the-art methods consider these two points a two-stage training pipeline, where they firstly train an auxiliary network by...

10.1609/aaai.v37i3.25373 article EN Proceedings of the AAAI Conference on Artificial Intelligence 2023-06-26

This paper focuses on the noise transmission process, presenting a comprehensive transfer model for velocity broad-band seismometers, which elucidate mechanisms of five distinct sources. We analyzed spectral characteristics functions across forward path, feedback and data acquisition stages, with focus gains, corner frequencies, 0 dB point. Numerical simulations experiments using CS60 seismometer showed excellent agreement theoretical predictions, validating proposed associated optimization...

10.3390/app142311393 article EN cc-by Applied Sciences 2024-12-06

<sec> <title>BACKGROUND</title> Real-time monitoring of pediatric epileptic seizures poses a significant challenge in clinical practice. In recent years, machine learning (ML) has attracted substantial attention from researchers for diagnosing and treating neurological diseases, leading to its application detecting seizures. However, systematic evidence substantiating feasibility remains limited. </sec> <title>OBJECTIVE</title> This review aimed consolidate the existing regarding...

10.2196/preprints.55986 preprint EN 2024-01-02
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