I am Lin Shih-Hsun, soon to graduate from the Department of Electrical Engineering, National Sun Yat-sen University, specializing in radar signal processing and RF circuit design. My research focuses on the application of Frequency- and Self-Injection Locking (FSIL) technology in vital sign radar, with a core emphasis on enhancing radar frequency stability, reducing phase noise effects, and improving physiological signal detection accuracy. The goal is to apply these advancements in smart healthcare, contactless physiological monitoring, and AIoT sensing.
During my research, under the guidance of Professor Tzyy-Sheng Horng and Professor Kang-Chun Peng, my work has gained international recognition. My paper, "Highly Sensitive Frequency- and Self-Injection-Locked Radar for Precise Vital Sign Detection”, has been accepted by IEEE IMS2025 and will be presented in San Francisco, USA, in June 2025. This research successfully validates the application value of FSIL technology in physiological sensing and provides new possibilities for the development of millimeter-wave radar in smart sensing applications. My current research continues to expand, focusing on through-wall radar experiments and FSK ranging applications, exploring more practical implementation scenarios.
RF Circuit Design:
1. Millimeter-wave radar front-end design
2. FSK / CW radar architecture
3. RF module tuning and debugging
Signal Processing:
1. Radar data analysis using MATLAB / LabVIEW
2. Signal filtering and feature extraction
Frequency & Phase Locking (FSIL):
1. FSIL technology applications
2. Phase tracking and frequency stability techniques
Ranging & Sensing Technologies:
1. Through-wall radar
2. FSK ranging technology
3. Radar scattering characteristic analysis
Programming & Development:
- Proficient in LabVIEW, ADS, MATLAB for testing and analysis
Project Management:
1. Cross-disciplinary research and experiment planning
2. Collaborative radar sensing system development
Problem-Solving Skills:
- Independently design experiments, analyze radar signal errors, and optimize system performance.
Team Collaboration:
- Work closely with researchers from various fields, contributing to experiment design and data analysis.
Fast Learning & Adaptability:
- Quickly grasp new technologies and flexibly apply them across different applications.
