Researcher, University of North Texas, United States
Thai Ha Dang is a passionate graduate student currently pursuing his Ph.D. in Electrical Engineering at the University of North Texas. With over 2 years of experience in wearable embedded devices and wireless sensing systems, he specializes in RF energy harvesting and machine learning for signal processing. His research spans human and animal models, and he has worked on projects related to cow behavior classification, energy harvesting systems, and underwater monitoring. Thaiโs commitment to research has led him to present at various international conferences and publish in high-impact journals. He has honed his skills in embedded system design, programming, and data analysis, making him a key player in the field of agricultural technology and sensor networks. His strong academic background and innovative contributions have made him a respected researcher among peers and mentors alike.
Profile
Orcid
Education
Thai Ha Dang’s educational journey began at Hanoi University of Science and Technology, Vietnam, where he earned his Degree of Engineer in Electrical Engineering, ranking in the top 15% of his class. He further advanced his studies by pursuing a Masterโs degree in Electrical Computer Engineering at Pukyong National University in South Korea, where he graduated with a GPA of 4.12/4.5. This rigorous academic background provided a strong foundation in embedded systems, machine learning, and wireless sensor networks. Currently, he is enrolled in the Ph.D. program in Electrical Engineering at the University of North Texas, where his research focuses on wearable embedded devices and RF energy harvesting. His dedication to academia is reflected in his continued pursuit of knowledge and excellence in his research endeavors, particularly in the application of machine learning techniques for signal processing in embedded systems.
Experience
Thai Ha Dang has built a solid foundation in research and industry through diverse experiences. As a Research Assistant in the Embedded Sensing & Processing Systems (ESPS) Lab at the University of North Texas, he is currently working on developing an underwater monitoring system, combining his interests in wireless sensing and energy harvesting. Before this, he contributed to a wide array of projects at the AIOT Lab, Pukyong National University, where he designed a multi-channel embedded device for monitoring cow behavior. This involved system design, firmware development, and experimentation. He also gained hands-on experience during his tenure as an engineer in Samsung Display Vietnamโs AI group, where he worked on training neural networks for computer vision tasks related to defect detection. His strong technical skills, combined with a practical understanding of industry needs, make him well-equipped to tackle complex research challenges in embedded systems and machine learning applications.
Awards and Honors
Thai Ha Dang has been recognized for his contributions to the research community through several prestigious awards. Notably, he received the Best Paper Award at the Korea Institute of Convergence Signal Processing (KICSP) in December 2021 for his work on deep learning approaches for food quality assessment using hyperspectral sensors. Additionally, he was honored with the Brain Korea 21 Scholarship for the years 2021-2023, further validating his potential as a leader in his field. Thaiโs academic excellence has been supported by research assistantships at both Pukyong National University and the University of California Irvine. These honors reflect his continuous pursuit of knowledge and the impact his work has had on advancing technology in agriculture and embedded systems. His recognition through these awards underscores his talent, dedication, and potential to drive innovation in his research.
Research Focus
Thai Ha Dang’s research primarily focuses on developing and applying wearable embedded systems for low-powered monitoring and energy harvesting, with a strong emphasis on machine learning techniques. His work includes creating self-powered systems, such as his wireless sensor network for monitoring cow behavior, which uses 915 MHz radio frequency energy harvesting. Another key area of his research is food quality monitoring, where he explores battery-free systems powered by RF energy harvesting to detect freshness in food products. Additionally, Thai has delved into underwater monitoring and aquaculture, with applications for shrimp larvae counting using multi-scale feature networks. His multidisciplinary research blends electrical engineering, machine learning, and sensor technology to address real-world challenges in agriculture, food safety, and environmental monitoring. Thai is particularly interested in developing sustainable and efficient systems that are capable of operating in challenging and remote environments, offering a glimpse into the future of intelligent, energy-efficient devices.
Publication Top Notes
- โSelf-Powered Cattle Behavior Monitoring System Using 915 MHz Radio Frequency Energy Harvesting,โ IEEE Access, 2024.
- โVAE-LSTM Data Augmentation for Cattle Behavior Classification Using a Wearable Inertial Sensor,โ IEEE Sensor Letters, 2024.
- โRadio-Frequency Energy Harvesting-based Self-Powered Dairy Cow Behavior Classification System,โ IEEE Sensors Journal, 2023.
- โA LoRaWAN-Based Smart Sensor Tag for Cow Behavior Monitoring,โ IEEE Sensors Conference, 2022.
- โB2EH: Batteryless BLE Sensor Network Using RF Energy Harvesting,โ IEEE Applied Sensing Conference, 2023.
- โShrimp Larvae Counting in Dense Environments Using Size-Adaptive Density Map Estimation and Multi-scale Feature Network,โ IEEE Transactions on Agrifood Electronics (accepted).