Mr. hang du | Programming | Internet of Things Award

Mr. hang du | Programming | Internet of Things Award

student, Nanjing Forestry University, China

Du Hang , a passionate researcher and aspiring innovator in the field of electronic information and FPGA-based hardware acceleration, is currently pursuing his Master’s degree at Nanjing Forestry University. Born in January 2000 in Nanyang, Henan Province, Du Hang has developed a strong academic and technical foundation in electrical engineering and embedded systems. As a member of the Communist Party of China, he is committed to academic excellence and practical innovation in electronic information systems. With a focus on hardware-software co-design, Du actively contributes to high-performance computing projects and has already published scholarly work in international journals.

Publication Profile

Scopus

🎓Education Background:

Du Hang is presently enrolled in the Master’s program in Electronic Information at Nanjing Forestry University (2022.09–2025.06). His coursework includes FPGA technology and application, C/C++ programming, embedded system design, and integrated circuit principles. He completed his undergraduate degree in Electrical Engineering and Automation at Henan University of Science and Technology (2018.09–2022.06), where he gained foundational knowledge in digital electronic technology, power systems, analog circuits, and motor control.

💼Professional Experience:

Du Hang has rich project experience involving advanced hardware design and system-level integration. His work includes the automatic focusing design of a thermal infrared camera using FPGA, FPGA-based machine vision defect detection systems, and the YOLOv4-tiny accelerator design using HLS for real-time object detection. He also led the development of a PC-based temperature and humidity data display system using STM32 and Visual Studio, and implemented a handwritten digit recognition system using PYNQ and LeNet. These projects highlight his strong command over Vivado, HLS, Vitis, and embedded systems, as well as his proficiency in hardware debugging using oscilloscopes and soldering tools.

🏆Awards and Honors:

Du Hang was awarded Second Prize in the 7th JiChuang Competition (East China Division) for his FPGA-based machine vision defect detection system. He holds two patents—one on a hardware acceleration system for target detection and another on a convolutional neural network acceleration architecture based on FPGA. His achievements demonstrate not only technical capability but also innovation under resource-constrained environments.

🔬Research Focus:

Du Hang’s research centers on FPGA-accelerated deep learning, hardware-software co-design, real-time signal processing, and embedded system optimization. He focuses on designing lightweight and high-speed neural network accelerators with techniques such as loop unrolling, channel parallelism, and double-buffer pipelining to enhance throughput and computational efficiency. His work contributes to applications in industrial automation, smart vision, and intelligent embedded systems.

🔚Conclusion:

Du Hang is a dynamic and highly motivated young engineer and researcher whose expertise lies in bridging the gap between algorithm design and hardware implementation. His ability to manage interdisciplinary projects, from coding to PCB-level execution, places him on a promising trajectory in the fields of electronic information and embedded AI systems. With a blend of academic excellence, innovation, and practical skills, Du Hang is poised to make significant contributions to future advancements in intelligent hardware systems.

📚 Top Publication Notes

FPGA Accelerated Deep Learning for Industrial and Engineering Applications: Optimal Design Under Resource Constraints
 Journal: Electronics (Switzerland), 2025
 Published Year: 2025
 Authors: Liu Yanyi, Du Hang, Wu Yin, Mo Tianli
 Cited by: 1 article

Jihyeon Ryu | Computer Science | Best Researcher Award

Assist Prof Dr. Jihyeon Ryu | Computer Science | Best Researcher Award

Professor, Kwangwoon University, South Korea

👩‍🏫 Jihyeon Ryu is an Assistant Professor at the School of Computer and Information Engineering, Kwangwoon University. She specializes in split learning, convolutional neural networks, and user authentication, contributing significantly to the field of computer science with her extensive research and numerous publications.

Profile

Google Scholar

 

Education

Ph.D. in Software (Integrated Master’s and Doctorate Course) from Sungkyunkwan University (Mar. 2018 – Feb. 2023), advised by Prof. Dongho Won and Prof. Hyoungshick Kim. B.S. in Mathematics and Computer Science and Engineering from Sungkyunkwan University (Mar. 2013 – Feb. 2018). Early Graduation from Sejong Science High School (Mar. 2011 – Feb. 2013).

 

Research Interests:

Split Learning. Convolutional Neural Networks. User Authentication

Awards

Family Company Workshop Lecture, Hoseo University (Feb. 2022). Invited Lecturer, Mental Women’s High School (Nov. 2021). Excellence Prize, Software Department Excellence Research Awards (Feb. 2021). Graduate Merit Scholarship, Sungkyunkwan University (Mar. 2018 – Feb. 2021). SimSan Scholarship, Multiple instances (Mar. 2018 – Sep. 2020). Excellence Award, 국가 암호기술 전문인력 양성과정 (Nov. 2019). Grand Prize, Software Department Excellence Research Awards (Feb. 2019). Samsung Science Scholarship (Mar. 2013 – Feb. 2018)

Publications

Lightweight Hash-Based Authentication Protocol for Smart Grids. Sensors, 2024. Sangjin Kook, Keunok Kim, Jihyeon Ryu, Youngsook Lee, Dongho Won.

Enhanced Lightweight Medical Sensor Networks Authentication Scheme Based on Blockchain. IEEE ACCESS, 2024. Taewoong Kang, Naryun Woo, Jihyeon Ryu.

Secure and Anonymous Authentication Scheme for Mobile Edge Computing Environments. IEEE Internet of Things Journal, 2024. Hakjun Lee, Jihyeon Ryu, Dongho Won.

Distributed and Federated Authentication Schemes Based on Updatable Smart Contracts. Electronics, 2023. Keunok Kim, Jihyeon Ryu, Hakjun Lee, Youngsook Lee, Dongho Won.

An Improved Lightweight User Authentication Scheme for the Internet of Medical Things. Sensors, 2023. Keunok Kim, Jihyeon Ryu, Youngsook Lee, Dongho Won.