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

Narmilan Amarasingam | Artificial Intelligence | Best Researcher Award

Mr. Narmilan Amarasingam | Artificial Intelligence | Best Researcher Award

PhD student, Queensland University of Technology, Australia

Researcher specializing in drone-based remote sensing solutions for environmental and surveillance needs, focusing on precision agriculture and biosecurity. Expertise includes UAV remote sensing, artificial intelligence, and multispectral/hyperspectral image processing. Currently pursuing a PhD in Precision Agriculture at Queensland University of Technology.

Profile

Google Scholar

 

🎓 Education

PhD: Precision Agriculture, Queensland University of Technology (2021 – Present). MSc: Agricultural Engineering, Eastern University, Sri Lanka (2016 – 2018). BSc: Agriculture, Agricultural Engineering, Eastern University, Sri Lanka (2010 – 2015). BIT: Software Engineering, University of Colombo School of Computing, Sri Lanka (2011 – 2015)

🔍 Experience

Research Assistant at Charles Sturt University and Sunshine Coast Council on projects integrating AI and drone technology for environmental monitoring and invasive species detection.

🏆 Awards

QUT/Accelerate Higher Education Development Expansion and Development (AHEAD) World Bank Project Scholarship. Vice Chancellor’s Award for Early Career Researcher, Faculty of Technology, 2022.

🌍 Research Interests

Precision Agriculture, UAV-based Remote Sensing, Multispectral Image Processing, AI, Biosystems Engineering, Environmental Management.

📚 Publications

Co-authored numerous peer-reviewed articles in Q1 and non-Q1 ranking journals on topics related to UAV-based remote sensing and AI applications in agriculture and environmental management.

A review of UAV platforms, sensors, and applications for monitoring of sugarcane crops
Predicting canopy chlorophyll content in sugarcane crops using machine learning algorithms and spectral vegetation indices derived from UAV multispectral imageryDetection of white leaf disease in sugarcane using machine learning techniques over UAV multispectral images
Detection of White Leaf Disease in Sugarcane Crops Using UAV-Derived RGB Imagery with Existing Deep Learning Models
N Amarasingam, F Gonzalez, ASA Salgadoe, J Sandino, K Powell
E-agricultural concepts for improving productivity: A review