Ning Zhang | Signal & Image Processing | Research Excellence Award

Dr. Ning Zhang | Signal & Image Processing | Research Excellence Award

Postdoctor | Beijing institute of technology | China

Ning Zhang is a researcher in deep learning and remote sensing at the Beijing Institute of Technology, with expertise in computer vision, real-time processing, and onboard intelligent systems. He holds bachelor’s, master’s, and doctoral degrees in electronic information and information and communication engineering, with specialized training in lightweight neural networks and FPGA-based algorithm–hardware co-design. His professional experience includes leading and contributing to nationally funded and institutional projects focused on airborne and satellite AI deployment, where he has played key roles in algorithm development, system architecture design, and technical leadership. His research centers on remote sensing scene classification, object detection, model compression, and energy-efficient neural network accelerators, resulting in high-impact publications in leading IEEE journals, multiple authorized and accepted patents, and a citation record of 337 citations with an h-index of 9 and an i10-index of 8. He has received numerous prestigious scholarships, graduate honors, national-level competition awards, and maintains active engagement in academic and professional communities.

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Featured Publications


UniGeoSeg: Towards Unified Open-World Segmentation for Geospatial Scenes

S. Ni, D. Wang, H. Chen, H. Guo, N. Zhang, J. Zhang

arXiv Preprint · Open-World Geospatial Segmentation


S2Net: Spatial-aligned and Semantic-discriminative Network for Remote Sensing Object Detection

J. Yao, H. Chen, Y. Xie, N. Zhang, M. Yang, L. Chen

IEEE Transactions on Geoscience and Remote Sensing · Top-Tier Journal


High-throughput Energy-efficient Accelerator with Collaborative-Trainable Sparse-Quantization Method for On-Board Remote Sensing Processing

T. Wang, H. Chen, N. Zhang, S. Ni, X. Zhang, L. Chen, W. Li

IEEE Transactions on Geoscience and Remote Sensing · Energy-Efficient AI Hardware


High-Throughput and Energy-Efficient FPGA-Based Accelerator for All Adder Neural Networks

N. Zhang, S. Ni, L. Chen, T. Wang, H. Chen

IEEE Internet of Things Journal · FPGA Acceleration


Q-A2NN: Quantized All-Adder Neural Networks for Onboard Remote Sensing Scene Classification

N. Zhang, H. Chen, L. Chen, J. Wang, G. Wang, W. Liu

Remote Sensing · Lightweight Neural Networks

Lei Guan | Machine Learning | Research Excellence Award

Mr. Lei Guan | Machine Learning | Research Excellence Award

Director | China Academy of Safety Science and Technology | China

Lei Guan is a Director and Professor at the Risk Monitoring and Early Warning Center, China Academy of Safety Science and Technology, with expertise in risk monitoring, early warning systems, artificial intelligence, and industrial safety engineering. He holds a Bachelor’s degree in Materials Science and Master’s and Doctoral degrees in Mechanical Engineering with specialization in precision instruments and safety-related systems. He has led major national and ministerial research programs, directed key laboratories and professional committees, supervised graduate researchers, and provided technical leadership for large-scale industrial and governmental safety initiatives. His research focuses on intelligent work safety systems, industrial internet applications, digital twins, data-driven risk modeling, and emergency management, with sustained contributions through peer-reviewed publications, patents, and standards development. His scholarly impact is reflected in 18 citations, an h-index of 3, and 13 published articles.

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Featured Publications

Numerical simulation of the double pits stress concentration in a curved casing inner surface
W. Yan, L. Guan, Y. Xu, J.G. Deng – Advances in Mechanical Engineering, 9(1) (3 citations)

Safety monitoring and management system for fluid catalytic cracking (FCC) process
L. Fang, Z. Wu, L. Wei, R. Kang, L. Guan – International Conference on Information and Automation (3 citations)

Study on SVM-based Flame Recognition and Fire Warning for Cotton and Linen Warehouses
X. Zhao, S. Hao, L. Guan, Y. Wang, Q. Zhao, D. Lv – IEEE Conference on Advances in Electrical Engineering (2 citations)

Industrial Internet of Things (IIoT) Identity Resolution Techniques: A Review
C. Dai, H. Li, L. Guan, M. Chi – IEEE BigDataSecurity (1 citation)

Dr. Swarup Ghosh | Energy Harvesting & Self-Powered Systems | Best Researcher Award

Dr. Swarup Ghosh | Energy Harvesting & Self-Powered Systems | Best Researcher Award

Assistant Professor | SR University | India

Dr. Swarup Ghosh is an Assistant Professor and Assistant Dean (Research) at the School of Computer Science and Artificial Intelligence, SR University, specializing in computational materials science, condensed matter physics, and AI-driven materials discovery. He earned his Ph.D. in Science from Jadavpur University with a focus on first-principles calculations, following an M.Sc. in Physical Sciences and a B.Sc. in Physics. Dr. Ghosh previously served as a Postdoctoral Research Associate at Jadavpur University and as a faculty member at Sammilani Mahavidyalaya, contributing to advanced computational materials research and student mentorship. His work spans density functional theory, molecular dynamics, many-body perturbation theory, electronic structure simulations, and machine-learning-enabled materials design, resulting in publications in high-impact journals and presentations at prestigious scientific forums. His research includes breakthroughs in 2D and nanomaterials, thermoelectrics, photovoltaics, spintronics, and catalytic systems, emphasizing data-centric scientific innovation. He has been honored with national research fellowships, merit-based academic distinctions, and awards for research excellence, while also serving as a reviewer for reputed international journals and participating in professional training programs and conferences. He maintains a strong scholarly impact, demonstrated by 245 citations, an h-index of 9, and an i10-index of 9, underscoring his growing influence in computational materials science and interdisciplinary research.

Profile: Google Scholar

Featured Publications

Swarup Ghosh*, Predicting photovoltaic efficiency of two-dimensional Janus materials for solar energy harvesting: A combined first-principles and machine learning study. Solar Energy Materials and Solar Cells, Accepted.

Swarup Ghosh*, First-principles study on structural, electronic, optical and photovoltaic properties of Sc₂C-based Janus MXenes for solar cell applications. Materials Today Communications, Accepted.

Swarup Ghosh, Predicting band gaps of ABN₃ perovskites: An account from machine learning and first-principles DFT studies. RSC Advances, Accepted.