Ning Zhang | Signal & Image Processing | Research Excellence Award

Published on by Electrical Engineering

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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View ORCID Profile View Google Scholar

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

Zhenghua Qian | Machine Learning | Research Excellence Award

Published on by Electrical Engineering

Prof. Dr. Zhenghua Qian | Machine Learning | Research Excellence Award

Professor | Nanjing University of Aeronautics and Astronautics | China

Professor Zhenghua Qian is a distinguished scholar in solid mechanics and aerospace engineering, serving as a Professor at the College of Aerospace Engineering, Nanjing University of Aeronautics and Astronautics, and a core member of the State Key Laboratory of Mechanics and Control of Aerospace Structures, with expertise spanning wave propagation, piezoelectric and smart structures, structural health monitoring, and machine learning–assisted nondestructive evaluation. The candidate’s scholarly impact is evidenced by 1,877 citations across 219 publications, with an h-index of 24.

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Adil Sultan | Environmental Modeling | Best Researcher Award

Published on by Electrical Engineering

Mr. Adil Sultan | Environmental Modeling | Best Researcher Award

PhD Student | National Yunlin University of Science and Technology | Taiwan

Adil Sultan is a dynamic researcher and postgraduate scholar in Computer Science and Information Engineering at the National Yunlin University of Science and Technology, Taiwan, with a Bachelor’s degree in Geophysics from Bahria University Islamabad, Pakistan. His interdisciplinary expertise bridges artificial intelligence, computational modeling, and earth sciences, emphasizing intelligent predictive frameworks for environmental and marine ecosystem dynamics. Adil’s research integrates machine learning, fractional calculus, and neurocomputational modeling to address complex ecological and climatological phenomena, with publications in high-impact Q1 journals such as Water Research, Engineering Applications of Artificial Intelligence, and Process Safety and Environmental Protection. He has contributed to advancing predictive neural architectures for modeling plankton dynamics, environmental toxin propagation, and climate-induced marine variations. His professional experience includes seismic data processing at Oil and Gas Development Company Limited, where he applied geophysical modeling and data analytics for subsurface evaluations, alongside earlier roles in communication and technical support at IBEX Global. Recognized for academic excellence and innovation, he ranked among the top three in his master’s program and earned a Bronze Medal for his undergraduate thesis. Adil has presented his work at international conferences, authored multiple manuscripts under review, and actively engages in interdisciplinary research collaborations. His memberships, scholarly achievements, and leadership in applied machine learning for environmental intelligence underscore his commitment to sustainable scientific innovation and global research excellence. His Scopus profile reflects 17 citations, 6 indexed documents, and an h-index of 2.

Profile: Scopus

Featured Publications

Adil Sultan*, Design of a Fractional-Order Environmental Toxin-Plankton System in Aquatic Ecosystems: A Novel Machine Predictive Expedition with Nonlinear Autoregressive Neuroarchitectures. Water Research, Q1, 12.4 I.F.

Adil Sultan*, Bayesian-Regularized Cascaded Neural Networks for Fractional Asymmetric Carbon-Thermal Nutrient-Plankton Dynamics under Global Warming and Climatic Perturbations. Engineering Applications of Artificial Intelligence, Q1, 8.0 I.F.

Adil Sultan*, Intelligent Predictive Networks for Nonlinear Oxygen-Phytoplankton-Zooplankton Coupled Marine Ecosystems under Environmental and Climatic Disruptions. Process Safety and Environmental Protection, Q1, 7.8 I.F.

Adil Sultan*, Prognostication of Zooplankton-Driven Cholera Pathoepidemiological Dynamics: Novel Bayesian-Regularized Deep NARX Neuroarchitecture. Computers in Biology and Medicine, Q1, 6.3 I.F.

Adil Sultan*, Predictive Modeling of Fractional Plankton-Assisted Cholera Propagation Dynamics Using Bayesian Regularized Deep Cascaded Exogenous Neural Networks. Process Safety and Environmental Protection, Q1, 7.8 I.F.