Dr. Ning Zhang | Deep Learning | Research Excellence Award

Dr. Ning Zhang | Deep Learning | Beijing Institute of Technology | China

Dr. Ning Zhang is a male researcher in the field of intelligent sensing and information and communication engineering, with a strong academic and applied background in deep learning–driven sensor systems, onboard artificial intelligence, and edge computing for aerospace and remote sensing platforms. He received his doctoral and master’s training in Information and Communication Engineering from Beijing Institute of Technology and completed his undergraduate education in Electronic Information Engineering at Wuhan University of Technology, forming a solid interdisciplinary foundation that integrates algorithms, hardware architecture, and embedded intelligence. Professionally, Dr. Ning Zhang has served as a project leader and key algorithm and hardware engineer in multiple nationally funded and internationally oriented research projects, including onboard AI systems for small satellites, UAV-based intelligent perception, and dynamic neural network deployment under constrained computing environments.

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Featured Publications

MFST: A Multi-Level Fusion Network for Remote Sensing Scene Classification

– IEEE Geoscience and Remote Sensing Letters, 2022 · 103 Citations

FPGA Implementation for CNN-Based Optical Remote Sensing Object Detection

– Electronics, 2021 · 96 Citations

Automatic Deployment of Convolutional Neural Networks on FPGA for Spaceborne Remote Sensing Application

– Remote Sensing, 2022 · 26 Citations

All Adder Neural Networks for On-Board Remote Sensing Scene Classification

– IEEE Transactions on Geoscience and Remote Sensing, 2023 · 22 Citations

Knowledge Distillation-Based Lightweight Change Detection in High-Resolution Remote Sensing Imagery

– IEEE JSTARS, 2024 · 22 Citations

Dr. Zobeir Raisi | Deep Learning | Chabahar Maritime University | Iran

Dr. Zobeir Raisi is a male expert in computer vision, machine learning, and deep learning, specializing in object detection, recognition, tracking, segmentation, 3D human pose estimation, and camera calibration, combining advanced theoretical knowledge with practical and applied research experience. He holds a Ph.D. in Systems Design Engineering from the University of Waterloo, Canada, and both M.E. and B.E. degrees in Electrical Engineering from the University of Sistan & Balouchestan, Iran, reflecting a strong academic foundation and interdisciplinary technical proficiency. Dr. Zobeir Raisi’s professional experience encompasses postdoctoral research focused on automating sports analytics using smartphone cameras, supervising master’s students in camera calibration projects with industry collaboration, and conducting Ph.D.-level research on transformer-based deep learning frameworks for arbitrary-shaped text detection and recognition in complex visual environments. He has contributed as a research assistant in computer vision and machine learning applications for automated assembly and anomaly detection systems, as well as serving as a lecturer and assistant professor at Chabahar Maritime University, teaching courses spanning digital image processing, digital systems, electrical circuits, computer architecture, microcontrollers, and electromagnetics, while mentoring undergraduate and graduate students and managing journal editorial processes.

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