Dr. Yingbin Wang, Xi’an Institute of Space Radio Technolog, China
Dr. Yingbin Wang is a leading researcher in space microwave communication, detection, and AI-driven signal processing. He earned his Ph.D. in Electronic Science and Technology from Xidian University in 2022 and currently serves as a Senior Engineer at the National Key Laboratory of Science and Technology on Space Microwave at the Xi’an Institute of Space Radio Technology. His research spans Integrated Sensing and Communication (ISAC), deep learning, and anti-jamming satellite systems. With over ten high-impact publications and contributions to national-level R&D projects, Dr. Wang is shaping the future of space-based communication and intelligent sensing. 🚀📡
🌍 Professional Profile:
Google Scholar
🏆 Suitability for the Best Researcher Award
Dr. Yingbin Wang is a highly qualified candidate for the Best Researcher Award, given his significant contributions to space microwave communication and AI-powered signal processing. His expertise in satellite-terrestrial integration, space-based radar target detection, and anti-jamming satellite systems plays a crucial role in advancing global space technology. With publications in top-tier IEEE journals, participation in national R&D projects, and contributions to cutting-edge ISAC applications, Dr. Wang is at the forefront of next-generation communication research. His work in AI-driven remote sensing is revolutionizing the field, making him a distinguished and deserving nominee. 🏆🚀
🎓 Education
Dr. Yingbin Wang pursued his entire higher education at Xidian University, China, a prestigious institution in electronic engineering and space communication. He obtained his Ph.D. in Electronic Science and Technology in June 2022, focusing on advanced space microwave systems and AI-enhanced signal processing. His doctoral research contributed to improving satellite communication resilience, radar detection, and deep learning applications in space technologies. Throughout his academic journey, he combined hardware engineering with AI-driven software models, enabling breakthroughs in integrated satellite-terrestrial communication. His strong foundation in electromagnetic waves, remote sensing, and computational intelligence defines his research excellence. 🎓📡🔬
💼 Experience
Dr. Yingbin Wang is a Senior Engineer at the National Key Laboratory of Science and Technology on Space Microwave, Xi’an Institute of Space Radio Technology. His role involves leading research in space microwave communication, detection, and AI-driven signal optimization. He has contributed to major national R&D projects, including space-based radar target detection, anti-jamming satellite communication, and integrated sensing for satellite-terrestrial networks. His work on AI-based signal processing and deep learning models has significantly enhanced real-time space communication efficiency. His expertise in high-frequency electromagnetic applications and AI-powered satellite technology is instrumental in shaping the future of space communications. 🚀📶
🏅 Awards & Honors
Dr. Yingbin Wang has received multiple recognitions for his contributions to space communication and AI-driven signal processing. His research in anti-jamming satellite networks has been awarded national-level research funding. He has received Best Paper Awards at leading IEEE conferences on signal processing and remote sensing. Additionally, his contributions to integrated satellite-terrestrial communication have been recognized by the National Science and Technology Innovation Program. As a reviewer for top IEEE journals, he actively contributes to the scientific community. His pioneering work in AI-enhanced space sensing continues to push the boundaries of satellite communication technologies. 🏆📡
🔬 Research Focus
Dr. Yingbin Wang’s research spans Artificial Intelligence, communication, deep learning, and signal processing, with a strong emphasis on space microwave communication and detection. His work explores AI-driven radar target detection, anti-jamming satellite communication, and integrated sensing and communication (ISAC) systems. He develops machine learning models for real-time adaptive signal processing, enhancing satellite-terrestrial connectivity. His studies in neural network-driven space communication systems optimize data transmission efficiency in complex space environments. His research is critical for next-generation deep-space exploration, smart communication networks, and high-performance microwave technology, ensuring global connectivity and security in aerospace applications. 🚀📡🛰️
📖 Publication Top Notes
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SPB-Net: A Deep Network for SAR Imaging and Despeckling with Downsampled Data
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Journal: IEEE Transactions on Geoscience and Remote Sensing
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Publication Year: 2020
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Citations: 27
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Lq-SPB-Net: A Real-Time Deep Network for SAR Imaging and Despeckling
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Journal: IEEE Transactions on Geoscience and Remote Sensing
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Publication Year: 2021
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Citations: 8
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Multi-Scale and Single-Scale Fully Convolutional Networks for Sound Event Detection
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Journal: Neurocomputing
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Publication Year: 2021
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Citations: 18
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MSFF-Net: Multi-Scale Feature Fusing Networks with Dilated Mixed Convolution and Cascaded Parallel Framework for Sound Event Detection
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Journal: Digital Signal Processing
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Publication Year: 2022
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Citations: 12
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A Convex Optimization Algorithm for Compressed Sensing in a Complex Domain: The Complex-Valued Split Bregman Method
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Journal: Sensors
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Publication Year: 2019
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Citations: 13