Da Chen | Wireless Communication | Best Researcher Award

Published on by Academic Awards

Prof. Dr. Da Chen | Wireless Communication | Best Researcher Award

Huazhong University of Science and Technology | China

Prof. Dr. Da Chen is a distinguished researcher in the field of wireless communications, focusing on multicarrier modulation, waveform design, communication signal processing, and millimeter-wave communication. His pioneering work on FBMC and OQAM/OFDM systems, cell-free massive MIMO, and hybrid precoding for wideband mmWave networks has significantly contributed to advancing next-generation wireless communication technologies. Prof. Dr. Da Chen has published extensively in high-impact IEEE journals, co-authored influential books with Elsevier and China Science Press, and holds numerous national patents. His research grants, funded by the National Natural Science Foundation of China and the Ministry of Science and Technology, underline his leadership in developing 5G and 6G air interface technologies. Recognized with multiple Best Paper and Editor Awards, he also serves as an editorial board member for leading IEEE journals. His work bridges theoretical innovations and real-world communication system applications.

Profile: Google Scholar

Featured Publications

Liu, P., Luo, K., Chen, D., & Jiang, T. (2019). Spectral efficiency analysis of cell-free massive MIMO systems with zero-forcing detector. IEEE Transactions on Wireless Communications, 19(2), 795–807.

Xiao, L., Li, S., Qian, Y., Chen, D., & Jiang, T. (2021). An overview of OTFS for Internet of Things: Concepts, benefits, and challenges. IEEE Internet of Things Journal, 9(10), 7596–7618.

Chen, Y., Xiong, Y., Chen, D., Jiang, T., Ng, S. X., & Hanzo, L. (2020). Hybrid precoding for wideband millimeter wave MIMO systems in the face of beam squint. IEEE Transactions on Wireless Communications, 20(3), 1847–1860.

Chen, D., Qu, D., Jiang, T., & He, Y. (2013). Prototype filter optimization to minimize stopband energy with NPR constraint for filter bank multicarrier modulation systems. IEEE Transactions on Signal Processing, 61(1), 159–169.

Chen, Y., Chen, D., Jiang, T., & Hanzo, L. (2019). Channel-covariance and angle-of-departure aided hybrid precoding for wideband multiuser millimeter wave MIMO systems. IEEE Transactions on Communications, 67(12), 8315–8328.

Eyob Abera Deboch | Communication Engineering | Best Researcher Award

Published on by Academic Awards

Mr. Eyob Abera Deboch | Communication Engineering | Best Researcher Award

Mr. Eyob Abera Deboch, Shenzhen institute of advance technology, Ethiopia

🌟 Eyob Abera Deboch is an Ethiopian computer vision specialist with a master’s degree from the University of Electronic Science and Technology of China. With over three years of experience, he excels in deep learning, particularly in image fusion, classification, and detection. Eyob has showcased his expertise through impactful research, winning awards such as first prize in the Scientific Innovation Competition Contest. He’s published in esteemed journals and demonstrated leadership in academia and extracurricular activities. Eyob is fluent in Python, MATLAB, and C/C++, with a passion for leveraging AI for real-world applications. 🖥️🔍

 

Publication Profile

Scopus

Education

🎓 Eyob Abera Deboch pursued his academic journey at the University of Electronic Science and Technology of China, where he attained both his Bachelor’s and Master’s degrees. Graduating with honors, Eyob’s Master’s thesis delved into “Research on Image fusion with the deep learning framework,” showcasing his expertise in computer vision. Prior to this, his Bachelor’s thesis focused on “Image Fusion Algorithms based on Machine learning,” laying the foundation for his subsequent research endeavors. With a strong academic background in Electronic Information Engineering and Information and Communication Engineering, Eyob has demonstrated a commitment to advancing the field of AI through innovative research and academic excellence. 📚✨

 

Experience

🌟 Eyob Abera Deboch has a diverse range of experiences, showcasing his expertise in computer vision and deep learning. During his master’s program, he designed and implemented a cutting-edge deep learning model for infrared and visible image fusion, under the guidance of Associate Prof. Qi Jin. As a dedicated school assistant, Eyob provided invaluable support to students and faculty at the School of Information and Communication Engineering. His bachelor’s thesis, advised by Associate Prof. Wu Ruiqing, focused on developing an Ensemble Network for Infrared and Visible image fusion. Additionally, Eyob excelled in freelance projects, including ball height detection and dataset preparation, and earned recognition for his Raspberry Pi face detection project, winning the first prize in a scientific innovation competition. 🖥️🏆

 

Awards

🏅 Eyob Abera Deboch has garnered numerous accolades and scholarships, underscoring his exceptional academic and extracurricular achievements. Notably, he secured the prestigious Chinese Government Scholarship, enabling him to pursue his master’s degree. Throughout his academic journey, Eyob earned the Academic Achievement Award multiple times, demonstrating consistent excellence. He clinched the top spot in the Scientific Innovation Competition Contest and received recognition for his contributions to various workshops and competitions. Eyob’s linguistic prowess extends to English and Amharic, with proficient communication in Chinese. His dedication to both scholarly pursuits and sports exemplifies a well-rounded individual committed to success. 🎓🌟

 

Research Focus

🔍 Eyob Abera Deboch’s research focuses primarily on computer vision and deep learning applications, with a specialization in image fusion and enhancement. Through projects like “A deep learning and image enhancement based pipeline for infrared and visible image fusion,” Eyob demonstrates a keen interest in leveraging advanced algorithms to improve image quality and analysis. His work extends to areas such as automatic image contrast enhancement, utilizing reinforcement learning techniques. Eyob’s dedication to enhancing image processing techniques showcases his commitment to advancing technology’s capabilities in visual data analysis. With a strong foundation in these domains, he continues to contribute innovative solutions to the field, driving progress in computer vision. 🖥️🌟

 

Publication Top Notes

Multi-scale feature fusion for prediction of IDH1 mutations in glioma histopathological images