Geamel Alyami | Engineering and Technology | Best Researcher Award

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Dr. Geamel Alyami | Engineering and Technology | Best Researcher Award

Dr. Geamel Alyami | Engineering and Technology | Best Researcher Award | Associate Research Professor | King Abdulaziz City for Science and Technology | Saudi Arabia 

Dr. Geamel Alyami is a distinguished researcher and engineer specializing in Electrical and Communication Engineering, currently serving at the National Center for Communication Systems and Command and Control Technology within the King Abdul-Aziz City for Science and Technology (KACST), Saudi Arabia. He obtained his Doctor of Science in Electrical Engineering from the Florida Institute of Technology in the United States, where he also earned his Master of Science degree. His academic foundation was laid at the University of Central Florida with a Bachelor of Science in Electrical Engineering, followed by a Diploma in Telecommunication from the Telecommunication and Information College in Jeddah. Throughout his career, Dr. Geamel Alyami has cultivated an extensive background in wireless communication systems, Massive MIMO technology, phased array antennas, and machine learning applications in telecommunication research. His research interests focus on 6G technologies, millimeter-wave communication, channel modeling, predictive antenna systems, and high-efficiency signal processing frameworks that aim to transform global communication infrastructures. With a strong commitment to scientific advancement, Dr. Alyami has contributed to several IEEE-indexed and peer-reviewed international journals and conferences, showcasing impactful work in areas such as multiuser separation, spatial channel modeling, and linear precoding for next-generation communication networks. His technical proficiency includes advanced software and programming tools such as MATLAB, Quartus II, PSpice, VHDL, Verilog HDL, and Microwave Studio, which he effectively integrates into his experimental and theoretical research frameworks. Professionally, Dr. Alyami has accumulated rich industrial and academic experience, having worked with Detecon Al Saudia Co. (DETASAD) as a Transmission SDH/TV Technician, gaining hands-on expertise in telecommunication systems installation, testing, and network optimization. His leadership extends beyond research, as he has actively participated in volunteer and academic communities, including IEEE, Phi Kappa Phi Honor Society, and the Center of Excellence for Telecommunication Applications (CETA). Recognized for his academic excellence, he has been featured on the Dean’s List and received honors from professional engineering societies. Dr. Geamel Alyami’s current research integrates machine learning and predictive modeling for smart THz antennas in 6G systems, reflecting his forward-looking vision for the future of telecommunication engineering. With fluency in Arabic and English and a working knowledge of Spanish, he brings a global perspective to collaborative projects. His unwavering dedication to innovation, leadership, and excellence in communication research underscores his continuing contributions to advancing scientific knowledge and promoting sustainable technology growth in the global research community.

Profile:  Scopus | Google Scholar

Featured Publications 

  1. Alyami, G., & Kostanic, I. (2016). On the spatial separation of multiuser channels using 73 GHz statistical channel models. IEEE International Conference on Communication, Networks and Satellite (COMNETSAT). Citations: 12

  2. Alyami, G., Kostanic, I., & Ahmad, W. (2016). Multiuser separation and performance analysis of millimeter wave channels with linear precoding. IEEE International Conference on Communication, Networks and Satellite (COMNETSAT). Citations: 15

  3. Alyami, G., & Kostanic, I. (2016). A low complexity user selection scheme with linear precoding for Massive MIMO systems. IAENG International Journal of Computer Science, 43(3). Citations: 20

  4. Alyami, G., Kostanic, I., & Ahmad, W. (2017). Performance modeling and analysis of millimeter-wave MIMO systems using linear precoding techniques. IEEE Transactions on Wireless Communications. Citations: 25

  5. Alyami, G., & Kostanic, I. (2018). Channel modeling and signal optimization for next-generation millimeter-wave communications. IEEE Access, 6, 12455–12464. Citations: 30

  6. Alyami, G., & Ahmad, W. (2019). Machine learning-assisted beamforming optimization in massive MIMO networks. IEEE Communications Letters, 23(12), 2245–2249. Citations: 35

 

 

Rongshun Chen | Engineering and Technology | Best Researcher Award

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Prof. Rongshun Chen | Engineering and Technology | Best Researcher Award

Prof. Rongshun Chen | Engineering and Technology | Best Researcher Award | Professor | National Tsing Hua University | Taiwan 

Prof. Rongshun Chen is a distinguished academic and accomplished researcher in the field of mechanical and power engineering, currently serving as a Professor in the Department of Power Mechanical Engineering at National Tsing Hua University, Hsinchu, Taiwan. Prof. Chen obtained his Bachelor of Science degree in Mechanical Engineering from the National Taiwan University of Science and Technology, followed by a Master of Science in Power Mechanical Engineering from National Tsing Hua University, and subsequently earned his Doctor of Philosophy in Mechanical Engineering from the University of Michigan, Ann Arbor, USA. Throughout his extensive academic career, Prof. Chen has made significant contributions to the advancement of robotics, control systems, and thermal management technologies, with a focus on developing intelligent sensing mechanisms, adaptive control, and mechatronic system integration. His research interests encompass robotics navigation, sensor technology, deep learning applications in thermal management, and micro-electromechanical systems (MEMS) design. Prof. Chen’s expertise extends across several domains of applied mechanics and computational modeling, enabling the development of efficient systems for industrial automation and energy-efficient engineering applications. His professional experience includes mentoring numerous graduate students, leading innovative research projects, and collaborating with interdisciplinary teams on global initiatives that bridge academia and industry. Prof. Chen has consistently demonstrated outstanding research skills in designing hybrid solvers for multi-agent motion control, developing dual-mode tactile sensors, and implementing deep learning models for predictive thermal management in data centers. His scholarly work has been published in high-impact journals and presented at major international conferences such as IEEE and Elsevier platforms, earning recognition for scientific rigor and innovation. A committed educator and leader, Prof. Chen is also an active member of the IEEE and has served in multiple academic and technical committees, contributing to the broader engineering research community. He has received numerous honors for his outstanding teaching and research achievements and continues to inspire through his leadership in robotics and thermal control engineering. Prof. Rongshun Chen’s career embodies the synergy of technical mastery, visionary thinking, and a lifelong dedication to advancing engineering science for societal benefit. His academic influence, publication record, and international collaborations firmly establish him as a leading scholar committed to advancing the future of intelligent mechanical systems and sustainable innovation through research excellence and mentorship.

Profile: Scopus | Google Scholar

Featured Publications

  1. Chen, R. (2022). Wearable and wireless performance evaluation system for sports science with an example in badminton. Scientific Reports. 7 citations.

  2. Chen, R. (2023). A Dual Spiral-Coils Tactile Sensor with Novel Driving Modes for Inductive Force and Capacitive Proximity Sensing. Conference Paper. 3 citations.

  3. Chen, R. (2023). Implementation of a Monolithic SoC Environmental Sensing Hub Using CMOS-MEMS Technique. Conference Paper. 1 citation.

  4. Chen, R. (2023). Collision-Free Navigation for Multiple Robots in Dynamic Environment. Conference Paper. 2 citations.

  5. Chen, R. (2023). Rack Inlet Temperature Prediction Based on Deep Learning. Conference Paper. 5 citations.

  6. Chen, R. (2023). A Dual Sensing Modes Capacitive Tactile Sensor for Proximity and Tri-Axial Forces Detection. Conference Paper. 12 citations.