Tuniyazi Abudoureheman | Intelligent Systems | Research Excellence Award

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Dr. Tuniyazi Abudoureheman | Intelligent Systems | Research Excellence Award

Dr. Tuniyazi Abudoureheman | Intelligent Systems | Hiroshima University | Japan

Dr. Tuniyazi Abudoureheman is a dedicated researcher and Ph.D. student at Hiroshima University whose work focuses on advanced sensing, machine vision, and robotic system diagnostics, contributing meaningfully to the fields of high-frame-rate (HFR) imaging, vibration analysis, and automated detection systems. Dr. Tuniyazi Abudoureheman began his academic journey with foundational studies that eventually led him to pursue graduate-level research, culminating in his current doctoral studies where he continues to expand his expertise in robotics and intelligent sensing technology. Throughout his professional experience, Dr. Tuniyazi Abudoureheman has actively engaged in collaborative research projects, working alongside multidisciplinary teams to design, implement, and validate methods involving HFR video, wing-beat frequency detection, and robot health monitoring across multiple postures. His early work also includes contributions to multi-target tracking using Kalman Filtering in complex environments, demonstrating both versatility and technical depth even before entering advanced doctoral research. The core research interests of Dr. Tuniyazi Abudoureheman include high-speed imaging, robotic vibration analysis, automated industrial inspection, bio-inspired detection systems, and machine vision algorithms, all of which align with the evolving demands of next-generation intelligent robotics. His research skills span HFR camera-based data acquisition, signal processing, vibration modeling, robotic motion evaluation, and applied machine learning, supported by strong analytical ability and experience with experimental system design. Dr. Tuniyazi Abudoureheman has also developed valuable competencies in publishing scientific results, presenting at conferences, and contributing to collaborative engineering investigations, which collectively strengthen his academic and professional profile. Although early in his academic career, Dr. Tuniyazi Abudoureheman has already earned recognition through peer-reviewed publications, citations, and participation in reputable conferences such as IEEE SENSORS, positioning him as an emerging scholar in robotics and sensing technology. His work has received growing scholarly attention, reflected in increasing citation counts and inclusion in respected journals covering robotics and mechatronics. In conclusion, Dr. Tuniyazi Abdurrahman continues to advance as a promising researcher whose technical contributions, methodological rigor, and commitment to innovation place him on a strong path toward future academic excellence and impactful scientific discovery.

Academic Profile: ORCID | Google Scholar

Featured Publications:

  1. Li, J., Shimasaki, K., Tuniyazi, A., Ishii, I., & Ogihara, M. (2023). HFR video-based hornet detection approach using wing-beat frequency analysis. 3 citations.

  2. Abudoureheman, T., Wang, F., Shimasaki, K., & Ishii, I. (2025). HFR-video-based vibration analysis of a multi-jointed robot manipulator. 1 citation.

  3. Abudoureheman, T., Otsubo, H., Wang, F., Shimasaki, K., & Ishii, I. (2025). High-frame-rate camera-based vibration analysis for health monitoring of industrial robots across multiple postures.

 

Goncalo Galvao | Electronics and Computer Engineering | Best Researcher Award

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Dr. Goncalo Galvao | Electronics and Computer Engineering | Best Researcher Award

International Society For Executive Learning | Portugal

Dr. Goncalo Galvao is a dedicated PhD student at ISEL whose research spans Electronics and Computer Engineering, Optoelectronics, and Machine Learning, with a strong focus on intelligent mobility systems. His academic trajectory reflects a commitment to innovation, particularly in the integration of Visible Light Communication and deep reinforcement learning to develop adaptive traffic control solutions that enhance vehicular communication, improve traffic flow efficiency, and reduce congestion in emerging smart-city environments. His earlier work explored advanced optical wireless systems for connected vehicles, leading to high-quality scientific contributions and recognition through a Best Paper Award at an international conference. Dr. Goncalo Galvao has authored a substantial body of research indexed in Scopus, comprising 21 documents with 64 citations across 42 citing sources and a 4 h-index, demonstrating the growing impact and visibility of his contributions to optical communication and AI-driven traffic management. His research involvement includes participation in a funded project centered on intelligent transportation and urban mobility challenges, where he applies data-driven engineering approaches to develop sustainable and efficient solutions. His ongoing doctoral research further advances this trajectory, positioning him at the forefront of innovative developments in smart mobility and next-generation transportation systems. Through his scholarly output, project engagements, and academic involvement, Dr. Goncalo Galvao continues to contribute meaningfully to advancements in optical wireless communication, machine learning applications in engineering, and the development of intelligent systems that support safer, smarter, and more efficient urban mobility infrastructures.

Profiles: Scopus | Orcid | Google Scholar | Researchgate

Featured Publications

Vieira, M. A., Galvão, G., Vieira, M., Louro, P., Vestias, M., & Vieira, P. (2024). Enhancing urban intersection efficiency: Visible light communication and learning-based control for traffic signal optimization and vehicle management. Symmetry, 16(2), 240.

Vieira, M., Vieira, M. A., Galvão, G., Louro, P., Véstias, M., & Vieira, P. (2024). Enhancing urban intersection efficiency: Utilizing visible light communication and learning-driven control for improved traffic signal performance. Vehicles, 6(2), 666–692.

Galvão, G., Vieira, M., Louro, P., Vieira, M. A., Véstias, M., & Vieira, P. (2023). Visible light communication at urban intersections to improve traffic signaling and cooperative trajectories. In 2023 7th International Young Engineers Forum (YEF-ECE) (pp. 60–65).

Vieira, M., Galvão, G., Vieira, M. A., Vestias, M., Louro, P., & Vieira, P. (2024). Integrating visible light communication and AI for adaptive traffic management: A focus on reward functions and rerouting coordination. Applied Sciences, 15(1), 116.

Galvão, G., Vieira, M., Louro, P., Vieira, M. A., Véstias, M., & Vieira, P. (2024). Multi agent reinforcement learning system for vehicular and pedestrian traffic control with visible light communication. In 2024 8th International Young Engineers Forum on Electrical and Computer Engineering.

Hyeryung Jang | Machine Learning | Best Researcher Award

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Assist. Prof. Dr Hyeryung Jang | Machine Learning | Best Researcher Award

Assistant Professor, Dongguk University, South Korea 🧑‍🏫

Hyeryung Jang is an Assistant Professor at the Division of AI Software Convergence at Dongguk University, Seoul, South Korea. His research interests lie at the intersection of communication systems, probabilistic graphical models, and networked machine learning. He has contributed significantly to the development of algorithms for large-scale communication networks, with applications in healthcare, manufacturing, and beyond. He has held academic and research positions at prestigious institutions, including King’s College London and KAIST.

Profile

Google Scholar

🎓 Education

Hyeryung Jang earned his Ph.D. in Electrical Engineering from the Korea Advanced Institute of Science and Technology (KAIST), South Korea, from March 2012 to February 2017. His doctoral thesis, titled Optimization and Learning of Graphical Models: A Stochastic Approximation Approach, was supervised by Prof. Yung Yi and co-advised by Prof. Jinwoo Shin. He also holds a Master’s degree in Electrical Engineering from KAIST, completed between March 2010 and February 2012, with a thesis on the Economic Benefits of ISP-CDN and ISP-ISP Cooperation, under the guidance of Prof. Yung Yi. Hyeryung Jang completed his Bachelor’s degree in Electrical Engineering at KAIST in February 2010.

💼 Experience

Hyeryung Jang currently serves as an Assistant Professor in the Division of AI Software Convergence at Dongguk University, where he has been leading the Intelligence and Optimization in Networks (ION) lab since March 2021. Before this, he was a Research Associate at King’s College London, in the Centre for Telecommunications Research, Department of Engineering, from March 2018 to February 2021. His post-doctoral research was conducted at KAIST from March 2017 to February 2018. Hyeryung also gained valuable experience as a Research Intern at Los Alamos National Laboratory in the USA during the summer of 2015.

🔬 Research Interests

Hyeryung Jang’s research interests are centered on mathematical modeling and communication systems, with a particular emphasis on networked machine learning. He explores innovative learning algorithms for probabilistic graphical models, deep learning, and reinforcement learning. His work aims to improve the stability and representation quality of generative models such as GANs, VAEs, and diffusion models. Jang is also focused on the learning and inference of graphical models, specifically for applications like robust recommendation systems and communication-efficient algorithms. Moreover, his research delves into efficient learning methods to address noisy data and real-world challenges in fields like healthcare, highlighting his broad interdisciplinary approach to solving complex problems in communication networks.

🏆 Awards

Hyeryung Jang has received recognition for his groundbreaking work in networked machine learning, contributing to innovative applications in healthcare and telecommunications. His research has been published in top-tier journals such as IEEE Transactions on Communications, IEEE Transactions on Neural Networks and Learning Systems, and Journal of Medical Internet Research (JMIR).

📚 Publications Top Notes

LinkFND: Simple Framework for False Negative Detection in Recommendation Tasks with Graph Contrastive Learning, IEEE Access, Dec. 2023.

In-Home Smartphone-based Prediction of Obstructive Sleep Apnea in Conjunction with Level 2 Home Polysomnography, JAMA Otolaryngology-Head & Neck Surgery, Nov. 2023.

Prediction of Sleep Stages via Deep Learning using Smartphone Audio Recordings in Home Environments, Journal of Medical Internet Research, June 2023.

Real-time Detection of Sleep Apnea based on Breathing Sounds and Prediction Reinforcement using Home Noises, Journal of Medical Internet Research, Feb. 2023.

Conclusion

Given his strong academic credentials, innovative contributions, and high-impact research, Hyeryung Jang is undoubtedly a strong contender for the Best Researcher Award. His work not only advances theoretical knowledge but also drives practical applications that address critical real-world challenges, particularly in communication systems and healthcare. Jang’s passion for interdisciplinary research and teaching further solidifies his suitability for this prestigious recognition.