Tuniyazi Abudoureheman | Information Technology | Research Excellence Award

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Dr. Tuniyazi Abudoureheman | Information Technology | Research Excellence Award

Hiroshima University | Japan

Dr. Tuniyazi Abudoureheman is an emerging researcher in intelligent imaging technologies whose work integrates high-frame-rate (HFR) video processing, digital signal processing, and intelligent systems to address complex challenges in robotics, motion analysis, and biological detection. His research focuses on developing advanced computational frameworks capable of extracting subtle temporal and spatial features from high-speed visual data, with applications spanning vibration monitoring, multi-joint robotic manipulators, and biological motion recognition. Tuniyazi’s contributions involve creating novel image- and signal-processing algorithms designed to improve the accuracy, stability, and efficiency of automated systems operating in dynamic environments. His work on HFR-video-based vibration analysis offers enhanced diagnostic capabilities for flexible robotic structures, while his research on hornet detection using wing-beat frequency analysis demonstrates the potential of high-speed imaging for environmental and biological applications. Furthermore, his earlier work on multi-person tracking in complex backgrounds reflects his strong foundation in computer vision and predictive filtering. Tuniyazi’s scholarly visibility continues to grow, with citations indexed in Google Scholar and Scopus, reflecting early-stage but steadily increasing academic impact. According to Google Scholar metrics, his work has accumulated citations, maintaining an h-index of 1 and an i10-index of 0, which is consistent with researchers developing specialized expertise in a rapidly advancing technical domain. His research outputs contribute to international conferences and peer-reviewed journals, demonstrating a commitment to scientific rigor and innovation. Tuniyazi’s ongoing research trajectory aligns strongly with the objectives of the Research Excellence Award, showcasing high-impact potential in intelligent video processing, adaptive computational models, and robotics-oriented signal analysis, reinforcing his role as a promising contributor to next-generation smart robotic and imaging systems.

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Featured Publications

  • Li, J., Shimasaki, K., Tuniyazi, A., Ishii, I., Ogihara, M., & Yoshiyama, M. (2023). HFR video-based hornet detection approach using wing-beat frequency analysis. IEEE Sensors, 1–4.

  • Abudoureheman, T., Wang, F., Shimasaki, K., & Ishii, I. (2025). HFR-video-based vibration analysis of a multi-jointed robot manipulator. Journal of Robotics and Mechatronics, 37(5), 1205–1218.

  • Tuniyazi Abudoureheman, T., & Abousharara, E. (2018). Multiple people tracking based on Kalman filter in complex background. Proceedings of the Shikoku-Section Joint Convention of Institutes of Electrical and Related Engineers.

Jinping Xue | Artificial Intelligence | Best Researcher Award

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Ms. Jinping Xue | Artificial Intelligence | Best Researcher Award

Renergy Overseas Limited | China

Ms. Jinping Xue is an emerging researcher and engineer specializing in the integration of Artificial Intelligence with sustainable urban environments. Her research focuses on Smart Cities, Industrial AI, Edge Intelligence, and Environmental AI—fields that explore how data-driven intelligence can transform urban infrastructure into more adaptive, efficient, and sustainable systems. She has made significant contributions to the development of a privacy-preserving AI framework for smart city environmental monitoring, integrating federated learning with LSTM and genetic algorithms to achieve high pollution source traceability accuracy while maintaining data privacy. Her interdisciplinary expertise bridges environmental modeling, urban systems optimization, and AI-driven perception technologies, contributing to innovative solutions in sustainable city management and smart mobility. Xue’s scholarly work demonstrates a strong interest in the application of distributed sensing and federated learning for pollution source analysis, contributing to the broader goal of achieving clean, data-secure, and intelligent urban ecosystems. She has collaborated with experts in environmental sensing and intelligent perception to enhance the real-time adaptability of urban monitoring systems. Her publications, indexed in Scopus and Google Scholar, reflect her growing impact in the domains of computational intelligence and environmental systems. With research outputs recognized in peer-reviewed international journals such as Sensors, her work has begun to gain citations across the environmental AI research community. Her citation records are progressively expanding, with verified documentation and indexing available through Scopus and Google Scholar, and her h-index count demonstrates her early but impactful research trajectory. Through her interdisciplinary approach, Xue continues to push the boundaries of AI applications for sustainable development and smart city transformation.

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Google Scholar

Featured Publications

Xue, J., Hu, X., Liu, Q., Yin, C., Ni, P., & Bo, X. (2025). Air pollutant traceability based on federated learning of edge intelligent perception agents. Sensors, 25(19), 6119.