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.

 

Ming-Hsiang Su | Signal Processing | Best Researcher Award

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Prof. Ming-Hsiang Su | Signal Processing | Best Researcher Award

Prof. Ming-Hsiang Su | Soochow University | Taiwan

Prof. Ming-Hsiang Su is a prominent researcher and assistant professor specializing in the fields of deep learning, natural language processing, and speech signal processing, with a particular focus on spoken dialogue systems, emotion recognition, and personality trait perception. His work integrates advanced computational techniques with real-world applications, developing intelligent systems capable of understanding, interpreting, and generating human-like speech and dialogue. Prof. Ming-Hsiang Su has contributed to the advancement of speech emotion recognition by considering both verbal and nonverbal vocal cues, and has designed sophisticated models for empathetic dialogue generation, text-to-motion transformation, and mood disorder detection through audiovisual signals. He has published extensively in high-impact journals and conferences, addressing topics such as few-shot image segmentation, sound source separation, automatic ontology population, and speaker identification. His research also extends to applied systems, including automated crop disease detection, question-answering systems, and industrial defect detection using deep learning architectures. By combining theoretical insights with practical implementations, Prof. Ming-Hsiang Su work bridges the gap between computational intelligence and human-centered applications, enhancing machine understanding of complex speech, language, and affective behaviors. Through his interdisciplinary approach, he continues to advance innovative methods for human-computer interaction, intelligent dialogue systems, and multimodal data analysis, establishing a significant impact on both academic research and practical technological applications across various domains, with 791 citations by 684 documents, 83 documents, and an h-index of 15.

Profiles: Scopus | Orcid | Google Scholar

Featured Publications

Huang, K. Y., Wu, C. H., Hong, Q. B., Su, M. H., & Chen, Y. H. (2019). Speech emotion recognition using deep neural network considering verbal and nonverbal speech sounds. ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech, and …, 138.

Su, M. H., Wu, C. H., Huang, K. Y., Hong, Q. B., & Wang, H. M. (2017). A chatbot using LSTM-based multi-layer embedding for elderly care. 2017 International Conference on Orange Technologies (ICOT), 70-74.

Hsu, J. H., Su, M. H., Wu, C. H., & Chen, Y. H. (2021). Speech emotion recognition considering nonverbal vocalization in affective conversations. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 29, 1675-1686.

Su, M. H., Wu, C. H., & Cheng, H. T. (2020). A two-stage transformer-based approach for variable-length abstractive summarization. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 28, 2061-2072.

Su, M. H., Wu, C. H., Huang, K. Y., & Hong, Q. B. (2018). LSTM-based text emotion recognition using semantic and emotional word vectors. 2018 First Asian Conference on Affective Computing and Intelligent …, 78.

 

Xiaojun Li | Control Science and Engineering | Best Researcher Award

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Dr Xiaojun Li | Control Science and Engineering | Best Researcher Award

PHD Candidate, School of Aerospace Science and Technology, Xidian University, China  🌟

Xiaojun Li is a dedicated Ph.D. candidate at the School of Aerospace Science and Technology, Xidian University. With a solid academic foundation and research acumen, he has been exploring innovative approaches to detection and tracking technologies. His commitment to advancing radar signal processing and LiDAR data analysis highlights his contributions to modern aerospace technologies.

Profile

Orcid

Education 📚

Xiaojun Li completed his B.S. in Detection, Guidance, and Control Technology at Xidian University, Shannxi, China, in 2023. He is currently pursuing his Ph.D. in Control Science and Technology at the same institution, focusing on cutting-edge advancements in aerospace engineering.

Experience 🛠️

As a student researcher, Xiaojun has been actively involved in developing innovative solutions for low, small, and slow target detection. He has contributed to significant radar signal processing projects and worked on consultancy assignments related to LiDAR data applications in aerospace.

Research Interests 🔍

Xiaojun Li’s research focuses on advancing detection and tracking technologies, particularly for low, small, and slow targets. His work delves into radar signal processing and LiDAR data analysis, exploring innovative approaches to enhance accuracy and efficiency in challenging environments. By bridging theoretical concepts with practical applications, Xiaojun addresses real-world challenges in aerospace engineering, contributing to the development of cutting-edge technologies that redefine detection and mapping systems.

Awards 🏆

While primarily focused on academic and research pursuits, Xiaojun Li has been recognized for his contributions to radar signal and LiDAR data processing technologies. His achievements reflect his dedication to innovation in the field.

Publications  Top Notes🖋️

Wang, W., Yan, B., Li, X., et al. (2024). “Multiple Pedestrian Tracking Using LiDAR Network in Complex Indoor Scenarios,” IEEE Sensors Journal, 24(8), pp. 13175–13192. DOI: 10.1109/JSEN.2024.3369947.

Cited by: 5 articles

Li, X., Hu, G., et al. (2024). “A Low-Cost 3D Mapping System for Indoor Scenes Based on 2D LiDAR and Monocular Cameras,” Remote Sensing, 16, 4712. DOI: 10.3390/rs16244712.

Cited by: 3 articles

Conclusion

Xiaojun Li is a promising candidate for the Best Researcher Award, with a solid foundation in innovative technologies and high-impact publications. Strengthening his profile through diversified outputs and applied research could further establish his eligibility. His demonstrated contributions and potential for impactful advancements in aerospace and tracking technology make him a strong contender for this recognition.