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.

 

Fernando Bruno Dovichi Filho | Engineering | Best Researcher Award

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Prof. Fernando Bruno Dovichi Filho | Engineering | Best Researcher Award

Professor, UNIFEI/UFSCAR, Brazil

Fernando Bruno Dovichi Filho 🇧🇷 is a Brazilian Mechanical Engineer with a Ph.D. in Mechanical Engineering, specializing in energy systems, renewable energy, and thermal modeling. With a rich blend of academic and research experience, he is currently a Substitute Professor at the Federal Institute of São Paulo (IFSP – Piracicaba campus). His work focuses on computational modeling, biomass energy, and sustainability-driven technologies, actively contributing to Brazil’s bioenergy development. Fernando’s background includes hands-on research in high-precision machining, hybrid propulsion, and energy conversion systems.

Profile

Orcid

Education 🎓

Fernando completed his Ph.D. in Mechanical Engineering (2017–2022) at the Federal University of Itajubá (UNIFEI), where he analyzed the technical and economic potential of electricity generation from biomass in Minas Gerais 🌱⚡. He earned his Master’s degree (2013–2015) at the same institution, refining thermal property estimation methods. His Bachelor’s in Industrial Mechanical Engineering (2008–2012) from ETEP Faculdades included a project on optical glass machining 🔧📐, showcasing his early inclination toward precision engineering and energy systems.

Experience 💼

With teaching and research roles across premier institutions, Fernando’s career spans from academia to aerospace research. Currently a full-time Substitute Professor at IFSP – Piracicaba (2023–present), he develops curricula, teaches engineering courses, and guides research and extension projects 🧑‍🏫📊. He previously served as a Substitute Professor at IFMS in 2016. As a PBIC/CNPq Research Fellow, he contributed to advanced propulsion research at both IAE and IEAv from 2009 to 2012, specializing in hybrid rocket engines, high-voltage discharges, and detonation studies using NASA CEA software 🚀💻.

Research Interest 🔍

Fernando’s research integrates renewable energy, thermal systems, and decision-making methodologies. His main focus is on biomass-based electricity generation, thermophysical property modeling, and multi-criteria decision analysis (MCDA) with GIS integration 🌍🧪. He is also keen on advancing thermal estimation techniques, applying hybrid modeling tools like MATLAB and EES, and evaluating the technology readiness of green energy solutions in Brazil and globally.

Awards 🏆

Fernando’s research integrates renewable energy, thermal systems, and decision-making methodologies. His main focus is on biomass-based electricity generation, thermophysical property modeling, and multi-criteria decision analysis (MCDA) with GIS integration 🌍🧪. He is also keen on advancing thermal estimation techniques, applying hybrid modeling tools like MATLAB and EES, and evaluating the technology readiness of green energy solutions in Brazil and globally.

Publications 📄

📖 Evaluation of TRL for biomass electricity technologies, Journal of Cleaner Production, 2021
DOI Link – Cited in renewable energy feasibility studies worldwide.

📖 GIS-MCDM methodology for biomass selection, Agriculture, 2025
DOI Link – A key reference for geo-spatial biomass planning.

📖 An approach to technology selection, Energy, 2023
DOI Link – Cited in works addressing clean technology prioritization.

📘 Book Chapter: From Crops and Wastes to Bioenergy, Woodhead Publishing, 2025

Publisher Link – Cited by authors in sustainable agriculture and energy.

Conclusion

Based on his research achievements, publications, and experience, Fernando Bruno Dovichi Filho is a suitable candidate for the Best Researcher Award. His contributions to sustainable energy solutions and his expertise in thermal systems optimization and renewable energy systems demonstrate his potential to make a significant impact in the field. With some further emphasis on international collaborations and publishing in top-tier journals, he is well-positioned to continue making meaningful contributions to research.

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.