Rong-Jong Wai | 3D Vision | Innovative Research Award

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Innovative Research Award

Rong-Jong Wai,
Affiliation National Taiwan University of Science and Technology
Country Taiwan
Scholar ID NnS_aNsAAAAJ
Documents 297
Citations 17,324
h-index 76
Subject Area 3D Vision
Event International Robotics and Automation Awards

Rong-Jong Wai

National Taiwan University of Science and Technology, Taiwan

Rong-Jong Wai, whose scholarly work has contributed significantly to the fields of intelligent systems, automation, robotics, control engineering, and 3D vision technologies. With an extensive publication record, strong citation performance, and substantial influence within the global research community, Professor Wai demonstrates the qualities associated with innovation-driven academic leadership and scientific advancement.[1]

Abstract

This article presents an academic overview of Rong-Jong Wai’s research achievements and scholarly influence in advanced engineering and intelligent automation systems. His research portfolio encompasses robotics, machine intelligence, control systems, power electronics, autonomous technologies, and three-dimensional perception methodologies. The breadth of his scientific output, combined with strong citation metrics and sustained academic productivity, reflects a distinguished research career characterized by innovation, interdisciplinary collaboration, and practical technological impact.[1]

Keywords

Innovative Research Award, Rong-Jong Wai, 3D Vision, Robotics, Intelligent Automation, Control Engineering, Artificial Intelligence, Autonomous Systems, Smart Technologies, Research Excellence.

Introduction

Innovation in robotics and automation increasingly depends on the integration of advanced perception systems, intelligent decision-making frameworks, and adaptive control methodologies. Researchers working at the intersection of these disciplines contribute substantially to industrial transformation, smart manufacturing, and autonomous technologies. Rong-Jong Wai has established a scholarly record that reflects long-term engagement with these research challenges through the development of novel theories, engineering solutions, and practical applications.[2]

Research Profile

Professor Rong-Jong Wai is affiliated with the National Taiwan University of Science and Technology and is recognized for his extensive contributions to intelligent control systems, robotics, power electronics, machine vision, and automation technologies. His academic output includes hundreds of peer-reviewed publications and substantial scholarly influence measured through citations and international recognition.[1]

  • Institution: National Taiwan University of Science and Technology
  • Country: Taiwan
  • Subject Area: 3D Vision
  • Documents Indexed: 297
  • Total Citations: 17,324
  • h-index: 76

Research Contributions

The research contributions associated with Professor Wai span multiple domains of modern engineering and intelligent systems development. His work has addressed challenges related to adaptive control, neural-network-based systems, intelligent motion control, autonomous robotics, and advanced sensing technologies. Such research efforts support the advancement of efficient, reliable, and intelligent automation platforms suitable for industrial and academic applications.[3]

  • Development of intelligent control algorithms for complex engineering systems.
  • Research in autonomous robotic platforms and smart automation technologies.
  • Advancement of 3D vision methodologies for perception and navigation tasks.
  • Contributions to machine learning integration within control and robotics frameworks.
  • Applications of intelligent systems in industrial and technological environments.

Publications

A substantial body of peer-reviewed publications forms the foundation of Professor Wai’s academic profile. His work appears across internationally recognized journals and conference proceedings in automation, robotics, control engineering, artificial intelligence, and intelligent systems research.[1]

  1. Intelligent Control Systems and Adaptive Automation Research.
  2. Advanced Robotics and Autonomous Navigation Studies.
  3. 3D Vision and Machine Perception Methodologies.
  4. Neural Network Applications in Engineering Systems.
  5. Smart Manufacturing and Industrial Automation Technologies.

Research Impact

Research impact can be assessed through publication influence, citation performance, technological relevance, and academic visibility. With more than seventeen thousand citations and a strong h-index, Professor Wai’s work has demonstrated broad scholarly engagement across multiple research communities. These indicators suggest that his findings have contributed to ongoing scientific discussions and technological developments within intelligent systems and automation research.[1]

  • Extensive international citation record.
  • Influence across robotics and automation disciplines.
  • Contribution to interdisciplinary engineering research.
  • Support for innovation in intelligent technologies.
  • Recognition through sustained scholarly productivity.

Award Suitability

The Innovative Research Award recognizes researchers whose work demonstrates originality, measurable academic influence, and contributions to scientific progress. Based on documented publication output, citation impact, interdisciplinary relevance, and technological significance, Rong-Jong Wai’s scholarly profile aligns closely with the objectives of this recognition. His contributions to robotics, automation, intelligent control, and vision-based technologies illustrate a sustained commitment to advancing both theoretical understanding and practical implementation within engineering research.[1][3]

Conclusion

Rong-Jong Wai’s academic career reflects a consistent commitment to innovation, research excellence, and technological advancement. Through extensive scholarly output, significant citation impact, and contributions to robotics, automation, intelligent systems, and 3D vision research, he has established a notable presence within the international scientific community. These achievements support his recognition within the framework of the Innovative Research Award and highlight the broader significance of his contributions to engineering and automation sciences.[1]

References

  1. Google Scholar. (n.d.). Rong-Jong Wai – Citation Profile and Scholarly Metrics. https://scholar.google.com/citations?user=NnS_aNsAAAAJ&hl=en&oi=sra
  2. High step-up converter with coupled-inductor. https://ieeexplore.ieee.org/abstract/document/1504873
  3. High-Performance Stand-Alone Photovoltaic Generation System.
    https://ieeexplore.ieee.org/abstract/document/4401197
  4. High-Efficiency DC-DC Converter With High Voltage Gain and Reduced Switch Stress. https://ieeexplore.ieee.org/abstract/document/4084723

Assoc. Prof. Dr. Entesar Eliwa | Deep Learning for Robotic Vision | Excellence in Research Award

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Assoc. Prof. Dr. Entesar Eliwa | Deep Learning for Robotic Vision | Excellence in Research Award

King Faisal University | Saudi Arabia

Dr. Entesar Hamed I. Eliwa is an Associate Professor at King Faisal University, Faculty of Science, Department of Mathematics and Statistics. She holds a B.Sc. in Computer Science from Minia University, where she also served as a Teaching Assistant before completing her M.Sc. and Ph.D. in the Computer Science Department. After joining King Faisal University as an Assistant Lecturer, her strong research productivity and academic contributions led to her promotion to Associate Professor. Her work focuses on data mining, knowledge discovery, predictive modeling, supervised learning, classification, association rule mining, Deep Learning for Robotic Vision and artificial intelligence. She has successfully completed 28 research projects and is currently leading 6 ongoing studies. Her scholarly influence is reflected in her most recent citation metrics, with 443 total citations across 373 citing documents, demonstrating a solid and expanding research footprint. She has produced 22 research documents contributing to advancements in computational intelligence, and she maintains an h-index of 8, underscoring the depth and consistency of her academic impact. Through her research, publications, and academic service, Dr. Eliwa continues to strengthen the fields of computer science, data analytics, and artificial intelligence within both regional and global research communities.

Profile: Scopus | Orcid | Google Scholar

Featured Publications

El Koshiry, A., Eliwa, E., Abd El-Hafeez, T., & Tony, M. A. A. (2026). The effectiveness of an e-learning platform in developing digital citizenship skills among blind students.  https://doi.org/10.1007/978-3-031-94770-4_21

Hamed, E., & Abd El-Hafeez, T. (2025). Deep learning for sustainable agriculture: Automating rice and paddy ripeness classification for enhanced food security. Egyptian Informatics Journal. https://doi.org/10.1016/j.eij.2025.100785

Amr, A., Eliwa, E., Tony, A. A., Shalgham, A., & Contributors from King Faisal University; Minia University; Arish University. (2025). The effectiveness of using Box-to-Box technology to develop some of the composite physical and technical capabilities of footballers. Fusion: Practice and Applications. https://doi.org/10.54216/fpa.170224

Eliwa, E. H. I., & Abd El-Hafeez, T. (2025). A robust deep learning pipeline for multi-class cervical cancer cell identification. Egyptian Informatics Journal. https://doi.org/10.1016/j.eij.2025.100787

Eliwa, E. H. I., & Abd El-Hafeez, T. (2025). A novel YOLOv11 framework for enhanced tomato disease detection. PeerJ Computer Science. https://doi.org/10.7717/peerj-cs.3200

Dr. Faisal Saeed | Object Detection | Excellence in Research Award

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Dr. Faisal Saeed | Object Detection | Excellence in Research Award

Shenzhen University | China

Dr. Faisal Saeed is an ai research scientist specializing in computer vision, deep learning, and intelligent manufacturing, with a strong research portfolio built through advanced academic training and international research appointments. He earned his master’s combined Ph.d. in computer science from Kyungpook National University, South Korea, where his work focused on transformer-based architectures for industrial small-object detection, culminating in the thesis feature enhanced assignment-based detection transformer for industrial small object detection. His academic contributions include 21 documents, a growing research footprint of 738 citations, and an h-index of 10, reflecting the global impact of his work across ai-driven automation, defect detection, and predictive maintenance. Professionally, he has served as a university research assistant and later as a postdoctoral fellow in both South Korea and China, contributing to deep learning theory, medical image analysis, multimodal ai, Object Detection and industrial visual inspection systems. His research integrates digital twins, time-series forecasting, and transformer models to advance intelligent manufacturing and robotics. Committed to bridging theoretical innovation with real-world applications, Dr. Saeed continues to publish influential work, secure funding for emerging ai research, and contribute to the scientific community through teaching, collaboration, and cutting-edge industrial ai development.

Profile: Scopus | Google Scholar

Featured Publications

Shah, H. A., Saeed, F., Yun, S., Park, J. H., Paul, A., & Kang, J. M. (2022). A robust approach for brain tumor detection in magnetic resonance images using finetuned EfficientNet. IEEE Access, 10, 65426–65438.

Saeed, F., Paul, A., Rehman, A., Hong, W. H., & Seo, H. (2018). IoT-based intelligent modeling of smart home environment for fire prevention and safety. Journal of Sensor and Actuator Networks, 7(1), 11.

Saeed, F., Paul, A., Karthigaikumar, P., & Nayyar, A. (2020). Convolutional neural network based early fire detection. Multimedia Tools and Applications, 79(13), 9083–9099.

Saeed, F., Ahmed, M. J., Gul, M. J., Hong, K. J., Paul, A., & Kavitha, M. S. (2021). A robust approach for industrial small-object detection using an improved faster regional convolutional neural network. Scientific Reports, 11(1), 23390.

Rehman, A., Rathore, M. M., Paul, A., Saeed, F., & Ahmad, R. W. (2018). Vehicular traffic optimisation and even distribution using ant colony in smart city environment. IET Intelligent Transport Systems, 12(7), 594–601.

Mr. Angelos Athanasiadis | Deep Learning for Robotic Vision | Research Excellence Award

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Mr. Angelos Athanasiadis | Deep Learning for Robotic Vision | Research Excellence Award

Aristotle University of Thessaloniki | Greece

Mr. Angelos Athanasiadis is a Ph.d. candidate in electrical and computer engineering at the Aristotle University of Thessaloniki, specializing in fpga-based acceleration of convolutional neural networks and heterogeneous computing systems. he holds an M.Eng. in electronics and computer systems and an mba with high distinction, combining strong technical expertise with strategic insight. his research focuses on full-precision CNN acceleration, FPGA architectures, cyber-physical systems, Deep Learning for Robotic Vision and distributed embedded system emulation. Angelos has contributed to major eu-funded research projects, including the adviser and redesign projects, and has completed industrial internships at cadence design systems in Munich. He has also worked in r&d and embedded development roles at exapsys and seems pc, strengthening his applied engineering experience. Academically, he has collaborated with Professor Ioannis papaefstathiou and assistant professor nikolaos tampouratzis, contributing to innovations in energy-efficient cnn inference and high-fidelity system emulation. his open-source framework, fusion, integrates qemu and omnet++ using hla/certi for deterministic, timing-accurate, multi-node execution. Although early in his publication journey, angelos has 1 citation, 1 scopus-listed document, and an h-index of 1, reflecting the initial impact of his contributions. Driven by interdisciplinary research, he aims to advance reconfigurable computing for next-generation autonomous and embedded intelligent systems.

Profiles: Orcid | Google Scholar

Featured Publications

Athanasiadis, A., Tampouratzis, N., & Papaefstathiou, I. (2025). An efficient open-source design and implementation framework for non-quantized CNNs on FPGAs. Integration, 102625.

Athanasiadis, A., Tampouratzis, N., & Papaefstathiou, I. (2024). An open-source HLS fully parameterizable matrix multiplication library for AMD FPGAs. WiPiEC Journal – Works in Progress in Embedded Computing, Article 62.

Katselas, L., Athanasiadis, A., Jiao, H., Papameletis, C., Hatzopoulos, A., & Marinissen, E. J. (2017). Embedded toggle generator to control the switching activity during test of digital 2D-SoCs and 3D-SICs. In 2017 27th International Symposium on Power and Timing Modeling, Optimization and Simulation (PATMOS) (pp. 1–8). IEEE.

Assoc. Prof. Dr. Yujin Liu | Computer Vision and AI | Best Researcher Award

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Assoc. Prof. Dr. Yujin Liu | Computer Vision and AI | Best Researcher Award

Xidian University | China

Author Profiles

SCOPUS

GOOGLE SCHOLAR

Summary

Liu Yujin, is an associate professor and researcher specializing in novel photodetectors and intelligent imaging methods. with academic training from Wuyi university and Jinan university, and professional roles at Xidian university, he has established a strong foundation in optical engineering.

Early academic pursuits

Liu Yujin’s academic journey began at Wuyi university, where he earned a bachelor’s degree in electronic information engineering. building on this foundation, he pursued a master’s degree in condensed matter physics at jinan university, under the guidance of professor zhao chuanxi. his dedication to research Computer Vision and AI and innovation led him to continue at jinan university for his ph.d. in optical engineering, supervised by professor mai wenjie, which he successfully completed.

Professional endeavors

After completing his doctorate, Liu joined the guangzhou research institute of Xidian university as a lecturer. He advanced his career by taking up a postdoctoral fellowship at the school of electronic science and Computer Vision and AI technology, xidian university. his dual roles highlight his growing influence as both a researcher and educator in the field of optical and electronic sciences.

Contributions and research focus

Liu’s research interests lie in novel photodetectors and intelligent imaging methods, areas that bridge optical engineering, materials science, and advanced electronics. his work focuses on developing next-generation photodetection Computer Vision and AI technologies and exploring intelligent imaging approaches with applications in sensing, communication, and machine vision. through his academic efforts, he aims to contribute to innovations that improve detection sensitivity, imaging precision, and device performance.

Impact and influence

As an associate professor and early-career researcher, Liu has already established a promising trajectory in optical engineering and imaging sciences. his contributions to the study of photodetectors and intelligent imaging are poised to Computer Vision and AI influence both theoretical development and practical applications in modern technology, particularly in fields such as smart sensors, biomedical imaging, and artificial intelligence-assisted optical systems.

Academic cites

Liu’s academic output reflects his growing engagement with the Computer Vision and AI scientific community. his publications and postdoctoral research contribute to a body of knowledge that strengthens collaboration across photonics, electronics, and materials science.

Legacy and future contributions

Looking ahead, Liu Yujin is expected to expand his influence in the development of novel photodetectors and intelligent imaging methods. his ongoing postdoctoral research at Xidian university positions him to play a Computer Vision and AI vital role in advancing optical technologies. as he continues his career, his legacy will be defined by fostering innovative solutions that connect fundamental research with real-world applications.

Publications

Title: Atomic‐Layer Deposition‐Assisted Double‐Side Interfacial Engineering for High‐Performance Flexible and Stable CsPbBr3 Perovskite Photodetectors
Authors: G. Cen, Y. Liu, C. Zhao, G. Wang, Y. Fu, G. Yan, Y. Yuan, C. Su, Z. Zhao, W. Mai
Journal: Small
Publication Year: 2019

Title: Visualized UV Photodetectors Based on Prussian Blue/TiO2 for Smart Irradiation Monitoring Application
Authors: M. Qiu, P. Sun, Y. Liu, Q. Huang, C. Zhao, Z. Li, W. Mai
Journal: Advanced Materials Technologies
Publication Year: 2018

Title: Perovskite-based color camera inspired by human visual cells
Authors: Y. Liu, Z. Ji, G. Cen, H. Sun, H. Wang, C. Zhao, Z.L. Wang, W. Mai
Journal: Light: Science & Applications
Publication Year: 2023

Title: Reducing current fluctuation of Cs3Bi2Br9 perovskite photodetectors for diffuse reflection imaging with wide dynamic range
Authors: Z. Ji, Y. Liu, W. Li, C. Zhao, W. Mai
Journal: Science Bulletin
Publication Year: 2020

Title: All-inorganic lead-free NiOx/Cs3Bi2Br9 perovskite heterojunction photodetectors for ultraviolet multispectral imaging
Authors: Y. Liu, Y. Gao, J. Zhi, R. Huang, W. Li, X. Huang, G. Yan, Z. Ji, W. Mai
Journal: Nano Research
Publication Year: 2022

Conclusion

Through his academic excellence, innovative research, and growing professional responsibilities, Liu is emerging as a significant contributor to optical sciences and advanced imaging technologies. His future work promises to strengthen the integration of photonics and intelligent systems for next-generation applications.