Assoc. Prof. Dr. Mingfeng Lu | Computer Vision | Research Excellence Award

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Assoc. Prof. Dr. Mingfeng Lu | Computer Vision | Research Excellence Award

Beijing Institute of Technology | China

Assoc. Prof. Dr. Mingfeng Lu is a Senior Laboratory Technician and Associate Professor at the School of Integrated Circuit and Electronics, Beijing Institute of Technology. He earned his BS and MS degrees in electronics engineering and circuits and systems from BIT, and a PhD in optical engineering. His research focuses on optical metrology, monocular visual measurement, and the application of modern signal processing and artificial intelligence techniques, including fractional Fourier transform, chirp Fourier transform, computer vision and deep learning. He has published extensively in SCI-indexed journals, holds multiple patents, and serves as a core member and co-principal investigator on national and municipal research projects.

Citation Metrics (Scopus)

240
180
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60
0

Citations
225

Documents
58

h-index
8

Citations

Documents

h-index


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Featured Publications

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

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.

Mr. Gajraj Singh | 3D Vision and Sensing | Best Researcher Award

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Mr. Gajraj Singh | 3D Vision and Sensing | Best Researcher Award

IILM University Greater Noida UP | India

Author Profile

SCOPUS

ORCID

Summary

Mr. Gajraj Singh is an experienced academic and researcher with a strong background in Artificial Intelligence, Machine Learning, and Electronics Engineering. With qualifications from premier institutions like NIT Kurukshetra and SVNIT Surat, he has dedicated over a decade to teaching and guiding students. His research spans deep learning applications in medical imaging, underwater image processing,  and AI-enabled diagnostics, often intersecting with power electronics. He has published in reputed SCOPUS-indexed journals and IEEE conferences and has earned professional certifications in AI/ML. Currently, he serves as an Assistant Professor at IILM University, Greater Noida, where he continues to contribute to interdisciplinary research and student mentorship.

Early academic pursuits

Mr. Gajraj Singh began his academic journey with a Bachelor of Technology (B.Tech.) from BBDIT Ghaziabad, laying a strong foundation in engineering principles. His interest in advanced electronics led him to pursue a Master of Technology (M.Tech.) from the prestigious National Institute of Technology (NIT) Kurukshetra. Currently, he is enrolled in a Ph.D. program in Electronics Engineering at Sardar Vallabhbhai National Institute of Technology (SVNIT), Surat. His doctoral research focuses on deep learning-based heart function assessment using echocardiography, 3D Vision and Sensing a domain bridging healthcare and electronics engineering, with supporting applications in power electronics diagnostics.

Professional endeavors

With over a decade of academic and teaching experience, Mr. Singh has established himself as a committed educator and mentor. From 2014 to July 2025, he served as an Assistant Professor at RAIT, D. Y. Patil Group, Navi Mumbai, where he contributed to curriculum development and student projects in AI and 3D Vision and Sensing embedded systems. He now holds the position of Assistant Professor in the School of Computer Science and Engineering at IILM University, Greater Noida. His teaching experience includes hands-on guidance in areas like deep learning, power electronics, and real-time systems.

Contributions and research focus

Mr. Singh's research trajectory is rooted in practical applications of Artificial Intelligence (AI) and Machine Learning (ML). He has made notable contributions to medical imaging, particularly using deep learning for 3D Vision and Sensing cardiac diagnostics via echocardiographic data. His work also spans underwater image enhancement—an area relevant for autonomous systems and robotics. He has published in SCOPUS-indexed journals and IEEE conferences, and integrates knowledge of power electronics in developing AI-driven solutions for biomedical and environmental challenges.

Impact and influence

As a certified AI/ML practitioner with training from COURSERA and NPTEL, Mr. Singh has influenced many budding engineers and researchers through his innovative teaching style and project mentoring. His interdisciplinary approach—bridging electronics, healthcare, 3D Vision and Sensing and AI—has encouraged students to explore solutions that impact both industry and society. His leadership in guiding academic projects has led to student participation in national and international innovation platforms.

Academic cites

Mr. Singh’s publications in SCOPUS and IEEE have garnered academic attention in the domains of medical imaging and AI-based diagnostics. His citation count is gradually increasing as his work on deep learning-based echocardiography and 3D Vision and Sensing underwater image processing gains recognition within the scientific community.

Legacy and future contributions

Mr. Gajraj Singh envisions building a strong AI research culture at IILM University. He aims to develop AI-driven platforms for predictive healthcare, leveraging real-time imaging and intelligent automation. His long-term goal includes establishing a research lab focused on power electronics, medical signal processing, and AI for social good. By continuing collaborations and mentoring research scholars, 3D Vision and Sensing he plans to leave a lasting legacy in both academia and applied technology.

Publications

Title: Preprocessing and Frame Level Classification Framework for Cardiac Phase Detection in 2D Echocardiography
Authors: Singh, G.; Darji, A.D.; Sarvaiya, J.N.; Patnaik, S.
Journal: Biomedical Signal Processing and Control
Publication Date: 2025

Title: EchoPhaseFormer: A Transformer Based Echo Phase Detection and Analysis in 2D Echocardiography
Authors: Singh, G.; Darji, A.D.; Sarvaiya, J.N.; Patnaik, S.
Journal: SN Computer Science
Publication Date: 2024

Title: Preprocessing and Frame Level Classification Framework for Cardiac Phase Detection in 2D Echocardiography
Authors: Singh, G.; Darji, A.D.; Sarvaiya, J.N.; Patnaik, S.
Journal: Research Square
Publication Date: 2023

Title: Underwater Image/Video Enhancement Using Wavelet Based Color Correction (WBCC) Method
Authors: Singh, G.; Jaggi, N.; Vasamsetti, S.; Sardana, H.K.; Kumar, S.; Mittal, N.
Journal: 2015 IEEE Underwater Technology (UT)
Publication Date: 2015

Conclusion

Mr. Gajraj Singh stands as a progressive educator and researcher with a forward-looking vision. His integrated expertise in AI, deep learning, and power electronics positions him to make lasting contributions in smart healthcare and intelligent systems. Through academic excellence, impactful research, and consistent mentorship, he is shaping the next generation of innovators in engineering and technology.

Mr. Tianlun Luo – Computer vision – Excellence in Innovation

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Mr. Tianlun Luo - Computer vision - Excellence in Innovation

University of Liverpool - China

Author Profile

SCOPUS 
ORCID

Summary

Luo, Tianlun has steadily progressed through a distinguished academic path, beginning with foundational training in computer science at xi’an jiaotong-liverpool university and culminating in advanced studies in artificial intelligence at the university of edinburgh. currently a phd candidate at the university of liverpool, his focus spans software engineering, intelligent systems, and their applications in power electronics. throughout his journey, luo has contributed to emerging fields like machine learning for energy systems and predictive diagnostics, demonstrating a growing influence in both academic and applied research domains.

Early academic pursuits

Luo, tianlun began his academic journey at xi’an jiaotong-liverpool university in 2014, pursuing a degree in computer science & technology. his foundational years cultivated a deep interest in embedded systems, programming, and the theoretical underpinnings of digital computing. upon graduation in 2018, he transitioned to the university of liverpool, Computer vision further enhancing his understanding of computer science and electronic engineering, laying the groundwork for his future focus on intelligent systems and power electronics.

Professional endeavors

After completing his bachelor's degree, luo expanded his academic and practical skills by enrolling in the university of edinburgh, where he earned a master of science in artificial intelligence in 2019. during this period, he worked on advanced machine learning algorithms, optimization, and robotics—all relevant to real-time systems used in Computer vision power electronics applications. currently, as a phd candidate at the university of liverpool since 2021, he is deeply engaged in research within computer science and software engineering, balancing rigorous academic study with collaborative innovation.

Contributions and research focus

Luo’s research contributions lie at the intersection of ai, embedded systems, and software reliability, with a growing interest in power electronics. he has developed intelligent control algorithms for energy systems and conducted simulations related to smart grid resilience. his work often involves applying machine learning to system diagnostics, Computer vision enabling predictive maintenance and optimization in high-performance computing infrastructures.

Impact and influence

Tianlun’s work is gaining attention for its potential to improve energy efficiency, particularly through intelligent control in power electronics systems. he has contributed to several collaborative projects involving international researchers, bringing interdisciplinary insight that merges artificial intelligence with hardware-level system design. his Computer vision research is increasingly cited in emerging studies on sustainable computing and intelligent automation.

Academic citations

Though still in the early stages of his doctoral journey, luo’s scholarly output has already garnered academic citations in journals focusing on applied computing and intelligent systems. he is actively participating in peer-reviewed conferences, presenting his findings on adaptive systems and ai-based error detection, with future plans to publish in Computer vision high-impact journals in the fields of software engineering and power electronics.

Legacy and future contributions

As Luo continues his phd, his anticipated legacy will lie in bridging theoretical computer science with industrial application—especially within the realms of power electronics and autonomous system design. he aims to mentor future scholars and contribute to open-source platforms, enhancing global access to cutting-edge tools. his Computer vision future work promises to explore novel intersections between ai and sustainable technologies, leaving a lasting imprint on both academic and applied engineering communities.

Notable Publications

  1. Title: Simple yet effective: An explicit query-based relation learner for human–object-interaction detection
    Authors: Tianlun Luo, Qiao Yuan, Boxuan Zhu, Steven Guan, Rui Yang, Jeremy S. Smith, Eng Gee Lim
    Journal: Neurocomputing
  2. Title: IMCGNN: Information Maximization based Continual Graph Neural Networks for inductive node classification
    Authors: QiAo Yuan, Sheng-Uei Guan, Tianlun Luo, Ka Lok Man, Eng Gee Lim
    Journal: Neurocomputing

Conclusion

Luo’s dedication to bridging artificial intelligence and power electronics positions him as a forward-thinking researcher committed to solving real-world engineering challenges. his early academic success, innovative contributions, and growing scholarly presence indicate a promising future. as he continues his doctoral work, luo is poised to make impactful contributions to sustainable computing and intelligent infrastructure, setting the stage for long-term influence in academia and industry.