Yucheng li | Digital image processing | Best Researcher Award

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Mr. Yucheng li | Digital image processing | Best Researcher Award

Mr. Yucheng li, Aviation maintenance NCO academy of Air Force Engineering University, China

Mr. Yucheng Li is a lecturer and researcher at the Aviation Maintenance NCO Academy of Air Force Engineering University, specializing in digital image processing. His research focuses on image enhancement, pattern recognition, image segmentation, feature extraction, and hyperspectral imaging, with practical applications in computer vision. From 2023 to 2025, he authored three SCI-indexed papers and publicly disclosed three invention patents, showcasing a strong contribution to both academic research and innovation. Mr. Li teaches undergraduate courses in digital image processing, signal analysis, and machine learning, integrating advanced methodologies such as deep learning, wavelet transforms, and compressive sensing into his instruction and research. Technically skilled in MATLAB, Python (OpenCV, TensorFlow, PyTorch), and C++, he brings a multidisciplinary approach to engineering education and applied technology. Based in Xinyang City, Henan Province, China, Mr. Li continues to advance high-impact research while fostering the next generation of digital technology professionals.

Publication Profile

Scopus

Professional Experience

Mr. Yucheng Li serves as a lecturer and researcher at the Aviation Maintenance NCO Academy of Air Force Engineering University, where he plays a vital role in shaping the academic and technical competencies of future engineers. In this capacity, he is responsible for teaching core undergraduate courses, including digital image processing, signal analysis, and machine learning. His instructional approach combines theoretical foundations with hands-on applications, ensuring students gain both conceptual understanding and practical expertise. By incorporating real-world case studies and advanced tools into his curriculum, Mr. Li fosters a dynamic and forward-looking learning environment. His professional experience is rooted in a deep knowledge of cutting-edge technologies, which he uses to bridge the gap between academic instruction and modern technological demands. Mr. Li’s commitment to academic excellence and student mentorship makes him a valuable contributor to the university’s mission of advancing engineering education in critical technological domains.

Innovative Researcher in Digital Image Processing

Mr. Yucheng Li is a dedicated lecturer and researcher at the Aviation Maintenance NCO Academy of Air Force Engineering University, with a specialized focus on digital image processing. His professional expertise centers on critical areas such as image enhancement, pattern recognition, and computer vision applications. Over the past two years, Mr. Li has demonstrated significant contributions to the field through the publication of high-impact research papers and the disclosure of innovative patents. His work not only advances theoretical understanding but also supports practical implementations in technologically intensive environments, especially in defense and aviation contexts. Mr. Li’s academic role involves teaching advanced topics like digital image processing and signal analysis, where he integrates cutting-edge methods and tools to prepare students for real-world challenges. With a strong commitment to innovation and academic excellence, Mr. Li continues to play a pivotal role in advancing the application of digital imaging technologies.

Research Focus

Mr. Yucheng Li’s research, as reflected in his recent work titled “Experimental Study on Glass Deformation Calculation Using the Holographic Interferometry Double-Exposure Method,” highlights his specialized focus in optical measurement techniques, image analysis, and experimental mechanics. This study, published in Applied Sciences (Switzerland) in 2025, demonstrates his expertise in applying advanced imaging methodologies to structural analysis problems. The use of holographic interferometry for deformation calculation showcases his integration of physics-based measurement with digital image processing, reinforcing his proficiency in non-contact, high-precision optical diagnostics. This research falls under the broader categories of digital image processing, optical engineering, photomechanics, and applied physics. His work contributes to the development of accurate and innovative techniques for detecting material changes and structural deformations, essential in aerospace, materials science, and defense applications. Through such research, Mr. Li continues to bridge traditional mechanical analysis with modern computational imaging approaches

Publication Top Notes

Experimental Study on Glass Deformation Calculation Using the Holographic Interferometry Double-Exposure Method

Conclusion

Mr. Yucheng Li demonstrates solid academic and technical credentials, particularly with recent contributions to the field of digital image processing through high-quality publications and innovation. His background in defense education adds a layer of national relevance, while his use of modern AI tools and methodologies aligns with cutting-edge research practices. While global engagement and broader impact indicators could further support his candidacy, his trajectory and research productivity from 2023–2025 make him a highly suitable and promising nominee for the Research for Best Researcher Award.

 

 

Getinet Yilma | Image processing | Best Researcher Award

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Assist. Prof. Dr Getinet Yilma| Image processing |Best Researcher Award

Getinet Yilma at Adama science and technology university

Abawatew Getinet Yilma is an assistant professor of software engineering at Adama Science and Technology University, Ethiopia. He earned his Ph.D. in Software Engineering from the University of Electronic Science and Technology of China, specializing in plant disease recognition using deep learning. With over 15 years of teaching and research experience, Getinet has led innovative projects in machine learning, big data analytics, and e-learning systems. His contributions include designing predictive models for power distribution networks and enhancing e-learning applications via social networks. He has guided numerous undergraduate and postgraduate research projects and has a strong academic and professional footprint in software engineering and IT systems.

Professional Profile

Education 🎓

  • Ph.D. in Software Engineering (2018–2022)
    University of Electronic Science and Technology of China, Chengdu, China
    Thesis Title: “Plant Disease Recognition Based on Deep Learning”
  • Master of Computer Applications (2009–2012)
    College of Engineering, Osmania University, Hyderabad, India
    Thesis Title: “Enhancing E-learning Application Based Social Networks”
  • Bachelor’s Degree in Information Technology (2002–2006)
    Institute of Technology, Jimma University, Jimma, Ethiopia

Research Interests 

  • Deep learning and machine learning applications in agriculture and industry.
  • Big data analytics and predictive analytics for the airline and power distribution sectors.
  • E-learning platforms and community service-based software solutions.

Professional Experience

  • Assistant Professor, Software Engineering (Sept 2018–Present)
    Adama Science and Technology University, Ethiopia

    • Teaching core courses such as machine learning, deep learning, big data, cloud computing, software architecture, and advanced programming.
    • Served as Associate Dean for the School of Electrical Engineering and Computing.
    • Supervised postgraduate research and undergraduate senior projects.
    • Contributed to curriculum development and participated in national-funded research initiatives.
  • Lecturer, Computer Science and Engineering (Sept 2013–Sept 2018)
    Adama Science and Technology University, Ethiopia

    • Taught advanced courses including database systems, data structures, and software requirement engineering.
    • Led university-funded research projects.
  • Lecturer, Information Technology (Jan 2009–Sept 2013)
    Debremarkos University, Ethiopia

    • Delivered undergraduate and postgraduate courses in programming, databases, and software development.
    • Advised capstone projects for undergraduate students.
  • Assistant Lecturer, Information Technology (Jan 2008–Jan 2009)
    Debremarkos University, Ethiopia

    • Taught foundational courses in programming, operating systems, and software development methods.
  • Technical Expert (July 2006–Jan 2008)
    Jimma University, Ethiopia

    • Managed IT equipment procurement, bid evaluation, and network system administration.

Top Notes Publications

  • “Self-Supervised Scene-Debiasing for Video Representation Learning via Background Patching”
    Authors: M. Assefa, W. Jiang, K. Gedamu, G. Yilma, B. Kumeda, M. Ayalew
    IEEE Transactions on Multimedia, 2023, 25, pp. 5500–5515
    Citations: 13
    Abstract: This study proposes a self-supervised method for scene-debiasing in video representation learning by leveraging background patching. This approach reduces the bias of the background in video datasets, improving the quality of representation learning.
  • “Self-Supervised Multi-Label Transformation Prediction for Video Representation Learning”
    Authors: M. Assefa, W. Jiang, G. Yilma, M. Ayalew, M. Seid
    Journal of Circuits, Systems, and Computers, 2022, 31(9), 2250159
    Citations: 6
    Abstract: This paper introduces a self-supervised multi-label transformation prediction technique aimed at enhancing video representation learning. It improves the learning process by predicting transformations across multiple labels in a self-supervised manner.
  • “Actor-Aware Contrastive Learning for Semi-Supervised Action Recognition”
    Authors: M. Assefa, W. Jiang, K. Gedamu, M. Ayalew, M. Seid
    Proceedings of the International Conference on Tools with Artificial Intelligence (ICTAI), 2022, October, pp. 660–665
    Citations: 2
    Abstract: This conference paper proposes an actor-aware contrastive learning method for semi-supervised action recognition, focusing on improving the recognition of actions in video sequences by emphasizing actor-specific features.
  • “Self-Supervised Representation Learning for Motion Control of Autonomous Vehicles”
    Authors: M. Ayalew, S. Zhou, M. Assefa, K. Gedamu, G. Yilma
    2022 19th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP), 2022
    Citations: 0
    Abstract: This paper presents a self-supervised representation learning approach for the motion control of autonomous vehicles. The model aims to improve decision-making and motion control by learning representations without labeled data.
  • “Spatio-temporal Dual-Attention Network for View-invariant Human Action Recognition”
    Authors: K. Gedamu, G. Yilma, M. Assefa, M. Ayalew
    Proceedings of SPIE – The International Society for Optical Engineering, 2022, 12342, 123420Q
    Citations: 5
    Abstract: This paper introduces a spatio-temporal dual-attention network for view-invariant human action recognition. The method uses both spatial and temporal attention mechanisms to enhance recognition accuracy, regardless of the viewing angle.

Conclusion

Dr. Getinet Yilma is undoubtedly a strong contender for the Best Researcher Award due to his deep expertise in software engineering, machine learning, and AI applications in diverse sectors. His innovative contributions to deep learning, along with his leadership in academic teaching and mentoring, set him apart as a pioneering researcher. With a few enhancements in interdisciplinary collaboration and broader international engagement, Dr. Yilma could further elevate his research to global prominence. He is highly deserving of recognition for his impactful contributions to both academia and industry.

Eyob Mersha Woldamanuel | Digital Image Processing | Best Researcher Award

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Mr. Eyob Mersha Woldamanuel | Digital Image Processing | Best Researcher Award

Mr. Eyob Mersha Woldamanuel, Haramaya University, Ethiopia

Based on the information provided for Eyob Mersha Woldamanuel, here is an evaluation considering the criteria for the Best Researcher Award

Publication profile

Scopus

Orcid

Academic and Professional Background

Eyob’s academic background includes a B.Sc. in Electrical and Computer Engineering and an M.Sc. in Electronics and Communication Engineering. His professional journey started as an assistant lecturer and evolved to his current role as a lecturer and researcher, highlighting a steady progression in his career.

Research and Innovations

Eyob has completed several research projects, notably in the areas of medical image enhancement and adaptive code modulation for rainfall fade mitigation. His ongoing projects further emphasize his focus on real-time waste management systems and comparative studies in image enhancement, showcasing his active involvement in applied research.

Publications

Hybrid Simulated Annealing‐Evaporation Rate‐Based Water Cycle Algorithm Application for Medical Image Enhancement

Grayscale Image Enhancement Using Water Cycle Algorithm

Enhanced adaptive code modulation for rainfall fade mitigation in Ethiopia

Contributions

Eyob has pioneered the application of the Water Cycle Algorithm for image enhancement, demonstrating innovation in his field. His work on adaptive code modulation for rain fade mitigation is particularly noteworthy for its practical application in Ethiopia’s unique climate.

Consultancy/Industry Projects, Books, and Patents

There are no consultancy or industry projects, books, or patents associated with Eyob, which might be a consideration for higher-level awards but does not detract from his strong research contributions.

Professional Memberships and Collaborations

Eyob has collaborated on university-level grant projects but lacks formal professional memberships, which could be an area for future development to enhance his profile.

Conclusion

Suitability for the Best Researcher Award:
Eyob Mersha Woldamanuel presents a solid case for consideration for the Best Researcher Award, particularly due to his innovative research in image processing and contributions to local technological advancements. While his citation metrics and lack of consultancy or industry projects suggest that he is still building his research impact, his ongoing projects and future PhD studies at Eindhoven University of Technology indicate strong potential for further significant contributions.

In summary, Eyob is a promising researcher with demonstrated expertise in his field, making him a suitable candidate for the Best Researcher Award, particularly if the award considers the trajectory and potential of emerging researchers in addition to established metrics.