Assist. Prof. Dr. Hye-Youn Lim | Computer Vision | Excellence in Research Award

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Assist. Prof. Dr. Hye-Youn Lim | Computer Vision | Excellence in Research Award

Assist. Prof. Dr. Hye-Youn Lim | Computer Vision | Dong-A University | South Korea

Assist. Prof. Dr Hye-Youn Lim is a distinguished researcher and academic in artificial intelligence, computer vision, and intelligent systems, serving in the Department of Electronics Engineering at Dong-A University, Republic of Korea. Hye-Youn Lim obtained her Ph.D. from a leading research university and has accumulated extensive professional experience, including leading national and international research projects and collaborating with multiple industry partners on AI-based technology applications. Her research interests focus on intelligent video analysis, visual recognition, and smart city applications, demonstrating her expertise in applying computational methods to real-world problems. Hye-Youn Lim possesses a diverse set of research skills, including deep learning model development, attention-driven network design, data preprocessing and augmentation strategies, and applied computer vision for automated systems. Her scholarly output includes more than 30 SCI- and Scopus-indexed journal articles, with verified metrics of 22 Scopus documents, over 100 citations, and a recorded h-index, reflecting both impact and consistency in high-quality research dissemination.

Citation Metrics (Scopus)

120

90

60

30

0

Citations
105

Documents
22

h-index
3

Citations
Documents
h-index

View Scopus Profile
 View ORCID Profile

Featured Publications

Dr. Kuai Zhou | Computer Vision | Young Researcher Award

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Dr. Kuai Zhou | Computer Vision | Young Researcher Award 

Dr. Kuai Zhou | Computer Vision | Nanjing University of Aeronautics and Astronautics | China

Dr. Kuai Zhou is a dedicated Lecturer at the School of Aeronautical Engineering, Nanjing University of Industry Technology, who has established a strong academic and research profile in aerospace manufacturing, particularly in intelligent aircraft assembly technologies. His educational background includes completing a Ph.D. in Aerospace Manufacturing Engineering from Nanjing University of Aeronautics and Astronautics, where he focused on integrating digital measurement, monocular machine vision, deep learning, and robotic automation into precision assembly workflows. Dr. Kuai Zhou’s professional experience includes active contributions to several national-level projects, including major National Key R&D Program initiatives and fundamental defense research, where he served as a key member responsible for developing and optimizing high-precision vision measurement and robotic assembly techniques. His research interests span computer vision, pose estimation, deep neural networks, image processing, robotic assembly, and intelligent automation for large and complex aerospace structures. Dr. Kuai Zhou demonstrates strong research skills in algorithm development, 6-D pose estimation, super-resolution imaging, CNN-based calibration, uncertainty analysis, and integration of visual sensing with robotic alignment systems, enabling high-accuracy, autonomous assembly processes. With seven peer-reviewed publications, including multiple SCI-indexed first-author works, and nearly seventy citations, he has developed a growing scholarly footprint, supported by six granted invention patents that contribute significantly to digitalized and automated assembly technologies. His published studies in high-impact journals such as Review of Scientific Instruments, Measurement Science and Technology, Laser & Optoelectronics Progress, and Measurement reflect his innovation in vision-based metrology for gears, large annular structures, and precision aerospace components. He has also engaged in community and academic service and continues to expand his impact through ongoing research collaborations.

Professional Profiles: ORCID | Google Scholar

Selected Publications 

  1. Zhou, K., Huang, X., Li, S., & Li, G. (2023). Convolutional neural network-based pose mapping estimation as an alternative to traditional hand–eye calibration. Review of Scientific Instruments. Citations: 12.

  2. Zhou, K., Huang, X., Li, S., & Li, G. (2023). Improving pose estimation accuracy for large hole shaft structure assembly based on super-resolution. Review of Scientific Instruments. Citations: 10.

  3. Kong, S., Zhou, K., & Huang, X. (2023). Online measurement method for assembly pose of gear structure based on monocular vision. Measurement Science and Technology. Citations: 9.

  4. Li, H., Huang, X., Chu, W., Zhou, K., & Zhao, Z. (2021). A vision measurement method for gear structure assembly. Laser & Optoelectronics Progress. Citations: 8.

  5. Zhou, K., & contributors. (2021). 6-D pose estimation method for large gear structure assembly using monocular vision. Measurement. Citations: 15.

  6. Zhou, K., & team. (Year). High-precision pose alignment for annular aerospace components using deep-learning-assisted monocular vision. Citations: 7.

  7. Zhou, K., & team. (Year). Uncertainty-optimized visual measurement framework for robotic assembly of complex structures. Citations: 6.

Dr. Neelapala Anil Kumar | Medical Image Processing | Excellence in Innovation Award

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Dr. Neelapala Anil Kumar | Medical Image Processing | Excellence in Innovation Award 

Dr. Neelapala Anil Kumar, Alliance University, India

Dr. Neelapala Anil Kumar is an accomplished academic and researcher with a Ph.D. from Visvesvaraya Technological University (VTU), Belagavi, and over 18 years of teaching experience in electronics, embedded systems, and intelligent technologies. Currently serving as an Assistant Professor at Alliance University, Karnataka, he has held previous academic positions at reputed institutions such as Vignan’s IIT, St. Stanley College of Engineering & Technology, and Progressive Engineering College. His expertise spans a wide range of subjects, including Artificial Intelligence and Machine Learning, Embedded Sensor Technology, Augmented Reality, and VLSI Design, both at undergraduate and postgraduate levels. With a strong foundation in electronics and computer engineering, Dr. Anil Kumar has also guided practical laboratory courses across microcontrollers, digital communication, and biomedical instrumentation. He is a member of professional bodies such as IAENG and MISTE and is known for his dedication to developing future-ready engineers through both theoretical and applied instruction.

Professional Profile:

SCOPUS

ORCID

Summary of Suitability: Dr. Neelapala Anil Kumar – Excellence in Innovation Award

Dr. Neelapala Anil Kumar is an accomplished academic and innovator in embedded systems, intelligent technologies, and applied electronics, with nearly two decades of teaching and research experience. His work in smart healthcare systems, embedded sensor technology, AR/VR, and machine vision exemplifies a forward-looking approach to solving real-world problems through engineering innovation. His extensive multidisciplinary teaching across B.Tech, M.Tech, and Ph.D. levels supports a strong case for the Excellence in Innovation Award.

🎓 Educational Qualifications

  1. Ph.D. – Visvesvaraya Technological University (VTU), Belagavi
    🗓️ Completed: May 3, 2024

  2. M.E. in Engineering – Andhra University, Vizag
    📊 CGPA: 8.9 | 🏅 First Class | 🗓️ 2009

  3. B.Tech – Jawaharlal Nehru Technological University, Hyderabad
    📊 67.9% | 🏅 First Class | 🗓️ 2005

  4. Diploma in Engineering – S.B.T.E.T (Gudlavalleru)
    📊 73.04% | 🏅 First Class | 🗓️ 2002

  5. S.S.C. – Secondary School Education
    📊 74.6% | 🏅 First Class | 🗓️ 1999

🏫 Work Experience

  1. Assistant Professor – Alliance University, Karnataka
    🗓️ June 2014 – Present | 📍 10+ Years

  2. Assistant Professor – Vignan’s IIT, Duvvada, Vizag
    🗓️ June 2007 – June 2014 | 📍 7 Years

  3. Assistant Professor – St. Stanley College of Engineering & Technology, Hyderabad
    🗓️ Dec 2005 – June 2007 | 📍 1.5 Years

  4. Assistant Professor – Progressive Engineering College, Nalgonda
    🗓️ July 2005 – Dec 2005 | 📍 6 Months

🏆 Achievements & Honors

  • 📜 Successfully completed Ph.D. in 2024 with specialization in Intelligent Systems.

  • 👨‍🏫 Over 18 years of teaching experience across premier engineering institutions.

  • 🎓 Handled a diverse range of subjects including AI & ML, Embedded Systems, and Smart Healthcare.

  • 🧪 Expert in lab development and instruction in electronics, digital communication, and microcontrollers.

  • 🌟 Recognized for academic excellence and mentoring students in cutting-edge tech areas like AR/VR and biomedical instrumentation.

Publication Top Notes:

A Novel Electrical Load Forecasting Model Using a Deep Learning Approach

Artificial intelligence-based energy efficiency models in green communications towards 6G

Severity Analysis Automation for Detection of Non-Proliferative Diabetic Retinopathy.

A combined fuzzy backtracking search optimization algorithm to localize retinal blood vessels for diabetic retinopathy

2nd International Symposium on Sustainable Energy and Technological Advancements (ISSETA 2023)” Feb 2023.NIT Meghalaya.

System-on-chip based Automated Optic Disk Segmentation in Retinal Images

Prof. Shuo-Tsung Chen | Image Processing Awards | Best Researcher Award

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Prof. Shuo-Tsung Chen | Image Processing Awards | Best Researcher Award 

Prof. Shuo-Tsung Chen, Chung Shan Medical University, Taiwan

Shuo-Tsung Chen is a dedicated academic and researcher specializing in signal processing, biomedical engineering, the Internet of Things (IoT), image processing, information hiding, big data, optimal control system design, and artificial intelligence. Dr. Chen earned his B.S. degree in Mathematics from National Cheng Kung University in 1996, followed by an M.S. in Applied Mathematics from Tunghai University in 2003. He completed his Ph.D. in Electrical Engineering at National Chi Nan University in 2010. Dr. Chen has extensive academic experience, having served as a Post-Doctoral Fellow at the Graduate Institute of Biomedical Engineering at National Taiwan University College of Medicine and as an Assistant Professor at the College of Future, National Yunlin University of Technology. He has also held positions as a Calculus Speaker in Electrical and Electronics Engineering at National Chi Nan University, a Research Assistant at Chaoyang University of Technology, and an Adjunct Assistant Professor in the Department of Mathematics at Tunghai University.

Professional Profile:

ORCID

Summary of Suitability for Best Researcher Award: Shuo-Tsung Chen, Ph.D.

Shuo-Tsung Chen is an accomplished academic currently serving as an Assistant Professor in Taiwan. With a comprehensive educational background in mathematics and electrical engineering, along with diverse research experience in various cutting-edge fields, he is a strong contender for the Best Researcher Award.

Education

🎓 Ph.D. in Electrical Engineering
National Chi Nan University, Puli, Nantou, Taiwan, 2010

📚 M.S. in Applied Mathematics
Tunghai University, Taichung, Taiwan, 2003

📏 B.S. in Mathematics
National Cheng Kung University, Tainan, Taiwan, 1996

Work Experience

🏫 Assistant Professor
National Yunlin University of Technology, College of Future, Taiwan (Present)

🔬 Post-Doctoral Fellow
Graduate Institute of Biomedical Engineering, National Taiwan University College of Medicine, Taiwan

👨‍🏫 Adjunct Assistant Professor
Department of Mathematics, Tunghai University (September 2010 – Present)

💼 Post-Doctoral Fellow
Department of Industrial Engineering and Enterprise Information, Tunghai University (August 2012 – Present)

📈 Research Assistant
Department of Information and Communication, Chaoyang University of Technology (August 2009 – January 2010)

📐 Calculus Speaker
Electrical and Electronics Engineering, National Chi Nan University (August 2005 – July 2006)

Achievements, Awards, and Honors

🏅 National Cheng Kung University Scholarship – Recognized for outstanding academic performance (Year unspecified).
🌟 Tunghai University Excellence Award – Acknowledged for contributions to academic research (Year unspecified).
📜 Publication in Leading Journals – Authored several papers on topics including Signal Processing, Biomedical Engineering, and Artificial Intelligence (Years unspecified).

Publication Top Notes:

Measurement Invariance Investigation for Performance of Deep Learning Architectures

Emergency Evacuation Planning via the Point of View on the Relationship Between Crowd Density and Moving Speed

Patient Confidential Information Transmission Using the Integration of PSO-Based Biomedical Signal Steganography and Threshold-Based Compression

Digital audio watermarking using minimum-amplitude scaling on optimized DWT low-frequency coefficients

Machine Learning Modeling for Failure Detection of Elevator Doors by Three-Dimensional Video Monitoring