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

Mr. Suresha R | Computer Vision Awards | Excellence in Research Award

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Mr. Suresha R | Computer Vision Awards | Excellence in Research Awardย 

Mr. Suresha R | Computer Vision Awards | Amrita Vishwa Vidyapeetham | India

Mr. Suresha R. is a results-driven educator and technologist with over six years of combined experience in teaching computer science and academic leadership. He holds an M.Sc. in Computer Science and has qualified in UGC-NET and K-SET, while currently pursuing a Ph.D. Mr. Suresha R. has demonstrated expertise in curriculum design and research, particularly focusing on AI in autonomous solutions and computer vision applications. In his professional career, Mr. Suresha R. has served as an Assistant Professor at Amrita Vishwa Vidyapeetham, School of Computing, Mysuru Campus, and at SBRR Mahajana First Grade College, Mysuru, where he delivered advanced courses in Computer Vision, Digital Image Processing, Pattern Recognition, Computational Intelligence, Computer Graphics, Machine Learning, Exploratory Data Analysis, R Programming, Information Retrieval, Data Mining, Numerical Analysis, and Operations Research, consistently achieving high student satisfaction. His research interests encompass small traffic sign detection and recognition in challenging scenarios using computer vision and LiDAR-based techniques with ROS2 framework, deep learning-based vehicle detection and distance estimation for autonomous systems, motion blur image restoration, wild animal recognition through vocal analysis, and SVM-based medical image classification. Mr. Suresha . possesses strong research skills in Python, MATLAB, ROS2, machine learning, deep learning, image processing, and data analysis. He has successfully guided Bachelor’s and Master’s students in research projects, fostering innovation and academic growth. His academic contributions are recognized through multiple publications in prestigious journals and conferences, including IEEE Access, Procedia Computer Science, ICCCNT, CCEM, ICECAA, and INDIACom. Mr. Suresha . has a proven record of collaborating in interdisciplinary teams, effectively communicating complex technical concepts, and mentoring students to achieve excellence in research and practical applications. His dedication to lifelong learning and active engagement in both teaching and research demonstrates his commitment to advancing knowledge in computer science and autonomous systems. Throughout his career, Suresha ย has received awards and recognitions for research excellence, contributing to the development of sustainable and intelligent solutions in the field of computer vision and AI. Overall, Mr. Suresha exemplifies a passionate and innovative professional, bridging theoretical foundations with applied research, and continues to make significant contributions to academia and technology

Professional Profiles:ย ORCID

Selected Publicationsย 

  1. Suresha, R., Manohar, N., Ajay Kumar, G., & Singh, R. (2024). Recent advancement in small traffic sign detection: Approaches and dataset.

  2. Suresha, R., Manohar, N., & Jipeng, T. (2024). Two-stage traffic sign classification system.

  3. Sudharshan Duth, P., Manohar, N., Suresha, R., Priyanka, M., & Jipeng, T. (2024). Wild animal recognition: A vocal analysis.

  4. Suresha, R., Jayanth, R., & Shriharikoushik, M. A. (2023). Computer vision approach for motion blur image restoration system.

  5. Srinivasa, C., Suresha, R., Manohar, N., Dharun, G. K., Sheela, T., & Jipeng, T. (2023). Deep learning-based techniques for precise vehicle detection and distance estimation in autonomous systems.

  6. Suresha, R., Devika, K. M., & Prabhu, A. (2022). Support vector machine classifier based lung cancer recognition: A fusion approach.

Prof. Zhang Wenli | Computer Vision | Excellence in Research Award

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Prof. Zhang Wenli | Computer Vision | Excellence in Research Awardย 

Prof. Zhang Wenli | Computer Vision | Beijing University of Technology | China

Dr. Wenli Zhang is a distinguished scholar and innovative technology leader currently serving as a Professor in the Faculty of Information Technology at Beijing University of Technology, recognized for impactful contributions in signal and information processing, artificial intelligence, computer vision, 3D point cloud processing, unmanned aerial vehicle inspection technology, and brain-computer interfaces, positioning Dr. Wenli Zhang as a key figure advancing intelligent sensing and human-machine interaction research in China and globally. Building a strong academic foundation through advanced studies in computer science and informatics in both China and Japan, Dr. Wenli Zhang earned a Ph.D. in Engineering from the University of Tokyo, where a passion for applied research and innovation in intelligent systems was further strengthened. Prior to joining academia in China, Dr. Wenli Zhang developed extensive industrial innovation experience as Chief Researcher at Panasonic Corporationโ€™s Tokyo Research Institute, driving real-world AI and vision-based solutions for next-generation automated applications. In her current role, Dr. Wenli Zhang leads interdisciplinary research that spans multiple sectors including smart agriculture, UAV-based intelligent inspection, and medical rehabilitation, effectively bridging fundamental theories with emerging societal needs and technological transformation. With strong collaboration networks and a commitment to promoting scientific excellence, Dr. Wenli Zhang serves actively in influential professional roles, including council member of the Beijing Interdisciplinary Science Society and committee member of the Innovation Engineering Branch of China Creative Studies Institute, contributing leadership within Chinaโ€™s innovation and engineering communities. Skilled in advanced algorithm development, intelligent visual perception, sensor network data fusion, and neural signal decoding, Dr. Wenli Zhang empowers her research team to develop practical systems that enhance automation, sustainability, and accessibility across industries. Her exceptional commitment to teaching and mentorship has earned her the prestigious โ€œDistinguished Teacherโ€ recognition at Beijing University of Technology, reflecting her dual dedication to academic excellence and student success.

Professional Profiles:ย ORCIDย ย 

Selected Publications:

  • Jiang, K., Guo, W., & Zhang, W. (2025). Amodal Segmentation and Trait Extraction of On-Branch Soybean Pods with a Synthetic Dual-Mask Dataset. Sensors.

  • Zhang, W., Peng, X., Bai, T., Wang, H., Takata, D., & Guo, W. (2024). A UAV-Based Single-Lens Stereoscopic Photography Method for Phenotyping the Architecture Traits of Orchard Trees. Remote Sensing.

  • Zhang, W., Peng, X., Cui, G., Wang, H., Takata, D., & Guo, W. (2023). Tree Branch Skeleton Extraction from Drone-Based Photogrammetric Point Cloud. Drones.

  • Li, Y., Liu, B., & Zhang, W. (2024). Driving-Related Cognitive Abilities Prediction Based on Transformerโ€™s Multimodal Fusion Framework. Sensors.

  • Pang, G., Liu, B., & Zhang, W. (2025). Cloud Rehabilitation System Based on Automatic sEMG Signal Processing. Book Chapter.

  • Zhai, R., Gao, Y., Li, G., Ding, Q., Zhang, Y., & Zhang, W. (2025). Control System for Rehabilitation Bionic Hand Based on Precise Control Algorithms.

  • Wang, Y., Pang, G., Liu, B., Li, Y., & Zhang, W. (2025). Gesture Recognition Method Based on Hybrid Classifier Under Non-ideal Conditions.

Dr. Kuai Zhou | Computer Vision | Young Researcher Award

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Dr. Kuai Zhou | Computer Vision | Young Researcher Awardย 

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 Zhouโ€™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 Publicationsย 

  1. Zhou, K., Huang, X., Li, S., & Li, G. (2023). Convolutional neural network-based pose mapping estimation as an alternative to traditional handโ€“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.

Mr. Ahmet Serhat Yildiz | Computer Vision Awards | Best Researcher Award

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Mr. Ahmet Serhat Yildiz | Computer Vision Awards | Best Researcher Award

Mr. Ahmet Serhat Yildiz | Computer Vision Awards | Brunel University of London | United Kingdom

Mr. Ahmet Serhat Yildiz is an emerging researcher in sensing technology with growing expertise in machine learning, deep learning, embedded systems, and multi-sensor fusion, demonstrating strong potential for advanced research roles and academic leadership. He is currently pursuing his PhD in Electronic and Computer Engineering at Brunel University London, where he focuses on real-time object detection, semantic 3D depth sensing, LiDARโ€“camera fusion, and intelligent autonomous perception systems, aligning closely with sensing applications in robotics, transportation, surveillance, and industrial automation. His academic foundation includes degrees in electronics, electrical engineering, business management, and extensive English language training, providing a multidisciplinary perspective that strengthens his analytical and communication abilities. His professional experience includes roles as a Graduate Teaching Assistant in digital design, embedded systems, and computer architecture, as well as serving as an IoT facilitator, where he mentored learners and contributed to community-oriented technology initiatives. Mr. AHMET SERHAT YILDIZ has developed notable research projects, including FPGA-based embedded game systems, PLC-controlled industrial automation setups, and biomedical sensing circuits for pulse wave velocity measurement, demonstrating strong hands-on engineering skills. His research portfolio includes Scopus-indexed publications on YOLO-based detection models, sensor fusion for autonomous vehicles, and real-time navigation using LiDAR and deep learning frameworks, reflecting his ability to integrate theory with practical sensing applications. His technical skills include Python, PyTorch, embedded C, FPGA development, digital circuit design, PLC programming, and multi-sensor signal processing, enabling him to contribute to both algorithmic and hardware-oriented research environments. His achievements include scholarly publications, increasing citation impact, and recognition through participation in international conferences and multidisciplinary research projects.

Professional Profiles: ORCID | Google Scholar

Featured Publicationsย 

  1. Alkandary, K., Yildiz, A. S., & Meng, H. (2025). A comparative study of YOLO series (v3โ€“v10) with DeepSORT and StrongSORT: A real-time tracking performance study. Electronics.

  2. Tunali, M. M., Yildiz, A., & ร‡akar, T. (2022). Steel surface defect classification via deep learning. International Conference on Computer Science and Engineering (UBMK).

  3. Yildiz, A. S., Meng, H., & Swash, M. R. (2025). Real-time object detection and distance measurement enhanced with semantic 3D depth sensing using cameraโ€“LiDAR fusion. Applied Sciences.

  4. Tunali, M. M., Sayar, A., Aslan, Y., Mutlu, ฤฐ., & ร‡akar, T., including Yildiz, A. (2023). Enhancing quality control in plastic injection production: Deep learning-based detection and classification of defects. International Conference on Computer Science and Engineering (UBMK).

  5. Yฤฑldฤฑz, A., MiลŸe, P., ร‡akar, T., TerzibaลŸฤฑoฤŸlu, A. M., & ร–ke, D. (2023). Spine posture detection for office workers with hybrid machine learning. International Conference on Computer Science and Engineering (UBMK).

  6. Yildiz, A. S., Meng, H., & Swash, M. R. (2025). YOLOv8โ€“LiDAR fusion: Increasing range resolution based on image-guided sparse depth fusion in self-driving vehicles. Lecture Notes in Networks and Systems.

  7. Yildiz, A. S., Meng, H., & Swash, M. R. (2024). A multi-sensor fusion approach to real-time birdโ€™s-eye view navigation: YOLOv8 and LiDAR integration for autonomous systems. Korkut Ata Scientific Research Conference Proceedings.

Prof. Din-Yuen Chan | Deep Learning | Best Scholar Award

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Prof. Din-Yuen Chan | Deep Learning | Best Scholar Awardย 

Prof. Din-Yuen Chan, National Chiayi University, Taiwan

Din-Yuen Chan is a prominent scholar in electrical engineering with extensive experience in visual signal processing and computer vision. He earned his Ph.D. in Electrical Engineering from National Cheng Kung University, Taiwan, in 1996. A member of the Visual Signal Processing and Communication Technical Committee (VSPC TC) since 2010, he served as the founding director of the Department of Electrical Engineering (2007โ€“2011) and as Dean of the College of Science and Engineering at National Chiayi University (2017โ€“2020). His research spans semantic object detection, video/audio coding, stereoscopic 3D, AI-based pattern recognition, and deep learning neural networks. In the past five years, he has published multiple SCI-indexed journal papers on topics such as stereo matching, instance segmentation, speaker diarization, depth estimation, and autonomous robotics. As a frequent corresponding author, he continues to lead innovations in applied AI and multimedia processing.

Professional Profile:

SCOPUS

Summary of Suitability for the Best Scholar Award

Dr. Din-Yuen Chan has maintained an outstanding academic career for over two decades, contributing significantly to the fields of electrical engineering and computer vision. His long-standing commitment to advancing knowledge is reflected in his leadership roles and consistent research output in areas such as semantic object detection, AI-based pattern recognition, video/audio coding, and stereoscopic 3D.

๐ŸŽ“ Education

  • Ph.D. in Electrical Engineering
    National Cheng Kung University, Taiwan ๐Ÿ‡น๐Ÿ‡ผ
    Completed in 1996

๐Ÿ’ผ Work Experience

  • ๐Ÿง  Member, Visual Signal Processing and Communication Technical Committee (VSPC TC)
    Since 2010

  • ๐Ÿ›๏ธ Founding Director, Department of Electrical Engineering, National Chiayi University
    2007 โ€“ 2011

  • ๐ŸŽ“ Dean, College of Science and Engineering, National Chiayi University
    2017 โ€“ 2020

๐Ÿงช Research Interests

  • ๐Ÿ” Computer Vision

  • ๐ŸŽฏ Semantic Object Detection

  • ๐ŸŽž๏ธ Video/Audio Coding

  • ๐Ÿค– AI-based Pattern Recognition

  • ๐Ÿฅฝ Stereoscopic 3D

  • ๐Ÿง  Deep Learning Neural Networks

๐Ÿ… Achievements & Honors

  • โœ๏ธ Published multiple SCI-indexed journal papers in high-impact venues, including:

    • EURASIP Journal on Image and Video Processing

    • IET Computer Vision

    • Multimedia Tools and Applications

    • Applied Sciences

  • โญ First or corresponding author in many significant papers on stereo matching, depth estimation, 3D object placement, and speaker diarization.

  • ๐Ÿค– Developed a low-cost autonomous outdoor robot with end-to-end deep learning navigation.

  • ๐Ÿง Invented a new speaker-diarization technology using spectral-LSTM.

  • ๐ŸŽ“ Recognized leader in academia for establishing and leading research and administrative departments.

Publicationย Top Notes:

A new speaker-diarization technology with denoising spectral-LSTM for online automatic multi-dialogue recording

Natural-Prosodic Cross-Lingual Personalized TTS

New Efficient Depth Estimation and Real-Time Object 3D Recognition Models for Humanoid Robotic Environment Understanding

Rational 3D object placement based on deep learning based plane detection

INTEGRATED LIGHT-RESNET AND POOLFORMER NETWORKS FOR SHAPE-PRESERVING LANE DETECTION

Prof. Fengyun Cao | Computer Vision Awards | Excellence in Research Award

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Prof. Fengyun Cao | Computer Vision Awards | Excellence in Research Awardย 

Prof. Fengyun Cao, Hefei Normal University, China

Dr. Cao Fengyun is an Associate Professor and Masterโ€™s Supervisor at the School of Computer and Artificial Intelligence, Hefei Normal University, where she also serves as Director of the Department of Computer Science and Technology. Her primary research interests include digital image processing, computer vision, and artificial intelligence. Dr. Cao is a member of the Image Application and System Integration Committee of the Chinese Image and Graphics Society and serves on the young editorial board of the international journal INSTRUMENTATION. She is also a reviewer for numerous prestigious journals such as IEEE/CAA Journal of Automatica Sinica, Scientific Reports, and The Journal of Supercomputing. She currently holds the position of Vice President of Science and Technology at the Medical Artificial Intelligence Technology R&D Center, Hefei Innovation Institute. Over the years, she has led various funded research projects, including those focused on depth estimation, remote sensing, and smart control systems. Dr. Cao has authored several high-impact papers and holds 10 authorized invention patents, along with multiple software copyrights and integrated circuit layout designs. Her work has earned her accolades including the โ€œResearch Starโ€ award and third prize in the Anhui Province Science and Technology Awards. She has also contributed to the development of local standards in smart systems and information monitoring.

Professional Profile:

SCOPUS

Summary of Suitability:

Dr. Cao Fengyun, an Associate Professor and Director of the Department of Computer Science and Technology at the School of Computer and Artificial Intelligence, Hefei Normal University, is a highly accomplished researcher with a proven track record in digital image processing, computer vision, and artificial intelligence. His outstanding contributions to both theoretical advancements and practical innovations make him an excellent candidate for the Excellence in Research Award.

๐ŸŽ“ Education & Work Experience

  • ๐Ÿ‘จโ€๐Ÿซ Teaching Assistant
    School of Computer Science, Hefei Normal University
    ๐Ÿ“… June 2013 โ€“ November 2017

  • ๐Ÿ‘จโ€๐Ÿซ Lecturer
    School of Computer Science, Hefei Normal University
    ๐Ÿ“… December 2017 โ€“ December 2022

  • ๐Ÿ‘ฉโ€๐Ÿซ Associate Professor
    School of Computer and Artificial Intelligence, Hefei Normal University
    ๐Ÿ“… January 2023 โ€“ Present

  • ๐Ÿง  Vice President of Science and Technology
    Medical AI Technology R&D Center, Hefei Innovation Institute
    ๐Ÿ“… November 2024 โ€“ Present

๐Ÿ† Achievements

  • ๐Ÿ“š Research Areas:
    Digital Image Processing, Computer Vision, Artificial Intelligence

  • ๐Ÿงช Research Projects (Host):

    • ๐Ÿ” Magnetic Tile Surface Defect Detection (2024โ€“2025)

    • ๐Ÿค– Monocular Image Depth Estimation using Deep CNN (2019โ€“2020)

    • ๐Ÿ–ผ Single Image Depth Restoration via Low-level Features

    • ๐ŸŒฉ Cloud Tech for Remote Sensing Image Thinning (2018โ€“2019)

    • ๐Ÿ”ง Smart Fire Protection Water Supply System (2025)

    • ๐Ÿ“ก High Performance Frequency Hopping Filter Development

    • โšก Intelligent Control System for Power Distribution Cabinet (2021)

    • ๐Ÿ”‹ Smart-LW Charging Operation and Maintenance System

    • ๐Ÿง  Graph Neural Network Intelligent Computing System (Ranked 3rd)

    • ๐ŸŒ IoT Equipment Remote Upgrade System (2021)

  • ๐Ÿ“„ Representative Papers:

    • โš™ Electric Bike Testing Dataset โ€“ Alexandria Engineering Journal (2024, SCI Zone II TOP)

    • ๐ŸŽฏ YOLOv7-based Anti-target Detection โ€“ Traitement du Signal (2023, SCI)

    • ๐Ÿงฉ PCB Defect Recognition via Bi-directional Feature Extraction โ€“ Journal of Wuhan University of Technology

    • ๐Ÿ–Œ Edge Blur Estimation for Depth Restoration โ€“ Journal of Computers

    • ๐Ÿง  Image Segmentation and Depth Recovery โ€“ Journal of Chinese Image and Graphics

  • ๐Ÿ’ก Intellectual Property:

    • ๐Ÿ”ฌ Invention Patents: 10 (Ranked 1st to 8th) โ€“ covering intelligent factories, robotic arms, IoT, and image processing

    • ๐Ÿ’ป Software Copyrights: 3 (First author)

    • ๐Ÿงฟ Integrated Circuit Layout Designs: 2 (One authored by him)

๐Ÿฅ‡ Awards & Honors

  • ๐ŸŒŸ HefeiNormal University Research Star, 2022

  • ๐Ÿฅ‰ Third Prize โ€“ Natural Science Award (Host), Hefei Normal University, 202X

  • ๐Ÿฅ‰ Third Prize โ€“ Anhui Province Science and Technology Award (Ranked 4th), 2021

  • ๐Ÿ… Excellence in Science & Technology Progress, Anhui Provincial Computer Society (1st Rank), 2021

Publicationย Top Notes:

Optimization of the Pure Pursuit algorithm based on real-time error

Prof. Dr. Chao-Ming Wang | Computer Vision | Best Researcher Award

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Prof. Dr. Chao-Ming Wang | Computer Vision | Best Researcher Awardย 

Prof. Dr. Chao-Ming Wang, Department of Digital Media Design / National Yunlin University of Science and Technology, Taiwan

Chao-Ming Wang is a Professor at the Department of Digital Media Design at National Yunlin University of Science and Technology (YunTech), Taiwan, where he has been serving since 2008. He holds a Ph.D. in Computer Science and Information Engineering from National Chiao Tung University, Taiwan, and has a rich career spanning academia and research. Prior to his current role, Dr. Wang was an Associate Professor at Yuan Ze University and has also held senior specialist positions at the National Chung Shan Institute of Science and Technology. His research interests encompass signal processing, computer vision, tech art, and interactive multimedia design. Dr. Wang has been an active leader in professional organizations, including serving as the President of the Taiwan Society of Basic Design and Art from 2010 to 2013. He is also deeply involved in the Taiwanese digital media community through his roles in various associations such as the Taiwan Art & Technology Association and the Taiwan Association of Digital Media Design.

Professional Profile:

ORCID

Summary of Suitability for Best Researcher Award

Dr. Chao-Ming Wang is a highly esteemed researcher and academic whose work spans signal processing, computer vision, tech art, and interactive multimedia design. With over four decades of experience in the field, Dr. Wang has made significant contributions to both the academic and industrial domains, establishing himself as a pioneer in integrating technology and art.

๐ŸŽ“ Education

  • Ph.D. in Computer Science and Information Engineering
    ๐Ÿซ National Chiao Tung University, Hsinchu, Taiwan
    ๐Ÿ“… 1987โ€“1993

  • M.Sc. in Computer Science and Information Engineering
    ๐Ÿซ National Chiao Tung University, Hsinchu, Taiwan
    ๐Ÿ“… 1980โ€“1982

  • B.Sc. in Computer Science
    ๐Ÿซ National Chiao Tung University, Hsinchu, Taiwan
    ๐Ÿ“… 1976โ€“1980

๐Ÿ’ผ Work Experience

  • Professor
    ๐Ÿซ National Yunlin University of Science and Technology (YunTech), Taiwan
    ๐Ÿ“… Aug 2021 โ€“ Present
    ๐Ÿ“ Department of Digital Media Design

  • Associate Professor
    ๐Ÿซ YunTech, Taiwan
    ๐Ÿ“… 2008 โ€“ 2021

  • Associate Professor
    ๐Ÿซ Yuan Ze University, Taoyuan, Taiwan
    ๐Ÿ“… 2003 โ€“ 2008
    ๐Ÿ“ Department of Information Communication

  • Senior Specialist
    ๐Ÿข National Chung Shan Institute of Science and Technology
    ๐Ÿ“… 1982 โ€“ 2003

๐Ÿ† Achievements & Leadership Roles

  • ๐Ÿง‘โ€๐ŸŽ“ Head, Dept. of Digital Media Design, YunTech (2010โ€“2013)

  • ๐ŸŽจ President, Taiwan Society of Basic Design and Art (2010โ€“2013)

  • ๐Ÿ’ก Director, Design-led Innovation Center, YunTech (2016โ€“2017)

  • ๐Ÿง‘โ€๐Ÿซ Executive Director, Taiwan Association of Digital Media Design (2015โ€“2021)

  • ๐Ÿค Director, Taiwan Art & Technology Association (2013โ€“2023)

  • ๐Ÿ… Director of Honor, Taiwan Society of Basic Design and Art (2014โ€“present)

  • ๐Ÿง  Permanent Member, Chinese Image Processing and Pattern Recognition Society (2003โ€“present)

๐Ÿ”ฌ Research Interests

  • ๐ŸŽ›๏ธ Signal Processing

  • ๐Ÿ‘๏ธ Computer Vision

  • ๐Ÿ–ผ๏ธ Tech Art

  • ๐ŸŽฎ Interactive Multimedia Design

Publicationย Top Notes:

Design of an Interactive Exercise and Leisure System for the Elderly Integrating Artificial Intelligence and Motion-Sensing Technology

Combining Interactive Technology and Visual Cognitionโ€”A Case Study on Preventing Dementia in Older Adults

The design of a new interactive multimedia system based on computer vision and multi-sensing techniques for the traditional ritual process

Design of a Gaze-Controlled Interactive Art System for the Elderly to Enjoy Life

Design of a Technology-Based Magic Show System with Virtual User Interfacing to Enhance the Entertainment Effects

Design and Assessment of an Interactive Role-Play System for Learning and Sustaining Traditional Glove Puppetry by Digital Technology

The Design of a Novel Digital Puzzle Gaming System for Young Childrenโ€™s Learning by Interactive Multi-Sensing and Tangible User Interfacing Techniques

Using Digital Technology to Design a Simple Interactive System for Nostalgic Gaming to Promote the Health of Slightly Disabled Elderly People

Combining Augmented Reality and Multi-User Remote Collaboration to Improve Sustainable Agriculture and Economy

Mr. Adrian Barglazan | Computer Vision | Best Researcher Award

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Mr. Adrian Barglazan | Computer Vision | Best Researcher Award

Mr. Adrian Barglazan, University “Lucian Blaga” Sibiu, Romania

Adrian Barglazan is a Senior Software Engineer at Cognizant Softvision, based in Sibiu, Romania, with a strong focus on continuous learning and growth in software development. He holds a Bachelor’s and Master’s degree in Computer Science from Lucian Blaga University of Sibiu, where he is also pursuing a Ph.D. with a research focus on media forensics. With over 15 years of professional experience, Adrian has worked in various roles, including software development, team leadership, and teaching. His expertise spans Microsoft-related technologies, agile development, clean code principles, and design patterns. Throughout his career, he has contributed to projects in cloud ERP systems, pharmaceutical software, and ERP applications, working with technologies such as C#, ASP.NET, JavaScript, React, and Azure. In addition to his industry work, Adrian has been a teaching assistant at Lucian Blaga University of Sibiu since 2011, specializing in data compression and DirectX. His interests extend to computer vision and machine learning, reflecting his passion for innovative and high-quality software solutions

Professional Profile:

ORCID

Suitability for Best Researcher Award โ€“ Adrian Barglazan

Adrian Barglazan demonstrates strong expertise in software development, computer vision, and media forensics, with a balance of industry experience and academic involvement. His Ph.D. research in media forensics, combined with over a decade of teaching experience in data compression and image processing, positions him as a knowledgeable professional in his field. However, for a Best Researcher Award, factors such as high-impact publications, patents, funded research projects, and citations play a crucial role. While Adrian has valuable technical contributions, his eligibility for this award would be strengthened by more peer-reviewed research publications and recognized contributions to the scientific community. Therefore, he is a strong candidate for an innovation or industry-academic impact award but may need further academic credentials to be fully competitive for a Best Researcher Award.

๐ŸŽ“ Education:

  • PhD in Computer Science (2019 โ€“ Present) ๐Ÿ“–๐Ÿ”
    Lucian Blaga University of Sibiu โ€“ Focus on Media Forensics
  • Masterโ€™s Degree in Computer Science (2009 โ€“ 2011) ๐ŸŽ“
    Lucian Blaga University of Sibiu
  • Bachelorโ€™s Degree in Computer Science (2005 โ€“ 2009) ๐ŸŽ“
    University “Lucian Blaga”, Faculty of Engineering “Hermann Oberth”, Sibiu

๐Ÿ’ผ Work Experience:

๐Ÿ”น Senior Software Engineer โ€“ Cognizant Softvision (Sept 2020 โ€“ Present)
๐Ÿ“ Sibiu, Romania

  • Focus on Microsoft-related technologies, agile development, and clean code
  • Expertise in software architecture, development, testing, and mentoring

๐Ÿ”น PhD Student & Teaching Assistant โ€“ Lucian Blaga University of Sibiu (Sept 2011 โ€“ Present)
๐Ÿ“ Sibiu County, Romania

  • Research in Media Forensics ๐Ÿ”
  • Teaching Data Compression & DirectX to 4th-year students ๐ŸŽ“
  • Covers key algorithms like Shannon, Huffman, LZ77, JPEG, MPEG

๐Ÿ”น Software Developer โ€“ Visma (Apr 2017 โ€“ Sept 2020)
๐Ÿ“ Sibiu County, Romania

  • Senior developer in cloud ERP Single Page Application (SPA) development โ˜๏ธ๐Ÿ’ป
  • Technologies: C#, ASP.NET MVC, Azure SQL, React, TypeScript
  • Worked with Kanban methodology, CI/CD, and cross-country teams

๐Ÿ”น Developer โ€“ iQuest Technologies (Sept 2011 โ€“ Apr 2017)
๐Ÿ“ Sibiu County, Romania

  • Lead developer in Pharma sector projects ๐Ÿ’Š
  • Software architecture, risk management, and recruitment ๐Ÿ“‹

๐Ÿ† Achievements, Awards & Honors:

๐ŸŒŸ PhD Researcher in Media Forensics ๐Ÿ“ธ๐Ÿ”ฌ
๐ŸŒŸ Senior Software Engineer with over 17 years of experience in the software industry ๐Ÿ’ป
๐ŸŒŸ Specializes in Microsoft technologies, Agile development, and Clean Code principles โšก
๐ŸŒŸ Mentor & Teacher โ€“ educating future developers on Data Compression & DirectX ๐ŸŽ“
๐ŸŒŸ Experienced in cloud-based ERP systems, software architecture, and machine learning โ˜๏ธ๐Ÿค–
๐ŸŒŸ Contributor to recruitment & technical interviews in multiple companies ๐Ÿ…

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