Ms. Priyanka Manchegowda | Computer Vision | Women Researcher Award

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Ms. Priyanka Manchegowda | Computer Vision | Women Researcher Award

Ms. Priyanka Manchegowda | Computer Vision | Amrita Vishwa Vidyapeetham | India

Ms. Priyanka Manchegowda is a results-driven Assistant Professor and researcher, currently pursuing her Ph.D., with over 12 years of combined experience in teaching computer science, academic leadership, and curriculum development. She holds an M.Sc. in Computer Science from Pooja Bhagavat Memorial Mahajana Post Graduate Centre, affiliated with the University of Mysore, Mysuru, India, and has actively contributed to higher education by delivering advanced courses in Exploratory Data Analysis using Python, Digital Image Processing, Design and Analysis of Algorithms, Data Structures, Problem Solving and Programming, Operations Research, Numerical Analysis, Statistical Techniques, Programming in C/C++, and Database Management Systems, consistently achieving strong student satisfaction. Professionally, she has served as an Assistant Professor at SBRR Mahajana First Grade College, Mysuru, from 2013 to 2020, and currently at Amrita Vishwa Vidyapeetham, School of Computing, Mysuru Campus since 2020, where she also contributes as a member of the Board of Studies and has developed curricula in alignment with university standards. In addition to teaching, she has guided Bachelor’s and Master’s students on research projects, focusing her research on computer vision-based human age estimation tailored for Indian medico-legal scenarios, demonstrating expertise in analytical methods, quantitative aptitude, image processing, and programming with Python, C, and C++, alongside database management using MS SQL Server and tools such as MATLAB and Anaconda. Ms. Manchegowda has actively contributed to institutional initiatives and student development, serving as the SWAYAM MOOC Nodal Officer, and as convener for the Rotaract Club and SARANTHA, while also engaging in faculty evaluations for the Internal Quality Assurance Cell (IQAC). She brings strong leadership, teamwork, administrative, and communication skills, alongside a commitment to lifelong learning and academic engagement. Her professional recognition includes citations in Scopus with an h-index reflecting the impact of her scholarly contributions.

Professional Profiles: ORCID | Scopus

Selected Publications

  • Priyanka, M., Divyashree, M., & Madhu, V. (2022). Computer Vision-Based Approach for Estimating Age and Gender using Wrist X-Ray Images.

  • Priyanka, M., Sreekumar, S., & Arsh, S. (2022). Detection of Covid-19 from the Chest X-Ray Images: A Comparison Study between CNN and Resnet-50.

Mr. Yuhang Meng | Control Awards | Best Researcher Award

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Mr. Yuhang Meng | Control Awards | Best Researcher Award

Mr. Yuhang Meng | Control Awards | Nanjing University of Science and Technology | China

Mr. Yuhang Meng is a highly motivated and accomplished researcher in the field of Electronic Information, specializing in advanced control systems, fault-tolerant mechanisms, and unmanned vehicle technologies, with a growing record of impactful publications and international recognition. He received his Bachelor’s degree in Electrical Engineering from Suzhou City University, followed by a Master’s degree in Electronic Information from Jiangsu University of Science and Technology, and is currently pursuing his EngD in Electronic Information at Nanjing University of Science and Technology under the supervision of Professor Zhengrong Xiang. Throughout his academic career, Mr. Meng has gained extensive experience in switched systems, adaptive control, sliding mode control, and the development of advanced algorithms for autonomous systems, with a specific emphasis on unmanned surface and amphibious vehicles. His professional experience reflects active engagement in high-impact research projects, both theoretical and application-oriented, resulting in publications in leading international journals such as IEEE Transactions on Mechatronics, IEEE Transactions on Industrial Electronics, Aerospace Science and Technology, and Applied Ocean Research, many of which are indexed in Scopus and widely cited within the research community. His expertise extends to designing robust trajectory-tracking controllers, developing hybrid amphibious platforms, and implementing artificial intelligence-based approaches such as bidirectional long short-term memory neural networks for adaptive control

Professional Profile: ORCID 

Selected Publications

  1. Design and Analysis of a Multimodal Hybrid Amphibious Vehicle, 2025 – Citations: 15

  2. Trajectory tracking control for unmanned amphibious surface vehicles with actuator faults, 2024 – Citations: 22

  3. An adaptive internal model control approach for unmanned surface vehicle based on bidirectional long short-term memory neural network: Implementation and field testing, 2024 – Citations: 18

  4. Trajectory‐tracking control of an unmanned surface vehicle based on characteristic modelling approach: Implementation and field testing, 2023 – Citations: 12

Prof. Dr. Hsien-Huang Wu | Automation Awards | Best Researcher Award

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Prof. Dr. Hsien-Huang Wu | Automation Awards | Best Researcher Award

Prof. Dr. Hsien-Huang Wu, National Yunlin University of Science and Technology, Taiwan

Dr. Hsien-Huang Wu is a Distinguished Professor in the Department of Electrical Engineering at National Yunlin University of Science and Technology, Douliu, Taiwan. He received his B.S. and M.S. degrees in Telecommunication Engineering from National Chiao Tung University in 1982 and 1986, respectively, and earned his Ph.D. in Electrical and Computer Engineering from the University of Arizona in 1993. His research focuses on artificial intelligence and computer vision, particularly for automated optical inspection (AOI) applications. With extensive industrial collaboration, Dr. Wu has worked with over 50 companies to develop innovative systems for automated inspection and production, bridging academic research and practical implementation.

Professional Profile:

SCOPUS

Summary of Suitability for Best Researcher Award – Dr. Hsien-Huang Wu

Dr. Hsien-Huang Wu stands out as a leading figure in the application of artificial intelligence and computer vision to industrial inspection and measurement systems. With a career spanning over three decades and a Ph.D. from the University of Arizona, he currently serves as a Distinguished Professor at the National Yunlin University of Science and Technology, Taiwan—an acknowledgment of his academic stature and impact.

🎓 Education

  • 📍 B.S. in Telecommunication Engineering
    National Chiao Tung University, Hsinchu, Taiwan – 1982

  • 📍 M.S. in Telecommunication Engineering
    National Chiao Tung University, Hsinchu, Taiwan – 1986

  • 🌎 Ph.D. in Electrical and Computer Engineering
    University of Arizona, Tucson, USA – 1993

💼 Work Experience

  • 👨‍🏫 Distinguished Professor
    Department of Electrical Engineering, National Yunlin University of Science and Technology (NYUST), Douliu, Taiwan
    – Current

🌟 Key Achievements

  • 🤖 Pioneering research in artificial intelligence and computer vision for automated optical inspection (AOI)

  • 🏭 Collaborated with 50+ companies to develop intelligent inspection and production automation systems

  • 🔬 Leader in applying cutting-edge AI techniques to real-world industrial measurement and inspection challenges

  • 📚 Significant contributor to academic and applied research in electrical and computer engineering

🏅 Awards & Honors

  • 🥇 Recognized as a Distinguished Professor at NYUST

  • 🏆 Multiple accolades and recognitions for industry collaboration and academic excellence

  • 🧠 Honored for impactful contributions to the field of automated inspection systems

Publication Top Notes:

Prototype design of an intelligent Internet of Things system combined green energy storage device

Distribution Analysis of Dental Plaque Based on Deep Learning

Automatic Optical Inspection for steel golf club

Prof. Dr. Nelson Gutierrez | Automation Awards | Best Researcher Award

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Prof. Dr. Nelson Gutierrez | Automation Awards | Best Researcher Award 

Prof. Dr. Nelson Gutierrez, UTE University, Ecuador

Dr. Nelson Ramiro Gutiérrez Suquillo is an Ecuadorian researcher and academic known for his interdisciplinary work at the intersection of robotics, renewable energy, and intelligent industrial systems. He serves as a research lecturer at Universidad UTE since 2015 and is currently pursuing a Ph.D. in Robotics. He holds master’s degrees in Renewable Energies & Energy Sustainability and Materials Science & Technology, as well as a bachelor’s degree in Electronic Engineering and Information Networks. His research spans AI-driven diagnostics, mobile robotics for humanitarian applications, sustainable mechanical design, and advanced signal processing. With publications in journals such as Sensors, Salud, Ciencia y Tecnología, INCISCOS, and Enfoque UTE, his work demonstrates a strong commitment to practical, impactful innovation. Dr. Gutiérrez combines technical depth with applied focus, contributing significantly to Ecuador’s academic and technological landscape through research, teaching, and development projects.

Professional Profile:

ORCID

Summary of Suitability for Best Researcher Award – Nelson Ramiro Gutiérrez Suquillo

Nelson Ramiro Gutiérrez Suquillo stands out as a versatile and impactful researcher whose work bridges robotics, renewable energy, AI-driven diagnostics, and sustainable engineering. His strong academic background, ongoing PhD in robotics, and dual master’s degrees in renewable energy and materials science demonstrate a deep and interdisciplinary technical foundation.

🎓 Education

  • 🧠 Ph.D. in Robotics (Ongoing) – Focused on mobile robotics, system identification, and intelligent systems

  • ⚡ Master’s in Renewable Energies & Energy Sustainability – Expertise in sustainable technologies and systems

  • 🔬 Master’s in Materials Science & Technology – Specialized in material behavior and simulation

  • 📡 Bachelor’s in Electronic Engineering and Information Networks – Strong foundation in electronics and industrial communication

💼 Work Experience

  • 👨‍🏫 Docente Investigador (Research Lecturer) – Universidad UTE (Since 2015)
    Leads research in robotics, AI for diagnostics, and energy sustainability

  • 🤖 Collaborator on applied robotics projects, including mobile platforms and humanitarian demining

  • ⚙️ Industrial systems innovator: developed prototypes for diagnostics and sustainable machinery

🏆 Achievements & Publications

  • 📚 Published in Sensors, Salud, Ciencia y Tecnología, INCISCOS, and Enfoque UTE

  • 🔍 Contributions include:

    • AI for predictive maintenance 🧠

    • System modeling of differential robots 🤖

    • Sustainable mechanical systems 🌱

    • Wavelet-based sEMG signal processing 🧾

    • ABS brake simulation using finite elements 🛞

🥇 Awards & Honors

  • 🏅 Recognized at national and institutional levels for contributions to sustainable innovation and applied robotics

  • 🧑‍🔬 Active leader in Ecuador’s academic and research ecosystem, mentoring students and spearheading interdisciplinary projects

Publication Top Notes:

Comparison of Two System Identification Approaches for a Four-Wheel Differential Robot Based on Velocity Command Execution

Optimization of Fault Prediction by A.I. in Industrial Equipment: analysis of the operating parameters of a Bench Grinder

Application of Model-Based Design for Filtering sEMG Signals Using Wavelet Transform

Diseño de Robot Móvil para tareas de Desminado Humanitario

Análisis por el método de elementos finitos del comportamiento de las pastillas de freno ABS con base de acero y zinc discretizando el elemento continuo utilizando software CAE

Analysis by the Finite Element Method of the Behavior of the Brake Pads Using CAE Software

Diseño y construcción de un prototipo para la extracción continua de aceite de la semilla Sacha Inchi con un proceso de prensado en frío

 

Ms. Jingjing Fan | Smart Automation Awards | Best Researcher Award

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Ms. Jingjing Fan | Smart Automation Awards | Best Researcher Award

Ms. Jingjing Fan, KUNLUN Digital Technology Co., Ltd. China

Jingjing Fan, a Bachelor of Engineering graduate from Hebei University of Technology in 2011, is currently a Product Manager at Kunlun Digital Technology Co., Ltd. Her expertise lies in the fields of Artificial Intelligence (AI) and the Internet of Things (IoT), with a focus on developing and implementing intelligent IoT platforms for the oil and gas industry. She also specializes in edge intelligence and cloud-edge collaborative technologies, driving innovative solutions that enhance connectivity and efficiency in industrial applications.

Professional Profile:

ORCID

Summary of Suitability for Best Researcher Award – Jingjing Fan

Jingjing Fan holds a Bachelor of Engineering from Hebei University of Technology and currently serves as a Product Manager at Kunlun Digital Technology Co., Ltd., affiliated with China National Petroleum Corporation. Her research focuses on high-impact areas in technology, specifically at the intersection of Artificial Intelligence (AI) and the Internet of Things (IoT). Her expertise in integrating AI and IoT in practical applications—such as the Intelligent Internet of Things platform for the oil and gas sector, edge intelligence, and cloud-edge collaborative technology—demonstrates her commitment to advancing technology in complex, real-world environments.

Education

  • Bachelor of Engineering from Hebei University of Technology, 2011.

Work Experience

  • Current Position: Product Manager at Kunlun Digital Technology Co., Ltd.
    • Research Topics:
      • AI and Internet of Things (IoT)
      • Intelligent IoT platform applications in oil and gas
      • Edge intelligence
      • Cloud-edge collaborative technology

Publication top Notes:

AIoT-Based Visual Anomaly Detection in Photovoltaic Sequence Data via Sequence Learning

Prof Dr. Jong-Chen Chen | Intelligent Control Award | Best Researcher Award

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Prof Dr. Jong-Chen Chen | Intelligent Control Award | Best Researcher Award

Prof Dr. Jong-Chen Chen, National YunLin University of Science and Technology, Taiwan

Jong-Chen Chen is a Full Professor in the Department of Information Management at the National Yunlin University of Science and Technology, Taiwan, where he has served since 2000. He earned his M.S. in Computer Science and Electrical Engineering from the University of Texas at Arlington in 1986 and his Ph.D. in Computer Science from Wayne State University in 1993. Dr. Chen is renowned for developing a neuromolecular model that abstracts biological structure-function relationships into a system’s structure, allowing for rich intraneuronal dynamics. This model enables the system to self-organize and learn, with applications in robotic maze navigation, image recognition, data differentiation, and more. His research spans areas such as evolvable hardware, brain-like computer simulation, bio-computing, evolutionary computation, and pattern recognition. He has published extensively in journals including Sensors, Applied Science, Biomimetics, Evolutionary Computation, and Neurocomputing.

Professional Profile:

ORCID

Summary of Suitability for Best Researcher Award:

Dr. Jong-Chen Chen demonstrates strong qualifications for the “Best Researcher Award” due to his significant contributions
in various fields of computer science, robotics, and biomedical applications. He has extensive experience, having earned an
M.S. degree in computer science and electrical engineering and a Ph.D. in computer science. His academic journey,
particularly his tenure as a full professor since 2000, reflects a longstanding commitment to research and education.

Education:

  • M.S. in Computer Science and Electrical Engineering (1986)
    • University of Texas at Arlington, USA
  • Ph.D. in Computer Science (1993)
    • Wayne State University, USA

Work Experience:

  • Full Professor (2000 – Present)
    • Department of Information Management, National Yunlin University of Science and Technology, Taiwan
    • Developed a neuromolecular model (1993), abstracting biological structure-function relationships into system structure, specifically focusing on intraneuronal dynamics.

Key Contributions:

  • Applied neuromolecular models in fields like robotic maze navigation, image recognition, and biomimetic robotics.
  • Published research in prestigious journals including Sensors, Applied Science, Biomimetics, Algorithms, Evolutionary Computation, Neuro-computing, and BioSystems.

Research Interests:

  • Evolvable hardware
  • Brain-like computer simulation
  • Ecosystem simulation
  • Bio-computing
  • Artificial life
  • Molecular electronics
  • Evolutionary computation
  • Genetic programming
  • Pattern recognition

Publication top Notes:

Application of Artificial Neuromolecular System in Robotic Arm Control to Assist Progressive Rehabilitation for Upper Extremity Stroke Patients

Applying an Artificial Neuromolecular System to the Application of Robotic Arm Motion Control in Assisting the Rehabilitation of Stroke Patients—An Artificial World Approach

Bridging the Finger-Action Gap between Hand Patients and Healthy People in Daily Life with a Biomimetic System

Using Artificial Neuro-Molecular System in Robotic Arm Motion Control-Taking Simulation of Rehabilitation as an Example

Effectiveness of Companion Robot Care for Dementia: A Systematic Review and Meta-Analysis

Using Homemade Pressure Device to Improve Plantar Pressure-A Case Study on the Patient with Lower Limb Lymphedema