Thai Ha Dang | Computers and Electronics in Agriculture | Best Researcher Award

Published on by Popular EngineerLeave a Comment on Thai Ha Dang | Computers and Electronics in Agriculture | Best Researcher Award

Mr. Thai Ha Dang | Computers and Electronics in Agriculture | Best Researcher Award

Researcher, University of North Texas, United States

Thai Ha Dang is a passionate graduate student currently pursuing his Ph.D. in Electrical Engineering at the University of North Texas. With over 2 years of experience in wearable embedded devices and wireless sensing systems, he specializes in RF energy harvesting and machine learning for signal processing. His research spans human and animal models, and he has worked on projects related to cow behavior classification, energy harvesting systems, and underwater monitoring. Thai’s commitment to research has led him to present at various international conferences and publish in high-impact journals. He has honed his skills in embedded system design, programming, and data analysis, making him a key player in the field of agricultural technology and sensor networks. His strong academic background and innovative contributions have made him a respected researcher among peers and mentors alike.

Profile

Orcid

Education

Thai Ha Dang’s educational journey began at Hanoi University of Science and Technology, Vietnam, where he earned his Degree of Engineer in Electrical Engineering, ranking in the top 15% of his class. He further advanced his studies by pursuing a Master’s degree in Electrical Computer Engineering at Pukyong National University in South Korea, where he graduated with a GPA of 4.12/4.5. This rigorous academic background provided a strong foundation in embedded systems, machine learning, and wireless sensor networks. Currently, he is enrolled in the Ph.D. program in Electrical Engineering at the University of North Texas, where his research focuses on wearable embedded devices and RF energy harvesting. His dedication to academia is reflected in his continued pursuit of knowledge and excellence in his research endeavors, particularly in the application of machine learning techniques for signal processing in embedded systems.

Experience

Thai Ha Dang has built a solid foundation in research and industry through diverse experiences. As a Research Assistant in the Embedded Sensing & Processing Systems (ESPS) Lab at the University of North Texas, he is currently working on developing an underwater monitoring system, combining his interests in wireless sensing and energy harvesting. Before this, he contributed to a wide array of projects at the AIOT Lab, Pukyong National University, where he designed a multi-channel embedded device for monitoring cow behavior. This involved system design, firmware development, and experimentation. He also gained hands-on experience during his tenure as an engineer in Samsung Display Vietnam’s AI group, where he worked on training neural networks for computer vision tasks related to defect detection. His strong technical skills, combined with a practical understanding of industry needs, make him well-equipped to tackle complex research challenges in embedded systems and machine learning applications.

Awards and Honors

Thai Ha Dang has been recognized for his contributions to the research community through several prestigious awards. Notably, he received the Best Paper Award at the Korea Institute of Convergence Signal Processing (KICSP) in December 2021 for his work on deep learning approaches for food quality assessment using hyperspectral sensors. Additionally, he was honored with the Brain Korea 21 Scholarship for the years 2021-2023, further validating his potential as a leader in his field. Thai’s academic excellence has been supported by research assistantships at both Pukyong National University and the University of California Irvine. These honors reflect his continuous pursuit of knowledge and the impact his work has had on advancing technology in agriculture and embedded systems. His recognition through these awards underscores his talent, dedication, and potential to drive innovation in his research.

Research Focus

Thai Ha Dang’s research primarily focuses on developing and applying wearable embedded systems for low-powered monitoring and energy harvesting, with a strong emphasis on machine learning techniques. His work includes creating self-powered systems, such as his wireless sensor network for monitoring cow behavior, which uses 915 MHz radio frequency energy harvesting. Another key area of his research is food quality monitoring, where he explores battery-free systems powered by RF energy harvesting to detect freshness in food products. Additionally, Thai has delved into underwater monitoring and aquaculture, with applications for shrimp larvae counting using multi-scale feature networks. His multidisciplinary research blends electrical engineering, machine learning, and sensor technology to address real-world challenges in agriculture, food safety, and environmental monitoring. Thai is particularly interested in developing sustainable and efficient systems that are capable of operating in challenging and remote environments, offering a glimpse into the future of intelligent, energy-efficient devices.

Publication Top Notes

  • “Self-Powered Cattle Behavior Monitoring System Using 915 MHz Radio Frequency Energy Harvesting,” IEEE Access, 2024.
  • “VAE-LSTM Data Augmentation for Cattle Behavior Classification Using a Wearable Inertial Sensor,” IEEE Sensor Letters, 2024.
  • “Radio-Frequency Energy Harvesting-based Self-Powered Dairy Cow Behavior Classification System,” IEEE Sensors Journal, 2023.
  • “A LoRaWAN-Based Smart Sensor Tag for Cow Behavior Monitoring,” IEEE Sensors Conference, 2022.
  • “B2EH: Batteryless BLE Sensor Network Using RF Energy Harvesting,” IEEE Applied Sensing Conference, 2023.
  • “Shrimp Larvae Counting in Dense Environments Using Size-Adaptive Density Map Estimation and Multi-scale Feature Network,” IEEE Transactions on Agrifood Electronics (accepted).

 

 

Qi Liang | Pattern Recognition | Excellence in Research

Published on by Popular EngineerLeave a Comment on Qi Liang | Pattern Recognition | Excellence in Research

Mr Qi Liang | Pattern Recognition | Excellence in Research

Master in Tongji University at China

Qi Liang is a dedicated researcher and master’s student at Tongji University, PR China, specializing in mechanical engineering. With a strong foundation in industrial engineering from Jiangsu University of Science and Technology, Qi has a keen interest in advancing technology through innovative research. Recognized for introducing self-supervised learning methods in semiconductor applications, Qi’s work aims to solve complex challenges in pattern recognition. Their publication in Engineering Applications of Artificial Intelligence reflects a commitment to high-impact research. With multiple ongoing projects and a focus on practical applications, Qi is paving the way for efficient solutions in the semiconductor industry.

Profile

Google Scholar

Strengths for the Award

  1. Innovative Research: Qi Liang has introduced a self-supervised learning method for few-shot learning in semiconductor applications, demonstrating originality and a significant contribution to the field.
  2. Publication Record: The recent publication in Engineering Applications of Artificial Intelligence showcases a commitment to high-quality research, adding to the credibility of the work.
  3. Diverse Research Interests: With a focus on computer vision, multi-modal learning, and fault diagnosis, Qi’s work spans multiple cutting-edge areas, which increases the potential impact of the research.
  4. Practical Applications: The research addresses real-world challenges in the semiconductor industry, offering low-cost, efficient methods that have immediate applicability.
  5. Academic Engagement: Qi’s active involvement in ongoing projects and industry collaborations indicates a robust engagement with both academic and practical aspects of research.

Areas for Improvement

  1. Broader Collaboration: Expanding collaborations with international researchers could enhance the research’s visibility and applicability on a global scale.
  2. Increased Publication Volume: While the current publication is commendable, a more extensive publication record could further establish Qi’s expertise and leadership in the field.
  3. Outreach and Communication: Engaging in more outreach activities, such as conferences and seminars, could help disseminate findings and foster connections within the research community.

Education 

Qi Liang graduated with a Bachelor’s degree in Industrial Engineering from Jiangsu University of Science and Technology, where foundational principles of engineering and technology were mastered. Currently, Qi is pursuing a Master’s degree in Mechanical Engineering at Tongji University, one of China’s prestigious institutions, now in their third year of the program. This advanced education has allowed Qi to engage deeply with cutting-edge topics, particularly in computer vision and machine learning. Through rigorous coursework and research, Qi has developed expertise in areas such as pattern recognition, self-supervised learning, and fault diagnosis, equipping them with the skills necessary to tackle complex engineering problems and contribute significantly to both academic and industrial advancements.

Experience

Qi Liang has gained substantial experience through multiple research projects, totaling five completed or ongoing initiatives that emphasize practical applications of machine learning in semiconductor manufacturing. In addition to academic research, Qi has participated in three consultancy and industry-sponsored projects, bridging the gap between theoretical knowledge and real-world applications. Their collaborative efforts in research have led to valuable partnerships and a broader understanding of the industry’s challenges and needs. As the first to implement self-supervised learning techniques in few-shot learning tasks related to wafer map pattern recognition, Qi has showcased exceptional innovation. This unique approach has opened new avenues for cost-effective and efficient solutions within the semiconductor sector, positioning Qi as an emerging leader in their field.

Research Focus 

Qi Liang’s research focuses on the intersection of computer vision and machine learning, with a strong emphasis on pattern recognition, keypoint detection, and image retrieval. Specializing in self-supervised and multi-modal learning, Qi aims to develop innovative methodologies that minimize the reliance on labeled data while maximizing efficiency and applicability in industrial contexts. Current research projects explore dynamic adaptation mechanisms for few-shot learning, specifically tailored for wafer map pattern recognition in the semiconductor industry. Qi is also interested in signal processing and fault diagnosis, seeking to improve reliability and performance in manufacturing processes. This research direction not only contributes to the academic community but also addresses pressing industry challenges, promoting advancements in automation and smart manufacturing.

Publication Top Notes

  • Masked Autoencoder with Dynamic Multi-Loss Adaptation Mechanism for Few Shot Wafer Map Pattern Recognition 📄

Conclusion

Qi Liang’s innovative contributions to the field of mechanical engineering and computer vision make a strong case for the Excellence in Research award. The unique approach to self-supervised learning in few-shot learning for wafer map pattern recognition signifies both a breakthrough in methodology and practical application in the semiconductor industry. With a few strategic improvements, Qi has the potential to further amplify the impact of their research and cement their status as a leading researcher in their field.

Hassan Alli | Industrial Design | Best Researcher Award

Published on by Popular EngineerLeave a Comment on Hassan Alli | Industrial Design | Best Researcher Award

Assoc Prof Dr Hassan Alli | Industrial Design | Best Researcher Award

Assoc Prof Dr Hassan Alli , University Putra Malaysia, Malaysia

Associate Professor Dr. Hassan  Alli is a Senior Lecturer at the Department of Industrial Design, Faculty of Design and Architecture, Universiti Putra Malaysia. With a PhD in Engineering Design from the University of Malaya, he has played pivotal roles including Head of Department and Deputy Dean. Hassan founded the Industrial Design program at UPM and authored numerous books and academic papers. His research spans new product development methodologies, innovation management, sustainable design, and SMART design strategies.

Publication Profile

Scopus

Education 

Dr. Hassan Alli holds a PhD in Engineering Design from the University of Malaya, a Master’s in Automotive Design from Coventry University, UK, and a Bachelor’s in Industrial Design from University Technology MARA, Malaysia. His educational journey integrates engineering principles with design methodologies, enriching his insights into consumer products, furniture, and automotive industries.

Experience 

Dr Hassan’s Alli career at University Putra Malaysia spans over two decades, beginning as a Tutor and advancing to Senior Lecturer and Associate Professor. His administrative roles include Head of Department and Deputy Dean, focusing on industry relations and program development. Hassan’s industrial experience as a designer for Malaysian firms complements his academic achievements, shaping his holistic approach to industrial design education and research.

Awards and Honors 

Dr Hassan Alli has received multiple awards for his contributions to design education and research excellence. As an educator, he has been recognized for setting high teaching standards and fostering innovation among students. His consultancies include projects with Malaysian governmental bodies and international corporations, further showcasing his impact on the industry. Notably, Hassan’s involvement in foresight projects for Malaysia’s furniture industry underscores his influence in shaping future industry trends and practices.

Research Focus 

Dr. Hassan’s research centers on advancing new product development through innovative methodologies, including design strategy, foresight, and sustainable practices. His expertise in SMART design and innovation management aims to enhance product marketability and sustainability across diverse industries. Hassan’s publications and consultations contribute significantly to the field of industrial design, addressing contemporary challenges and opportunities in product design and development.

Publication Top Notes

“A Weighted Fuzzy Approach for the Agility of Sustainable Product Development Process Assessment: A Case Study in Chinese Medium-sized Enterprises”

“A Current Design Approach for Ming Chairs”

“A Systematic Review of Research on Sitting and Working Furniture Ergonomic from 2012 to 2022: Analysis of Assessment Approaches”

“Preserving Zibo Cultural Heritage: Exploring the Symbolism, Visual Identity, and Conservation Efforts of the Fish Motif Pattern Design”

“GA Optimization-based BRB AI Reasoning Algorithm for Determining the Factors Affecting Customer Churn for Operators”

“The Trends of Potential User Research from 2014-2023 Based on Bibliometric and Bertopic”

“Crossover and Extension of Hand-drawn Map in Heritage Tourism: A Case Study”

“The Impact of User Preference and Perceived Value on Customer Satisfaction and Marketability at Traditional Handicraft Product”

“Optimal Measurement of Visual Transmission Design Based on CAD and Data Mining”

“A Thematic Review on Traditional Handicraft from Design Perspective Publications from 2002–2022: Analysis of Trends for Future Studies”