Kim Bjerge | Signal Processing | Best Researcher Award

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Kim Bjerge | Signal Processing | Best Researcher Award

Mr. Kim Bjerge, Aarhus University, Denmark.

Kim Bjerge is an Associate Professor at Aarhus University in the Department of Electrical and Computer Engineering, specializing in Signal Processing and Machine Learning. With a Ph.D. focused on Computer Vision and Deep Learning for Insect Monitoring, Kim combines academic expertise with significant industry experience. He has held various teaching and leadership positions at Aarhus University and has contributed to research projects in computer vision. His work has resulted in a notable H-index of 14 and 1080 citations on Google Scholar. Kim is dedicated to advancing technology in engineering education and research. 🎓💻📈

Publication Profiles 

Googlescholoar

Education and Experience

  • Ph.D. in Computer Vision and Deep Learning for Insect Monitoring (Aarhus University, 2022 – present) 📚
  • M.Sc. Eng. in Information Technology (Aarhus University, 2013) 📖
  • B. Eng. in Electronics Engineering (Engineering College of Aarhus, 1989) 🔧
  • Associate Professor and Group Leader (Aarhus University, 2021 – present) 🎓
  • Associate Professor and Group Leader, Signal Processing (Aarhus University, 2009 – 2020) 📊
  • Senior Consultant, IT-Development (Danish Technological Institute, 2007 – 2009) 🛠️
  • Software Development Manager (TC Electronic A/S, 1999 – 2007) 🎶
  • System Developer (Crisplant A/S, 1996 – 1999) 📦
  • System Manager (Sam-system A/S, 1989 – 1996) 💼

Suitability For The Award

Mr. Kim Bjerge, Associate Professor at Aarhus University’s Department of Electrical and Computer Engineering, is an exemplary candidate for the Best Researcher Award due to his outstanding contributions to computer vision, deep learning, and signal processing. With a remarkable career spanning academia and industry, he has made groundbreaking advancements in the fields of artificial intelligence, embedded systems, and digital signal processing, impacting both research and application development globally.

Professional Development

Kim Bjerge has pursued extensive professional development through various programs. He completed the Pedagogical Programme in Engineering at the Center for Engineering Education Research and Development, earning 10 ECTS credits. Additionally, he participated in project management training at Provinu and various management courses at Aarhus Business College, enhancing his skills in human resources, organizational strategy, and software engineering. His commitment to ongoing learning ensures that he remains at the forefront of engineering education and technology. 📚🔧🌱

Research Focus

Kim Bjerge’s research focuses on the intersection of computer vision, deep learning, and machine learning, particularly in the context of insect monitoring. His work aims to develop innovative solutions that enhance the understanding and management of ecological systems through advanced image analysis and artificial intelligence techniques. By leveraging his expertise in signal processing, he contributes to the development of cutting-edge technologies that have practical applications in various fields, including agriculture and environmental science. 🌱🔍🤖

Publication Top Notes 

  • Deep learning and computer vision will transform entomology – Cited by: 362, Year: 2021 📖
  • Towards the fully automated monitoring of ecological communities – Cited by: 141, Year: 2022 🌱
  • An automated light trap to monitor moths (Lepidoptera) using computer vision-based tracking and deep learning – Cited by: 119, Year: 2021 🦋
  • Real-time insect tracking and monitoring with computer vision and deep learning – Cited by: 110, Year: 2021 📹
  • A computer vision system to monitor the infestation level of Varroa destructor in a honeybee colony – Cited by: 85, Year: 2019 🐝
  • Accurate detection and identification of insects from camera trap images with deep learning – Cited by: 61, Year: 2023 🔍
  • A living laboratory exploring mobile support for everyday life with diabetes – Cited by: 40, Year: 2010 📱
  • Hierarchical classification of insects with multitask learning and anomaly detection – Cited by: 26, Year: 2023 📊
  • Enhancing non-technical skills by a multidisciplinary engineering summer school – Cited by: 19, Year: 2017 🎓

Mr. Qiang Yu | Signal Theory Award | Best Researcher Award

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Mr. Qiang Yu | Signal Theory Award | Best Researcher Award

Mr. Qiang Yu, Shanxi Normal University, China

Dr. Qiang Yu is a distinguished professor in the Department of Mathematical Sciences at Shanxi Normal University, China. With a solid academic foundation, Dr. Yu obtained his B.S. in Mathematics Education from Shi He Zi University in 2003, followed by an M.S. in Applied Mathematics and a Ph.D. in Basic Mathematics from Shaanxi Normal University in 2009 and 2014, respectively. His doctoral research, under the guidance of Professor Baowei Wu, focused on nonlinear systems, stability, stabilization, and control. Dr. Yu’s professional career began as a mathematics teacher at Fukang No.1 Senior School in Xinjiang. He then advanced to academic positions at Heng Shui University and Shaanxi Normal University, where he steadily rose through the ranks from Lecturer to Associate Professor, and ultimately to Professor in December 2023. He also served as a visiting professor at the Shibaura Institute of Technology in Japan from 2019 to 2020, collaborating with Prof. Guisheng Zhai.

Professional Profile:

GOOGLE SCHOLAR

Summary of Suitability for Best Researcher Award

Dr. Qiang Yu demonstrates an exceptional academic and professional background that makes him highly suitable for the Best Researcher Award. His educational foundation in mathematics (B.S., M.S., and Ph.D. from Shi He Zi University and Shaanxi Normal University) underpins his rigorous work in complex areas like control theory, dynamical systems, and nonlinear systems. He has further specialized in switched systems, robust control, and time-delay systems—highly relevant research areas with widespread applications in engineering and mathematics.

📚 Academic Background

  • 🎓 B.S. in Mathematics Education (1999-2003) – Shi He Zi University
  • 🎓 M.S. in Applied Mathematics (2006-2009) – Shaanxi Normal University
  • 🎓 Ph.D. in Basic Mathematics (2011-2014) – Shaanxi Normal University
    • 📑 Advisor: Professor Baowei Wu
    • 🔍 Research Theme: Nonlinear Systems, Stability, Stabilization, and Control

🏫 Professional Experience

  • 🧑‍🏫 Math Teacher – Fukang No.1 Senior School, Xinjiang (2003-2006)
  • 📈 Lecturer – Heng Shui University, Hebei (2009-2011)
  • 🎓 Lecturer, Associate Professor, and Professor – Shaanxi Normal University, Shanxi (2014-present)
  • 🌏 Visiting Professor – Shibaura Institute of Technology, Saitama, Japan (2019-2020)

🔬 Research Interests

  • 🛠️ Switched Systems
  • 📏 Robust Control
  • ⏳ Time-Delay Systems
  • 📊 Control Theory and Engineering
  • 🧮 Applied Mathematics
  • 🔄 Dynamical Systems
  • 🤖 Neural Networks

🌐 Professional Memberships and Activities

  • 🔍 Reviewer for Mathematical Reviews
  • 🏅 Member, IEEE Control Systems Society
  • 🧩 Member, Chinese Mathematical Society
  • 🤖 Member, Chinese Association of Automation
  • 🇨🇳 Member, Society of Industry and Applied Mathematics of China
  • ✒️ Editorial Board Member, American Journal of Applied Mathematics (Since 2020)

🏆 Honors and Awards

  • 🥇 First Prize Gardener’s Scholarship, Shaanxi Normal University (2013)
  • 🥈 Second Prize in National College Student Academic Competitions (2013)
  • 🎖️ National Scholarship for Doctoral Students, Ministry of Education of China (2013)
  • 🏅 Excellent Postgraduate Student Award, Shaanxi Normal University (2014)
  • 🥉 Third Prize in Teachers’ Skills Competition, Shanxi Normal University (2015)
  • 🥈 Second Prize in National Mathematics Micro Course Design Competition (2016)
  • 🌟 Advanced Individual for Educational Excellence, Shanxi Normal University (2017)
  • 🥉 Third Prize for Excellent Academic Paper, Shanxi Science and Technology Department (2018)
  • 🥇 First Prize Science and Technology Award, Shaanxi Higher Education Institutions (2019)
  • 🏆 Second Prize of the Natural Science Award, Shaanxi Science and Technology (2022)

Publication top Notes:

Sampled-data synchronization of delayed multi-agent networks and its application to coupled circuit

CITED:62

Analysis of mixed convection flow in an inclined lid-driven enclosure with Buongiorno’s nanofluid model

CITED:56

Stability analysis for discrete-time switched systems with stable and unstable modes based on a weighted average dwell time approach

CITED:45

Coiflets solutions for Föppl-von Kármán equations governing large deflection of a thin flat plate by a novel wavelet-homotopy approach

CITED:39

Robust stability analysis of uncertain switched linear systems with unstable subsystems

CITED:38

Stability analysis of discrete-time switched linear systems with unstable subsystems

CITED:36

Mr. Yeonjae Park | Signal Cleaning Award | Best Scholar Award

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Mr. Yeonjae Park | Signal Cleaning Award | Best Scholar Award

Mr. Yeonjae Park, The Graduate School of Yonsei University, South Korea

Yeonjae Park is a Master’s student at Yonsei University in the Department of Medical Informatics and Biostatistics, under the guidance of Professor Dae Ryong Kang. With a strong foundation in Computer and Telecommunication Engineering as well as Information and Statistics, Park obtained dual B.S. degrees from Yonsei University, where they were mentored by Professors Cho Young-rae and Na Seongyong. Their research interests span machine learning, deep learning, generative models, multi-modal data analysis, and time series forecasting. Park has gained valuable research experience through various positions, including as a researcher intern at the Artificial Intelligence-Information Retrieval Lab, a researcher at the Applied Data Science Lab, and their current role at the National Health BigData Clinical Research Institute. Their projects encompass a range of topics, from text extraction and OCR recognition to complex analyses in genomics, disease correlations, and the effectiveness of medical treatments.

Professional Profile:

Summary of Suitability for Best Scholar Award:

Yeonjae Park has a strong academic foundation, holding dual Bachelor’s degrees in Computer and Telecommunication Engineering and Information and Statistics from Yonsei University, one of South Korea’s most prestigious institutions. Currently, Yeonjae is pursuing a Master’s degree in Medical Informatics and Biostatistics at the same university, under the guidance of a notable advisor, Dae Ryong Kang.

Education 📚

  • Samseon Middle School, Seoul, Korea (Mar. 2010 ~ Jul. 2010)
  • SungSan Middle School, Seoul, Korea (Jul. 2010 ~ Feb. 2013)
  • Kwangsung High School, Seoul, Korea (Mar. 2013 ~ Feb. 2016)
  • Yonsei University, Department of Computer and Telecommunication Engineering 🖥️ (Mar. 2016 ~ Aug. 2021)
    • B.S. in Computer and Telecommunication Engineering
    • Advisor: Prof. Cho Young-rae
  • Yonsei University, Department of Information and Statistics 📊 (Feb. 2016 ~ Aug. 2021)
    • B.S. in Information and Statistics
    • Advisor: Prof. Na Seongyong
  • Yonsei University, Department of Medical Informatics and Biostatistics 🧬 (Aug. 2021 ~ Present)
    • Master Student
    • Advisor: Prof. Dae Ryong Kang

Research Interests 🔍

  • Machine Learning / Deep Learning 🤖
  • Generative Models 🌀
  • Multi Modal 🧠
  • Time Series Forecasting ⏳

Research Experiences 💼

  • Researcher Intern at Artificial Intelligence-Information Retrieval Lab, Yonsei University, Korea (May. 2019 ~ Apr. 2020)
  • Researcher at Applied Data Science Lab, Yonsei University, Korea (May. 2020 ~ Jan. 2021)
  • Researcher at National Health BigData Clinical Research Institute, Korea (Jan. 2021 ~ Present)

 

Publication top Notes:

Development and Validation of a Real-Time Service Model for Noise Removal and Arrhythmia Classification Using Electrocardiogram Signals

Intracardiac Echocardiogram: Feasibility, Efficacy, and Safety for Guidance of Transcatheter Multiple Atrial Septal Defects Closure

 

 

 

Dr. Zhigang Zhu | Signal Processing Award | Best Researcher Award

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Dr. Zhigang Zhu | Signal Processing Award | Best Researcher Award

Dr. Zhigang Zhu, Xidian University, China

Zhigang Zhu, born on October 27, 1989, is a distinguished postdoctoral researcher in the School of Electronic Engineering at Xidian University. With a robust educational foundation, Zhigang holds a Ph.D. in Control Science and Engineering from Xidian University. His academic journey began at Qingdao University of Technology, where he earned his undergraduate degree in Telecommunication Engineering in 2009.Zhigang’s expertise lies in deep learning and signal processing, with a keen focus on signal representation and recognition. His research achievements are substantial, having published over 20 SCI-indexed papers in prestigious journals such as Remote Sensing, IEEE TAES, IEEE TIM, and IEEE SPL. He is a recognized member of both the Chinese Institute of Electronics (CIE) and the Institute of Electrical and Electronics Engineers (IEEE).

Professional Profile

🎓 Education & Academic Achievements:

I hold a Ph.D. in Control Science and Engineering from Xidian University, completed in 2015. I began my academic journey with a Bachelor’s degree in Telecommunication Engineering from Qingdao University of Technology in 2009. Currently, I am a postdoctoral researcher in the School of Electronic Engineering at Xidian University. My specialization lies in deep learning and signal processing, particularly in signal representation and signal recognition.

📚 Experience & Professional Engagements:

Since 2015, I have been deeply involved in research and academia. I have led numerous projects, including a significant initiative by the National Natural Science Foundation of China focused on deep learning. My work in electronics science and technology has earned me accolades such as the Shaanxi Higher Education Institutions Scientific Research Outstanding Achievement Award. Additionally, I have made substantial contributions to the field by publishing over 20 SCI-indexed papers in renowned journals like IEEE TAES and IEEE TIM.

🌐 Research & Contributions:

My research interests include computer vision, signal processing, and deep learning. I have been recognized with multiple national and provincial awards for my innovative research and entrepreneurial efforts. As a member of both the Chinese Institute of Electronics (CIE) and the Institute of Electrical and Electronics Engineers (IEEE), I actively contribute to the scientific community. I have also guided a student team to win prestigious awards in competitions such as the Shaanxi Provincial Internet+ Innovation and Entrepreneurship Competition.

🏆 Recognition & Impact:

My dedication to advancing technology and fostering innovation has been recognized through various awards, including the Excellence Award at the National Post-Doctoral Innovation and Entrepreneurship Competition. I strive to inspire the next generation of researchers and apply my work for the benefit of society.

 

.Publications Notes:📄

 

 

 

Dr. Sangyeop Lee | Signal Processing | Best Researcher Award

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Dr. Sangyeop Lee | Signal Processing | Best Researcher Award

Dr. Sangyeop Lee, LG Electronics, South Korea

Sangyeop Lee, Ph.D., is a seasoned Senior Researcher and Data Scientist at LG Electronics, currently based at the Life Data Fusion Laboratory within the B2B Advanced Technology Center in Seoul, Republic of Korea. With a robust academic background, including a Ph.D. in Computer Science from Yonsei University, Sangyeop has been actively involved in both research and academia. His research interests span various domains, notably including LLM fine-tuning, artificial neural networks for biomedical signal processing, and context-awareness in the clinical domain using machine learning techniques. Throughout his career, he has contributed significantly to cutting-edge projects such as Smartcare in Kindergarten and neptuNE, addressing critical issues like child behavior monitoring and home healthcare. Sangyeop’s expertise extends to teaching and mentoring, evident from his engagements as a lecturer and teaching assistant at Yonsei University. His dedication to advancing technology and solving real-world problems underscores his commitment to innovation in the fields of data science and healthcare.

Professional Profile

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Affiliation:

Sangyeop is currently affiliated with the LEAD technology task at the Life Data Fusion Laboratory within the B2B Advanced Technology Center at LG Electronics, located in Seocho R&D Campus, Seoul, Republic of Korea.

Research Interests:

His research interests include LLM fine-tuning, artificial neural networks for biomedical signal processing, and context-awareness using machine learning techniques in clinical settings.

Teaching Experience:

Sangyeop has contributed to education as a lecturer and teaching assistant at Yonsei University, covering subjects like AI for Medical Problems and Engineering Information Processing, where he taught Python practice.

Projects:

  1. Smartcare in Kindergarten: Collaborated with DNX Kidsnote and Severance Hospital to utilize AI technology in studying children’s behavior and location in kindergartens using wearables/radars.
  2. neptuNE: Developed sensors and mobile devices for home monitoring, addressing nocturnal enuresis in children, in collaboration with Samsung Electronics and Severance Hospital.
  3. Ready-Made Implant: Conducted a confidential study on mass production with pre-made implants and recommending customized implant models through dental data analysis, in collaboration with Ostem Implant and Yonsei University.

Publications:

Sangyeop has several publications in prestigious conferences and journals, including IEEE Radar Conference and Sensors, focusing on topics like artificial intelligence, biomedical engineering, and healthcare.

Application:

Sangyeop has contributed to the development of in-home monitoring with wearables and NE Diary Application, enhancing healthcare solutions through technology.

Sangyeop’s dedication to advancing data-driven solutions in healthcare underscores his commitment to innovation and improving patient outcomes. 🌟

Publications Notes:📄

Wearable-Based Integrated System for In-Home Monitoring and Analysis of Nocturnal Enuresis

Continuous body impedance measurement to detect bladder volume changes during urodynamic study: A prospective study in pediatric patients