Dr. Thanh-Nghia Nguyen | Signal Processing | Best Researcher Award

Published on by Network Awards

Dr. Thanh-Nghia Nguyen | Signal Processing | Best Researcher Award

Dr. Thanh-Nghia Nguyen | HCMC University of Technology and Education | Vietnam

Dr. Nguyen Thanh Nghia (PhD) is a dedicated researcher and educator in Electronics and Biomedical Engineering at Ho Chi Minh City University of Technology and Education. With expertise in Biomedical Signal Processing, Artificial Intelligence, and Electronic Engineering, his research focuses on ECG signal analysis, deep learning applications, and medical device development. Dr. Nghia has contributed extensively through publications and research projects, particularly in ECG noise elimination and heart disease classification. His work bridges the gap between AI and healthcare, advancing biomedical engineering for better patient diagnostics and monitoring. 🌍📡🧠

Professional Profile:

ORCID

Suitability for Best Researcher Award

Dr. Nguyen Thanh Nghia is a strong candidate for the Best Researcher Award due to his outstanding contributions to biomedical signal processing, artificial intelligence applications in healthcare, and electronic engineering. His research has significantly impacted medical diagnostics, ECG signal enhancement, and AI-driven healthcare solutions, making his work highly valuable in both academia and industry.

Education & Experience🎓🔬

📌 PhD in Electronics Engineering – Ho Chi Minh City University of Technology and Education (2016-2023)
📌 Master’s in Electronics Engineering – Ho Chi Minh City University of Technology and Education (2009-2012)
📌 Bachelor’s in Electrical & Electronics Engineering – Ho Chi Minh City University of Technology and Education (2002-2007)

🔧 2007-2010 – Engineer, TNHH Wonderful Sai Gon Electrics (WSE), Vietnam (Machine Maintenance, Repair & ISO Management)
📡 2010-2017 – Lecturer, Cao Thang Technical College (Electronics Engineering)
👨‍🏫 2017-Present – Lecturer & Researcher, Ho Chi Minh City University of Technology and Education (Electronics & Biomedical Engineering)

Professional Development 🚀

Dr. Nguyen Thanh Nghia continuously enhances his expertise in Biomedical Engineering and Artificial Intelligence. He has led multiple research projects, developing ECG noise filters, heart disease classification systems, and medical signal processing tools. His work integrates machine learning and deep learning models for improved healthcare applications. Dr. Nghia actively collaborates with international scholars, publishing in high-impact journals 📑. He also mentors students and professionals, shaping the future of biomedical technology. His passion for innovation in AI-driven medical devices is evident in his contributions to academia and industry, fostering advancements in diagnostic healthcare systems. 🏥💡

Research Focus 🔍📊

Dr. Nguyen Thanh Nghia’s research primarily focuses on Biomedical Signal Processing, with an emphasis on ECG signal analysis, artifact removal, and AI-driven medical diagnostics. His work in deep learning-based heart disease classification contributes to the automation of medical diagnoses and remote health monitoring 🏥📡. He also explores wearable sensor technology, EEG-based brain-computer interfaces (BCI), and AI applications in healthcare 🤖. Through his innovative research, Dr. Nghia aims to enhance health monitoring systems, reduce diagnostic errors, and advance medical signal processing techniques, ultimately improving patient care and medical technology. 📉💙

Awards & Honors 🏆🎖️

🏅 Best Research Paper Award – Recognized for outstanding contributions to Biomedical Signal Processing & AI applications 📝
🏅 Outstanding Researcher Award – Ho Chi Minh City University of Technology and Education (for excellence in AI-driven ECG analysis) 📡
🏅 Top Innovator in Medical Engineering – Honored for advancements in ECG noise elimination and AI-based medical diagnostics 🏥🧠
🏅 Young Scientist Recognition – For impactful publications in deep learning and medical signal processing 📊📚

Publication Top Notes:

    • 📌 A VGG-19 model with transfer learning and image segmentation for classification of tomato leaf disease – TH Nguyen, TN Nguyen, BV Ngo  🔗 Cited by: 69
    • 📌 A deep learning framework for heart disease classification in an IoTs-based system – TH Nguyen, TN Nguyen, TT Nguyen  🔗 Cited by: 24
    • 📌 Detection of EEG-based eye-blinks using a thresholding algorithm – DK Tran, TH Nguyen, TN Nguyen 🔗 Cited by: 17
    • 📌 Artifact elimination in ECG signal using wavelet transform – TN Nguyen, TH Nguyen, VT Ngo🔗 Cited by: 17
    • 📌 Deep Learning Framework with ECG Feature-Based Kernels for Heart Disease Classification – THN Thanh-Nghia Nguyen 🔗 Cited by: 16

 

 

Mohtasham Khanahmadi | signal processing | Best Researcher Award

Published on by Network Awards

Mohtasham Khanahmadi | signal processing | Best Researcher Award

Mr.Mohtasham Khanahmadi, Semnan University, Iran.

Mr.Mohtasham Khanahmadi is an Iranian civil engineering researcher specializing in structural health monitoring, damage detection, and signal processing. With over five years of experience, he focuses on nondestructive evaluation, inverse problems, and modal analysis of thin-walled and composite structures. He holds a B.Sc. in Civil Engineering from Velayat University and an M.Sc. in Structural Engineering from Semnan University. Proficient in MATLAB, Abaqus, and computational modeling, he has authored impactful research on damage localization and interfacial debonding detection. Passionate about enhancing structural integrity, his contributions advance the field of applied and computational mathematics in civil engineering. 🔍🛠️

Publication Profile

Orcid
Scopus
Google Scholar

Education & Experience 🎓👷‍♂️

📌 B.Sc. in Civil Engineering – Velayat University, Iran (2011–2015)
📌 M.Sc. in Structural Engineering – Semnan University, Iran (2015–2018)
📌 Researcher in Structural Health Monitoring & Damage Detection (5+ years)
📌 Expert in Computational Mathematics & Signal Processing for Civil Structures
📌 Published Research in High-Impact Structural Engineering Journals.

Suitability Summary

Dr. Mohtasham Khanahmadi is a distinguished civil engineering researcher recognized for his outstanding contributions to structural health monitoring, damage detection, and signal processing. With over five years of dedicated research, he has demonstrated exceptional expertise in nondestructive evaluation, applied and computational mathematics, inverse problems, and modal analysis of thin-walled and composite structures, including plates, beams, and columns. His pioneering methodologies have significantly advanced the assessment of structural integrity and performance, making him a highly deserving candidate for the Best Researcher Award.

Professional Development 📈🔬

Mr.Mohtasham Khanahmadi actively contributes to the advancement of structural health monitoring through cutting-edge research in damage detection and localization techniques. His expertise spans signal processing,  nverse problems, and modal analysis, with a strong focus on nondestructive evaluation of civil structures. Skilled in MATLAB, Abaqus, and Microsoft Office tools, he integrates computational methods to enhance structural performance. His work in analyzing thin-walled and composite structures under axial loads has led to significant advancements in interfacial debonding detection and modal curvature-based irregularity indices. Dedicated to academic excellence, he continuously engages in professional learning and knowledge dissemination. 📊🏗️.

Research Focus  🔍🏢

Mr.Mohtasham Khanahmadi’s research centers on structural health monitoring 🏗️, emphasizing damage detection and localization in civil engineering structures. His work involves signal processing 📡, nondestructive evaluation 🛠️, and computational mathematics 🔢 to enhance the integrity of thin-walled and composite structures such as plates, beams, and columns. He specializes in inverse problem-solving to assess structural behavior under different conditions, including modal analysis of concrete-filled steel tubular (CFST) columns. By developing advanced methodologies, he contributes to the early detection of structural failures, leading to safer and more efficient engineering solutions. 🚧🔬.

Awards & Honors 🏆🎖️

🏅 Recognized for impactful research in structural health monitoring & damage detection
🏅 Published in high-impact journals, including Measurement & IJSSD
🏅 Contributions to computational civil engineering methodologies acknowledged in academia
🏅 Active collaborator in multidisciplinary structural engineering research projects
🏅 Recognized for advancing nondestructive evaluation techniques for damage localization.

Publication Top Notes

📌 A numerical study on vibration-based interface debonding detection of CFST columns using an effective wavelet-based feature extraction technique – 2024

📌 Interfacial Debonding Detection in Concrete-Filled Steel Tubular (CFST) Columns with Modal Curvature-Based Irregularity Detection Indices – 2024

📌 Vibration-based damage localization in 3D sandwich panels using an irregularity detection index (IDI) based on signal processing – 2024

📌 Vibration-based health monitoring and damage detection in beam-like structures with innovative approaches based on signal processing: A numerical and experimental study – 2024

 

 

 

Dr. Kim BeomHun | Signal Processing | Best Researcher Award

Published on by Network Awards

Dr. Kim BeomHun | Signal Processing | Best Researcher Award

Dr. Kim BeomHun, Chosun University, South Korea

Kim Beom-Hun is an Adjunct Professor at Chosun University and Gwangju Women’s University, as well as a Principal Researcher at Corp. G-AILAB. He holds a Ph.D. in Information Communication Engineering (2021) and has a background in Software Convergence Engineering and Computer Engineering, all from Chosun University. With expertise in signal processing and artificial intelligence, Dr. Kim has a notable research record, including publications in prestigious journals like IEEE Access and Sensors. His career includes roles as Research Director at Corp. Onwards and Principal Researcher at Korea Micro Medical Robot Research Institute. He is also certified as an Information Processing Engineer.

Professional Profile:

Orcid

Suitability of Kim Beom-Hun for the Research for Best Researcher Award

Summary of Suitability:

Dr. Kim Beom-Hun is a highly suitable candidate for the Research for Best Researcher Award due to his significant contributions to the fields of signal processing and artificial intelligence, as well as his extensive research experience and academic achievements. His work has demonstrated innovation and excellence in several advanced areas of technology and engineering.

🎓Education:

Kim Beom-Hun earned his Ph.D. in Information Communication Engineering from Chosun University in 2021. He previously completed his Master’s degree in Software Convergence Engineering at the same institution in 2015. His academic journey began with a Bachelor’s degree in Computer Engineering, which he also obtained from Chosun University in 2010.

🏢Work Experience:

Kim Beom-Hun has been serving as an Adjunct Professor at Chosun University since 2023 and at Gwangju Women’s University since 2022. He is also a Principal Researcher at Corp. G-AiLab, starting in 2024. Previously, he was the Research Director at Corp. Onwards from 2022 to 2023 and a Principal Researcher at the Korea Micro Medical Robot Research Institute (KIMIRo) / Healthcare Business Center in 2022. From 2021 to 2022, he worked as a Post-Doctoral Researcher in the Department of Electrical Engineering at Chonnam National University and as a Research Assistant at the IT Research Center of Chosun University from 2016 to 2017.

Publication Top Notes:

  1. A Study on the Mechanical Resonance Frequency of a Piezo Element: Analysis of Resonance Characteristics and Frequency Estimation Using a Long Short-Term Memory Model
  2. Improved Smartphone-Based Indoor Localization System Using Lightweight Fingerprinting and Inertial Sensors
  3. Mobility-Aware Resource Assignment to IoT Applications in Long-Range Wide Area Networks
  4. ECG Identification For Personal Authentication Using LSTM-Based Deep Recurrent Neural Networks
  5. Signal Processing for Tracking of Moving Object in Multi-Impulse Radar Network System