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

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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

 

 

Dr. Deepa Beeta thiyam | EEG Signal Processing | Women Researcher Award

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Dr. Deepa Beeta thiyam | EEG Signal Processing | Women Researcher Award

Dr. Deepa Beeta thiyam | Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology | India

🎓 Thiyam Deepa Beeta, Ph.D., is a researcher in Biomedical Engineering at Vel Tech University 🏫, with expertise in EEG signal processing 🧠 and Brain-Computer Interface (BCI) systems. She completed her co-directed PhD from VIT University, India 🇮🇳, and University of Seville, Spain 🇪🇸, focusing on motor imagery movement classification. Thiyam’s work aims to design robust algorithms for paralyzed patients using BCI technology 🌐. With extensive research experience and several publications 📚, she also contributes to teaching and mentoring future engineers and scientists 👩‍🏫. Her work is funded by various prestigious grants 💡.

Professional Profile:

ORCID

SCOPUS

Suitability for Women Researcher Award

Thiyam Deepa Beeta is highly suitable for the Women Researcher Award due to her outstanding contributions in the field of Biomedical Engineering, specifically in EEG signal processing and Brain-Computer Interface (BCI) systems. Her expertise in developing algorithms for motor imagery movement classification holds immense potential in improving the quality of life for paralyzed patients. This innovative work directly aligns with advancing healthcare through cutting-edge technologies, which makes her an exemplary candidate.

Education and Experience 🎓💼

  • PhD in Biomedical Engineering (VIT University, India) & Automática, Electrónica y Telecomunicaciones (University of Seville, Spain) (2012–2018)
    Research: EEG Signal Processing for Motor Imagery BCI Systems
  • M.Tech in Biomedical Engineering (VIT University, India) (2008–2010)
  • B.Tech in Biomedical Instrumentation Engineering (Dr. MGR Educational & Research Institute, India) (2004–2008)
  • Associate Professor at Vel Tech Rangarajan Dr. Sagunthala R & D Institute (2023–Present)
  • Assistant Professor at Vel Tech Rangarajan Dr. Sagunthala R & D Institute (2019–2023)
  • Teaching & Research Associate at VIT University, Vellore (2012–2017)

Professional Development 

Thiyam Deepa Beeta has demonstrated her leadership in Biomedical Engineering 🏥 by mentoring students 👩‍🏫 and contributing to academic journals 📖. As an Associate Professor at Vel Tech University, she teaches subjects like Biomedical Instrumentation and Microcontrollers 💻. Her expertise in EEG signal processing 🧠 and Brain-Computer Interfaces has shaped her research and helped her secure funding for projects 💡. Thiyam has also been a reviewer for international journals and conferences 🌍, such as IEEE Access and Biosignal Processing and Control, making her a prominent contributor to the field 📚.

Research Focus 

Thiyam Deepa Beeta’s research focuses on EEG signal processing 🧠, specifically in Brain-Computer Interface (BCI) systems for motor imagery movements 💡. Her goal is to develop robust algorithms for paralyzed individuals 🦽, using BCI to help them regain control of their motor functions. She works on signal classification techniques 📊 for motor tasks and explores hybrid BCI systems for improved performance. Her research integrates AI 🤖 and machine learning models, especially CNN-based systems for medical applications 💉, pushing the boundaries of biomedical engineering towards life-changing innovations for patients.

Awards and Honors 🏆

  • 🏅 CSIR Travel Grant for attending IEEE TENCON 2016 (Singapore)
  • 🎓 Heritage Erasmus Mundus Scholarship for research at University of Seville, Spain
  • 💡 Vel Tech University Internal Seed Fund for research on Motor Imagery EEG Signal Classification
  • 🏅 Project Funding from Ministry of Economy and Competitiveness, Spain

Publication Top Notes:

  • “A hybrid CNN model for classification of motor tasks obtained from hybrid BCI system,” Scientific Reports  🌐🧠
  • “Motor Imagery EEG Signal Classification Using Optimized Convolutional Neural Network,” Przeglad Elektrotechniczny  ⚡🧠
  • “Performance Analysis of HybridA-BCI Signals Using CNN for Motor Movement Classification,” Traitement du Signal  📊💻 |
  • “Simulational Study for Designing Lung on-Chip,” ICBSII Conference
  • “Biocompatibility of oxide nanoparticles,” Oxides for Medical Applications  📚🧪
  • “Signal Processing for Hybrid BCI Signals,” Journal of Physics: Conference Series )📡🔧
  • “A customized knee brace for osteoarthritis patient using 3D printing,” ICICV Conference

 

 

 

Mohtasham Khanahmadi | signal processing | Best Researcher Award

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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