Rasool Baghbani | Biomedical Sensors | Excellence in Research Award

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Prof. Dr. Rasool Baghbani | Biomedical Sensors | Excellence in Research Award

Hamedan University of Technology | Iran

Rasool Baghbani is a dedicated biomedical engineering scholar and faculty member at Hamedan University of Technology whose work bridges advanced engineering with clinical innovation through the development of intelligent medical devices, embedded microsystems, bioimpedance technologies, and IoMT solutions. His academic background spans electrical engineering, biomedical instrumentation, and bioelectrics, culminating in doctoral research focused on bioimpedance-based lung cancer diagnostics, which led to multiple scientific publications and validated methods for intraoperative assessment. His professional experience includes academic teaching, supervising graduate research, leading departmental initiatives, and contributing to national evaluation of medical equipment in alignment with international safety and performance standards. His research interests encompass biomedical sensors, firmware programming, biophysical signal analysis, medical robotics, bioelectromagnetics, and machine learning for health applications, supported by strong skills in Python, C++, MATLAB, microcontroller programming, PCB design, COMSOL modelling, laboratory instrumentation, and hardware prototyping. His achievements include multiple recognitions in teaching, research excellence, peer-review service for leading journals, and patents in medical technology. Through his interdisciplinary expertise and sustained commitment to innovation, he continues to advance biomedical engineering by designing practical, patient-centered solutions while inspiring future researchers, ultimately aiming to elevate healthcare through technology-driven problem solving.

Profile: Google scholar

Featured Publications

Baghbani, R., Rad, M. A., & Pourziad, A. (2015). Microwave sensor for non-invasive glucose measurements: Design and implementation of a novel linear method. IET Wireless Sensor Systems, 5(2), 51–57.
Citations: 58

Hamouleh-Alipour, A., Forouzeshfard, M., Baghbani, R., & Vafapour, Z. (2022). Blood hemoglobin concentration sensing by optical nano biosensor-based plasmonic metasurface: A feasibility study. IEEE Transactions on Nanotechnology, 1–8.
Citations: 49

Baghbani, R., Shadmehr, M. B., Ashoorirad, M., Molaeezadeh, S. F., & Moradi, M. H. (2021). Bioimpedance spectroscopy measurement and classification of lung tissue to identify pulmonary nodules. IEEE Transactions on Instrumentation and Measurement, 70, 1–7.
Citations: 45

Alipour, A. H., Khani, S., Ashoorirad, M., & Baghbani, R. (2023). Trapped multimodal resonance in magnetic field enhancement and sensitive THz plasmon sensor for toxic materials accusation. IEEE Sensors Journal, 23(13), 14057–14066.
Citations: 35

Junkang Zheng | Fault diagnosis | Best Researcher Award

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Mr. Junkang Zheng | Fault diagnosis | Best Researcher Award

Zhejiang Industry Polytechnic College | China

Junkang Zheng is a dedicated teacher at Zhejiang Industry Polytechnic College, specializing in the integration of artificial intelligence with industrial applications, particularly in intelligent fault diagnosis and numerical simulation. He has built his academic training and professional experience around computational analysis and smart diagnostic systems, applying AI-driven models to enhance accuracy, efficiency, and predictive analysis in industrial fault detection. His work demonstrates strong engagement with intelligent diagnosis research, producing peer-reviewed publications that contribute to developing more reliable and automated maintenance systems. His research interests include artificial intelligence algorithms, simulation-based equipment monitoring, and data-driven fault prediction, reflecting a commitment to improving industrial safety and performance through advanced computational tools. He possesses research skills in machine learning, numerical modeling, algorithm optimization, data processing, and diagnostic model implementation, enabling him to contribute to innovative solutions in equipment fault analysis. Zheng has also showcased his innovative capabilities through multiple patent contributions, supporting the practical translation of AI-based diagnostic technologies. His research outputs and patents have earned citations and recognition for their relevance in intelligent industrial systems. Overall, Zheng exemplifies a researcher who combines theoretical expertise with applicable innovations, helping advance intelligent condition monitoring and strengthening the role of AI in engineering reliability and industrial development.

Profile: ORCID

Featured Publications

Zheng, J., Han, S., Xue, M., Hu, H., & Wu, M. (2025). Numerical simulation-based intelligent fault detection for rotary vector reducers with imbalanced classes. Results in Engineering.

 

Dan-Alexandru Szabo | Biomedical Engineering | Best Review Paper Award

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Assoc. Prof. Dr. Dan-Alexandru Szabo | Biomedical Engineering | Best Review Paper Award

George Emil Palade University of Medicine | Romania

Associate Professor Dr. Dan-Alexandru Szabo is a distinguished scholar and educator in the field of biomedical engineering and human motor sciences, whose professional journey reflects a profound commitment to education, research, and sports science. He obtained his academic qualifications from leading Romanian universities, specialising in kinesiology, physiology, pedagogy, and performance management in sport. His extensive teaching and coaching career spans multiple institutions, where he has taught subjects including human motor activity, kinesiology, psychomotor education, and rehabilitation sciences. Dr. Szabo’s research interests encompass biomechanics, kinesiology, physical education, sports performance, and the integration of biomedical principles in human motion analysis. His research skills extend to experimental design, performance optimisation, data analysis in sports science, and the application of technology in rehabilitation. A respected peer reviewer for several Web of Science-indexed journals, he has contributed significantly to the academic community through scholarly publications and active participation in international conferences. His achievements include multiple national volleyball championships and involvement in curriculum development for physical education in Romania. Dr. Szabo’s dedication to academic excellence and innovation in human movement sciences has earned him recognition and numerous professional affiliations, underscoring his impact on education, research, and sports development in Romania and beyond.

Profile: ORCID

Featured Publications

Partha Sengupta | Structural Health Monitoring | Best Researcher Award

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Dr.Partha Sengupta | Structural Health Monitoring | Best Researcher Award

 

AECOM,India

Profile

Education

He holds a Ph.D. in Civil Engineering from the Indian Institute of Engineering Science and Technology (IIEST), Shibpur (2018–2023), with a perfect CGPA of 10/10. His doctoral research focused on “Finite Element Model Updating of Structures in Bayesian Framework and Enhanced Model Reduction Techniques.” Prior to this, he completed his M.Tech in Civil Engineering from IIEST, Shibpur (2014–2016), earning a CGPA of 9.03/10, with a thesis on the “Application of Ground Penetrating Radar in Concrete Evaluation, Pavement Profile, and Utility Detection.” He obtained his B.Tech in Civil Engineering from West Bengal University of Technology (2010–2014) with a CGPA of 9.02/10.

Professional Experience

Dr. Partha Sengupta conducts research in Structural Health Monitoring, focusing on model updating within a Bayesian framework using an enhanced model reduction technique with incomplete modal and time history response data. His work involves developing an iterative model reduction technique in the frequency domain by eliminating stiffness terms from the transformation equation, effectively mapping the full model and predicting its dynamic responses. The modified equation depends on measured modal responses and invariant mass matrices, eliminating the need for repeated evaluations of stiffness terms typically required in structural health monitoring (SHM) updating algorithms. Furthermore, this model reduction approach is integrated with a sub-structuring scheme, making it applicable to large finite element models. Additionally, Dr. Sengupta has developed an improved Bayesian model updating technique within the Transitional Markov Chain Monte Carlo (TMCMC) framework in the frequency domain, incorporating modifications to enhance the TMCMC algorithm.

AWARDS & ACHIEVEMENTS:

He received the Professor Amiya K. Basu Research Award in Structural Dynamics from the Department of Civil Engineering, IIEST Shibpur, and an additional monthly stipend of ₹10,000 from MHRD in 2022, along with his institute fellowship. He was also awarded the Best Paper Award in “Control and Health Monitoring” at the International Conference on Materials, Mechanics, and Structures (ICMMS 2020) in Kozhikode, India. As a reviewer, he has contributed to the G20 C20 International Conference on Interdisciplinary Approaches in Civil Engineering for Sustainable Development, published by Springer Nature, as well as Engineering Structures and Computer Methods in Applied Mechanics and Engineering, prestigious SCI journals published by Elsevier. Additionally, he received the Ministry of Human Resource and Development (MHRD) Institute Fellowship for pursuing his Ph.D. and M.Tech, having qualified GATE 2014 with a 99 percentile.

Publications