Yue-Der Lin | Engineering | Best Researcher Award

Prof. Yue-Der Lin | Engineering | Best Researcher Award

Professor Feng Chia University/Department of Automatic Control Engineering Taiwan

๐ŸŽ“ Dr. Yue-Der Lin is a Professor at the Department of Automatic Control Engineering, Feng Chia University, Taiwan. With extensive experience in biomedical engineering, he specializes in biopotential amplifier design, adaptive signal processing, and EEG signal analysis. His research bridges biomedical and electronic engineering, contributing significantly to advancements in biopotential measurement and bioinformatics.

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Education

๐Ÿ“˜ Ph.D. in Electrical Engineering (Biomedical Engineering Program)
National Taiwan University, Taiwan (1992โ€“1998)
๐Ÿ“˜ Masterโ€™s in Electrical Engineering (Biomedical Engineering & Electronic Circuit Programs)
National Taiwan University, Taiwan (1987โ€“1989)
๐Ÿ“˜ Bachelorโ€™s in Electrical Engineering (Control Program)
Chung Yuan Christian University, Taiwan (1983โ€“1987)

Professional Experience

๐Ÿ’ผ Professor, Department of Automatic Engineering, Feng Chia University (2019โ€“Present)
๐Ÿ’ผ Director, Department of Automatic Engineering (2016โ€“2017)
๐Ÿ’ผ Director, Masterโ€™s Program of Biomedical Informatics and Biomedical Engineering (2014โ€“2017)
๐Ÿ’ผ Visiting Scholar, Department of Electrical and Computer Engineering (2007)

Research Interests

๐Ÿ”ฌ Dr. Lin’s research focuses on:

  • Biopotential amplifier design
  • Adaptive signal processing
  • Biomedical signal analysis (EEG, EMG)
  • Bioinformatics systems
  • Advanced imaging techniques in biomedical engineering

Honors and Awards

๐Ÿ† Excellent Research Award, Feng Chia University (2005, 2007โ€“2009, 2011โ€“2019, 2021โ€“2024)
๐Ÿ† Distinguished Scholar Award, National Science and Technology Council, Taiwan (2014โ€“2015, 2018โ€“2020, 2022โ€“2024)
๐Ÿ† Albert Nelson Marquis Lifetime Achievement Award (2017โ€“2018, 2020)
๐Ÿ† Marquis Whoโ€™s Who in the World (2008โ€“2010, 2012โ€“2020)
๐Ÿ† IBC Leading Engineers of the World (2008, 2013, 2015)

Publications Top Notes:

๐Ÿ“š Below are Dr. Yue-Der Lin’s selected publications with publication year, journal, and citation details:

Lin, Y.-D., Chong, F.-C., Sung, S.-M., et al. “The calculation of complexity in normal and apoplectic EEG signals.” Journal of the Chinese Institute of Engineers, Vol. 21, No. 5, pp. 585-594, 1998. Cited by 56

Lin, Y.-D., Wu, C.-P., et al. “An active comb filter structure for harmonic interference removal.” Journal of the Chinese Institute of Engineers, Vol. 21, No. 5, pp. 605-610, 1998. Cited by 40

Lin, Y.-D., et al. “Preamplifier with a second-order high-pass filtering characteristic.” IEEE Transactions on Biomedical Engineering, Vol. 46, No. 5, pp. 609-612, 1999. Cited by 95

Lin, Y.-D., et al. “An adaptive power-line interference removal technique for biopotential measurement.” Biomedical Engineeringโ€”Applications, Basis and Communications, Vol. 12, No. 1, pp. 24-32, 2000. Cited by 23

Lin, Y.-D., et al. “Comments on โ€˜Line patterns in the mosaic electric properties of human skinโ€”A cross-correlation studyโ€™.” IEEE Transactions on Biomedical Engineering, Vol. 49, No. 3, pp. 274, 2002. Cited by 15

Use of acupressure to improve gastrointestinal motility in women after trans-abdominal hysterectomy

Aminoguanidine prevents the impairment of cardiac pumping mechanics in rats with streptozotocin and nicotinamideโ€induced type 2 diabetes

A novel approach for decomposition of biomedical signals in different applications based on data-adaptive Gaussian average filtering

Soroush Zare | Engineering | Best Researcher Award

Dr. Soroush Zare | Engineering | Best Researcher Award

Greadaute Research Assistant University of Virginia United States

๐Ÿ“š Soroush Zare is a dedicated Ph.D. Candidate in Mechanical and Aerospace Engineering at the University of Virginia. With expertise in robotics, soft exoskeletons, and Brain-Computer Interface (BCI) technologies, he specializes in designing advanced systems for rehabilitation and assistive applications. His research integrates AI-driven controls with cutting-edge mechanical design.

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Education

๐ŸŽ“ University of Virginia (2023โ€“Present)

  • Ph.D. in Mechanical and Aerospace Engineering (GPA: 4.0/4.0)
  • Focus: EEG-based motor imagery for wearable textile robotics under an NSF-funded project.

๐ŸŽ“ University of Tehran (2018โ€“2021)

  • M.S. in Mechanical Engineering (GPA: 3.9/4.0)
  • Thesis: Deep Reinforcement Learning Control of Suspended Cable-Driven Robots.

๐ŸŽ“ Shiraz University (2014โ€“2018)

  • B.S. in Mechanical Engineering (GPA: 3.6/4.0)
  • Thesis: Modeling and analysis of bladeless wind turbines.

Experience

๐Ÿ’ผ Research Assistant, University of Virginia (2023โ€“Present)

  • Developed wearable soft rehabilitation exoskeletons integrating EEG technologies.
  • Innovated reinforcement learning frameworks for intuitive robotic control.

๐Ÿ’ผ Research Assistant, York University (2022โ€“2023)

  • Led projects on robotic grasping using deep reinforcement learning.

๐Ÿ’ผ Research Assistant, University of Tehran (2018โ€“2022)

  • Advanced control techniques for Cable-Driven Parallel Robots (CDPRs).

Research Interests

๐Ÿ”ฌ Robotics, soft exoskeletons, Brain-Computer Interface (BCI) technologies, deep reinforcement learning, EEG-based motor imagery classification, and AI-driven assistive technologies.

Awards

๐Ÿ† Honors & Achievements

  • NSF Student Travel Award, IEEE/ACM CHASE (2024)
  • GRADESTAR Fellowship (2023, 2024)
  • Chairpersonโ€™s Fellowship (2023)
  • Ranked 2nd among solid design students at Shiraz University.

Publications Top Notes:ย 

๐Ÿ“„ Recent Publications

NeuroMotion: EEG-Based Motor Imagery Control of Wearable Exoskeleton (In preparation).

Kinematic analysis of an under-constrained cable-driven robot using neural networks

Wearable upper limb robotics for pervasive health: A review

Experimental study on the control of a suspended cable-driven parallel robot for object tracking purpose

Reconstructing 3-D Graphical Model Using an Under-Constrained Cable-Driven Parallel Robot

Understanding Human Motion Intention from Motor Imagery Eeg Based on Convolutional Neural Network

A Low-Cost Wearable Exoskeleton for Sitting and Standing Assistance

EEG Motor Imagery Classification using Integrated Transformer-CNN for Assistive Technology Control

MIMO Dynamic Control of a Suspended Underactuated Cable Robot Using Genetic Algorithm

 

 

Jingyang Mao | Engineering | Best Researcher Award

Dr. Jingyang Mao | Engineering | Best Researcher Award

Lecturer Shanghai Institute of Technology China

๐Ÿง‘โ€๐Ÿซ Dr. Jingyang Mao is a dedicated lecturer at the School of Electrical and Electronic Engineering, Faculty of Intelligence Technology, Shanghai Institute of Technology. With a Ph.D. in Control Science and Engineering from the University of Shanghai for Science and Technology (2022), he specializes in cutting-edge research on networked control systems and cyber-physical systems. His academic journey also includes a visiting scholar tenure at Louisiana State University, USA (2019โ€“2021). Dr. Mao’s work bridges theoretical innovations with practical applications in modern engineering systems.

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Education

๐ŸŽ“ Ph.D. in Control Science and Engineering (2022)

  • University of Shanghai for Science and Technology, Shanghai, China

โœˆ๏ธ Visiting Scholar (2019โ€“2021)

  • Department of Electrical and Computer Engineering, Louisiana State University, USA

Experience

๐Ÿ‘จโ€๐Ÿ’ป Lecturer (2022โ€“Present)

  • School of Electrical and Electronic Engineering, Shanghai Institute of Technology
  • Focus: Cyber-physical systems, networked control, and adaptive filtering

Research Interests

๐Ÿ” Dr. Mao’s research interests lie in the fields of:

  • Cyber-physical systems ๐ŸŒ
  • Multi-rate systems โฑ๏ธ
  • Joint recursive filtering ๐Ÿ”„
  • Unknown input estimation โ“
  • Adaptive event-triggered mechanisms โš™๏ธ

Awards

๐Ÿ† Award Nomination: Best Researcher Award
Recognized for groundbreaking contributions to the theory and application of cyber-physical systems.

Publications Top Notes:

๐Ÿ“„ “Recursive filtering of multi-rate cyber-physical systems with unknown inputs under adaptive event-triggered mechanisms”

Eventโ€based reducedโ€order Hโˆž$H_{\infty }$ estimation for switched complex networks based on Tโ€S fuzzy model

Recursive filtering of multi-rate cyber-physical systems with unknown inputs under adaptive event-triggered mechanisms

Event-Based Distributed Adaptive Kalman Filtering With Unknown Covariance of Process Noises

Geleta Fikadu | Engineering | Best Researcher Award

Assist. Prof. Dr. Geleta Fikadu | Engineering | Best Researcher Award

Ph.D adama science and Technology university Ethiopia

๐ŸŽ“ Dr. Geleta Fekadu Daba, an accomplished expert in Thermal Engineering, earned his Ph.D. from the prestigious Indian Institute of Technology Roorkee (IITR) in 2020. His career spans academia, research, and engineering innovation, focusing on renewable energy systems and automotive engineering. Currently an Assistant Professor at Wollega University, Ethiopia, Dr. Daba actively contributes to energy-efficient technologies, especially solar-assisted liquid desiccant air conditioning systems.

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Education

๐ŸŽ“ Ph.D. in Thermal Engineering – Indian Institute of Technology Roorkee, India (2020)
๐ŸŽ“ M.Sc. in Automotive Engineering – Adama Science and Technology University, Ethiopia (2013)
๐ŸŽ“ B.Sc. in Automotive Technology – Adama Science and Technology University, Ethiopia (2005)

Professional Experience

๐Ÿ›  Assistant Professor, Department of Mechanical Engineering, Wollega University, Ethiopia (2015โ€“Present)

  • Delivered courses on thermodynamics, fluid mechanics, and renewable energy systems.
  • Supervised graduate students and coordinated departmental research projects.
  • Led innovative studies on solar energy applications and energy optimization.

Research Interests

๐Ÿ”ฌ Dr. Dabaโ€™s research encompasses renewable energy, automobile engineering, thermodynamics, heat and mass transfer, nano-fluids for heat transfer, and solar energy systems. His work emphasizes practical solutions for sustainable development and energy efficiency.

Awards and Recognition

๐Ÿ† Received 33 lac INR funding for his Ph.D. project on solar-assisted liquid desiccant air conditioning from the Department of Science and Technology, India.

Publications

๐Ÿ“š Dr. Daba has published extensively in reputed journals and conferences. Below are selected works with links:

Renewable energy for liquid desiccants air conditioning system: A review

Energy and Exergy Analysis of Marquise Shaped Channel Flat Plate Solar Collector Using Al2O3โ€“Water Nanofluid and Water

Comparative performance evaluation of gasoline and its blends with ethanol in gasoline engine

Liquid desiccant air conditioning using single Storage solution tank, evaporative cooling, and marquise-shaped solar collector

Study of Performance of Solar Flat Plate Collector Using Al2O3/Water Nanofluids

Experimental Study of Internallyโˆ’ Cooled Dehumidification of Liquid Desiccant for a Single Storage Tank

Energy and Exergy Analysis of Marquise Shaped Channel Flat Plate Solar Collector Using Al2O3โ€“Water Nanofluid and Water

Performance analysis of a compact liquid desiccant cooling system

Weijun Wang | Engineering | Best Researcher Award

Dr. Weijun Wang | Engineering | Best Researcher Award

Student Changchun University of Technology China

๐ŸŒŸ Weijun Wang (็Ž‹ๅจ็บ) is a researcher in the field of Statistics, currently pursuing a Ph.D. at Changchun University of Technology, China. With expertise spanning fault diagnosis, data-driven designs, and dynamic systems, Weijun Wang has made significant contributions to high-speed train traction systems and other advanced engineering applications.

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Education

๐ŸŽ“ Weijun Wang’s academic journey includes:

  • Ph.D. in Statistics (2021โ€“Present): Changchun University of Technology.
  • Master’s in Information and Communication Engineering (2018โ€“2021): Changchun University of Technology.
  • Bachelor’s in Electronic Information Engineering (2014โ€“2018): Changchun University of Technology.

Experience

๐Ÿ‘ฉโ€๐Ÿ”ฌ Weijun Wang has extensive experience in developing innovative fault detection methods, dynamic system models, and data-driven algorithms. Collaborating on multidisciplinary projects, she has co-authored several impactful journal and conference papers.

Research Interests

๐Ÿ”ฌ Weijun Wang’s research focuses on:

  • Fault detection and diagnosis.
  • Data-driven modeling for high-speed train traction systems.
  • Dynamic systems, robust designs, and performance prediction.

Awards

๐Ÿ† First Prize: National Finals at RAICOM, 2024.
๐Ÿ… Innovation Award: 3rd China Urban Rail Transit Science and Technology Innovation Competition, 2022.

Publications

A Martingale Posterior-Based Fault Detection and Estimation Method for Electrical Systems of Industry

Dynamic Fault Detection Method of Traction Systems in High-Speed Trains Based on Joint Observer

Data-Driven Robust Designs of Performance Prediction and Its Application in High-speed Trains

Enhanced Fault Diagnosis Using Broad Learning for Traction Systems in High-Speed Trains

State Estimation with Partial Random Walk

State-degradation-oriented fault diagnosis for high-speed train running gears system

Fault prediction of high-speed train running gears based on hidden markov model and analytic hierarchy process

Multi-sensor system filtering and fault detection under unbiased constraint and colored measurement noise