Prof. Dr. Ahmad Jalal | Portable Sensors Awards | Best Researcher Award

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Prof. Dr. Ahmad Jalal | Portable Sensors Awards | Best Researcher Award 

Prof. Dr. Ahmad Jalal, Air University, Pakistan

R. Ahmad Jalal is an accomplished academic and researcher, currently serving as an Associate Professor in the Department of Computer Science and Engineering at Air University, Islamabad, Pakistan. He also leads the Intelligent Media Center (IMC) as its Director, overseeing a team of 15 MS and Ph.D. students, researchers, and developers contributing to innovative R&D activities with both national and international collaborations.

Professional Profile:

GOOGLE SCHOLAR

Suitability of R. Ahmad Jalal for the Best Researcher Award

Dr. R. Ahmad Jalal’s extensive academic and professional contributions position him as a highly suitable candidate for the Best Researcher Award. With his role as an Associate Professor in the Department of Computer Science and Engineering at Air University, Islamabad, and as the Director of the Intelligent Media Center (IMC), he has demonstrated leadership in research and innovation.

Education 🎓

  • Ph.D. in Computer Engineering, Pohang University of Science and Technology (POSTECH), South Korea
  • Master’s in Computer Science, (Details not provided, assumed prior to Ph.D.)

Work Experience 💼

  1. Associate Professor (March 2019 – Present)
    • Air University, Department of Computer Science and Engineering, Islamabad, Pakistan.
  2. Director, Intelligent Media Center (IMC) (2017 – Present)
    • Leading a team of 15 MS and Ph.D. students, researchers, and developers working on R&D for international and national collaborations.

Achievements & Contributions 🌟

  • Supervision: Successfully supervised 9 Ph.D. students and 21 MS students.
  • Patents:
    • Depth-based invariant human activity recognition using R transformation features (Co-inventor), Korea Copyright Commission, Patent No. 134571-0003856, 2011.
  • Publications: Multiple impactful papers with notable awards (details below).

Awards and Honors 🏆

  1. Best Paper Award (Runner-up) – ICAEM 2018
    • Facial Expression Recognition in Image Sequences Using 1D Transform and Gabor Wavelet Transform
    • Presented at IEEE Conference of Applied and Engineering Mathematics, pp. 82-87.
  2. Best Paper Award – ICOST 2011
    • Daily Human Activity Recognition Using Depth Silhouettes and R Transformation for Smart Home
    • Presented at the 9th ICOST, Lecture Notes in Computer Science (Springer), LNCS 6719, pp. 25-32.

Notable Roles ✨

  • Leader: Spearheading advanced research in AI, human activity recognition, and media technologies.
  • Innovator: Developed patented solutions in depth-based human activity recognition.

Publication Top Notes:

Robust human activity recognition from depth video using spatiotemporal multi-fused features

CITED:399

A Depth Video Sensor-based Life Logging Human Activity Recognition System for Elderly Care in Smart Indoor Environments

CITED:301

Depth video-based human activity recognition system using translation and scaling invariant features for life logging at smart home

CITED:249

Human activity recognition via recognized body parts of human depth silhouettes for residents monitoring services at smart home

CITED:208

Students’ Behavior Mining in E-learning Environment Using Cognitive Processes with Information Technologies

CITED:186

Vision-based human activity recognition system using depth silhouettes: A smart home system for monitoring the residents

CITED:117

Mr. Chunhui Xu | Sensor Integration Awards | Best Researcher Award

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Mr. Chunhui Xu | Sensor Integration Awards | Best Researcher Award

Mr. Chunhui Xu, Shenyang Institute of Automation, Chinese Academy of Sciences, China

Xu Chunhui is a distinguished male researcher and Master Supervisor at the Shenyang Institute of Automation, part of the Chinese Academy of Sciences. He holds a Master of Engineering and a Bachelor of Engineering from Harbin Engineering University. Xu has extensive experience in autonomous underwater vehicles (AUVs), specializing in areas such as software architecture, path planning, navigation control, and fault diagnosis. His professional journey includes roles as an Assistant Researcher and Research Intern at the Shenyang Institute, where he has made significant contributions to the field, earning several awards including the Special Prize for the Science and Technology Promotion Award of the Chinese Academy of Sciences. Xu has a robust patent portfolio with numerous inventions related to underwater robotics, including collision avoidance technologies and navigation methods. His research continues to advance the capabilities of AUVs, with a focus on applications in deep-sea exploration and resource management.

Professional Profile:

SCOPUS

Xu Chunhui for the Best Researcher Award

Xu Chunhui is a distinguished male Master Supervisor at the Shenyang Institute of Automation, Chinese Academy of Sciences. His expertise lies in autonomous underwater vehicle (AUV) technologies, particularly in software architecture, path planning, navigation control, and fault diagnosis. His extensive educational background includes a Master of Engineering and a Bachelor of Engineering from Harbin Engineering University.

Education 🎓

  • Master of Engineering
    Harbin Engineering University
    September 2005 – March 2008
  • Bachelor of Engineering
    Harbin Engineering University
    September 2001 – August 2005

Work Experience 💼

  • Associate Researcher
    Shenyang Institute of Automation, Chinese Academy of Sciences
    April 2016 – Present
  • Assistant Researcher
    Shenyang Institute of Automation, Chinese Academy of Sciences
    October 2010 – March 2016
  • Research Intern
    Shenyang Institute of Automation, Chinese Academy of Sciences
    April 2008 – September 2010

Achievements 🏆

  • Science and Technology Promotion Award of the Chinese Academy of Sciences
    Special Prize, 2021
  • 3D Real-time Collision Avoidance Technology of Autonomous Underwater Robot
    Second Prize, Provincial Level, 2019
  • Research and Application of Key Technologies for Autonomous Exploration System of Deep-sea Resources
    First Prize, Ministry Level, 2018

Publication Top Notes:

Applications of Autonomous Underwater Vehicle in Submarine Hydrothermal Fields: A Review

Guided Trajectory Filtering for Challenging Long-Range AUV Navigation

A fault diagnosis method with multi-source data fusion based on hierarchical attention for AUV

Ocean Temperature Prediction Based on Stereo Spatial and Temporal 4-D Convolution Model

Accurate two-step filtering for AUV navigation in large deep-sea environment

A fault diagnosis method based on attention mechanism with application in Qianlong-2 autonomous underwater vehicle

Gabriel Danciu | Intelligent sensing | Excellence in Research

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Mr. Gabriel Danciu | Intelligent sensing | Excellence in Research

Lecturer at Transilvania University, Romania

Danciu Gabriel is a prominent researcher and educator from Romania, specializing in electrical engineering and computer science. Currently serving as a Şef lucrări at the University of Transilvania in Brașov, he combines his academic role with practical experience as an engineer and manager at Siemens. With a robust publication record exceeding 50 papers, Gabriel is recognized for his contributions to artificial intelligence, image processing, and software architecture. He is an active member of the IEEE and has presented at numerous international conferences. His commitment to education is reflected in his mentoring roles and project coordination, making him a vital part of the academic community. Gabriel’s expertise in developing algorithms for RGB-D cameras and his innovative research approaches have earned him respect in the field. He aims to bridge theoretical knowledge with practical applications, enhancing technological advancements and shaping the next generation of engineers.

Profile:

Google Scholar Profile

Strengths for the Award:

  1. Extensive Experience: Gabriel has over 15 years of experience in academia and industry, demonstrating a strong commitment to both education and research.
  2. Publication Record: With over 50 published works, he shows a robust contribution to fields such as AI, image processing, and software architectures, indicating high productivity and impact in his research area.
  3. Diverse Skill Set: His competencies in various programming languages (C++, C#, Python) and expertise in software architecture showcase his technical proficiency, which is critical for modern research.
  4. Leadership Roles: As a Şef lucrări (Head of Department) and an engineer at Siemens, he has proven leadership capabilities, indicating his ability to manage projects and mentor others effectively.
  5. International Engagement: Participation in over 5 European projects and presentations at numerous conferences reflects his active engagement with the global research community.
  6. Research Innovation: His focus on cutting-edge topics like AI and image processing highlights his relevance and adaptability to current technological trends.

Areas for Improvement:

  1. Language Proficiency: While he is proficient in English, improving his German skills could enhance his collaboration opportunities in Europe, particularly in multilingual environments.
  2. Broader Collaboration: Expanding his research network beyond existing affiliations could lead to more interdisciplinary projects and greater innovation.
  3. Public Engagement: Increasing visibility through popular science publications or community outreach could enhance his impact beyond the academic sphere.
  4. Mentoring: Actively seeking to mentor younger researchers or students could foster new talent in the field and enhance his leadership profile.

Education:

Danciu Gabriel pursued his academic journey at the University of Transilvania in Brașov, where he obtained his Bachelor’s degree in Automatică și Informatică Industrială in 2004. He continued his studies at the same institution, completing a Master’s degree in Electrical Engineering and Telecommunications in 2006. Gabriel then earned his Ph.D. in 2014, focusing on developing algorithms for image processing using RGB-D cameras. His educational background laid a solid foundation for his future roles in academia and industry. As an Asistent universitar from 2007 to 2022, he dedicated himself to teaching and research, culminating in his current position as Șef lucrări, where he engages in educational leadership, research activities, and administrative duties. Gabriel’s academic achievements are complemented by ongoing professional development, ensuring that he stays at the forefront of technological advancements and educational methodologies in his field.

Experience:

Danciu Gabriel boasts extensive professional experience spanning over 15 years in both academia and industry. He began his career as a Software Engineer at Dynamic Ventures from 2005 to 2017, where he focused on research, mentorship, and software development. In 2018, he transitioned to Siemens as an Engineer, Researcher, and Manager, where he continues to work on innovative research projects while mentoring emerging talent. Concurrently, he has held various academic positions at the University of Transilvania, serving as an Asistent universitar for 15 years before advancing to Şef lucrări in 2022. His dual role allows him to integrate theoretical knowledge with practical applications, contributing to the growth of his students and the advancement of technology. Gabriel’s experience is characterized by a commitment to education, research innovation, and leadership, positioning him as a key figure in the fields of electrical engineering and computer science.

Research Focus:

Danciu Gabriel’s research primarily revolves around artificial intelligence, image processing, and software architecture, with a specific emphasis on RGB-D cameras. His work in developing innovative algorithms for depth image analysis has significantly contributed to advancements in computer vision and signal processing. Gabriel has published over 50 papers in renowned journals and conferences, exploring various topics, including noise pollution monitoring, functional verification in digital designs, and object tracking methods. He actively participates in European projects, collaborating with interdisciplinary teams to address real-world challenges through technology. Gabriel is passionate about integrating theoretical concepts with practical applications, aiming to improve the efficiency and accuracy of image processing techniques. His ongoing research endeavors focus on enhancing machine learning models and exploring new avenues in automated systems, positioning him at the cutting edge of technological innovation in the fields of engineering and computer science.

Publication Top Notes:

  • Shadow removal in depth images morphology-based for Kinect cameras 🌌
  • Objective erythema assessment of Psoriasis lesions for PASI evaluation 🌿
  • A novel approach for face expressions recognition 😊
  • Improved contours for ToF cameras based on vicinity logic operations 🖼️
  • Cost-efficient approaches for fulfillment of functional coverage during verification of digital designs 💻
  • Coverage fulfillment automation in hardware functional verification using genetic algorithms 🔍
  • Extended control-value emotional agent based on fuzzy logic approach 🤖
  • Scale and rotation-invariant feature extraction for color images of iris melanoma 🌈
  • Level up in verification: Learning from functional snapshots 📊
  • Noise pollution monitoring using mobile crowd sensing and SAP analytics 📱
  • Debugging FPGA projects using artificial intelligence 🧩
  • Debug FPGA projects using machine learning 📈
  • Efficient analysis of digital systems’ supplied data ⚙️
  • Method proposal for blob separation in segmented images 🔍
  • Solutions for Roaming and Interoperability Problems Between LTE and 2G or 3G Networks 📶
  • Methods of Object Tracking 🕵️‍♂️
  • Adaptive Scaling for Image Sensors in Embedded Security Applications 🔒
  • A method proposal of scene recognition for RGB-D cameras 🌍
  • Genetic algorithm for depth images in RGB-D cameras 🔧
  • Hierarchical contours based on depth images 🗺️

Conclusion:

Gabriel Danciu demonstrates a strong profile as a candidate for the Best Researcher Award, with a solid foundation in research, a wealth of experience, and a proven track record of publications and collaborations. By addressing the suggested areas for improvement, particularly in broader engagement and mentorship, he could further strengthen his candidacy and impact in the research community.

Dr. Yue Wang | Sensor development Award | Best Researcher Award

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Dr. Yue Wang | Sensor development Award | Best Researcher Award 

Dr. Yue Wang, University of Science and Technology Liaoning, China

Dr. Yue Wang is an Associate Professor at the School of Chemical Engineering at the University of Science and Technology Liaoning in China. He earned his Bachelor’s degree from the University of Science and Technology Anshan and both his Master’s and Doctorate degrees from the University of Science and Technology Liaoning and Saitama Institute of Technology, Japan, respectively. Since joining the University of Science and Technology Liaoning in 2006, Dr. Wang has focused his research on sensors and biosensors, biofuel cells, supercapacitors, energy harvesting, and artificial muscles. His work has resulted in over 60 published scientific papers, garnering approximately 600 citations, reflecting his significant contributions to the field. Dr. Wang has secured multiple research grants from various institutions, including the Education Department of Liaoning Province and the Natural Science Foundation of Liaoning Province, to advance his projects on conductive sensors, pesticide sensors, electrochemical biosensors, and wearable smart sensing technologies. Additionally, he completed a visiting scholarship at the University of Texas at Dallas in 2019-2020, further enhancing his academic and research expertise.

Professional Profile:

SCOPUS

Summary of Suitability for Best Researcher Award: Yue Wang

Yue Wang is an exemplary candidate for the Best Researcher Award, primarily due to his substantial academic qualifications, extensive research contributions, and impactful work in the field of Material Science, specifically within sensor and biosensor technologies.

Education

  • Bachelor’s Degree in Material Science
    University of Science and Technology Anshan, China
    September 1998 – July 2002
  • Master’s Degree in Material Science
    University of Science and Technology Liaoning, China
    September 2003 – March 2006
  • Ph.D. in Material Science
    Saitama Institute of Technology, Japan
    April 2008 – March 2011

Work Experience

  • Associate Professor
    University of Science and Technology Liaoning, China
    April 2006 – Present
  • Visiting Scholar
    University of Texas at Dallas
    April 2019 – March 2020

Publication top Notes:

A carbon black–doped chalcopyrite–based electrochemical sensor for determination of hydrogen peroxide

Glucose oxidase, horseradish peroxidase and phenothiazine dyes-co-adsorbed carbon felt-based amperometric flow-biosensor for glucose

Crab gill–derived nanorod-like carbons as bifunctional electrochemical sensors for detection of hydrogen peroxide and glucose

Cellulose-derived hierarchical porous carbon based electrochemical sensor for simultaneous detection of catechol and hydroquinone

A triphenylamine based fluorescent probe for Zn2+ detection and its applicability in live cell imaging

1,8-naphthalimide-triphenylamine-based red-emitting fluorescence probes for the detection of hydrazine in real water samples and applications in bioimaging in vivo

Prof Wanrun Li | Vision Sensing | Best Researcher Award

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Prof Wanrun Li | Vision Sensing | Best Researcher Award

Prof Wanrun Li, Lanzhou University of Technology, China 

Professor Wanrun Li is a distinguished academic and researcher in Structural Health Monitoring, currently serving as the Vice Dean of the School of Civil Engineering at Lanzhou University of Technology, China. With a focus on fatigue analysis, wind turbine vibration control, and structural health monitoring, he has led numerous research projects funded by national and regional foundations. His work has significantly contributed to understanding the seismic performance of super-tall buildings, damage identification, and fatigue life prediction of wind turbines. He has held multiple leadership roles, including Associate Professor and department head, and has been a visiting scholar at prestigious institutions.

Professional Profile:

Suitability for the Best Researcher Award: 

Professor Wanrun Li’s extensive research portfolio, leadership in high-stakes projects, and contributions to structural engineering make him a strong candidate for the Best Researcher Award. His work in wind turbine vibration control and fatigue analysis is critical for the advancement of sustainable energy infrastructure. However, to strengthen his candidacy, broadening the impact of his research on industry standards and further enhancing global outreach would be valuable steps forward. Overall, his dedication to innovative research and significant contributions to civil engineering position him as a deserving nominee for this prestigious award.

Education

Professor Wanrun Li earned his Ph.D. in Structural Engineering from Lanzhou University of Technology in 2013, specializing in Structural Health Monitoring under the guidance of Prof. Yongfeng Du and Prof. Y.Q. Ni. He was also a joint-supervised Ph.D. candidate at Hong Kong Polytechnic University from 2011-2012. His academic background includes an M.S. in Disaster Prevention and Mitigation from Lanzhou University of Technology (2010) and a B.S. in Civil Engineering from the same institution (2008). His education laid the foundation for his expertise in monitoring structural health and analyzing the fatigue of civil structures.

Work Experience

Wanrun Li has over a decade of academic and research experience. Currently, he is a Professor and Vice Dean at Lanzhou University of Technology. He previously served as an Associate Professor and Head of the Department of Building Engineering. His international experience includes being a visiting scholar at the University of Illinois Urbana-Champaign and Southeast University, China. Over the years, he has led research initiatives focused on vibration control, fatigue life prediction, and damage identification in large structures such as wind turbines and high-rise buildings.

Skills

Professor Li’s technical expertise includes advanced knowledge in Structural Health Monitoring, Wind Turbine Vibration Control, and Weld Fatigue Analysis. His skills extend to predictive modeling, statistical pattern recognition, and seismic data analysis. He is proficient in developing new devices for vibration reduction, using machine vision technology for turbine blade detection, and applying experimental and multi-scale simulation techniques for assessing fatigue in steel structures. His proficiency with numerical modeling, experimental research, and structural design underpins his research and teaching efforts.

Awards and Honors

Wanrun Li has been recognized with multiple prestigious awards, including the Hongling Outstanding Young Scholar Award at Lanzhou University of Technology (2019-2021) and the Science Fund for Distinguished Young Scholars of Gansu Province (2021-2024). His work has earned several grants from the National Natural Science Foundation of China (NSFC), totaling over ¥1.9 million for projects related to wind turbine structures and vibration control. These honors affirm his significant contributions to structural engineering and disaster prevention.

Membership

Professor Li is actively engaged in several professional organizations, contributing to the advancement of structural health monitoring and civil engineering. He collaborates closely with renowned research groups and institutions, including the University of Illinois Urbana-Champaign and Southeast University. His membership in national scientific communities has enabled him to secure significant research funding and present his findings in both domestic and international conferences.

Teaching Experience

With a passion for mentoring, Professor Li has been an academic instructor at Lanzhou University of Technology since 2013, progressing from instructor to professor. As a faculty member, he has taught courses related to Structural Engineering, Civil Engineering, and Structural Health Monitoring. His role as the Chief Duty Professor for the Hongliu Top-class Major in Civil Engineering reflects his commitment to academic excellence. He also supervises graduate students, guiding them through research projects and fostering a collaborative learning environment.

Research Focus

Professor Li’s research is centered on Structural Health Monitoring, Weld Fatigue Analysis, and Vibration Control of Wind Turbines. He is particularly interested in seismic data analysis of tall structures, wind-induced fatigue of turbines, and developing new devices for reducing tower vibrations. His projects include studies on vibration control using tuned liquid column dampers and fatigue life prediction of wind turbines in harsh environments. His innovative work integrates machine vision technology and UAVs for turbine blade detection, and he has contributed significantly to enhancing structural safety and durability.

Publication top Notes:

“Wind turbine blade defect detection and measurement technology based on improved SegFormer and pixel matching”

    • Year: 2024
    • Journal: Optics & Laser Technology
    • DOI: 10.1016/j.optlastec.2024.111381

“Mitigation of In-Plane Vibrations in Large-Scale Wind Turbine Blades with a Track Tuned Mass Damper”

    • Year: 2023
    • Journal: Structural Control and Health Monitoring
    • DOI: 10.1155/2023/8645831

“Dynamic Characteristic Monitoring of Wind Turbine Structure Using Smartphone and Optical Flow Method”

    • Year: 2022
    • Journal: Buildings
    • DOI: 10.3390/buildings12112021

“Dynamic Characteristics Monitoring of Large Wind Turbine Blades Based on Target-Free DSST Vision Algorithm and UAV”

    • Year: 2022
    • Journal: Remote Sensing
    • DOI: 10.3390/rs14133113

“Seismic Vibration Mitigation of Wind Turbine Tower Using Bi-Directional Tuned Mass Dampers”

    • Year: 2020
    • Journal: Mathematical Problems in Engineering
    • DOI: 10.1155/2020/8822611

“Low-cycle fatigue test and life assessment of carbon structural steel GB Q235B butt joints and cruciform joints”

    • Year: 2019
    • Journal: Advances in Structural Engineering
    • DOI: 10.1177/1369433218795292

“Seismic Performance of a New Precast Concrete Shear Wall with Bolt Connection”

    • Year: 2019
    • Journal: Gongcheng Kexue Yu Jishu/Advanced Engineering Science
    • DOI: 10.15961/j.jsuese.201801163

“Time-Varying Nonlinear Parametric Identification of Isolated Structure Based on Wavelet Multiresolution Analysis”

    • Year: 2019
    • Journal: Zhendong Ceshi Yu Zhenduan/Journal of Vibration, Measurement and Diagnosis
    • DOI: 10.16450/j.cnki.issn.1004-6801.2019.03.024

“Welding Residual Stress Simulation and Experimental Verification in Beam-to-Column Joints of Q345B Steel”

    • Year: 2019
    • Journal: Huanan Ligong Daxue Xuebao/Journal of South China University of Technology (Natural Science)
    • DOI: 10.12141/j.issn.1000-565X.190044

 

Best Machine Learning for Sensing

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Introduction Best Machine Learning for Sensing

Welcome to the ‘Best Machine Learning for Sensing‘ award, honoring innovative solutions that leverage machine learning to advance sensing technologies. This award recognizes outstanding contributions in developing algorithms, models, and systems that enhance sensing capabilities across various domains.

About the Award:
  • Eligibility: Open to individuals, teams, academic institutions, and organizations worldwide.
  • Age Limits: None.
  • Qualification: Projects or research work showcasing the application of machine learning in sensing technologies.
  • Publications: Relevant publications or patents are encouraged but not required.
  • Requirements: Submissions must demonstrate innovative use of machine learning in sensing, with clear impact and potential for advancement in the field.
Evaluation Criteria:
  • Innovation: Uniqueness and originality of the approach.
  • Impact: Significance and relevance of the work in advancing sensing technologies.
  • Technical Merit: Soundness of the methodology and technical rigor.
  • Applicability: Potential for practical application and scalability.
  • Presentation: Clarity, organization, and effectiveness of the submission.
Submission Guidelines:
  • Submissions should include a detailed description of the project or research work.
  • Supplementary materials such as videos, code repositories, and datasets are welcome.
  • All submissions must be in English.
Recognition:
  • The winner will receive a prestigious award and recognition at a special ceremony.
  • Winners and finalists will be featured on our website and in press releases.
Community Impact:
  • Projects with demonstrated positive impact on society or the environment will be highly regarded.
  • Community engagement and collaboration will be considered favorably.
Biography:
  • Provide a brief biography highlighting the key contributors and their roles in the project.
Abstract:
  • A concise summary of the project, highlighting its significance and key findings.
Supporting Files:
  • Upload any relevant files such as research papers, presentations, or supplementary materials.