Dr.Reza Askari Moghadam | Bio Sensor Awards | Best Researcher Award

Dr.Reza Askari Moghadam | Bio Sensor Awards | Best Researcher Award-5093

Dr.Reza Askari Moghadam, Sorbonne Universitรฉ, France

Reza Askari Moghadam is a distinguished academic and researcher currently serving as a Lecturer at Sorbonne Universitรฉ in Paris, France, specializing in electronics and mechatronics. He holds a Ph.D. in Electronics from the Islamic Azad University (IUST), where he conducted innovative research on intelligent fault detection in RF MEMS, funded by the Iranian Telecommunications Research Center. With over a decade of experience as a Tenured Lecturer at the University of Tehran, Reza has significantly contributed to the fields of sensors, actuators, microfluidics, and artificial intelligence. His extensive teaching background encompasses more than 4,600 hours of instruction across various degree programs, from bachelor’s to doctoral levels. Reza’s research output includes 58 articles in international journals and 59 conference papers, highlighting his active engagement in advancing knowledge in his field. He has also participated in multiple collaborations and projects in Europe, further enriching his academic portfolio. In addition to his research and teaching, he possesses a robust skill set in various software tools, including Python, MATLAB, and COMSOL, which support his ongoing contributions to engineering and technology.

Professional Profile:

SCOPUS

Summary of Suitability for Best Researcher Award:ย 

Reza Askari Moghadam is an accomplished academic and researcher in the field of Electronics and Engineering, with a solid track record of teaching, research, and publication. His diverse experiences, educational background, and substantial contributions to the field make him a strong candidate for the Best Researcher Award.

Education

  1. Ph.D. in Electronics
    • Institution: Islamic Azad University (IUST), Tehran, Iran
    • Years: 2001 – 2007
    • Thesis: “Intelligent Detection of Faults in RF MEMS”
    • Funding: Iranian Telecommunications Research Center (ITRC)
  2. Masterโ€™s Degree in Electrical Engineering (Specialization: Control)
    • Institution: Islamic Azad University (IUST), Tehran, Iran
    • Years: 1998 – 2001
    • Thesis: “Design, Implementation, and Control of a Robotic Arm”
    • Funding: Electronics Research Center, IUST
  3. Bachelorโ€™s Degree in Electrical Engineering (Specialization: Electronics)
    • Institution: University of Petroleum Industry, Iran
    • Years: 1993 โ€“ 1998
    • Thesis: “Design and Implementation of an EEPROM Programmer”

Professional Experience

  1. Lecturer
    • Institution: Campus Pierre et Marie Curie, Sorbonne Universitรฉ, Paris, France
    • Years: Sep. 2023 โ€“ Present
  2. Temporary Teaching and Research Attachรฉ (ATER)
    • Institution: Laboratoire Images, Signaux et Systรจmes Intelligents (LISSI), UPEC, France
    • Years: Jan. 2022 โ€“ Sep. 2023
    • Notes: Contract renewed in September 2022
  3. Tenured Lecturer
    • Institution: Department of “Mechatronics & MEMS”, Faculty of New Sciences and Technologies, University of Tehran (UT), Iran
    • Years: Sep. 2012 โ€“ Jan. 2022

Research Activities

  • Collaborated with LISSI Laboratory, UPEC, France since 2016.
  • Visiting Researcher at Nano Center, University of Southampton, UK (2010, three months).
  • Attended Synchrotron Summer School at Daresbury Synchrotron Laboratory, UK (2004, one month).

Publication top Notes:

Simplified U-Net as a deep learning intelligent medical assistive tool in glaucoma detection

High speed universal NAND gate based on weakly coupled RF MEMS resonators

Microfluidics chip inspired by fish gills for blood cells and serum separation

Theoretical and experimental evaluation of small flow rate ultrasonic flowmeter

Design optimization of a heat-to-cool Stirling cycle using artificial neural network

A novel Gamma-type duplex Stirling system to convert heat energy to cooling power: Theoretical and experimental study

Gabriel Danciu | Intelligent sensing | Excellence in Research

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 ลžef lucrฤƒri at the University of Transilvania in Braศ™ov, 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 ลžef lucrฤƒri (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 Braศ™ov, where he obtained his Bachelor’s degree in Automaticฤƒ ศ™i Informaticฤƒ Industrialฤƒ in 2004. He continued his studies at the same institution, completing a Masterโ€™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 ศ˜ef lucrฤƒri, where he engages in educational leadership, research activities, and administrative duties. Gabrielโ€™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 ลžef lucrฤƒri 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. Gabrielโ€™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 ๐ŸŒŒ
  • Objective erythema assessment of Psoriasis lesions for PASI evaluation ๐ŸŒฟ
  • A novel approach for face expressions recognition ๐Ÿ˜Š
  • Improved contours for ToF cameras based on vicinity logic operations ๐Ÿ–ผ๏ธ
  • Cost-efficient approaches for fulfillment of functional coverage during verification of digital designs ๐Ÿ’ป
  • Coverage fulfillment automation in hardware functional verification using genetic algorithms ๐Ÿ”
  • Extended control-value emotional agent based on fuzzy logic approach ๐Ÿค–
  • Scale and rotation-invariant feature extraction for color images of iris melanoma ๐ŸŒˆ
  • Level up in verification: Learning from functional snapshots ๐Ÿ“Š
  • Noise pollution monitoring using mobile crowd sensing and SAP analytics ๐Ÿ“ฑ
  • Debugging FPGA projects using artificial intelligence ๐Ÿงฉ
  • Debug FPGA projects using machine learning ๐Ÿ“ˆ
  • Efficient analysis of digital systems’ supplied data โš™๏ธ
  • Method proposal for blob separation in segmented images ๐Ÿ”
  • Solutions for Roaming and Interoperability Problems Between LTE and 2G or 3G Networks ๐Ÿ“ถ
  • Methods of Object Tracking ๐Ÿ•ต๏ธโ€โ™‚๏ธ
  • Adaptive Scaling for Image Sensors in Embedded Security Applications ๐Ÿ”’
  • A method proposal of scene recognition for RGB-D cameras ๐ŸŒ
  • Genetic algorithm for depth images in RGB-D cameras ๐Ÿ”ง
  • Hierarchical contours based on depth images ๐Ÿ—บ๏ธ

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.

Yongquan Wang | Remote Sensing | Best Researcher Award

Dr.Yongquan Wang | Remote Sensing | Best Researcher Award

PhD atย  shenzhen university, China

Yongquan Wang is a dedicated researcher specializing in ocean color and radiative transfer. With a robust academic background, he holds an M.S. and Ph.D. in Urban Informatics from Shenzhen University and a B.S. in Geodesy and Geomatics from Anhui Agriculture University. Recognized as an Outstanding Graduate Student of Guangdong Province, Yongquan’s research focuses on innovative techniques for environmental monitoring, particularly in retrieving oceanic particulate organic nitrogen (PON) concentrations. His work integrates advanced imaging technologies and data processing skills, reflecting a commitment to addressing pressing ecological challenges.

Profile:

Scopus Profile

Strengths for the Award:

Yongquan Wang has demonstrated exceptional research capabilities in the fields of ocean color and radiative transfer. His focus on retrieving oceanic particulate organic nitrogen (PON) concentrations from image data shows innovative thinking and application of advanced techniques. His research work is well-supported by a solid academic background, achieving high GPAs and recognition as an Outstanding Graduate Student of Guangdong Province. The breadth of his publications in reputable journals like IEEE Transactions and Remote Sensing further establishes his expertise. Additionally, his contributions to novel methods using aerial imaging and UAV technology in environmental monitoring underscore his ability to address real-world challenges effectively.

Areas for Improvement:

While Yongquan has made significant strides in his research, he could enhance his impact by diversifying his research collaborations, particularly with interdisciplinary teams that include ecologists and data scientists. Engaging more with broader environmental policy discussions could also strengthen the societal relevance of his work. Additionally, expanding his outreach to communicate research findings to non-specialist audiences may increase public engagement and understanding of his work.

Education:

Yongquan Wang completed his M.S. and Ph.D. at the School of Architecture and Urban Planning, Shenzhen University, where he achieved a GPA of 86.7/100. Prior to this, he earned a B.S. in Geodesy and Geomatics from Anhui Agriculture University, graduating with a GPA of 88.8/100. His educational journey has been marked by academic excellence, including multiple scholarships for outstanding performance. This strong foundation has equipped him with the knowledge and skills to engage in impactful research in ocean color remote sensing and related fields.

Experience:

Yongquan Wang has amassed significant research experience since September 2018, focusing on the retrieval of oceanic particulate organic nitrogen (PON) concentrations from image data. He has explored the development of retrieval models for global ocean monitoring and atmospheric corrections under weak light conditions. Additionally, he has engaged in innovative projects using tethered UAVs for emergency surveying and mapping, demonstrating versatility in applying technology to real-world problems. His work reflects a commitment to advancing remote sensing methodologies for environmental applications.

Research Focus:

Yongquanโ€™s research centers on ocean color and radiative transfer, particularly the retrieval of oceanic particulate organic nitrogen (PON) concentrations. He investigates bio-optical proxies for PON retrieval and develops models to analyze monthly variations in global ocean PON levels. His work also addresses atmospheric correction techniques in optically complex waters, enhancing the accuracy of remote sensing data. By leveraging advanced imaging technologies and data processing skills, Yongquan aims to contribute valuable insights into oceanic health and environmental sustainability.

Publications Top Notes:

  1. Towards Applicable Retrieval Models of Oceanic Particulate Organic Nitrogen Concentrations for Multiple Ocean Color Satellite Missions ๐Ÿ“„
  2. Ocean Colour Atmospheric Correction for Optically Complex Waters under High Solar Zenith Angles: Facilitating Frequent Diurnal Monitoring and Management ๐ŸŒŠ
  3. Remote Sensing Video Production and Traffic Information Extraction Based on Urban Skyline ๐Ÿšฆ
  4. Spatiotemporal Dynamics and Geo-environmental Factors Influencing Mangrove Gross Primary Productivity during 2000โ€“2020 in Gaoqiao Mangrove Reserve, China ๐ŸŒณ
  5. Estimating Particulate Organic Nitrogen Concentrations in the Surface Ocean from Ocean Color Remote Sensing Data ๐Ÿ”
  6. Satellite Retrieval of Oceanic Particulate Organic Nitrogen Concentration ๐ŸŒ
  7. A Glimpse of Ocean Color Remote Sensing From Moon-Based Earth Observations ๐ŸŒ™
  8. Framework to Create Cloud-Free Remote Sensing Data Using Passenger Aircraft as the Platform โœˆ๏ธ
  9. Dynamic Earth Observation Based on an Urban Skyline: A New Remote Sensing Approach for Urban Emergency Response ๐Ÿ™๏ธ
  10. Volunteered Remote Sensing Data Generation with Air Passengers as Sensors ๐Ÿš

Conclusion:

Yongquan Wang is a strong candidate for the Research for Best Researcher Award, with notable achievements and contributions in oceanographic research. His innovative approaches and demonstrated academic excellence position him well for recognition. Continued efforts to broaden his collaboration network and enhance public engagement will further solidify his status as a leading researcher in his field.