Ms. Xinlu Bai | Sensing Awards | Best Researcher Award

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Ms. Xinlu Bai | Sensing Awards | Best Researcher Award

Ms. Xinlu Bai, Changchun university, China

Xinlu Bai is a dedicated researcher currently pursuing a Master’s degree in Computer Science at Changchun University, following an Engineering Degree from Zhengzhou University of Finance and Economics (2018-2022). Xinlu has made significant contributions to the field of computer vision, particularly in dense pedestrian detection. His research includes the development of the GR-YOLO algorithm, which improves detection performance over existing methods like YOLOv8, with notable advancements in accuracy across various datasets. Xinlu’s work has been published in Sensors and has been guided by esteemed professors Deyou Chen and Nianfeng Li. He has been recognized for his excellence in competitions, winning the first prize in the Jilin Province Virtual Reality Competition, the second prize in the China Virtual Reality Competition (Data Visualization Track), and the third prize in the Jilin Province Ruikang Robot Competition.

Professional Profile:

Orcid

Suitability Summary for Best Researcher Award

Researcher: Xinlu Bai

Summary:

Xinlu Bai is a highly qualified candidate for the Best Researcher Award, distinguished by his innovative research and significant contributions to the field of computer science, particularly in pedestrian detection technology. Bai’s work demonstrates a clear commitment to advancing technology through rigorous research and practical applications.

🎓Education:

Xinlu Bai is a dedicated researcher currently pursuing a Master’s degree in Computer Science at Changchun University, which he has been enrolled in since 2023. He previously completed his Engineering Degree at Zhengzhou University of Finance and Economics, where he studied from 2018 to 2022. Xinlu has made significant contributions to the field of computer vision, particularly in dense pedestrian detection. His development of the GR-YOLO algorithm, which enhances detection performance compared to YOLOv8, has been recognized through publications in Sensors and has been guided by esteemed professors Deyou Chen and Nianfeng Li. His excellence has been acknowledged in various competitions, including winning the first prize in the Jilin Province Virtual Reality Competition, the second prize in the China Virtual Reality Competition (Data Visualization Track), and the third prize in the Jilin Province Ruikang Robot Competition.

🏆Awards:

Xinlu Bai is a dedicated researcher currently pursuing a Master’s degree in Computer Science at Changchun University, having previously completed his Engineering Degree at Zhengzhou University of Finance and Economics. His contributions to computer vision, particularly through the development of the GR-YOLO algorithm, have been published in Sensors and guided by Professors Deyou Chen and Nianfeng Li. Xinlu’s excellence in the field has been recognized with several prestigious awards: he won the First Prize in the Jilin Province Virtual Reality Competition, the Second Prize in the China Virtual Reality Competition (Data Visualization Track), and the Third Prize in the Jilin Province Ruikang Robot Competition.

Publication Top Notes:

Title: Dense Pedestrian Detection Based on GR-YOLO

 

 

 

Mr. Mohammad Ahmadi | physiological Sensors | Best Researcher Award

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Mr. Mohammad Ahmadi | physiological Sensors | Best Researcher Award 

Mr. Mohammad Ahmadi, University of Auckland, New Zealand

Ted Ahmadi is a seasoned game developer based in Toronto, with a strong focus on designing Mixed/Augmented/Virtual Reality (MR/AR/VR) games using Unity3D and C#. With over 6 years of experience, he is proficient in utilizing the Microsoft Mixed Augmented Reality Toolkit (MRTK) and has expertise in designing Mixed Reality games for platforms such as Magic Leap, Vive/Vive Pro Eye, Oculus Quest/Quest 2&3/Quest Pro, HP Omnicept, Hololens 2, and Apple Vision Pro. Ted’s career spans across various aspects of game development, including 2D game design for Android using Unity3D, game networking with Photon and Ubiq, and integrating technologies like OpenGL, Blender, and iClone 3D animation toolkit. He is also skilled in using Leap Motion for enhancing interactive experiences in game applications. Beyond game development, Ted is proficient in C++/C# programming across different applications and has experience in Agile/Rapid development methodologies, Waterfall, and Continuous Integration. His expertise extends to embedded systems such as ROS in Linux/Windows, particularly in VR applications for robotics, and enterprise web server applications where he excels in Java programming, software optimization, debugging, and troubleshooting.

Professional Profile:

ORCID

 

Education

University of Auckland

  • Bachelor of Science in Computer Science
    Date: Graduated in 2018

Work Experience

Design School, University of Auckland
Teaching and Tutoring Assistant
July 2022 – Nov 2022

  • Responsibilities: Assisted in teaching and tutoring the course “Designing Mix Realities” at the School of Design.
  • Skills: Unity3D, Blender (3D modeling and animation for rapid prototyping), Adobe Aero (3D modeling).

Skills

  • Game Design: Unity3D, MRTK and XR SDK, AR Kit, AR Core, Leap Motion, OpenGL, Vuforia, Blender, iClone 7.
  • Programming: C++/C#, Java, JavaScript, PHP/CSS/HTML, jQuery, mySQL, JSON/XML, Matlab.
  • HMD: Vive/Vive Pro Eye, Oculus Quest/Quest 2/Quest 3/Quest Pro, HP Omnicept, Magic Leap, Hololens 2, Apple Vision Pro.
  • API: WebGL, OpenGL.
  • Web API: .Net/ASP.Net MVC.
  • J2EE API: Java Servlet and EJB.
  • Version Control: git and GitHub.
  • OS: Linux, Windows.
  • Embedded Systems: ROS.

Employment History

🏫 Design School, University of Auckland
Teaching and Tutoring Assistant (July 2022 – Nov 2022)

  • Teaching and tutoring assistant for the course “Designing Mix Realities” at the school of design.
  • Skills: Unity3D, Blender (3D modeling and animation for rapid prototyping), Adobe Aero (3D modeling).

Publication top Notes:

EEG, Pupil Dilations, and Other Physiological Measures of Working Memory Load in the Sternberg Task

Cognitive Load Measurement with Physiological Sensors in Virtual Reality during Physical Activity

Comparing Performance of Dry and Gel EEG Electrodes in VR using MI Paradigms

PlayMeBack – Cognitive Load Measurement using Different Physiological Cues in a VR Game

Heba-Allah El-Sayed | Biological Sensors Award | Best Researcher Award

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Dr. Heba-Allah El-Sayed | Biological Sensors Award | Best Researcher Award

Assistant researcher at Agriculture Research Center, Egypt

Dr. Heba-Allah El-Sayed is a dedicated researcher with a background in entomology, specializing in honey bee health and beekeeping. She holds a Ph.D. in Entomology from Cairo University, with a focus on the antiviral properties of Egyptian propolis and bee venom on honey bee health. Heba-Allah has extensive experience in laboratory work, including diagnosing diseases and pests of honey bees, RNA and DNA extraction, and gene expression analysis. She is proficient in using laboratory equipment such as PCR machines and spectrophotometers. Heba-Allah is also skilled in raising queens in apiaries and has experience in chemical analysis of honey bee products. She is fluent in Arabic and has a good command of English. Heba-Allah is known for her strong communication skills, ability to work well in a team, and her ambition to contribute significantly to her field.

Professional Profile

Education:

Heba-Allah El-Sayed has an impressive academic background, culminating in a Ph.D. in Entomology from Cairo University in 2023. Her doctoral thesis, titled “In Vivo and in Vitro Evaluation of the Effect of Egyptian Propolis and Bee Venom on the Honey Bee (Apis mellifera L.) Health,” delved into the chemical profile of honey bee venom (HBV) and Egyptian ethanolic propolis extract (EP) using advanced analytical techniques like FTIR spectroscopy, GC-MS, and HPLC. The study identified methyl gallate and phthalic acid in EP and demonstrated the antiviral activity of both HBV and EP against honey bee cell lines infected with DWV, BQCV, VDV-1, and KV, as detected by RT-PCR and RT-qPCR. Moreover, the research showed that HBV and EP could improve cell health and increase the lifespan, activities, and density of bee workers when used as supplements in bee nutrition. These findings suggest potential applications of HBV and EP as supplements and antiviral drugs in honeybee apiaries. Prior to her Ph.D., Heba-Allah completed her Master’s degree, focusing on the detection of Deformed wing virus (DWV) and Kakugo virus (KV) in honeybees in Egypt. Her thesis, titled “First detection of Deformed wing virus (DWV) and Kakugo virus (KV) in honeybees (Apis mellifera L.) (Hymenoptera-Apidae) in Egypt by RT-PCR,” utilized molecular techniques to isolate and identify these viruses, providing valuable insights into their presence and prevalence in Egyptian honeybee populations. Heba-Allah’s academic journey began with a Bachelor of Science degree from Cairo University in 2007, laying the foundation for her subsequent research and achievements in the field of Entomology. She has also contributed to published research articles, including a recent publication on the antiviral activities of Egyptian ethanolic propolis extract and honey bee venom against honey bees infected with multiple viruses in vitro.

Research:

Heba-Allah El-Sayed has been actively involved in research, demonstrating a high level of participation and contribution. She has conducted all laboratory work, showcasing her hands-on expertise and proficiency in various experimental techniques and methodologies. Additionally, Heba-Allah has been instrumental in generating ideas and contributing to the writing process of research projects. Her involvement in both the practical and theoretical aspects of research highlights her comprehensive understanding and commitment to advancing scientific knowledge in the field of Entomology.

Work Experiences:

Heba-Allah El-Sayed possesses a wealth of practical experience in the field of Entomology, particularly in diagnosing diseases and pests of honey bees. She has conducted laboratory molecular and microscopic biological examinations to identify viral, bacterial, fungal, and external and internal parasitic infections in honey bee populations. Additionally, Heba-Allah is proficient in RNA and DNA extraction, cDNA conversion, and amplification templates using RT-PCR. She has also conducted gene expression analyses and developed standard curves for viruses in laboratory settings. Heba-Allah has hands-on experience in cultivating cells and tissues, as well as a deep understanding of the anatomy of insects, specifically honey bees. Her practical skills extend to raising queens in apiaries, showcasing her expertise in beekeeping practices. Moreover, Heba-Allah has sufficient experience in conducting chemical analyses of honey bee products in laboratory settings, highlighting her comprehensive skill set in bee-related research. She is adept at using various laboratory devices, including conventional PCR and quantitative RT-PCR machines, gel and protein electrophoresis equipment, and spectrophotometers. These experiences demonstrate her proficiency in employing advanced laboratory techniques and equipment to further her research objectives. Heba-Allah is also actively engaged in professional development, having attended the online workshop “Winter school of Bioinformatics” Level 1, organized by the Elite Scientists Platform (ESP) in 2022. Additionally, she has served as a trainer in chemical analysis for honeybee products, further showcasing her commitment to sharing knowledge and expertise within the scientific community.

Skills:

Heba-Allah El-Sayed is proficient in Arabic, her mother tongue, and has a good command of English. She also has elementary proficiency in French. In terms of computer skills, Heba-Allah has completed a course in Digital Transformation in 2023 and received the ICDL certification in 2010. She possesses a very good knowledge of Microsoft Windows Vista and Microsoft Office 2007 and 2013, along with extensive experience in using the Internet. Heba-Allah is also skilled in typing. On a personal level, Heba-Allah demonstrates strong communication and presentation skills, making her adept at conveying complex information effectively. She has a high ability to instruct others in her field of specialization, indicating her proficiency in knowledge sharing and teaching. Heba-Allah works well in groups, displaying a helpful and collaborative attitude towards team members. She excels at working under pressure, demonstrating resilience and a strong work ethic. Heba-Allah is known for her punctuality and dedication to her work. She possesses advanced research abilities and is driven by ambition to achieve her goals.

Publications:

Effect of honeybee venom and Egyptian propolis on the honeybee (Apis mellifera L.) health in vivo

Authors: H. Seyam, A.A.A. Metwally, A.H. El-Deeb, M.S. Badr, E.M. Abd-El-Samie

Journal: Egyptian Journal of Biological Pest Control

Year: 2022

Citations: 1

Molecular characterization of viruses found in honeybee (Apis mellifera) colonies infested with Varroa destructor and Nosema cerana in Egypt

Authors: E.M. Abd-El-Samie, N.K. Basuny, H. Seyam

Journal: Molecular and Cellular Probes

Year: 2021

Citations: 4

First detection of deformed wing and kakugo viruses in honeybees (Apis mellifera L.) in Egypt by real-time polymerase chain reaction (RT-PCR)

Authors: E.M. Abd-El-Samie, F.K. Adham, S. El-Mohandes, H. Seyam

Journal: African Journal of Biotechnology

Year: 2017

Citations: Not specified