Lijun Zong | Robotics and AI | Best Researcher Award

Assoc. Prof. Dr Lijun Zong | Robotics and AI | Best Researcher Award

Associate Professor, Northwestern Polytechnical University, China

Lijun Zong, born on April 28, 1991, in Zhangye, Gansu, China, is an Associate Professor at Northwestern Polytechnical University. A prolific researcher in aerospace robotics, his contributions focus on modular, reconfigurable robots and space manipulator systems. He earned his B.Sc. in Detection, Guidance, and Control Technology from Beijing Institute of Technology, followed by M.Sc. and Ph.D. degrees in Aerospace Vehicle Design at Northwestern Polytechnical University. As a visiting scholar at the University of Toronto Institute for Aerospace Studies, he honed his expertise in hardware-in-the-loop synthesis for space manipulators. Dr. Zong’s groundbreaking research has led to numerous publications in top-tier journals, reflecting his leadership in aerospace robotics.

PROFISSIONAL PROFILE

Google Scholar

Scopus

STRENGTHS FOR THE AWARD

Dr. Lijun Zong’s distinguished research contributions to aerospace robotics, particularly in the domain of space manipulators and their control systems, make him an outstanding candidate for the Best Researcher Award. His work on reactionless control, trajectory optimization, and hardware-in-the-loop simulations addresses critical challenges in modern aerospace engineering.

  1. Pioneering Publications: Dr. Zong has authored impactful papers in high-ranking journals such as IEEE Transactions on Aerospace and Electronic Systems and Aerospace Science and Technology. Key works include advancements in reactionless control for free-floating space manipulators and concurrent rendezvous control of underactuated manipulators.
  2. Global Research Exposure: As a visiting scholar at the University of Toronto Institute for Aerospace Studies, Dr. Zong collaborated internationally, enhancing the global applicability and validation of his research.
  3. Advanced Methodologies: His research employs cutting-edge approaches, such as mixed-integer predictive control, concurrent learning, and game-theoretic optimization, to address practical and theoretical aerospace challenges.
  4. Proven Impact: His work has been cited frequently, reflecting its relevance and influence in academia and industry. Topics like modular and reconfigurable robotics demonstrate innovative solutions for future aerospace missions.
  5. Leadership in Aerospace Research: As an Associate Professor at Northwestern Polytechnical University, Dr. Zong has demonstrated his capability in leading research teams, publishing prolifically, and mentoring future aerospace engineers.

AREAS FOR IMPROVEMENT

  1. Industry Collaboration: While Dr. Zong’s academic achievements are remarkable, deeper collaborations with aerospace industries could further validate his methodologies in real-world applications.
  2. Public Engagement: Increasing the visibility of his work through outreach programs or public talks could help bridge the gap between cutting-edge research and societal understanding of aerospace advancements.
  3. Interdisciplinary Expansion: Expanding his research to include intersections with artificial intelligence and machine learning could further enhance the robustness of his control systems for aerospace applications.

EDUCATION 

  • Ph.D. in Aerospace Vehicle Design (2015–2020)
    Northwestern Polytechnical University, Xi’an, China
    Thesis: “Optimal Trajectory Planning and Coordinated Control for Space Manipulators Capturing a Tumbling Target” | Advisor: Prof. Jianjun Luo
  • Visiting Scholar (2016–2018)
    University of Toronto Institute for Aerospace Studies, Toronto, Canada
    Subject: “Hardware-in-the-loop Synthesis and Analysis of Space Manipulators”
  • M.Sc. in Aerospace Vehicle Design (2013–2015)
    Northwestern Polytechnical University, Xi’an, China
    Thesis: “Occasion Determination and Control for Space Manipulators Capturing Tumbling Targets”
  • B.Sc. in Detection, Guidance, and Control Technology (2009–2013)
    Beijing Institute of Technology, Beijing, China

EXPERIENCE 

  • Associate Professor (Present)
    Northwestern Polytechnical University, Xi’an, China
    Specializing in aerospace robotics, modular systems, and trajectory optimization.
  • Visiting Researcher (2016–2018)
    University of Toronto Institute for Aerospace Studies, Toronto, Canada
    Conducted research in hardware-in-the-loop simulations for space manipulators under Prof. M. Reza Emami.
  • Postdoctoral Researcher (2020)
    Focused on control strategies for space manipulators and robotic systems.
  • Early Research Experience (2013–2020)
    Developed concurrent learning and control techniques for space manipulators and obstacle-avoidance strategies during doctoral and master’s studies.

AWARDS AND HONORS 

  • Best Researcher Award in Aerospace Robotics (2023)
  • IEEE Outstanding Contribution Award (2021)
  • Young Scientist Award by Northwestern Polytechnical University (2019)
  • Journal of Aerospace Excellence Reviewer Recognition (2018)
  • Top 10 Innovators in Robotics by China Robotics Forum (2017)

RESEARCH FOCUS 

Dr. Zong’s research focuses on aerospace robotics, including modular and reconfigurable robots, reactionless control mechanisms, and trajectory optimization. His pioneering work addresses critical challenges in space manipulator systems—particularly in the rendezvous and capture of tumbling targets. He is advancing technologies in hardware-in-the-loop simulations, obstacle avoidance strategies, and predictive control mechanisms. Dr. Zong is also investigating energy-efficient robotic systems and adaptive learning techniques for aerospace applications, driving the future of modular robotic designs and dynamic system stability.

PUBLICATION TOP NOTES

  1. 🚀 Concurrent Rendezvous Control of Underactuated Space Manipulators
  2. 🌌 Parameters Concurrent Learning and Reactionless Control in Post-capture of Unknown Targets by Space Manipulators
  3. 🤖 Reactionless Control of Free-floating Space Manipulators
  4. 🛰️ Concurrent Base-Arm Control of Space Manipulators with Optimal Rendezvous Trajectory
  5. 🌍 Obstacle Avoidance Handling and Mixed Integer Predictive Control for Space Robots
  6. 🌠 Optimal Capture Occasion Determination and Trajectory Generation for Space Robots Grasping Tumbling Objects
  7. 🔧 Optimal Concurrent Control for Space Manipulators Rendezvous and Capturing Targets under Actuator Saturation
  8. 🔬 Kinematics Modeling and Control of Spherical Rolling Contact Joint and Manipulator
  9. ⚙️ Control Verifications of Space Manipulators Using Ground Platforms
  10. ✨ Energy Sharing Mechanism for a Freeform Robotic System-Freebot

CONCLUSION

Dr. Lijun Zong’s expertise and impactful contributions to aerospace robotics position him as a strong contender for the Best Researcher Award. His innovative work addresses pivotal challenges in space exploration, offering practical and theoretical solutions that elevate the field of aerospace engineering. With continued advancements and increased interdisciplinary collaborations, Dr. Zong is well-poised to maintain his trajectory as a leader in aerospace research.

Everton – Artificial Intelligence – Best Researcher Award

Everton - Artificial Intelligence - Best Researcher Award

Universidade Federal da Grande Dourados - Brazil

AUTHOR PROFILE

SCOPUS

ACADEMIC AFFILIATION

Everton is associated with Universidade Católica Dom Bosco, where he contributes to cutting-edge research in computer vision and its applications in agriculture and urban studies.

PRECISION AGRICULTURE RESEARCH

His research in precision agriculture includes the integration of UAV technology and machine learning to optimize farming practices. By improving weed and pest detection methods, his work supports sustainable agriculture and food security.

COMPUTER VISION IN AGRICULTURE

Everton's expertise in computer vision extends to various agricultural applications, from crop monitoring to automated harvesting systems. His innovative solutions help in increasing agricultural productivity and efficiency.

REAL-TIME WEED DETECTION IN SOYBEAN USING UAV IMAGES

Everton Castelão Tetila specializes in the real-time detection of weeds in soybean fields through the innovative use of UAV (Unmanned Aerial Vehicle) images. His work significantly contributes to precision agriculture, enabling farmers to identify and manage weeds more efficiently.

YOLO PERFORMANCE ANALYSIS FOR SOYBEAN PEST DETECTION

Everton has conducted extensive performance analysis of the YOLO (You Only Look Once) algorithm for the real-time detection of soybean pests. His research enhances pest management practices, ensuring timely interventions and reducing crop damage.

URBAN AREA CLASSIFICATION AND MONITORING USING COMPUTER VISION

He applies advanced computer vision techniques for the classification and monitoring of urbanized areas. This work aids in urban planning and development, providing detailed and accurate assessments of urban growth and infrastructure.

EDUCATIONAL CONTRIBUTIONS

He is dedicated to advancing education in his field, sharing his knowledge and findings through publications and presentations. His contributions help train the next generation of researchers and professionals in computer vision and its agricultural applications.

NOTABLE PUBLICATION

YOLO performance analysis for real-time detection of soybean pests
Authors: E.C. Tetila, F.A.G. da Silveira, A.B. da Costa, H. Pistori, J.G.A. Barbedo
Year: 2024
Journal: Smart Agricultural Technology, 7, 100405

Title: Classification and monitoring of urbanized areas using computer vision techniques | Classificação e monitoramento de áreas urbanizadas usando técnicas de visão computacional
Authors: E.C. Tetila, P.M. de Moraes, M. Constantino, M.M.D.M. Greco, H. Pistori
Year: 2023
Journal: Desenvolvimento e Meio Ambiente, 61, pp. 32–42

Title: An approach for applying natural language processing to image classification problems
Authors: G. Astolfi, D.A. Sant'Ana, J.V.D.A. Porto, E.T. Matsubara, H. Pistori
Year: 2022
Journal: Neurocomputing, 513, pp. 372–382

Title: Performance Analysis of YOLOv3 for Real-Time Detection of Pests in Soybeans
Authors: F.A.G. Silveira, E.C. Tetila, G. Astolfi, A.B. Costa, W.P. Amorim
Year: 2021
Conference: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 13074 LNAI, pp. 265–279

Title: Associative classification model for forecasting stock market trends
Authors: E.C. Tetila, B.B. MacHado, J.F. Rorigues, M. Constantino, H. Pistori
Year: 2021
Journal: International Journal of Business Intelligence and Data Mining, 19(1), pp. 97–112

Aswani Devi Aguru – Computer Science – Women Researcher Award

Aswani Devi Aguru - Computer Science & Engineering - Women Researcher Award

NIT warangal - India

EARLY ACADEMIC PURSUITS

Aswani Devi Aguru began her academic journey with a Bachelor's degree in Computer Science & Engineering from IIIT Basara, India. Excelling in her studies, she continued her education with a Master's degree from JNTU Kakinada, where she earned a remarkable CGPA of 9.32 and was awarded a Gold Medal. Her academic prowess led her to pursue a Ph.D. in Computer Science & Engineering from the prestigious National Institute of Technology, Warangal, where she is currently in pursuit of advanced research.

PROFESSIONAL ENDEAVORS

With a strong foundation in computer science, Aguru has embarked on a professional journey marked by research and innovation. She has served as a Junior Research Fellow (JRF), contributing significantly to projects focused on cutting-edge technologies such as Deep Learning, Cryptography, Blockchain, and Internet of Things (IoT). Her role in developing real-time solutions, including a prototype for text-to-speech conversion for the visually impaired using deep learning, demonstrates her commitment to leveraging technology for societal benefit.

CONTRIBUTIONS AND RESEARCH FOCUS ON COMPUTER SCIENCE & ENGINEERING

Aguru's research focus spans several interdisciplinary domains, including cybersecurity, computer vision, and blockchain technology. Her notable contributions include the development of lightweight intrusion detection mechanisms, trust-based blockchain systems, and innovative frameworks for securing IoT networks. Her research papers have been published in reputable journals and presented at international conferences, showcasing her expertise in addressing contemporary challenges in the field of computer science & engineering.

IMPACT AND INFLUENCE

Aguru's work has left a significant impact on academia and industry, with her research findings contributing to the advancement of cybersecurity protocols, blockchain-enabled solutions, and IoT applications. Her collaborations with peers and co-guidance of projects have facilitated knowledge dissemination and skill development among students and professionals alike. Through lectures and workshops, she has shared her insights and expertise, inspiring the next generation of researchers and practitioners in the field computer science & engineering.

ACADEMIC CITATIONS

Aguru's research publications have garnered citations from peers and experts in the field, underscoring the relevance and significance of her contributions. Her work has been cited in reputable journals and conferences, indicating its recognition and influence within the academic community.

LEGACY AND FUTURE CONTRIBUTIONS

Aswani Devi Aguru's legacy lies in her dedication to advancing the frontiers of computer science & engineering through rigorous research, innovative solutions, and knowledge dissemination. Her future contributions are poised to further propel the field forward, addressing emerging challenges and harnessing the potential of disruptive technologies for societal impact. With her expertise in cryptography, blockchain, and cybersecurity, she remains at the forefront of transformative research, shaping the future of technology-enabled solutions in a rapidly evolving digital landscape.

NOTABLE PUBLICATION

Smart Contract Based Next-Generation Public Key Infrastructure (PKI) Using Permissionless Blockchain.  2022 (1)

A Lightweight DDoS Detection Mechanism in IoT Networks using Entropy and Expectation of Packet Size.  2022 (1)