Juxian Zhao | Computer Science | Best Researcher Award

Dr. Juxian Zhao | Computer Science | Best Researcher Award

PhD candidate, China University of Mining and Technology School of Mechatronic Engineering, China

馃摎 Juxian Zhao is a PhD candidate at the China University of Mining and Technology, specializing in robotics, computer vision, and deep learning. He focuses on developing innovative technologies for intelligent firefighting equipment and autonomous operations. Currently leading R&D for a key provincial project, Juxian has made significant contributions to the field through his research and innovations.

Profile

Scopus

 

Education

馃帗 Juxian Zhao is pursuing a PhD at the China University of Mining and Technology in the School of Mechatronic Engineering. His academic journey has been marked by a strong focus on robotics, computer vision, and deep learning technologies, which he integrates into his research on intelligent firefighting equipment.

Experience

馃捈 Juxian Zhao has extensive experience in the research and development of intelligent firefighting equipment, multi-agent collaboration, and autonomous firefighting operations. He is currently leading a key provincial-level R&D project and actively collaborating with XCMG Fire Fighting Equipment Co., Ltd., and Xuzhou XCMG Daojin Special Robot Technology Co., Ltd.

Research Interests

馃敩 Juxian Zhao’s research interests include robotics, computer vision, and deep learning technologies. He is particularly focused on applying these technologies to intelligent firefighting equipment and autonomous firefighting operations, aiming to enhance efficiency and effectiveness in emergency response scenarios.

Awards

馃弳 Juxian Zhao has been recognized for his contributions to the field of robotics and firefighting technology through various accolades. His work on the CG-DALNet model for autonomous firefighting has garnered attention for its innovative approach and significant performance improvements.

Publications

Accurate and Fast Fire Alignment Method Based on a Mono-binocular Vision System

Visual predictive control of fire monitor with time delay model of fire extinguishing jet

An efficient firefighting method for robotics: A novel convolution-based lightweight network model guided by contextual features with dual attention

Najmeh Zamani | Engineering | Best Researcher Award

Dr. Najmeh Zamani | Engineering | Best Researcher Award聽

Postdoc researcher, Concordia university, Canada

馃専 Najmeh Zamani (b. 29th July 1989) is a dedicated researcher in the field of electrical and computer engineering. She is currently a Postdoctoral Researcher at Concordia University, Canada, with a strong academic background from Isfahan University of Technology, Iran. Najmeh’s expertise spans control systems, nonlinear multi-agent systems, and deep learning applications. Married and proficient in both Persian and English, she is recognized for her significant contributions to her field.

 

Profile

Google Scholar

 

Education

馃帗 Najmeh Zamani has an impressive academic record, starting with a Bachelor’s degree in Electrical Engineering from Isfahan University (2007-2011), where she ranked 1st. She continued at the same university for her Master’s degree (2011-2014), ranking 3rd, and then pursued a Ph.D. in Electrical Engineering at Isfahan University of Technology (2015-2021), achieving a GPA of 19/20. Her Ph.D. dissertation focused on the “Consensus of Nonlinear Multi-Agent Systems with Time-Delays and Actuator Faults.” Najmeh also completed postdoctoral research at Concordia University, Canada, in 2023.

Experience

馃捈 Najmeh Zamani has a robust professional background, including roles as a researcher at Isfahan University of Technology, an R&D researcher at Control Farayand Pasargad Company, and an electronic designer at Sirco Company. Her experience spans industrial projects like COVID-19 pandemic prediction models and power electronic boost converter design. Najmeh has also served as a visiting professor and teaching assistant in various institutions, sharing her expertise in control systems and electronics.

Research Interests

馃敩 Najmeh Zamani‘s research interests are diverse and cutting-edge. They include distributed control systems and multi-agent systems, adaptive control of nonlinear systems with time-delays and faults, fault estimation and control theory, and control of distributed applications like mobile sensors. She is also passionate about deep learning, probabilistic graphical models (PGM), data-driven control, reinforcement learning, and data analysis.

Awards

馃弳 Najmeh Zamani has received several accolades throughout her academic journey. She was ranked 1st among all graduated students in Electrical and Computer Engineering at Isfahan University in 2011. She was also recognized as an outstanding B.Sc. and M.Sc. student from 2007 to 2014. Notably, she received admission offers for both her graduate and doctoral studies at Isfahan University of Technology without needing to take the National Entrance Exam for Graduate Schools.

Publications

N. Zamani, M. Ataei, M. Niroomand, “Analysis and Control of Chaotic Behavior in Boost Converter by Ramp Compensation Based on Lyapunov Exponents Assignment: Theoretical and Experimental Investigation”, Chaos, Solitons and Fractals, 2015, cited by 20 articles. Link.

N. Zamani, J. Askari, M. Kamali, A. Aghdam, “Distributed adaptive consensus tracking control for non-linear multi-agent systems with time-varying delays”, IET Control Theory & Applications, 2020, cited by 15 articles. Link.

H. Kalantari, M. Mojiri, S. Dubljevic, N. Zamani, “Fast l1-MPC Based on Sensitivity Analysis Strategy”, IET Control Theory & Applications, 2020, cited by 10 articles. Link.

N. Zamani, J. Askari, M. Kamali, H. Kalantari, A. Aghdam, “Adaptive Tracking Control for Nonlinear Multi-Agent Systems with Stuck Failures and Unknown Control Directions”, Journal of the Franklin Institute, 2024. Link.

H. Kalantari, M. Mojiri, Najmeh Zamani, “Urban Traffic Control By Fast l1 Model Predictive Control Based on Sensitivity Analysis”, IET Control Theory & Applications, 2024. Link.