Jingxian Liu | Computer Vision | Best Researcher Award

Posted on by Academic achievements

Mr. Jingxian Liu | Computer Vision | Best Researcher Award

Associate Research Fellow‌ at Guangzhou Maritime University, China.

Mr. Jingxian Liu is an Associate Research Fellow at Guangzhou Maritime University. Born in November 1984 in Guangzhou, China, he specializes in remote sensing and communication systems. His research focuses on digital twins, intelligent state prediction, and maneuvering-target tracking using advanced computational methods. Liu has authored numerous high-impact publications and has led several national and regional research projects, contributing significantly to the field of geoscience and remote sensing.

Profile Verification

Scopus 

Education

Jingxian Liu pursued his doctoral studies in Communication and Information Systems at Beihang University (2013-2018) after completing a Master’s degree in Geodetection and Information Technology from China University of Geoscience (Beijing, 2007-2010). He holds a Bachelor’s degree in Electronic Information Engineering from China University of Geoscience (Beijing, 2003-2007). His educational background has equipped him with a solid foundation in engineering and remote sensing technologies.

Experience

Jingxian Liu currently serves as an Associate Research Fellow at Guangzhou Maritime University since March 2024. Prior to this, he was an Associate Research Fellow at Guangxi University of Science and Technology from December 2018 to December 2023. During this period, he contributed significantly to research projects focusing on remote sensing and digital twin technologies. Earlier in his career, Liu worked as an Engineer at the China Shipbuilding Industry Corporation’s 760th Research Institute from July 2010 to June 2013. His role there involved conducting research and development activities aimed at advancing engineering technologies in shipbuilding and marine industries.

Research Interests

Jingxian Liu’s research primarily revolves around remote sensing image processing, digital twins, intelligent state prediction, and maneuvering-target tracking. His innovations include fast arbitrary-oriented object detection for remote sensing images, differential correction based shadow removal methods, and deep learning algorithms for maneuvering-target tracking. His work significantly advances understanding in these areas, applying cutting-edge computational techniques to solve complex challenges.

Publications

Fast arbitrary-oriented object detection for remote sensing images

Authors: Liu, J.; Tang, J.; Yang, F.; Zhao, Y.

Citations: 0

Year: 2024

Task Demands-Oriented Collaborative Offloading and Deployment Strategy in Software-Defined UAV-Assisted Edge Networks

Authors: Yan, J.; Wang, W.; Liu, J.; Yuan, H.; Zhu, Y.

Citations: 0

Year: 2024

HDDet: A More Common Heading Direction Detector for Remote Sensing and Arbitrary Viewing Angle Images

Authors: Ding, S.; Liu, J.; Yang, F.; Xu, M.

Citations: 1

Year: 2024

Digital Twins Based Intelligent State Prediction Method for Maneuvering-Target Tracking

Authors: Liu, J.; Yan, J.; Wan, D.; Al-Dulaimi, A.; Quan, Z.

Citations: 5

Year: 2023

Locating the propagation source in complex networks with observers-based similarity measures and direction-induced search

Authors: Yang, F.; Li, C.; Peng, Y.; Wen, J.; Yang, S.

Citations: 7

Year: 2023

Diffusion characteristics classification framework for identification of diffusion source in complex networks

Authors: Yang, F.; Liu, J.; Zhang, R.; Yao, Y.

Citations: 1

Year: 2023

A differential correction based shadow removal method for real-time monitoring

Authors: Liu, S.; Chen, M.; Li, Z.; Liu, J.; He, M.

Citations: 0

Year: 2023

A cross-and-dot-product neural network based filtering for maneuvering-target tracking

Authors: Liu, J.; Yang, S.; Yang, F.

Citations: 6

Year: 2022

Micro-Knowledge Embedding for Zero-shot Classification

Authors: Li, H.; Wang, F.; Liu, J.; Zhang, T.; Yang, S.

Citations: 3

Year: 2022

An identification strategy for unknown attack through the joint learning of space–time features

Authors: Wang, H.; Mumtaz, S.; Li, H.; Liu, J.; Yang, F.

Citations: 6

Year: 2021

 

Conclusion

Jingxian Liu is a highly deserving candidate for the Research for Best Researcher Award due to his significant contributions to remote sensing, digital twins, and maneuvering-target tracking. His innovative research methodologies, high-impact publications, and leadership in large-scale research projects position him as a leader in his field. Continued efforts to enhance industry collaborations and community engagement will further solidify his status as a key figure in advancing technological solutions for environmental and geospatial challenges.

 

Haoran Lu | Electrochemistry | Best Researcher Award

Posted on by Academic achievements

Mr. Haoran Lu | Electrochemistry | Best Researcher Award

Graduate student at Beijing Forestry University, China.

Haoran Lu, was born in Jinan, Shandong Province in January 1999. He specializes in environmental engineering, focusing on water pollution control and water resources utilization. He is currently pursuing a Master of Engineering in Environmental Engineering at Beijing Forestry University. Haoran is dedicated to advancing sustainable water treatment technologies and improving water quality through innovative research.

Profile Verification

Scopus 

Education

Bachelor of Engineering in Environmental Engineering (September 2011 – June 2021)
School of Resources and Environmental Engineering, Shandong University of Technology, Jinan, Shandong Province, China. During this period, Haoran Lu gained foundational knowledge in environmental science, engineering principles, and sustainable practices with a focus on water resources management and pollution control. His coursework and projects were aimed at developing solutions for managing environmental challenges.

Master of Engineering in Environmental Engineering (September 2021 – June 2024)
College of Environmental Science and Engineering, Beijing Forestry University, Beijing, China. During his master’s studies, Haoran concentrated on advanced research topics related to water pollution control and water resources utilization. His research projects were focused on developing innovative wastewater treatment technologies and strategies to enhance water quality and sustainability, aiming to address the challenges associated with industrial wastewater management.

Experience

Haoran Lu has gained valuable experience in environmental engineering through his studies and research. His role as a researcher at the Beijing Key Lab for Source Control Technology of Water Pollution, College of Environmental Science & Engineering at Beijing Forestry University, allowed him to work on innovative wastewater treatment technologies. He has actively participated in experimental design, data analysis, and project development, contributing to advancements in water quality management and environmental sustainability.

Research Interests

Haoran Lu’s research is centered on water pollution control and water resources utilization. He explores advanced electrochemical methods for wastewater treatment, focusing on optimizing the degradation of complex organic pollutants. His work aims to develop efficient and sustainable solutions for wastewater management, particularly in addressing the challenges posed by industrial effluents.

Awards and Honors

Haoran Lu has been recognized for his research excellence and dedication to environmental sustainability. He received the Best Thesis Award in 2024 from Beijing Forestry University for his work on innovative water treatment technologies. His research contributions have also been acknowledged through invitations to international conferences on environmental engineering.

Publications

Efficient degradation and Ni recovery from Ni-EDTA wastewater by Ni/GAC electrodes

Authors: Yang, T.; Lu, H.; Qin, Y.; Han, X.; Liang, W.

Citations: 0

Year: 2024

Journal: Journal of Water Process Engineering, 68, 106410

Catalytic applications of amorphous alloys in wastewater treatment: A review on mechanisms, recent trends, challenges, and future directions

Authors: Liu, Y.; Lu, H.; Yang, T.; Han, X.; Liang, W.

Citations: 1

Year: 2024

Journal: Chinese Chemical Letters, 35(10), 109492

Photosynthetic microalgae microbial fuel cells for bioelectricity generation and microalgae lipid recovery using Gd-Co@N-CSs/NF as cathode

Authors: Qin, L.; Qin, Y.; Cui, N.; Yang, T.; Liang, W.

Citations: 1

Year: 2024

Journal: Chemical Engineering Journal, 490, 151647

Electrocatalytic degradation of nitrogenous heterocycles on confined particle electrodes derived from ZIF-67

Authors: Liu, Y.; Qin, L.; Qin, Y.; Zhang, Q.; Liang, W.

Citations: 6

Year: 2024

Journal: Journal of Hazardous Materials, 463, 132899

Electrocatalysis degradation of biochemical tail water from coking wastewater using particle electrode with persulphate

Authors: Lu, H.; You, X.; Yang, T.; Han, X.; Liang, W.

Citations: 0

Year: 2024

Journal: Environmental Technology (United Kingdom)

Distribution, source, risk, and phytoremediation of polycyclic aromatic hydrocarbons (PAHs) in typical urban landscape waters recharged by reclaimed water

Authors: Zhu, Z.; Li, L.; Yu, Y.; Lu, H.; Liang, W.

Citations: 13

Year: 2023

Journal: Journal of Environmental Management, 330, 117214

Gd-Co nanosheet arrays coated on N-doped carbon spheres as cathode catalyst in photosynthetic microalgae microbial fuel cells

Authors: Qin, L.; Liu, Y.; Qin, Y.; Yang, T.; Liang, W.

Citations: 5

Year: 2022

Journal: Science of the Total Environment, 849, 15771

 

Conclusion

Haoran Lu is an emerging leader in the field of environmental engineering, showing promise through innovative research and strong methodological rigor. His study provides a compelling case for the application of advanced electrochemical techniques in treating complex industrial wastewater. His work aligns well with the objectives of the Best Researcher Award, highlighting contributions to environmental sustainability and practical applications in water pollution control. Continued research and exploration in diverse environmental contexts will likely solidify his status as a leading researcher in the field.

 

Rajesh Pashikanti – Signal processing – Best Researcher Award

Posted on by Academic achievements

Rajesh Pashikanti - Signal processing - Best Researcher Award

COEP TECH University - India

Professional Profiles

Early Academic Pursuits

Rajesh Pashikanti's academic journey commenced with a strong foundation in electronics and instrumentation. He pursued his Bachelor's degree at Vaagdevi College of Engineering, followed by a Master's degree in Embedded Systems from Ramappa Engineering College. His educational background equipped him with a comprehensive understanding of technology and laid the groundwork for his future endeavors in signal processing.

Professional Endeavors

Following his academic pursuits, Rajesh embarked on a fulfilling career in education, accumulating a decade of teaching experience at MBES College of Engineering in Maharashtra. During his tenure, he honed his skills as an educator, imparting knowledge and nurturing the next generation of engineers. Additionally, Rajesh ventured into the realm of research, joining COEP Technological University in Pune, Maharashtra, where he has been engaged in signal processing research since 2021.

Contributions and Research Focus On Signal processing

Rajesh's research focus lies in the domain of signal processing, a field essential for various technological applications, including telecommunications, medical imaging, and audio processing. Through his doctoral studies at COEP Technological University, he delves into the intricacies of signal analysis, processing algorithms, and their real-world implementations. His contributions aim to advance the understanding and application of signal processing techniques, addressing contemporary challenges and driving innovation in the field.

Accolades and Recognition In Signal processing

Rajesh Pashikanti's dedication to academia and research has garnered recognition and accolades from peers and institutions alike. His commitment to excellence in teaching earned him admiration from students and colleagues during his tenure at MBES College of Engineering. Additionally, his foray into research at COEP Technological University showcases his ambition and passion for advancing knowledge in signal processing.

Impact and Influence

Rajesh's influence extends beyond the classroom and laboratory. As a teacher, he has shaped the academic journeys of countless students, instilling in them a passion for technology and a thirst for knowledge. His research endeavors hold the potential to make significant contributions to the field of signal processing, potentially impacting industries ranging from telecommunications to healthcare. Through his work, Rajesh aims to leave a lasting imprint on the academic and technological landscape.

Legacy and Future Contributions For Signal processing

As Rajesh continues his doctoral research and academic pursuits, his legacy is one of dedication, perseverance, and innovation. His efforts in signal processing research have the potential to shape the future of technology, improving communication systems, medical diagnostics, and more. With a steadfast commitment to excellence and a vision for the future, Rajesh Pashikanti is poised to make enduring contributions to academia and industry alike.

Rajesh Pashikanti's journey from student to educator to researcher exemplifies the transformative power of education and the pursuit of knowledge. Through his academic and professional endeavors, he embodies the spirit of inquiry and innovation, leaving an indelible mark on the field of signal processing and inspiring future generations of engineers and researchers.

Notable Publications

An adaptive Marine Predator Optimization Algorithm (MPOA) integrated Gated Recurrent Neural Network (GRNN) classifier model for arrhythmia detection 2024

Adaptive Predictive Control-Based Noise Cancellation with Deep Learning for Arrhythmia Classification from ECG Signals 2022

Cardiac Arrhythmia Classification using Deep Convolutional Neural Network and Fuzzy Inference System 2022

Daniela Lupu – Artificial intelligence – Best Researcher Award

Posted on by Academic achievements

Daniela Lupu - Artificial intelligence - Best Researcher Award

National University of Science and Technology Politehnica BUcharest - Romania

Professional Profiles

Early Academic Pursuits

Daniela Lupu embarked on her academic journey at the National University of Science and Technology Politehnica Bucharest, where she pursued her Bachelor's degree in Systems Engineering from 2013 to 2017. Building upon this foundation, she continued her academic pursuits with a Master's program in Complex Systems from 2017 to 2019 at the same institution. Her dedication to learning and exploration led her to pursue a Doctor of Philosophy degree from 2019 to 2023, specializing in stochastic optimization, artificial intelligence, and model predictive control at the National University of Science and Technology Politehnica Bucharest.

Professional Endeavors

Throughout her academic journey, Daniela Lupu has engaged in various professional endeavors, demonstrating her commitment to research and teaching. As a teaching assistant at the National University of Science and Technology Politehnica Bucharest since 2019, she has contributed to seminars on Numerical methods and optimization techniques. Additionally, she served as a researcher for the Ecient Learning and Optimization Tools for Hyperspectral Imaging Systems (ELO-Hyp) project from 2020 to 2023, where she delved into dimensionality reduction methods and developed stochastic higher-order methods for hyperspectral image analysis.

Contributions and Research Focus On Artificial intelligence

Daniela Lupu's research focus revolves around the intersection of stochastic optimization, artificial intelligence, and model predictive control. Her doctoral thesis, titled "Contributions to the analysis of some higher-order methods and applications," underscores her dedication to advancing methodologies in optimization and their practical applications. She has made significant contributions to the field, as evidenced by her journal papers on topics such as exact representation and efficient approximations of linear model predictive control laws and the convergence analysis of stochastic higher-order majorization-minimization algorithms.

Accolades and Recognition

Daniela Lupu's contributions to academia have been recognized through various accolades and publications. She has authored papers in reputable journals such as Systems & Control Letters and Optimization Methods and Software, showcasing her expertise and research prowess. Moreover, her participation in workshops and collaborations with international institutions, such as NTNU in Trondheim, further highlights her commitment to academic excellence and knowledge dissemination.

Impact and Influence

Through her research endeavors and teaching engagements, Daniela Lupu has made a significant impact on the academic community and beyond. Her work in stochastic optimization and artificial intelligence has the potential to revolutionize various fields, including hyperspectral imaging systems and control theory. As a teaching assistant, she has inspired and mentored students, nurturing the next generation of scholars and engineers.

Artificial intelligence (AI) is revolutionizing industries worldwide by enabling machines to replicate human-like intelligence. Through algorithms and computational models, AI systems can analyze vast amounts of data, learn from patterns, and make autonomous decisions. From self-driving cars to personalized recommendation systems, AI powers various applications, enhancing efficiency and innovation. With continuous advancements in machine learning and neural networks, artificial intelligence is poised to reshape how we live, work, and interact with technology in the future.

Legacy and Future Contributions

Looking ahead, Daniela Lupu's legacy in the realm of optimization and control theory is poised to endure, with her research serving as a cornerstone for future advancements in the field. Her dedication to pushing the boundaries of knowledge and her commitment to excellence will continue to shape the landscape of academia and industry. As she progresses in her academic journey, Daniela Lupu remains steadfast in her pursuit of innovation and discovery, leaving an indelible mark on the scientific community and society as a whole.

Notable Publications

Control of a wastewater treatment process using linear and nonlinear model predictive control 2023

Dimensionality reduction of hyperspectral images using an ICA-based stochastic second-order optimization algorithm 2023