Qiaoning Yang | Signal & Image Processing | Best Researcher Award

Published on by Electrical Engineering

Assoc. Prof. Dr. Qiaoning Yang | Signal & Image Processing | Best Researcher Award

Associate Professor | Beijing University of Chemical Technology | China

Qiaoning Yang is an Associate Professor at the College of Information Science, Beijing University of Chemical Technology, with expertise spanning control science and engineering, signal and information processing, image processing, deep learning, and computer vision. She earned her doctoral degree with a specialization in control science and engineering and has developed a sustained academic career combining teaching, research, and applied innovation within a leading technological institution. Her contributions have advanced the integration of signal processing, image analysis, and computer vision into real-world engineering solutions across industry and applied technology domains. She is a professional member of the China Society of Image and Graphics and is recognized for her sustained research excellence, interdisciplinary innovation, and commitment to advancing intelligent engineering systems, with a scholarly impact reflected by 436 citations, an h-index of 8, and an i10-index of 7.

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Featured Publications

Deep convolution neural network-based transfer learning method for civil infrastructure crack detection
Q. Yang, W. Shi, J. Chen, W. Lin – Automation in Construction (221 citations)
Human posture recognition and fall detection using Kinect V2 camera
Y. Xu, J. Chen, Q. Yang, Q. Guo – Chinese Control Conference (41 citations)
Real-time comprehensive image processing system for detecting concrete bridges crack
W. Lin, Y. Sun, Q. Yang, Y. Lin – Computers and Concrete (15 citations)

Qingguo Lü | Electrical Engineering  | Research Excellence Award

Published on by Electrical Engineering

Dr. Qingguo Lü | Electrical Engineering  | Research Excellence Award

Associate Researcher | Chongqing University | China

Qingguo Lü is an Associate Researcher at Chongqing University specializing in distributed optimization, privacy-preserving machine learning, and smart grid systems. He earned his doctoral degree in computer science and technology with a strong focus on optimization theory and networked systems and subsequently advanced his expertise through postdoctoral research and international academic collaboration.  His scholarly contributions have delivered both theoretical advances and practical engineering solutions, achieving strong international visibility and impact. His academic influence is reflected by 1,174 citations, an h-index of 18, and an i10-index of 26. In recognition of his expertise and service, he holds multiple editorial and guest editorial appointments in international journals, serves on conference program committees, contributes extensively to peer review, holds innovation patents, and maintains professional standing as an IEEE Senior Member, demonstrating sustained excellence in research, leadership, and academic service suitable for a prestigious research award.

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Featured Publications

Distributed projection subgradient algorithm over time-varying general unbalanced directed graphs
H. Li, Q. Liu, T. Huang – IEEE Transactions on Automatic Control · Citations: 115
Accelerated convergence algorithm for distributed constrained optimization under time-varying general directed graphs
H. Li, Q. Lü, X. Liao, T. Huang – IEEE Transactions on Systems, Man, and Cybernetics: Systems · Citations: 104
Achieving acceleration for distributed economic dispatch in smart grids over directed networks
Q. Liu, X. Liao, H. Li, T. Huang – IEEE Transactions on Network Science and Engineering · Citations: 100
Convergence analysis of a distributed optimization algorithm with a general unbalanced directed communication network
H. Li, Q. Liu, T. Huang – IEEE Transactions on Network Science and Engineering · Citations: 96
Privacy masking stochastic subgradient-push algorithm for distributed online optimization
Q. Lü, X. Liao, T. Xiang, H. Li, T. Huang – IEEE Transactions on Cybernetics · Citations: 80

Aseel Basheer | Machine Learning | Excellence in Research Award

Published on by Electrical Engineering

Dr. Aseel Basheer | Machine Learning | Excellence in Research Award

Postdoc | University of Oklahoma | United States

Aseel Basheer is a Graduate Research Assistant and Ph.D. candidate in Computer Science at the University of Oklahoma, with expertise in machine learning, data science, and large-scale data analytics. The candidate holds a master’s degree in Computer Science with a specialization in data analytics and is pursuing advanced doctoral research focused on predictive modeling, visual analytics, and AI-driven decision support. Professionally, Aseel has contributed to interdisciplinary research projects in public health intelligence and pandemic surveillance, developing AI/ML models, data-driven forecasting systems, and visualization platforms, while also demonstrating academic leadership through teaching, mentoring, and curriculum support in higher education. The candidate’s professional profile is further strengthened by recognized certifications in data analytics, machine learning, healthcare data science, and research rigor, alongside active engagement in scholarly communities. The scholarly impact is reflected through 22 citations, an h-index of 2, and an i10-index of 1.

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