Ljiljana Trajkovic received the Dipl. Ing. degree from the University of Pristina, Yugoslavia, the M.Sc. degree in electrical engineering and computer engineering from Syracuse University,  Syracuse, NY, and the Ph.D. degree in electrical engineering from the University of California at  Los Angeles. She is currently a professor in the School of Engineering Science, Simon Fraser University, Burnaby, British Columbia, Canada. Her research interests include communication networks and dynamical systems. She served as IEEE Division X Delegate/Director and President of the IEEE Systems, Man, and Cybernetics Society and the IEEE Circuits and Systems Society. Dr. Trajkovic serves as Editor-in-Chief of the IEEE Transactions on Human-Machine Systems and Associate Editor-in-Chief of the IEEE Open Journal of Systems Engineering. She is a Distinguished Lecturer of the IEEE Circuits and System Society and the IEEE Systems, Man, and Cybernetics Society and a Fellow of the IEEE.

Title: Data Mining and Machine Learning for Analysis of Network Traffic

Abstract: Collection and analysis of data from deployed networks is essential for understanding modern communication networks. Data mining and statistical analysis of network data are often employed to determine traffic loads, analyze patterns of users' behavior, and predict future network traffic while various machine learning techniques proved valuable for predicting anomalous traffic behavior. In described case studies, traffic traces collected from various deployed networks and the Internet are used to characterize and model network traffic, analyze Internet topologies, and classify network anomalies.

Prof. Shaohua Luo

IEEE Member

Guizhou University, China

Shaohua Luo is currently a Professor at the School of Mechanical Engineering, Guizhou University. He is a provincial outstanding young scientific and technological talents, leader of provincial university innovation research group, special invited expert of ‘Enterprise Innovation Post’ and academic leader of Guizhou University.

His research interests include the dynamics analysis of special electromechanical systems and network, analog circuit design and intelligent control, adaptive and optimal controls, application of artificial intelligence technology in electromechanical systems. In recent years, he have presided over several research projects supported by the National Natural Science Foundation of China and the Provincial Natural Science Foundation of China, and participated in several research works such as the National 863 Program and the National Natural Science Foundation of China, e.g., key projects, general projects and international (regional) cooperation and exchange  projects. Some theoretical and application achievements have been made in key science and technology such as intelligent manufacturing, autonomous intelligent system, high-performance  electromechanical drive system and equipment. He have published 50 research papers in authoritative academic journals at home and abroad, acquired 22 authorized invention patents including four British invention patents and one America invention patents, did the transformation of two invention patents, and served as the director of innovation research group of provincial universities and key projects of ESI potential discipline promotion plan of Guizhou University. Meanwhile he have won the second prize of Excellence, and relevant research results  have been positively cited by well-known experts in this field.

Prof. Yang Yue

IEEE Senior Member

Xi'an Jiaotong University, China

Yang Yue received the B.S. and M.S. degrees in electrical engineering and optics from Nankai University, China, in 2004 and 2007, respectively. He received the Ph.D. degree in electrical engineering from the University of Southern California, USA, in 2012. He is a Professor with the School of Information and Communications Engineering, Xi'an Jiaotong University, China. Dr. Yue’s current research interest is intelligent photonics, including optical communications, optical perception, and optical chip. He has published over 260 journal papers (including Science) and conference proceedings with >10,000 citations, six edited books, two book chapters, >60 issued or pending patents, >200 invited presentations (including 1 tutorial, >30 plenary and >50 keynote talks). Dr. Yue is a Fellow of SPIE, a Senior Member of IEEE and Optica. He is an Associate Editor for IEEE Access and Frontiers in Physics, Editor Board Member for four other scientific journals, Guest Editor for >10 journal special issues. He also served as Chair or Committee Member for >100 international conferences, Reviewer for >70 prestigious journals.

Title: Optical Performance Monitoring for Multiparameter Communications Channels Using Machine Learning

Abstract: In recent years, machine learning has come to the forefront as a promising technology to aid in optical performance monitoring for multiparameter communications channels. In this talk, we will introduce CNN-based techniques to effectively monitor multiple system performance parameters of optical channels using eye diagram measurements. Experimental results demonstrate this method achieves a prediction accuracy >98% when tasked with identifying the modulation format (QPSK, 8-QAM, or 16-QAM), as well as the optical signal-to-noise ratio (OSNR), roll-off factor (ROF), and timing skew for 32 GBd coherent channels. For PAM-based intensity-modulation direct detection (IMDD) channel eye-diagram-based CNN method maintain >97% identification accuracy for 432 classes under different combinations of probabilistic shaping (PS), ROF, baud rate, OSNR, and chromatic dispersion (CD) by each modulation format. Furthermore, we undertake on an extensive comparison of ResNet-18, MobileNetV3 and EfficientNetV2. Our designed VGG-based model of reduced layers, alongside the lightweight MobileNetV3, demonstrates enhanced cost-effectiveness while maintaining high accuracy.

Associate Professor Pavel Loskot has over 25 years of experience in the design, analysis, implementation, and deployment of telecommunication systems through numerous academic and industrial collaborative projects and consultancy contracts. He possesses expert-level knowledge of digital and statistical signal processing, algorithms, and methods, as well as a solid background in applied probability and statistics. Additionally, he is an avid Linux programmer and user since 1996. In 2014/2015, as a Visiting Researcher at CSRC of the Chinese Academy of Engineering Physics, he began working on computational molecular biology. From 1999 to 2001, he served as a Research Scientist and Project Manager at CWC in Oulu, Finland. Furthermore, Dr. Loskot is a Fellow of the Higher Education Academy of the UK and a Recognised Research Supervisor of the UK Council for Graduate Education. He has been a Senior Member of the IEEE since 2013.