- Keynote Speakers
- General Information
- Organizing Committee
- Contact us
Title: Modeling and Analysis of Fuzzy Systems in The Frequency Domain
Ebrahim Navid Sadjadi, Universidad Carlos III de Madrid.
The objective of this half-day workshop is to cover the state-of-the-art in modeling and analysis of fuzzy systems in the frequency domain. During the last years, we have witnessed major successes of fuzzy logic systems in academia and industries. From beating professionals at games like chess, to fast detection of diseases like cancer, classification of complex images, and generation of captions for images in the personalized media of the incomplete and noisy information. In many AI fields, fuzzy systems could outperform all the existing machine learning and model-based control methods. Hence, they are one of the few areas that received the on-going interest of the researchers and engineers.
Although fuzzy models have been employed for a long time so far, however, the recent research in the fuzzy systems demonstrated that some kinds of fuzzy compositions can enlarge the design space into the higher dimensions through the Fourier expansion of the membership functions and thereby, facilitate the frequency analysis and modeling of the fuzzy systems.
Frequency analysis of the fuzzy systems could facilitate the modeling and analysis of fuzzy systems in different aspects. The first is the dedication of the sufficient number of rules to the fuzzy system, without making the fuzzy structure complicated beyond what is really required. The contribution in this area could facilitate the understanding, utilization, tuning, and performance of the consequent algorithms. The second challenge is the ability to handle the system disturbances and noises soft and smoothly. The increase of the system robustness will facilitate the operation of the industrial processes inside their margins and operational limits. The third aspect is the ability to perform on-line and fast processing and decision makings for the processes, considering their affordable computational complexities.
Hence, the purpose of providing this workshop is to give a detailed introduction to the fundamental developments in the recent field of fuzzy systems for the researchers, graduate students, and practitioners. The main focus of the course is on comprehensive study of the new achievements on the structural properties of fuzzy models in the frequency domain for various applications, which include control, identification, and signal processing as well as the design of smooth fuzzy models for various applications in image processing and biomedical engineering.
Biography of the Speakers:
Ebrahim Navid Sadjadi has studied a first degree and then a master degree in Engineering at the Technology University of Madrid. He continued Ph.D. in Information Systems at the University of Carlos III in Madrid. He has contributed to the literature of the smooth fuzzy systems through working on the structural approximation properties of the smooth fuzzy model and on their applications for systems modeling, optimization, control, and signal processing.
Mohammad Bagher Menhaj received the Ph.D. degree from Oklahoma State University (OSU), Stillwater, in 1992. Then, he became a Postdoctoral Fellow with OSU. In 1993, he joined the Amirkabir University of Technology, Tehran, Iran, where he is a Professor now. From December 2000 to August 2003, he was with the School of Electrical and Computer Engineering, Department of Computer Science, OSU, as a Visiting Faculty Member and a Research Scholar. He has also been a Project Director for projects in the areas such as computational intelligence, cognitive science, real-time simulator design, and adaptive control, sponsored by private and government institutions. He is author and co-author of more than 350 technical papers and four books: Fundamentals of Neural Networks, application of Computational Intelligence in Control, Neural Networks, and Fuzzy Computations.
Title: Security, and Privacy in Human Cybernetics, Smart Cities and IoT (SPHCSI-2020)
Sk Md Mizanur Rahman, Centennial College, Canada.
Mehrdad Tirandazian, Ryerson University, Canada.
Raylin Tso, National Chengchi University, Taiwan.
Human Cybernetics (HC) is the science of control and communication in the human being and the machine and it can be broadly classified with the combination of different areas, as examples cyborg, body sensor network, unmanned aerial vehicles networks (UAVN), Internet of Things (IoT), Cyber-Physical Systems (CPS), smart electronic systems, etc. With the rapid development of 5G communication/networking, sensing technologies, Artificial Intelligence (AI) and machine learning (ML), and cloud-edge cooperative computing, this domain has been becoming increasingly prosperous, which constitute a variety of interconnected, smart, cooperative environments.
Artificial Intelligence (AI) and Mobile based technologies are widely used in computer applications to perform tasks such as monitoring, forecasting, recommending, prediction, and statistical reporting. They are deployed in a variety of systems including robot-controlled warehouses, financial forecasting applications, and security enforcement and are increasingly integrated with cloud, fog and edge computing, big data analytics, robotics, Internet-of-Things, mobile computing, smart cities, smart homes, intelligent healthcare, etc.
IoT, CPS, and other abovementioned systems are multi-dimensional complex systems that are the combinations of multiple computing, networking, and physical environments through the integration and cooperation of communication, computation, and control. Such a complex system can monitor, realize real-time perception, dynamically control, and provide information services for large engineering systems.
However, due to the characteristics of complex systems, vulnerable end devices, limited computation/communication/storage/energy resources, heterogeneous networking, etc., security and privacy for the above systems are extremely challenging problems. There exist a lot of security threat incidents, from system invasion, cyber-attack, industrial control damage, to data privacy leakage.
This workshop collects novel solutions and offers a venue for researchers and industry partners to publish, present and discuss their latest research results in the area of security and privacy in HC, smart cooperative IoT and CPS.
Biography of the Speakers:
Dr. Sk Md Mizanur Rahman is a fulltime professor in the department of Information and Communication Engineering Technology, School of Engineering Technology and Applied Science, Centennial College. Prior to his current appointment, he worked as an Assistant Professor for five years in the Information Systems Department at the College of Computer and Information Sciences, King Saud University. He also worked for several years in cryptography and security engineering in the high-tech industry in Ottawa, Canada. In addition, he worked as a postdoctoral researcher for several years at the University of Ottawa, University of Ontario Institute of Technology (UOIT), and University of Guelph, Canada. He completed a Ph.D. in Engineering (Major: Cybersecurity Risk Engineering) in the Laboratory of Cryptography and Information Security, Department of Risk Engineering, University of Tsukuba, Japan, in 2007. The Information Processing Society Japan (IPSJ) awarded Dr. Rahman its Digital Courier Funai Young researcher Encouragement Award for his excellent contributions to IT security research. He is awarded a Gold Medal for distinction in his undergraduate and graduate programs. He has published approximately one-hundred peer-reviewed journal and conference research articles. Also, he has a granted industrial patent (US Patent) on cryptographic key generation and protection. Dr. Rahman’s primary research interests are cryptographic protocol design, software and network security, reverse engineering and ethical hacking, privacy-enhancing technology, sensor and mobile ad-hoc network security, cloud and the Internet of Things (IoT) security.
Dr. Mehrdad Tirandazian received his B.Sc., M.Sc., and Ph.D. degrees in Computer Science. His main Ph.D. research was dedicated to developing algorithms for VLSI Design and Optimization where he explored the advantages of embedding Boolean algebra into the Polynomial Ring. Mehrdad is a researcher and Assistant Professor at Ryerson University, and he has taught a variety of computer science and engineering courses at the University of Ontario Institute of Technology, Sheridan College, Humber College, and Centennial College. For e.g. Digital Computation and Programming, Extreme Programming and Agile Processes, Database Systems, Data Structure, Software Design and Analysis, Digital Media Production, Information Technology for Engineers, Introduction to Programming for scientists, and the list goes on. In addition to his vast teaching experience, Mehrdad has had over 10 years of corporate experience working in IT project management in local and international companies as a Senior Systems Architect and Senior Systems Analyst. Mehrdad has introduced, designed and developed University-level computer science and engineering courses and has published several computer science and engineering research papers, including Computer Graphics, Artificial Intelligence and Fuzzy Logic, and has designed algorithms for numerous computer science and engineering projects. Mehrdad has worked as a technical committee member and is a current member of IEEE (Institute of Electrical and Electronics Engineers) and a Professional member of CEEA (Canadian Engineering Education Association).
Title: Symbiotic Driving and Cross-Disciplinary Shared Control
David A. Abbink, Delft University of Technology.
Dr. Tom Carlson, University College London.
Half-a-day tutorial for the dissemination of the findings from my Symbiotic Driving project, which will end in fall this year and for which we recently did real-world experiments together with Nissan. I’d be honoured if I could organize the final scientific event at SMC. This includes lectures by my team and me, discussions, and an interactive demo session (if COVID allows), using a portable driving simulator which we are now constructed and plan to bring to make the workshop truly interactive.
Half-a-day workshop with several key international speakers (who use shared control for different application domains than automotive) to reflect on the automotive findings. We will use workshop techniques to generate discussions in the spirit of our Shared Control paper that aims to find common ground in the diversity of application domains (e.g., robotics, intelligent wheel chairs, BMI). The workshop is highly interactive in nature, and will ideally take place physically, if safely possible. If the event is to be virtual, we will create an interactive virtual environment and use several pre-recorded movies to enhance communication and stimulate discussions and exchanges.
Biography of the Speaker:
Prof. dr. ir. David A. Abbink (1977) received his MSc. degree (2002) and Ph.D. degree (2006) in Mechanical Engineering from Delft University of Technology. He is a full Professor at the Delft University of Technology, leading the section of Human-Robot Interaction in the department of Cognitive Robotics. His research interests include system identification, neuroscience, haptic assistance, human factors, human-robot interaction and the impact of robotic systems on society. David was voted best teacher of his department for seven consecutive years, best teacher of his faculty twice, and received an international open courseware award for his course “The Human Controller.” He has always worked 4 days a week to ensure enough time for the other pleasures in life, such as drumming in rock bands, cooking, traveling and being a dad. His Ph.D. thesis on haptic assistance for car-following was awarded the best Dutch Ph.D. dissertation in movement sciences (2006), and contributed to the market release of Nissan’s Distance Control Assist system. David received two prestigious personal grants – VENI (2010) and VIDI (2015) and was co-PI on the H-Haptics programme, where 16 PhD students and 3 postdocs collaborate on designing human-centered haptic shared control for tele-robotics, across various application domain. His research on human-automation interaction has received funding from major industry partners such as Nissan, Renault, Boeing. David is a member of Human Factors and Ergonomics Society, IEEE SMC, and is an IEEE senior member. He served as an associate editor for IEEE Transaction on Human-Machine Systems, and IEEE Transactions on Haptics. He co-founded the IEEE SMC Technical Committee on Shared Control, which he chaired for many years.
Title: Systems Biology
Xingming Zhao, Fudan University, China.
Yufei Huang, University of Texas at San Antonio, U.S.A.
The workshop of systems biology is organized by the technical committee on systems biology of IEEE SMC society, and aims to present the recent progress on the systems biology. With the accumulating of a huge amount of biomedical data, it is becoming a big challenge to understand those data. The objective of this half-day workshop is to show the recent progress of new machine learning and artificial intelligent algorithms developed for data mining of those big data, and the mathematic models used for understanding the complex biological systems.
Biography of the Speakers:
Xingming Zhao (Senior Member, IEEE) received the Ph.D. degree from the University of Science and Technology of China, Hefei, China. Currently, he is a full professor at the Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, China, and serves as a Vice Director of the Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, China. His research interests include data mining, bioinformatics, and computational systems biology. He has published more than 90 peer-reviewed journal papers. He is a Co-Chair of the IEEE SMC Technical Committee on Systems Biology. He is also the lead guest editor and the editorial member of several journals, e.g., IEEE/ACM TCBB, Neurocomputing, Journal of Theoretical Biology, IET Systems Biology, and so on.
Yufei Huang received his Ph.D. degree in electrical engineering from the State University of New York at Stony Brook in 2001. Since 2002, he has been with the Department of Electrical and Computer Engineering at the University of Texas at San Antonio (UTSA), where he is now Professor. He is also an adjunct professor at the Dept. of Epidemiology and Biostatistics at the University of Texas Health Science Center at San Antonio. Dr. Huang’s expertise is in the areas of computational biology, computational neuroergonomics, brain computer interface, statistical modeling, and Bayesian methods. He is an Associate Editor of IEEE Transactions on Signal Processing, BMC Systems Biology, EURASIP Journal on Bioinformatics and Computational Biology, and International Journal Machine Learning and Cybernetics.
Please click here to see the technical program schedule.