The goal of this project is to achieve high-bandwidth underwater wireless communication using a flock of small Autonomous Underwater Vehicles (AUVs) that relay a laser beam from the seabed to the surface of the ocean. The approach is advanced control of specially-designed AUVs, along with prediction of ocean currents, so that each AUV unit can reliably receive the signal from a unit at a lower depth, amplify the signal and send it to the next unit above, until the signal reaches the surface where it can easily reach satellites and hence anywhere in the world.
Deep Neural Networks (DNN) enabled Cyber-Physical Systems (CPS) hold great promise for revolutionizing many industries, such as drones and self-driving cars. However, the current generation of DNN cannot provide analyzable behaviors and verifiable properties that are necessary for safety assurance. This critical flaw in purely data-driven DNN sometimes leads to catastrophic consequences, such as vehicle crashes linked to self-driving and driver-assistance technologies.
http://publish.illinois.edu/cpsintegrationlab/people/lui-sha/
This project seeks to develop low-interference mitigation options for cybersickness; that is motion sickness like symptoms in response to virtual reality use. With several new virtual reality (VR) systems such as the Oculus rift, Google VR, and HTC Vive now available to the general population with increased usage in education and training, the need for cybersickness mitigation options has dramatically increased.
The objective of this CAREER project is to develop a theoretical and computational framework for the co-design of information and incentive mechanisms targeted at humans in Societal-Scale Cyber-Physical Systems (SCPS) in order to encourage efficient shared resource consumption while mitigating unintended consequences. The application focus is on intelligent transportation systems, a prototypical SCPS with humans in the loop, rapid technology adoption, and emerging mobility markets.
In the US, agricultural drainage infrastructure benefits >22.6 Mha of cropland and is valued at ~$100B. As a proportion of total croplands, drained croplands produce a disproportionately large amount of grain but also release a disproportionately large amount of eutrophying nutrients to aquatic ecosystems. Drainage systems include individually-owned field drains that depend on the function of community-owned main drains.
Liang Dong is an associate professor of electrical and computer engineering at Baylor University. His research interests include Digital Communications and Signal Processing, Green Wireless Networks, Cyber-Physical System and Security, Social Internet of Things, and E-health Applications.
Liang Dong is a senior member of the Institute of Electrical and Electronics Engineers (IEEE), a member of the American Physical Society (APS), and a member of the American Society for Engineering Education (ASEE). He served on the executive board of IEEE West Michigan Section from 2006 to 2011 and the executive board of ASEE North Central Section from 2007 to 2008. He also served as a TPC member for IEEE HealthCom 2015, IEEE GlobalSIP 2015 and IEEE GlobalSIP 2016, and a session chair for IEEE WCNC 2013 and IEEE GlobalSIP 2016. He is a member of Sigma Xi, Phi Kappa Phi, and Tau Beta Pi, and a faculty advisor of Eta Kappa Nu.
The goal of the project is to develop a methodology, supported by tools, that uses formal methods to gain clarity into the standards of network protocols and test the conformance of implementations to these standards. The work will be demonstrated on QUIC, a new complex protocol that is currently carrying about 10% of the internet traffic and is likely to carry much more of it in the near future. The standardization of QUIC is ongoing under the IETF. The software and the experimental results developed under this project will be stored in GitHub.
With the growing world population and diminishing agricultural lands, it becomes imperative to maximize crop yield by protecting crop health and mitigating against pests and diseases. Though there are decades-old practices still in place, there is also growing adoption of so-called precision agriculture solutions, which employ emerging technologies in sensing, automation, and analytics in daily farmland operations.
Increasing wildfire costs---a reflection of climate variability and development within wildlands---drive calls for new national capabilities to manage wildfires. The great potential of unmanned aerial systems (UAS) has not yet been fully utilized in this domain due to the lack of holistic, resilient, flexible, and cost-effective monitoring protocols.
Kyriakos G. Vamvoudakis was born in Athens, Greece. He earned his Diploma in Electronic and Computer Engineering (equivalent to a Master of Science) from the Technical University of Crete, Greece, in 2006, graduating with highest honors. After relocating to the United States, he pursued further studies at The University of Texas at Arlington under the guidance of Frank L. Lewis, obtaining his M.S. and Ph.D. in Electrical Engineering in 2008 and 2011, respectively. From May 2011 to January 2012, he served as an Adjunct Professor and Faculty Research Associate at the University of Texas at Arlington and the Automation and Robotics Research Institute. Between 2012 and 2016, he was a project research scientist at the Center for Control, Dynamical Systems, and Computation at the University of California, Santa Barbara. He then joined the Kevin T. Crofton Department of Aerospace and Ocean Engineering at Virginia Tech as an assistant professor, a position he held until 2018.
He currently serves as the Dutton-Ducoffe Endowed Professor at The Daniel Guggenheim School of Aerospace Engineering at Georgia Tech. He holds a secondary appointment in the School of Electrical and Computer Engineering. His expertise is in reinforcement learning, control theory, game theory, cyber-physical security, bounded rationality, and safe/assured autonomy.
He has received numerous prestigious awards, including the 2019 ARO YIP Award, the 2018 NSF CAREER Award, the 2018 DoD Minerva Research Initiative Award, and the 2021 GT Chapter Sigma Xi Young Faculty Award. His work has also been recognized with several best paper nominations and international awards, such as the 2016 International Neural Network Society Young Investigator (INNS) Award, the Best Paper Award for Autonomous/Unmanned Vehicles at the 27th Army Science Conference in 2010, the Best Presentation Award at the World Congress of Computational Intelligence in 2010, and the Best Researcher Award from the Automation and Robotics Research Institute in 2011. Dr. Vamvoudakis has served on various international program committees and has organized special sessions, workshops, and tutorials for several international conferences. He is the Editor-in-Chief of Aerospace Science and Technology and currently serves on the IEEE Control Systems Society Conference Editorial Board. Additionally, he is an Associate Editor for several journals, including Automatica, IEEE Transactions on Automatic Control, IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Systems, Man, and Cybernetics: Systems, IEEE Transactions on Artificial Intelligence, Neural Networks, and the Journal of Optimization Theory and Applications. He is also a Senior Guest Editor for the IEEE Open Journal of Control Systems for the special issue on the intersection of machine learning with control. Previously, Dr. Vamvoudakis has served as a Guest Editor for various special issues, including those in IEEE Transactions on Automation Science and Engineering, IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Industrial Informatics, and IEEE Transactions on Intelligent Transportation Systems. He is a registered Professional Engineer (PE) in Electrical/Computer Engineering, a member of the Technical Chamber of Greece, an Associate Fellow of AIAA, and a Senior Member of IEEE.
The gradual deployment of self-driving cars will inevitably lead to the emergence of a new important class of cyber-physical-human systems where autonomous vehicles interact with human-driven vehicles via on-board sensors or vehicle-to-vehicle communications. Reinforcement learning along with control theory can help meet the safety requirements for real-time decision making and Level 5 autonomy in self-driving vehicles.
Kyriakos G. Vamvoudakis was born in Athens, Greece. He earned his Diploma in Electronic and Computer Engineering (equivalent to a Master of Science) from the Technical University of Crete, Greece, in 2006, graduating with highest honors. After relocating to the United States, he pursued further studies at The University of Texas at Arlington under the guidance of Frank L. Lewis, obtaining his M.S. and Ph.D. in Electrical Engineering in 2008 and 2011, respectively. From May 2011 to January 2012, he served as an Adjunct Professor and Faculty Research Associate at the University of Texas at Arlington and the Automation and Robotics Research Institute. Between 2012 and 2016, he was a project research scientist at the Center for Control, Dynamical Systems, and Computation at the University of California, Santa Barbara. He then joined the Kevin T. Crofton Department of Aerospace and Ocean Engineering at Virginia Tech as an assistant professor, a position he held until 2018.
He currently serves as the Dutton-Ducoffe Endowed Professor at The Daniel Guggenheim School of Aerospace Engineering at Georgia Tech. He holds a secondary appointment in the School of Electrical and Computer Engineering. His expertise is in reinforcement learning, control theory, game theory, cyber-physical security, bounded rationality, and safe/assured autonomy.
He has received numerous prestigious awards, including the 2019 ARO YIP Award, the 2018 NSF CAREER Award, the 2018 DoD Minerva Research Initiative Award, and the 2021 GT Chapter Sigma Xi Young Faculty Award. His work has also been recognized with several best paper nominations and international awards, such as the 2016 International Neural Network Society Young Investigator (INNS) Award, the Best Paper Award for Autonomous/Unmanned Vehicles at the 27th Army Science Conference in 2010, the Best Presentation Award at the World Congress of Computational Intelligence in 2010, and the Best Researcher Award from the Automation and Robotics Research Institute in 2011. Dr. Vamvoudakis has served on various international program committees and has organized special sessions, workshops, and tutorials for several international conferences. He is the Editor-in-Chief of Aerospace Science and Technology and currently serves on the IEEE Control Systems Society Conference Editorial Board. Additionally, he is an Associate Editor for several journals, including Automatica, IEEE Transactions on Automatic Control, IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Systems, Man, and Cybernetics: Systems, IEEE Transactions on Artificial Intelligence, Neural Networks, and the Journal of Optimization Theory and Applications. He is also a Senior Guest Editor for the IEEE Open Journal of Control Systems for the special issue on the intersection of machine learning with control. Previously, Dr. Vamvoudakis has served as a Guest Editor for various special issues, including those in IEEE Transactions on Automation Science and Engineering, IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Industrial Informatics, and IEEE Transactions on Intelligent Transportation Systems. He is a registered Professional Engineer (PE) in Electrical/Computer Engineering, a member of the Technical Chamber of Greece, an Associate Fellow of AIAA, and a Senior Member of IEEE.
Rapid growth in additive manufacturing (AM) has improved the accessibility, customizability and affordability of making products using personal printers. Designs can be developed by consumers, if they have enough knowledge of mechanical design and 3D modeling, or they can be obtained from third parties. However, the process of translating a design to a program that can be successfully executed by a 3D printer often requires specialized domain knowledge that many end-users currently lack.