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This Cyber-Physical Systems (CPS) project supports research to enable the automated monitoring of building and infrastructure construction projects. The purpose of construction monitoring is to provide developers, contractors, subcontractors, and tradesmen with the information they need to easily and quickly make project control decisions. These decisions have a direct impact on the overall efficiency of a construction project. Given that construction is a $800 billion industry, gains in efficiency could lead to enormous cost savings, benefiting both the U.S. economy and society.
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This project combines Cyber Physical Systems (CPS) and gigabit networks to address major health concerns due to air pollution. A wide range of health outcomes are affected by air pollution. In March 2014 the World Health Organization (WHO) released a report that in 2012 alone, a staggering 7 million people died as a result of air pollution exposure, one in eight of the total global deaths. A major component of this pollution is airborne particulate matter. Approximately 50 million Americans have allergic diseases.
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In this project we consider the development of a Cyber Physical Freight Transportation System for load balancing in multimodal transportation networks. We use on line simulation models to capture the nonlinear and complex dynamical characteristics of the transportation networks. The simulation models generate the states of the network that are used to solve an optimization problem which finds the optimum routes.
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This project is developing tools for traffic management and control using formal methods. By applying techniques such as model-checking and correct-byconstruction synthesis, we ensure that traffic flow satisfies high-level objectives expressed using temporal logics that guarantee desirable behavior such as avoiding congestion, maintaining high throughput, ensuring fairness of ramp metering strategies, and reacting to incidents or unexpected conditions.
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Performance monitoring data (e.g., measurements, logs, events) are becoming increasingly accessible and abundant (in terms of cost and availability) in modern distributed complex systems such as computer systems and networks, integrated buildings, industrial systems, transportation networks and power-grids. With efficient exploration of such data, health monitoring, diagnosis and prognosis can be greatly improved beyond the current state-of-the-art.
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Our research addresses urgent challenges in high confidence testing of automotive systems due to on-going and anticipated introduction of advanced, connected, and autonomous vehicle technologies. We pursue the development of tools for maneuver and data optimization to determine test trajectories and scenarios to facilitate vehicle testing. Our approaches exploit game theoretic traffic interaction modeling to inform in-traffic relevant trajectories, model-free optimization to identify trajectories falsifying time domain specifications, and the development of Smart Black Box
