The terms denote engineering domains that have high CPS content.
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Abstract:
Data-driven intelligence is an essential foundation for physical systems in transportation safety and efficiency, area surveillance and security, as well as environmental sustainability. While sophisticated data analysis and synthesis can be well supported in large data centers, future intelligent systems require on-the-scene processing with faster responses and less dependence on the unreliable (often wireless) data communications in the field.
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In order to improve the current capabilities of automotive active safety control systems (ASCS) one needs to take into account the interactions between driver/vehicle/ASCS/environment. To achieve this goal, this research will infer longterm and short-term driver behavior via the use of Bayesian networks and neuromorphic algorithms to estimate the driver's skills and current state of attention from eye movement data, together with dynamic motion cues obtained from steering and pedal inputs.
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One of the challenges for the future cyber-physical systems is the exploration of large design spaces. Genetic algorithms (GAs), which embody a simplified computational model of the mutation and election mechanisms of natural evolution, are known to be effective for design optimization. However, the traditional formulations are limited to choosing values for a predetermined set of parameters within a given fixed architecture.
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This project will design next-generation defense mechanisms to protect critical infrastructures, such as power grids, large industrial plants, and water distribution systems. These critical infrastructures are complex primarily due to the integration of cyber and physical components, the presence of high-order behaviors and functions, and an intricate and large interconnection pattern.
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This project addresses highly dynamic Cyber-Physical Systems (CPSs) understood as systems where a computing delay of a few milliseconds or an incorrectly computed response to a disturbance can lead to catastrophic consequences. Such is the case of advanced safety systems on passenger cars, unmanned air vehicles performing critical maneuvers such as landing, or disaster and rescue response bipedal robots rushing through the rubble to collect information or save human lives.
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Transit hub is a city scale smart phone application that provides real-time, contextual transit travel planning services. It uses a centralized decision support system for integrating the different sensor data streams including the automated vehicle locator, the usage data gathered from the application, and other related data streams that are publically available.
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Project Description
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Rapid industrialization and increased pollution impose severe threats to the availability and quality of water resources worldwide. Currently, many wastewater treatment plants are discharging treated wastewater containing significant amounts of nutrients, such as nitrogen, ammonium, and phosphate ions, directly into the water system, posing significant threats to the environment. Finding a solution for effective treatment and safe discharge of wastewater has become an urgent and challenging task.
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Despite the contributions of automation to vehicular (aircraft, automobile, etc.) safety and efficiency, the problems associated with technology-centered rather than human-centered automation are well known: decreased operator situation awareness, deterioration of manual control skills, difficulties operators experience when trying to jump into the loop when needed, and so forth.
