Systems able to process data as it comes in, typically without buffering delays.
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Fault tolerance is vital to ensuring the integrity and availability of safety critical systems. Current solutions are based almost exclusively on physical redundancy at all levels of the design. The use of physical redundancy, however, dramatically increases system size, complexity, weight, and power consumption.
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This project focuses on the formal design of semi-autonomous automotive Cyber Physical Systems (CPS). Rather than disconnecting the driver from the vehicle, the goal is to obtain a vehicle where the degree of autonomy is continuously changed in real-time as a function of certified uncertainty ranges in driver behavior and environment reconstruction.
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Quality control in high-volume manufacturing is commonly performed using Statistics-based quality control techniques. These techniques require large data sets in order to specify acceptable variation limits. These statistics-based QC approaches are not applicable in small-lot high-value manufacturing where it is important to ensure the quality of each one of the final products. Previous model based process control used simplified representations of the actual physics of the process.
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Continuous real-time tracking of the eye and field-of-view of an individual is profoundly important to understanding how humans perceive and interact with cyber-physical systems. Such continuous monitoring can enable detection of hazardous behaviors such as drowsiness while driving, mental health issues such as schizophrenia, addictive behavior and substance abuse, neurological disease progression, head injuries, and others.
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Growing demands on our civil infrastructure have heightened the need for smart structural components and systems whose behavior and performance can be controlled under a variety of loading scenarios such as high winds and earthquakes.
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The objective of this NSF-CPS Synergy proposal is to develop a distributed algorithmic framework, supported by a highly fault-tolerant software system, for executing critical transmission-level operations of the North American power grid using gigantic volumes of Synchrophasor data.
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The project will demonstrate a holonic multi-agent system architecture (HMSA) capable of adaptively controlling future electrical power distribution systems, which are expected to include a large number of renewable power generators, energy storage devices, and advanced metering and control devices. The project will produce a general, extensible, and secure cyber architecture based on holonic multi-agent principles to support adaptive PDS.
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This cross-disciplinary project brings together a team of engineering and computer science researchers to create, validate, and demonstrate the value of new techniques for ensuring that systems composed of combinations of hardware, software, and people are designed to operate in a truly synergistic and safe fashion.
