National CPS PI Meeting 2013
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Abstract:
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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Motivation: Reliable and resilient operation of cyber-physical systems (CPS) of societal importance such as Smart Electric Grids is critical. Efficient models and tools to perform timely fault diagnostics and prognostics are needed for curtailing systemic failures such as power blackouts. Varying system state caused by the fluctuating power consumption, dynamic control actions, physical component degradation, and interactions with possible software anomalies make the failure analysis, prediction, and mitigation difficult.
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Multicore platforms have the potential of revolutionizing the capabilities of embedded cyber-physical systems but lack predictability in execution time due to shared resources. Safety-critical systems require such predictability for certification. This research aims at resolving this multicore "predictability problem.'' It will develop methods that enable to share hardware resources to be allocated and provide predictability, including support for real-time operating systems, middleware, and associated analysis tools.
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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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Cerebral Palsy (CP) is the most common motor disorder of central origin in childhood and affects at least 2 children per 1000 live births every year. This project will research new methods and tools in motor/cognitive assessment for small children (5-8 years old) with Cerebral Palsy.
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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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The purpose of this grant was to develop clothing-like material with embedded sensors and synthetic muscles. When eventually worn by brain-injured individuals over one or more limbs, this clothing may be used to restore their capability for independent mobility. The material, called a "second skin", is a cyberphysical system designed as a soft robot that cooperates with the biological muscles of the body.
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Functional electrical stimulation (FES) is a promising technology for activating muscles in spinal cord injured (SCI) patients. The objective of our project is to develop an intuitive user interface and control system that allows high--level tetraplegic patients to regain the use of their own arm.
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Today, the assembly of safety-critical software involves many distinct agents, including control engineers and software engineers. Due in part to the existing regulatory framework, little, if any, semantic information is passed from control engineers (who specify the real-time software) to software engineers (who implement the specifications). As a result, high-level, system-wide information is lost at the time of software assembly, and only during system validation do software specification and semantics re-appear.
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The research is along two directions and a number of results have been published or submitted. Physical side: Algorithms have been developed to detect isotopes based on the ideas of group positive Lasso and the total least squares. Two papers have been either published or to appear in Radiation Measurements. Cyber-side: The research focused on detection of unknown sources.
