Monitoring and control of cyber-physical systems.
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Cyber-Physical Systems (CPS) are being increasingly deployed in critical infrastructures such as electric-power, water, transportation, and other networks. These deployments are facilitating real-time monitoring and closed-loop control by exploiting the advances in wireless sensor-actuator networks, the internet of "everything," data-driven analytics, and machine-to-machine interfaces. CPS operations depend on the synergy of computational and physical components.
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Autonomy-enabled transportation networks are rapidly becoming a prominent Cyber-Physical-Systems (CPS) application area with tremendous potential for societal impact, as the autonomous systems technology penetrates into aerial/road vehicles and as the concept of connected vehicles emerge. The potential opportunities are not gone unnoticed. For example, unmanned aerial vehicle (UAV) based delivery networks has already attracted innovative companies like Amazon, Google, and Matternet.
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Designing software that can properly and safely interact with the physical world is an important cyber-physical systems design challenge. The proposed work includes the development of a novel approach to designing planning and control algorithms for high-performance cyber physical systems. The new approach was inspired by statistical mechanics and stochastic geometry. It will (i) identify behavior such as phase transitions in cyber-physical systems and (ii) capitalize this behavior in order to design practical algorithms with provable correctness and performance guarantees.
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Parking can take up a significant amount of the trip costs (time and money) in urban travel. As such, it can considerably influence travelers' choices of modes, locations, and time of travel. The advent of smart sensors, wireless communications, social media and big data analytics offers a unique opportunity to tap parking's influence on travel to make the transportation system more efficient, cleaner, and more resilient. A cyber-physical social system for parking is proposed to realize parking's potential in achieving the above goals.
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The CyberCardia project will lead to significant advances in the state of the art for system verification and cardiac therapies based on the use of formal methods and closed-loop control and verification. The animating vision for the work is to enable the development of a true in silico design methodology for medical devices that can be used to speed the development of new devices and to provide greater assurance that their behavior matches designer intentions, and to pass regulatory muster more quickly so that they can be used on patients needing their care.
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This project is a component of a larger effort is to develop the foundations of modeling, synthesis and development of verified medical device software and systems from verified closed-loop models of the device and organ(s). This research spans both implantable medical devices such as cardiac pacemakers and physiological control systems such as drug infusion pumps which have multiple networked medical systems. Here we focus on advancing two aspects of this work: (1) development of patient-specific models and therapies and (2) multi-scale modeling of complex physiological phenomena.
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This project advances the scientific knowledge on design methods for improving the resilience of civil infrastructures to disruptions. To improve resilience, critical services in civil infrastructure sectors must utilize new diagnostic tools and control algorithms that ensure survivability in the presence of both security attacks and random faults, and also include the models of incentives of human decision makers in the design process.
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The presentation materials cover results obtained for the two above-mentioned projects. The poster presents material on an algebraic approach to modeling systems with both continuous and discrete behavior. The framework is based on process algebra, which was developed for discrete systems, and features the development of a tree-based semantic model, called generalized synchronization trees, that uniformly captures a very general notions of time.
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Starting from a database of 100's of real patient electrogram records, we describe how to develop and use a large
in-silico cohort consisting of 10,000+ heart models to improve the planning and execution of a clinical trial (CT)
for implantable cardioverter defibrillators (ICDs). We illustrate our approach by applying it retrospectively to a
real CT that compares two discrimination algorithms (DA) within ICDs for the detection of potentially fatal
cardiac arrhythmias. The CT posited that one algorithm would be better than the other but the results of the trial
