The objective of the research is to develop tools for comprehensive design and optimization of air traffic flow management capabilities at multiple spatial and temporal resolutions: a national airspace-wide scale and one-day time horizon (strategic time-frame); and at a regional scale (of one or a few Centers) and a two-hour time horizon (tactical time-frame). The approach is to develop a suite of tools for designing complex multi-scale dynamical networks, and in turn to use these tools to comprehensively address the strategic-to-tactical traffic flow management problem. The two directions in tool development include 1) the meshed modeling/design of flow- and queueing-networks under network topology variation for cyber- and physical- resource allocation, and 2) large-scale network simulation and numerical analysis. This research will yield aggregate modeling, management design, and validation tools for multi-scale dynamical infrastructure networks, and comprehensive solutions for national-wide strategic-to-tactical traffic flow management using these tools. The broader impact of the research lies in the significant improvement in cost and equity that may be achieved by the National Airspace System customers, and in the introduction of systematic tools for infrastructure-network design that will have impact not only in transportation but in fields such as electric power network control and health-infrastructure design. The development of an Infrastructure Network Ideas Cluster will enhance inter-disciplinary collaboration on the project topics and discussion of their potential societal impact. Activities of the cluster include cross-university undergraduate research training, seminars on technological and societal-impact aspects of the project, and new course development.

The objective of this research is to improve the ability to track the orbits of space debris and thereby reduce the frequency of collisions. The approach is based on two scientific advances: 1) optimizing the scheduling of data transmission from a future constellation of orbiting Cubesats to ground stations located worldwide, and 2) using satellite data to improve models of the ionosphere and thermosphere, which in turn are used to improve estimates of atmospheric density. Intellectual Merit Robust capacity-constrained scheduling depends on fundamental research on optimization algorithms for nonlinear problems involving both discrete and continuous variables. This objective depends on advances in optimization theory and computational techniques. Model refinement depends on adaptive control algorithms, and can lead to fundamental advances for automatic control systems. These contributions provide new ideas and techniques that are broadly applicable to diverse areas of science and engineering. Broader Impacts Improving the ability to predict the trajectories of space debris can render the space environment safer in both the near term---by enhancing astronaut safety and satellite reliability---and the long term---by suppressing cascading collisions that could have a devastating impact on the usage of space. This project will impact real-world practice by developing techniques that are applicable to large-scale modeling and data collection, from weather prediction to Homeland Security. The research results will impact education through graduate and undergraduate research as well as through interdisciplinary modules developed for courses in space science, satellite engineering, optimization, and data-based modeling taught across multiple disciplines.

The objective of this research is to improve the ability to track the orbits of space debris and thereby reduce the frequency of collisions. The approach is based on two scientific advances: 1) optimizing the scheduling of data transmission from a future constellation of orbiting Cubesats to ground stations located worldwide, and 2) using satellite data to improve models of the ionosphere and thermosphere, which in turn are used to improve estimates of atmospheric density. Intellectual Merit Robust capacity-constrained scheduling depends on fundamental research on optimization algorithms for nonlinear problems involving both discrete and continuous variables. This objective depends on advances in optimization theory and computational techniques. Model refinement depends on adaptive control algorithms, and can lead to fundamental advances for automatic control systems. These contributions provide new ideas and techniques that are broadly applicable to diverse areas of science and engineering. Broader Impacts Improving the ability to predict the trajectories of space debris can render the space environment safer in both the near term---by enhancing astronaut safety and satellite reliability---and the long term---by suppressing cascading collisions that could have a devastating impact on the usage of space. This project will impact real-world practice by developing techniques that are applicable to large-scale modeling and data collection, from weather prediction to Homeland Security. The research results will impact education through graduate and undergraduate research as well as through interdisciplinary modules developed for courses in space science, satellite engineering, optimization, and data-based modeling taught across multiple disciplines.

The objective of this research is to develop models, methods and tools for capturing and processing of events and actions in cyber-physical systems (CPS) in a manner that does not violate the underlying physics or computational logic. The project approach uses a novel notion of cyber-physical objects (CPO) to capture the mobility and localization of computation in cyber-physical systems using recent advances in geolocation and the Internet infrastructure and supports novel methods for spatiotemporal resource discovery. Project innovations include a model for computing spatiotemporal relationships among events of interests in the physical and logical parts of a CPS, and its use in a novel cyberspatial reference model. Using this model the project builds a framework for locating cyber-physical application services and an operating environment for these services. The project plan includes an experimental platform to demonstrate capabilities for building new OS services for CPS applications including collaborative control applications drawn from the intermodal transportation system. The project will enable design and analysis of societal scale applications such as the transportation and electrical power grid that also include a governance structure. It will directly contribute to educating an engineering talent pool by offering curricular training that range from degree programs in embedded systems to seminars and technology transfer opportunities coordinated through the CalIT2 institute at UCSD and the Institute for Sensing Systems (ISS) at OSU. The team will collaborate with the non-profit Milwaukee Institute to explore policies and mechanisms for enterprise governance systems.

The objective of this research is to develop a real-time operating system for a virtual humanoid avatar that will model human behaviors such as visual tracking and other sensori-motor tasks in natural environments. This approach has become possible to test because of the development of theoretical tools in inverse reinforcement learning (IRL) that allow the acquisition of reward functions from detailed measurements of human behavior, together with technical developments in virtual environments and behavioral monitoring that allow such measurements to be obtained. The central idea is that complex behaviors can be decomposed into sub-tasks that can be considered more or less independently. An embodied agent learns a policy for actions required by each sub-task, given the state information from sensori-motor measurements, in order to maximize total reward. The reward functions implied by human data can be computed and compared to those of an avatar model using the newly-developed IRL technique, constituting an exacting test of the system. The broadest impact of the project would provide a formal template for further investigations of human mental function. Modular RL models of human behavior would allow realistic humanoid avatars to be used in training for emergency situations, conversation, computer games, and classroom tutoring. Monitoring behavior in patients with diseases that exhibit unusual eye movements (e.g., Tourettes, Schizophrenia, ADHD) and unusual body movement patterns (e.g., Parkinsons), should lead to new diagnostic methods. In addition the regular use of the laboratory in undergraduate courses and outreach programs promotes diversity.

The objective of this research is to develop an intuitive user interface for functional electrical stimulation (FES), which uses surgically-implanted electrodes to stimulate muscles in spinal cord-injured (SCI) patients. The challenge is to enable high-level tetraplegic patients to regain the use of their own arm. The approach is to develop a multi-modal Bayesian user-intent decoder; use natural muscle synergies to generate appropriate low-dimensional muscle activation signals in a feedforward controller; develop a feedback controller to enhance the performance of the feedforward controller; and test the system with SCI patients on daily living tasks, such as reaching, grasping, and eating. The challenge problem of restoring arm use to SCI patients will lead to new design principles for cyber-physical systems interfacing neural and biological systems with engineered computation and electrical power systems. The tight integration of the proposed user interface and controller with the users own control system requires a deep understanding of biological design principles such as nested feedback loops at different time and length scales, noisy signals, parallel processing, and highly coupled neuromechanical systems. This work will lead to new technology that dramatically improves the lives of spinal cord-injured patients. These patients often have no cognitive impairment and have long life spans after injury. The goal is to enable these patients to eat, reach, and grasp nearby objects. These tasks are critical for independent living and quality of life. This work will also help train a new generation of students in human-machine interfaces at the undergraduate, graduate, and postdoctoral levels.

CPS: Small: Collaborative Research: Localization and System Services for SpatioTemporal Actions in Cyber-Physical Systems The objective of this research is to develop models, methods and tools for capturing and processing of events and actions in cyber-physical systems (CPS) in a manner that does not violate the underlying physics or computational logic. The project approach uses a novel notion of cyber-physical objects (CPO) to capture the mobility and localization of computation in cyber-physical systems using recent advances in geolocation and the Internet infrastructure and supports novel methods for spatiotemporal resource discovery. Project innovations include a model for computing spatiotemporal relationships among events of interests in the physical and logical parts of a CPS, and its use in a novel cyberspatial reference model. Using this model the project builds a framework for locating cyber-physical application services and an operating environment for these services. The project plan includes an experimental platform to demonstrate capabilities for building new OS services for CPS applications including collaborative control applications drawn from the intermodal transportation system. The project will enable design and analysis of societal scale applications such as the transportation and electrical power grid that also include a governance structure. It will directly contribute to educating an engineering talent pool by offering curricular training that range from degree programs in embedded systems to seminars and technology transfer opportunities coordinated through the CalIT2 institute at UCSD and the Institute for Sensing Systems (ISS) at OSU. The team will collaborate with the non-profit Milwaukee Institute to explore policies and mechanisms for enterprise governance systems.

The objective of this research is to investigate and implement a software architecture to improve productivity in the development of rapidly deployable, robust, real-time situational awareness and response applications. The approach is based on a modular cross-layered architecture that combines a data-centric descriptive programming model with an overlay-based communication model. The cross-layer architecture will promote an efficient implementation. Simultaneously, the data-centric programming model and overlay-based communication model will promote a robust implementation that can take advantage of heterogeneous resources and respond to different failures. There is currently no high-level software architecture that meets the stringent requirements of many situational awareness and response applications. The proposed project will fill this void by developing a novel data-centric programming model that spans devices with varying computational and communication capabilities. Similarly, the overlay communication model will extend existing work by integrating network resources with the programming model. This cross-layer design will promote the implementation of efficient and robust applications. This research will benefit society by providing emergency responders with software tools that present key information in a timely fashion. This, in turn, will increase safety and reduce economic and human loss during emergencies. The productivity gains in deploying sensors and mobile devices will benefit other domains, such as field research using sensor networks. Software will be released under an open-source license to promote the use by government agencies, research institutions, and individuals. Products of this research, including the software, will be used in courses at the University of North Carolina.

The objective of this research is to address fundamental challenges in the verification and analysis of reconfigurable distributed hybrid control systems. These occur frequently whenever control decisions for a continuous plant depend on the actions and state of other participants. They are not supported by verification technology today. The approach advocated here is to develop strictly compositional proof-based verification techniques to close this analytic gap in cyber-physical system design and to overcome scalability issues. This project develops techniques using symbolic invariants for differential equations to address the analytic gap between nonlinear applications and present verification techniques for linear dynamics. This project aims at transformative research changing the scope of systems that can be analyzed. The proposed research develops a compositional proof-based approach to hybrid systems verification in contrast to the dominant automata-based verification approaches. It represents a major improvement addressing the challenges of composition, reconfiguration, and nonlinearity in system models The proposed research has significant applications in the verification of safety-critical properties in next generation cyber-physical systems. This includes distributed car control, robotic swarms, and unmanned aerial vehicle cooperation schemes to full collision avoidance protocols for multiple aircraft. Analysis tools for distributed hybrid systems have a broad range of applications of varying degrees of safety-criticality, validation cost, and operative risk. Analytic techniques that find bugs or ensure correct functioning can save lives and money, and therefore are likely to have substantial economic and societal impact.

The objectives of this research are to design a heterogeneous network of embedded systems so that faults can be quickly detected and isolated and to develop on-line and off-line fault diagnosis and prognosis methods. Our approach is to develop functional dependency models between the failure modes and the concomitant monitoring mechanisms, which form the basis for failure modes, effects and criticality analysis, design for testability, diagnostic inference, and the remaining useful life estimation of (hardware) components. Over the last few years, the electronic explosion in automotive vehicles and other application domains has significantly increased the complexity, heterogeneity, and interconnectedness of embedded systems. To address the cross-subsystem malfunction phenomena in such networked systems, it is essential to develop a common methodology that: (i) identifies the potential failure modes associated with software, hardware, and hardware-software interfaces; (ii) generates functional dependencies between the failure modes and tests; (iii) provides an on-line/off-line diagnosis system; (iv) computes the remaining useful life estimates of components based on the diagnosis; and (iv) validates the diagnostic and prognostic inference methods via fault injection prior to deployment in the field. The development of functional dependency models and diagnostic inference from these models to aid in online and remote diagnosis and prognosis of embedded systems is a potentially novel aspect of this effort. This project seeks to improve the competitiveness of the U.S. automotive industry by enhancing vehicle reliability, performance and safety, and by improving customer satisfaction. Other representative applications include aerospace systems, electrification of transportation, medical equipment, and communication and power networks, to name a few.