The objective of this research is to meet the urgent global need for improved safety and reduced maintenance costs of important infrastructures by developing a unified signal processing framework coupling spatiotemporal sensing data with physics-based and data-driven models. The approach is structured along the following thrusts: investigating the feasibility of statistical modeling of dynamic structures to address the spatiotemporal correlation of sensing data; developing efficient distributed damage detection and localization algorithms; investigating network enhancement through strategic sensor placement; addressing optimal sensor collaboration for recursive localized structural state estimation and prediction. Intellectual merit: This innovative unified framework approach has the potential of being more reliable and efficient with better scalability compared to the current state-of-the-art in structural health monitoring. The proposed research is also practical as it allows analysis of real-world data that accounts for structural properties, environmental noise, and loss of integrity over sensors. Probabilistic representation of significant damages allows more informative risk assessment. Broader impacts: The outcome of this project will provide an important step toward safety and reliability albeit increasing complexity in dynamic systems. New models and algorithms developed in this project are generic and can contribute in many other areas and applications that involve distributed recursive state estimation, distributed change detection and data fusion. This project will serve as an excellent educational platform to educate and train the next generation CPS researchers and engineers. Under-represented groups such as female students and Native American students will be supported in this project, at both the graduate and undergraduate levels.

The objective of this research is to develop methods to monitor and ensure the robustness of a class of cyber-physical systems termed "physical networks," such as electric, water, sewage, and gas networks. The approach is to analyze such networks using the mathematical formalism of graphical models. The project models a physical network as a graph, whose variables have a concrete physical interpretation, such as voltage, satisfying known physical laws, such as Kirchoff laws. The machinery of graphical models is used to develop methods to monitor and ensure the robustness of such networks, using the electric power network as a representative. By studying puzzling network-wide interactions, the project has the potential to clarify the role of complexity in large scale networks. Potential contributions will be made to the fields of distributed inference algorithms and fast numerical methods. Physical networks play a crucial role in modern society, and yet, often exhibit fragile behavior, such as black-outs in electric power networks, resulting in economic loss, as well as causing a security risk. This project seeks to understand the robustness behavior of such networks and to train a broad class of students in their theory and practice. Results from this research are to be incorporated into courses and disseminated via research publications. The Carnegie Mellon Conference on the Electricity Industry allows students to interact with faculty and electricity industry veterans. Interaction with the electricity industry aims to provide it with an understanding of cyber-intelligence, to ensure effective robustness monitoring capabilities in the power grid.

The objective of this research is to scale up the capabilities of fully autonomous vehicles so that they are capable of operating in mixed-traffic urban environments (e.g., in a city such as Columbus or even New York or Istanbul). Such environments are realistic large-city driving situations involving many other vehicles, mostly human-driven. Moreover, such a car will be in a world where it interacts with other cars, humans, other external effects, and internal and external software modules. This is a prototypical CPS with which we have considerable experience over many years, including participation in the recent DARPA Urban Challenge. Even in the latter case, though, operation to date has been restricted to relatively “clean” environments (such as multi-lane highways and simpler intersections with a few other vehicles). The approach is to integrate multidisciplinary advances in software, sensing and control, and modeling to address current weaknesses in autonomous vehicle design for this complex mixed-traffic urban environment. All work will be done within a defined design-and-verification cycle. Theoretical advances and new models will be evaluated both by large-scale simulations, and by implementation on laboratory robots and road-worthy vehicles driven in real-world situations. The research address significant improvements to current methods and tools to enable a number of formal methods to move from use in limited, controlled environments to use in more complex and realistic environments. The theory, tools, and design methods that are investigated have potential application for a broad class of cyber-physical systems consisting of mobile entities operating in a semi-structured environment. This research has the potential to lead to safer autonomous vehicles and to improve economic competitiveness, the nation's transportation infrastructure, and energy efficiency. The richness of the domain means that expected research contributions can apply not only to autonomous vehicles but, also, to a variety of related cyber-physical systems such as service robots in hospitals and rescue robots used after natural disasters. The experimental research laboratory for the project is used for undergraduate and graduate courses and supports new summer outreach projects for high-school students. Research outcomes are integrated with undergraduate and graduate courses on component-based software.

The goal of this project is to develop a semantic foundation, cross-layer system architecture and adaptation services to improve dependability in instrumented cyberphysical spaces (ICPS) based on the principles of "computation reflection". ICPSs integrate a variety of sensing devices to create a digital representation of the evolving physical world and its processes for use by applications such as critical infrastructure monitoring, surveillance and incident-site emergency response. This requires the underlying systems to be dependable despite disruptions caused by failures in sensing, communications, and computation. The digital state representation guides a range of adaptations at different layers of the ICPS (i.e. networking, sensing, applications, cross-layer) to achieve end-to-end dependability at both the infrastructure and information levels. Examples of techniques explored include mechanisms for reliable information delivery over multi-networks, quality aware data collection, semantic sensing and reconfiguration using overlapping capabilities of heterogeneous sensors. Such adaptations are driven by a formal-methods based runtime analysis of system components, resource availability and application dependability needs. Responsphere, a real-world ICPS infrastructure on the University of California at Irvine campus, will serve as a testbed for development and validation of the overall ?reflective? approach and the cross-layer adaptation techniques to achieve dependability. Students at different levels (graduate, undergraduate, K-12) will be given opportunities to gain experience with using and designing real-world applications in the Responsphere ICPS via courses, independent study projects and demonstration sessions. Students will benefit tremendously from exposure to new software development paradigms for the ICPSs that will be a part of the future living environments.

The goal of this project is to develop a semantic foundation, cross-layer system architecture and adaptation services to improve dependability in instrumented cyberphysical spaces (ICPS) based on the principles of "computation reflection". ICPSs integrate a variety of sensing devices to create a digital representation of the evolving physical world and its processes for use by applications such as critical infrastructure monitoring, surveillance and incident-site emergency response. This requires the underlying systems to be dependable despite disruptions caused by failures in sensing, communications, and computation. The digital state representation guides a range of adaptations at different layers of the ICPS (i.e. networking, sensing, applications, cross-layer) to achieve end-to-end dependability at both the infrastructure and information levels. Examples of techniques explored include mechanisms for reliable information delivery over multi-networks, quality aware data collection, semantic sensing and reconfiguration using overlapping capabilities of heterogeneous sensors. Such adaptations are driven by a formal-methods based runtime analysis of system components, resource availability and application dependability needs. Responsphere, a real-world ICPS infrastructure on the University of California at Irvine campus, will serve as a testbed for development and validation of the overall ?reflective? approach and the cross-layer adaptation techniques to achieve dependability. Students at different levels (graduate, undergraduate, K-12) will be given opportunities to gain experience with using and designing real-world applications in the Responsphere ICPS via courses, independent study projects and demonstration sessions. Students will benefit tremendously from exposure to new software development paradigms for the ICPSs that will be a part of the future living environments.

This project is developing techniques for secured real-time services for cyber-physical systems. In particular, the research is incorporating real-time traffic modeling techniques into the security service, consequently enhancing both system security and real-time capabilities in an adverse environment. While this proposed methodology has not yet been fully tested, it is potentially transformative. To defend against traffic analysis attacks, the research is developing algorithms that can effectively mask the actual operational modes of cyber-physical applications without compromising the guaranteed quality of service. This is achieved by using the traffic modeling theory, developed by the PIs, to precisely manage the network traffic at the right time and the right place. This traffic modeling theory can also help in develop efficient attack detection and suppression methods that can identify and restrain an attack in real-time. The proposed methods are expected to be more effective, efficient, and scale-able than traditional methods.

The objective of this research is to develop formal verification tools for human-computer interfaces to cyber-physical systems. The approach is incorporating realistic assumptions about the behavior of humans into the verification process through mathematically constructed "mistake models" for common types of mistakes committed by the operator during an interactive task. Exhaustive verification techniques are used to expose combinations of human mistakes that can lead to system-wide failures. The techniques are evaluated using case studies involving medical device interfaces. The problem of verifying human-machine interfaces requires new approaches that combine rigorous formal verification techniques with the empirical human-centered approach to user-interface evaluation. The research addresses challenges of integrating empirical user-study data into formal game-based models that describe common types of operator mistakes. Using these models to detect subtle flaws in user-interface design is also a challenge. It is well-known that a poorly designed interface will enable harmful operator errors, which remain a major cause of failures in a wide variety of safety-critical cyber-physical systems. This project will automate user-interface verification by detecting likely defects, early in the design process. Open source verification tools will be made freely available to the community at large. The ongoing research will be integrated into a set of graduate-level computer science courses focused on the theme of "Safety in Human Computer Interfaces". Results from the project will also be integrated into educational materials for the ongoing eCSite GK12 project with the goal of promoting awareness of user-interface design issues amongst high school students.

The objective of this research is to develop an atomic force microscope based cyber-physical system that can enable automated, robust and efficient assembly of nanoscale components such as nanoparticles, carbon nanotubes, nanowires and DNAs into nanodevices. The proposed approach is based on the premise that automated, robust and efficient nanoassembly can be achieved through tip based pushing in an atomic force microscope with intermittent local scanning of nanoscale components. In particular, in order to resolve temporally and spatially continuous movement of nanoscale components under tip pushing, we propose the combination of intermittent local scanning and interval non-uniform rational B-spline based isogeometric analysis in this research. Successful completion of this research would lead to foundational theories and algorithmic infrastructures for effective integration of physical operations (pushing and scanning) and computation (planning and simulation) for robust, efficient and automated nanoassembly. The resulting theories and algorithms will also be applicable to a broader set of cyber physical systems. If successful, this research will lead to leap progress in nanoscale assembly, from prototype demonstration to large-scale manufacturing. Through its integrated research, education and outreach activities, this project will provide advanced knowledge in cyber-physical systems and nanoassembly for students from high schools to graduate schools and will increase domestic students? interest in science and engineering and therefore strengthen our competitiveness in the global workforce.