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 the physical world. This work advances both the technology and engineering of cyber-physical systems by designing an innovative paradigm involving next-generation computational eyeglasses that interact with a user's mobile phone to provide the capability for real-time visual context sensing and inference. This research integrates novel research into low-power embedded systems, image representation, image processing and machine learning, and mobile sensing and inference, to advance the state-of-art in continuous sensing for CPS applications. The activity addresses several fundamental research challenges including: 1) design of novel, highly integrated, computational eyeglasses for tracking eye movements, the visual field of a user, and head movement patterns, all in real-time; 2) a unified compressive signal processing framework that optimizes sensing and estimation, while enabling re-targeting of the device to perform a broad range of tasks depending on the needs of an application; 3) design of a novel real-time visual context sensing system that extracts high-level contexts of interest from compressed data representations; and 4) a layer of intelligence that combines contexts extracted from the computational eyeglass together with contexts obtained from the mobile phone to improve energy-efficiency and sensing accuracy. This technology can revolutionize a range of disciplines including transportation, healthcare, behavioral science and market research. Continuous monitoring of the eye and field-of-view of an individual 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. The research provides the foundations for such applications through the design of a prototype platform together with real-time sensor processing algorithms, and making these systems available through open source venues for broader use. Outreach for this project includes demonstrations of the device at science fairs for high-school students, and integration of the platform into undergraduate and graduate courses.

Effective engineering of complex devices often depends critically on the ability to encapsulate responsibility for tasks into modular agents and ensure those agents communicate with one another in well-defined and easily observable ways. When such conditions are followed, it becomes possible to detect where problems lie so they can be corrected. It also becomes possible to optimize the agents and their communications to improve performance. Cyber-physical systems (like robots, self-piloting aircraft, etc.) modify themselves to improve performance break those conditions in that some agent modules negotiate their own communications and decide their own actions, sometimes taking advantage of the physics of the world in ways we did not anticipate. This renders difficult application of standard engineering tools to accomplish critical fault diagnosis and design optimization. This project will produce analysis methods address the specific needs of cyber-physical systems that, by their natures, break the rules of convention. We will apply these new methods to the design and analysis of self-improving controllers for flapping-wing micro air vehicles. This work will provide advances in both model-checking related formal design methodologies and in module-based self-adaptive control in computationally resource constrained cyber-physical systems. The formal methods advances will significantly expand our ability to properly design and verify systems that tightly couple computation, sensors, and actuators. The specific test application addressed is significant to a number of nationally important security and defense efforts and will directly impact identified national priorities.

This project is to develop a dynamical systems model of distributed computation motivated from recent work on the distributed computation of averages. The key idea is that static optimization problems (particularly convex optimization problems) can be solved by designing a dynamic system that stabilizes around the optimal solution of the problem. Moreover, when the optimization problem is separable, then the designed dynamic system decomposes into a set of locally-interacting dynamic systems. This is expected to open a door to a host of new computational approaches that take advantage of recent developments in control engineering including robust control (providing a mechanism for errors introduced by discretization), Markovian Jump Linear Systems (providing a mechanism for random discretization time), event-driven control (providing a mechanism for assured asynchronous execution), control over networks (providing a mechanism for improved performance of distributed computational systems in general). The new approach is essential in emerging applications, where the optimization runs on physically separated agents, operating in a noisy environment and communicating over unreliable channels. As a test bed, the project will make use of a two-vehicle robotic system developed by the PI designed to monitor a crop of corn plants, where the dynamic systems perspective of this grant will, for example, allow for distributed optimal estimation toward the goal of optimal station-keeping. By studying how natural systems can collectively compute and optimize, this research has potential to impact many disciplines involving networked systems, from controlling the electric power grid, to modeling the behavior of social, biological or economic systems. It is directly applicable to cooperative networked multi-agent systems like robotic search and rescue missions and disaster-relief operations, distributed machine learning problems, and intelligent systems. An intriguing mix of motivating applications and theoretical problems offer a unique multidisciplinary educational opportunity to students who will be involved in the project, and provide exciting innovative material for courses and labs. Software developed will be distributed as open source via the CPS Virtual Organization.

This project explores balancing performance considerations and power consumption in cyber-physical systems, through algorithms that switch among different modes of operation (e.g., low-power/high-power, on/off, or mobile/static) in response to environmental conditions. The main theoretical contribution is a computational, hybrid optimal control framework that is connected to a number of relevant target applications where physical modeling, control design, and software architectures all constitute important components. The fundamental research in this program advances state-of-the-art along four different dimensions, namely (1) real-time, hybrid optimal control algorithms for power management, (2) power-management in mobile sensor networks, (3) distributed power-aware architectures for infrastructure management, and (4) power-management in embedded multi-core processors. The expected outcome, which is to enable low-power devices to be deployed in a more effective manner, has implications on a number of application domains, including distributed sensor and communication networks, and intelligent and efficient buildings. The team represents both a research university (Georgia Institute of Technology) and an undergraduate teaching university (York College of Pennsylvania) in order to ensure that the educational components are far-reaching and cut across traditional educational boundaries. The project involves novel, inductive-based learning modules, where graduate students team with undergraduate researchers.

The project aims at making cities "smarter" by engineering processes such as traffic control, efficient parking services, and new urban activities such as recharging electric vehicles. To that end, the research will study the components needed to establish a Cyber-Physical Infrastructure for urban environments and address fundamental problems that involve data collection, resource allocation, real-time decision making, safety, and security. Accordingly, the research is organized along two main directions: (i) Sensing and data acquisition using a new mobile sensor network paradigm designed for urban environments; and (ii) Decision Support for the "Smart City" relying on formal verification and certification methods coupled with innovative dynamic optimization techniques used for decision making and resource allocation. The work will bring together and build upon methodological advances in optimization under uncertainty, computer simulation, discrete event and hybrid systems, control and games, system security, and formal verification and safety. Target applications include: a "Smart Parking" system where parking spaces are optimally assigned and reserved, and vehicular traffic regulation. The research has the potential of revolutionizing the way cities are viewed: from a passive living and working environment to a highly dynamic one with new ways to deal with transportation, energy, and safety. Teaming up with stakeholders in the Boston Back Bay neighborhood, the City of Boston, and private industry, the research team expects to establish new collaborative models between universities and urban groups for cutting-edge research embedded in the deployment of an exciting technological, economic, and sociological development.

Large-scale critical infrastructure systems, including energy and transportation networks, comprise millions of individual elements (human, software and hardware) whose actions may be inconsequential in isolation but profoundly important in aggregate. The focus of this project is on the coordination of these elements via ubiquitous sensing, communications, computation, and control, with an emphasis on the electric grid. The project integrates ideas from economics and behavioral science into frameworks grounded in control theory and power systems. Our central construct is that of a ?resource cluster,? a collection of distributed resources (ex: solar PV, storage, deferrable loads) that can be coordinated to efficiently and reliably offer services (ex: power delivery) in the face of uncertainty (ex: PV output, consumer behavior). Three topic areas form the core of the project: (a) the theoretical foundations for the ?cluster manager? concept and complementary tools to characterize the capabilities of a resource cluster; (b) centralized resource coordination strategies that span multiple time scales via innovations in stochastic optimal control theory; and (c) decentralized coordination strategies based on cluster manager incentives and built upon foundations of non-cooperative dynamic game theory. These innovations will improve the operation of infrastructure systems via a cyber-physical-social approach to the problem of resource allocation in complex infrastructures. By transforming the role of humans from passive resource recipients to active participants in the electric power system, the project will facilitate energy security for the nation, and climate change mitigation. The project will also engage K-12 students through lab-visits and lectures; address the undergraduate demand for power systems training through curricular innovations at the intersection of cyber-systems engineering and physical power systems; and equip graduate students with the multi-disciplinary training in power systems, communications, control, optimization and economics to become leaders in innovation.

This grant provides funding for the development of Cyber Enabled Manufacturing (CeMs) process control for small lot manufacturing that incorporates a model of the process directly into the control algorithm. Such a model can be used to accommodate changes in the physical product and the manufacturing process and thus the manufacturing monitoring and control algorithm, so that changing conditions are easily accommodated without extensive additional experiments. A set of objectives of this physics and cyber-enabled manufacturing process control system are rational setting of manufacturing tolerances, real time prediction of manufacturing defects, real time control of process to eliminate defects, and real time monitoring and control for small lot manufacturing. The methodologies we propose to achieve these goals are high fidelity, physics based models including models of faults/defects, uncertainty quantification, reduced order models that run in real time, measurement, real time prediction, real time computer architecture, real time control with inverse solutions, and automating the CeMs process for generic manufacturing processes If successful, the results of this research will greatly reduce cycle time in producing new or modified products and improve the quality of manufacturing processes with accompanying reduction in waste, energy use, and cost. The development of such accurate control algorithms and their application to manufacturing processes can provide a competitive edge to US manufacturers. Perhaps more importantly, the education of engineers involved this research will supply US industry with employees who can apply this technology to many industrial processes.

The most compelling problem confronting detection of nuclear material in a large area is the level of manifest uncertainty. Furthermore, detection and localization problems involve nontrivial and nonlinear non-convex optimization often stuck at local minima. This project develops fundamentally new techniques by using cheap detectors for rough detection and localization, placing detectors to expunge local minima, achieving fast distributed localization with reduced communication overheads, simultaneously localizing multiple sources and optimally placing detectors and assisting in their autonomous self-organization. There is a growing recognition of the inadequacy of current capabilities with respect to nuclear material detection and localization in public events and areas. This project develops an integrated cyber-physical system for public protection against nuclear and radiological threats. Clearly the project addresses a national security issue. If successful, the contribution and results of the project likely open a new framework for detection or monitoring in a large area using a wireless detector network. One of the key aspects of this project is the inter-disciplinary training of our graduate and undergraduate students including female and minority students in the areas of signal processing, statistical methods, modeling and performance analysis. K-12 students are also targeted through the First competition and Project-Lead-The-Way that connects the College of Engineering at the University of Iowa to almost all high school students in Iowa. It is expected that the project will generate enthusiasm and interests in science, mathematics and engineering for high school students.

Autonomous navigation in unknown and dynamic environments has been a major challenge for synthetic mobile robotic agents. On the other hand, insects can easily solve such complex navigational problems and demonstrate remarkably stable and optimized locomotion skills in almost any environment. This project aims to develop a mobile sensor network where insects are used as mobile biological-robotic (biobotic) nodes. Insects, in fact, build a "natural" sensor network through the use of their biological sensing organs and release of chemical, mechanical and optical cues to communicate the information to the rest of the group. In the scope of this project, a novel cyber-physical communication network will be established among the individual insect in addition to the aforementioned natural one. For this, insects will be equipped with synthetic electronic sensors to sense additional cues, neuromuscular stimulation systems to direct the control of the insect and microcontrollers with radios to establish an RF link between the insects. This novel network will enable operation of insect biobots in complicated and uncertain dynamic environments for applications such as environmental sensing and search-and-rescue operations after natural disasters. The unique interdisciplinary nature of this project will help engineers to reach to younger generations and train them to be able to look at engineering problems from a cyberphysical systems point of view. Planned activities include development of lab modules and demos by undergraduate and graduate students to teach K-12 students and their teachers through our on-going collaborations with educational partners. These demos will also be instrumental during nation level efforts to promote graduate education to underrepresented minority students.