The goal of this project is to establish a theoretical and empirical foundation for secured and efficient energy resource management in the smart grid - a typical energy-based cyber-physical system and the future critical energy infrastructure for the nation. However, as a large distributed and complex system, the smart grid inherently operates under the presence of various uncertainties, which can be raised from natural disasters, malicious attacks, distributed renewable energy resources, plug-in electrical vehicles, habits of energy usage, and weather. These uncertainties make the development of a secured and efficient energy resource management system challenging. To address this challenging problem, a novel modeling framework and techniques to deal with these uncertainties will be developed. Threats and their impact on both system operations and end users will be studied and effective defensive schemes will be developed. The outcomes of this project will have broader impacts on the higher education system and national economy and will provide a scientific foundation for designing a secured and efficient energy-based critical infrastructure. The contributions of this project include: a theoretical framework, techniques, and toolkits for smart grid research and education. Specifically, a modeling framework for secured and efficient energy resource management will be developed to quantify uncertainties from both the cyber and physical power grids. Techniques based on statistical modeling, data mining, forecasting, and others will be developed to manage energy resources efficiently. Based on the developed framework, the space of attacks against system operations and end users from key function modules, attack venue, abilities of adversaries, and system knowledge will be studied systematically. Based on the deep understanding of attacks, novel schemes to prevent, detect, and attribute attacks will be developed. An integrated cyber and physical power grid simulation tool and testbed will be developed to evaluate the proposed modeling framework and techniques using realistic scenarios. This project will integrate research, education, and outreach. The outcomes of the project will be integrated into curriculum development and provide research and educational opportunities for both graduate and undergraduate students, including underrepresented minorities and CyberCorps: Scholarship for Service students.

This NSF-FDA Scholar-In-Residence award supports translational research in modeling to inform future medical device design and approval processes. It is supported by the NSF Cyber-Physical Systems program in the Division of Computer and Network Systems in the Directorate for Computer and Information Science and Engineering. Sudden cardiac death is the leading cause of fatalities in the industrialized world. One in five people in the United States is affected by some sort of heart disease and one third of all deaths are due to cardiac diseases with an economic impact of about $200 billion a year. Most of these deaths result from arrhythmias, particularly fibrillation, which is rapid, disorganized electrical activity. The classification of arrhythmias as either reentrant or focal is of clinical significance, yet is difficult to assess. The FDA is responsible for regulating the systems and algorithms that aim to make this important differentiation. Such differentiation is a complex task involving the analysis of complex spatio-temporal patterns of electrical activity. The objectives of this project are to identify the key features of fibrillation that models should represent, to compare how well (or poorly) existing models correspond to measured values of these features, and to develop models that better represent fibrillation. The project develops and extends cell and tissue models and explores the analysis of clinical, experimental and simulation data from the perspective of regulatory science at the FDA, including verification, validation, and uncertainty quantification (VVUQ). The project seeks to 1) validate and create new models that reproduce not only single-cell dynamics, but also experimental and clinically relevant physiological dynamics in tissue and 2) initiate a new developmental framework that the FDA can use not only to test cardiac electrophysiology devices but also to characterize and verify massive submissions of therapeutic compounds obtained by computer-aided drug design methods. The research is conducted in collaboration with the Center for Devices and Radiological Health at FDA, and is aimed at developing tools that can characterize and evaluate real-world performance of devices. This will help the FDA to better regulate and verify the safety and effectiveness of devices that are developed to treat and terminate cardiac arrhythmias. All results from this project will be made freely available to the research community and to the general public.

This INSPIRE award is partially funded by the Cyber-Physical Systems Program in the Division of Computer and Network Systems in the Directorate for Computer and Information Science and Engineering, the Information and Intelligent Systems Program in the Division of Information and Intelligent Systems in the Directorate for Computer and Information Science and Engineering, the Computer Systems Research Program in the Division of Computer and Network Systems in the Directorate for Computer and Information Science and Engineering, and the Software and Hardware Foundations Program in the Division of Computing and Communications Foundations in the Directorate for Computer and Information Science and Engineering. Sound plays a vital role in the ocean ecosystem as many organisms rely on acoustics for navigation, communication, detecting predators, and finding food. Therefore, the 3D underwater soundscape, i.e., the combination of sounds present in the immersive underwater environment, is of extreme importance to understand and protect underwater ecosystems. This project is creating a transformative distributed ocean observing system for studying the underwater soundscape at revolutionary spatial (~100 meters) and temporal (~100 seconds) resolutions that is also able to simultaneously resolve small-scale ocean current flow. These breakthroughs are achieved using a distributed collective of small hydrophone-equipped subsurface floats, which utilize group management techniques and sensor fusion to understand the ocean soundscape in a Lagrangian manner. The ability to record soundscapes provides a novel sensing technology to understand the effects of sound on marine ecosystems and the role that sound plays for species development. Experiments off the coast of San Diego, CA, and a research campaign in the Cayman Islands provide concrete scientific studies that are tightly interwoven with the engineering research. Oceans are drivers of global climate, are home to some of the most important and diverse ecosystems, and represent a substantial contribution to the world's economy as a major source of food and employment. The technological and scientific advances in this project provide crucial tools to understand natural ocean resources, by studying soundscapes at spatio-temporal scales that were heretofore extremely burdensome and expensive to obtain.

The objective of this project is to improve the performance of autonomous systems in dynamic environments, such as disaster recovery, by integrating perception, planning paradigms, learning, and databases. For the next generation of autonomous systems to be truly effective in terms of tangible performance improvements (e.g., long-term operations, complex and rapidly changing environments), a new level of intelligence must be attained. This project improves the state of robotic systems by enhancing their ability to coordinate activities (such as searching a disaster zone), recognize objects or people, account for uncertainty, and "most important" learn, so the system's performance is continuously improving. To do this, the project takes an interdisciplinary approach to developing techniques in core areas and at the interface of perception, planning, learning, and databases to achieve robustness. This project seeks to significantly improve the performance of cyber-physical systems for time-critical applications such as disaster monitoring, search and rescue, autonomous navigation, and security and surveillance. It enables the development of techniques and tools to augment all decision making processes and applications which are characterized by continuously changing operating conditions, missions and environments. The project contributes to education and a diverse engineering workforce by training students at the University of California, Riverside, one of the most diverse research institutions in US and an accredited Hispanic Serving Institution. Instruction and research opportunities cross traditional disciplinary boundaries, and the project serves as the basis for undergraduate capstone design projects and a new graduate course. The software and testbeds from this project will be shared with the cyber-physical system research community, industry, and end users. The project plans to present focused workshops/tutorials at major IEEE and ACM conferences. The results will be broadly disseminated through the project website. For further information see the project website at: http://vislab.ucr.edu/RESEARCH/DSLC/DSLC.php

This project develops the theory and technology for a new frontier in cyber-physical systems: cyber-physical manipulation. The goal of cyber-physical manipulation is to enable groups of hundreds or thousands of individual robotic agents to collaboratively explore an environment, manipulate objects in that environment, and transport those objects to desired locations. The project embraces realistic assumptions about the communication, computation, and sensing capabilities of simple individual robots, leading to algorithmic solutions that intrinsically leverage population size in favor of complex agents. Cyber-physical solutions for locating, grasping, and characterizing objects require tools based on distributed computational geometry, while the tasks of planning a path, initiating motion, and controlling the trajectory require tools from decentralized control and consensus. The project lays the theoretical and algorithmic foundations of cyber-physical manipulation, and proves the feasibility of the concept experimentally in hardware tests with up to 100 individual robots. The project uses the problem of manipulation as a stage on which to explore the deeper cyber-physical issue of information asymmetry; the difference in the state of the world as perceived by different agents in the system due to differences in their history of observations, and limitations in their communication capabilities. The object retrieval problem studied in this project is an elemental building block for enabling more complex cyber-physical manipulation tasks. It provides crucial algorithmic components for numerous applications of broad societal benefit, including automated construction (in which hundreds or thousands of robots fabricate large, complex structures), autonomous emergency response (in which large teams of robots find and retrieve incapacitated human survivors after a disaster), and automated environmental cleanup (in which robots secure a dangerous environment by removing debris or hazardous substances). Furthermore, distributed algorithms for multi-agent systems are of broad scientific relevance beyond the realm of cyberphysical systems. The natural world is, in its algorithmic essence, decentralized at many levels. Hence, any advancement in the understanding of how groups of individual agents collaborate to accomplish a coherent task will have broad scientific ramifications. The project has a robust educational and outreach program. One aspect is a hands-on curriculum for robotics outreach activities, called the 'Cyber-Physical Manipulation Lab.' Using a custom-designed robot platform, this educational module introduces the theory and practice of cyber-physical systems to young students to attract them to STEM subject areas at an early age. Results of the project are also incorporated into several graduate and undergraduate level courses at Rice University and Boston University.

This project focuses on the problem of information acquisition, state estimation and control in the context of cyber physical systems. In our underlying model, a (set of) decision maker(s), by controlling a sequence of actions with uncertain outcomes, dynamically refines the belief about stochastically time-varying parameters of interest. These parameters are then used to control the physical system efficiently and robustly. Here the cyber system collects, processes, and acquires information about the underlying physical system of interest, which is used for its control. The proposed work will develop a new theoretical framework for stochastic learning, decision-making, and control in stochastically-varying cyber physical systems. In order to obtain analytical insights into the structure of efficient design, we first consider the case where the actions of the cyber system only affect the estimate of the underlying physical system. This class of problems arises in the context of (distributed) sensing/tracking of a physical system in isolation from cyber system control of the physical system's state. Joint state estimation and control for cyber-physical systems will then be considered. Here the most natural first step is to obtain sufficient conditions and/or special classes of systems where a separated approach to the information acquisition and efficient control is (near) optimal. To demonstrate its utility in practice, our theoretical framework will be applied in the specific context of energy efficient control of data centers and robust control of the smart grid under limited sensing. The intellectual merit of this work will be to develop a theoretical framework for the design of cyber-physical systems including information acquisition, state estimation, and control. In addition, separation theorems for the optimality of separate state estimation and control will be explored. In terms of broader impacts, significant performance improvement of control systems closed over communication networks will impact a wide range of applications for societal benefit, including smart buildings, intelligent transportation systems, energy-efficient data centers, and the future smart-grid. The PIs plan to disseminate the research results widely through conferences and journals, as well as by organizing specialized workshops and conference sessions related to cyber physical systems. The proposed project will train Ph.D. students as well as enrich the curriculum taught by the PIs in communications, stochastic control, and networks. The PIs have a strong track record in diversity and outreach activities, which for this project will include exposure and involvement of high school and undergraduate students, including under-represented minorities and women.

This cross-disciplinary project brings together a team of engineering and computer science researchers to create, validate, and demonstrate the value of new techniques for ensuring that systems composed of combinations of hardware, software, and humans are designed to operate in a truly synergistic and safe fashion. One notable and increasingly common feature of these "Cyber-Physical-Human" (CPH) systems is that the responsibility for safe operation and performance is typically shared by increasingly sophisticated automation in the form of hardware and software, and humans who direct and oversee the behavior of automation yet may need to intervene to take over manual or shared system control when unexpected environmental situations or hardware or software failures occur. The ultimate goal is to achieve levels of safety and performance in system operation that exceed the levels attainable by either skilled human operators or completely autonomous systems acting alone. To do so, the research team will draw upon their expertise in the design of robust, fault-tolerant control systems, in the design of complexity-reduction architectures for software verification, and in human factors techniques for cognitive modeling to assure high levels of human situation awareness through effective interface design. By doing so, the safety, cost and performance benefits of increasingly sophisticated automation can be achieved without the frequently observed safety risks caused by automation creating greater distance between human operators and system operation. The techniques will be iteratively created and empirically evaluated using experimentation in human-in-the-loop simulations, including a medium-fidelity aircraft and flight simulator and a simulation of assistive automation in a medical context. More broadly, this research is expected to impact and inform the engineering of future CPH systems generally, for all industries and systems characterized by an increasing use of hardware and software automation directed and overseen by humans who provide an additional layer of safety in expected situations, Examples include highway and automotive automation, aerospace and air traffic control automation, semi-automated process control systems, and the many forms of automated systems and devices increasingly being used in medical contexts, such as the ICU and operating room. This research is also expected to inform government and industry efforts to provide safety certification criteria for the technologies used in CPH systems, and to educate a next generation of students trained in the cross-disciplinary skills and abilities needed to engineer the CPH systems of the future. The investigators will organize industry, academic, and government workshops to disseminate results and mentor students who are members of underrepresented groups through the course of this research project.

This project investigates new reinforcement learning algorithms to enable long-term real-time autonomous learning by cyber-physical systems (CPS). The complexity of CPS makes hand-programming safe and efficient controllers for them difficult. For CPS to meet their potential, they need methods that enable them to learn and adapt to novel situations that they were not programmed for. Reinforcement learning (RL) is a paradigm for learning sequential decision making processes with potential for solving this problem. However, existing RL algorithms do not meet all of the requirements of learning in CPS. Efficacy of the new algorithms for CPS is evaluated in the context of smart buildings and autonomous vehicles. Cyber-physical systems (CPS) have the potential to revolutionize society by enabling smart buildings, transportation, medical technology, and electric grids. Success of this project could lead to a new generation of CPS that are capable of adapting to their situation and improving their performance autonomously over time. In addition to the traditional methods of dissemination, this project will develop and release open-source code implementing the new reinforcement learning algorithms. Education and outreach activities associated with the project include a Freshman Research Initiative course, participation in a UT Austin annual open house that draws in many underrepresented minorities to interest the public in computer science and science in general, and the department's annual summer school for high school girls called First Bytes.

This project develops the theory and technology for a new frontier in cyber-physical systems: cyber-physical manipulation. The goal of cyber-physical manipulation is to enable groups of hundreds or thousands of individual robotic agents to collaboratively explore an environment, manipulate objects in that environment, and transport those objects to desired locations. The project embraces realistic assumptions about the communication, computation, and sensing capabilities of simple individual robots, leading to algorithmic solutions that intrinsically leverage population size in favor of complex agents. Cyber-physical solutions for locating, grasping, and characterizing objects require tools based on distributed computational geometry, while the tasks of planning a path, initiating motion, and controlling the trajectory require tools from decentralized control and consensus. The project lays the theoretical and algorithmic foundations of cyber-physical manipulation, and proves the feasibility of the concept experimentally in hardware tests with up to 100 individual robots. The project uses the problem of manipulation as a stage on which to explore the deeper cyber-physical issue of information asymmetry; the difference in the state of the world as perceived by different agents in the system due to differences in their history of observations, and limitations in their communication capabilities. The object retrieval problem studied in this project is an elemental building block for enabling more complex cyber-physical manipulation tasks. It provides crucial algorithmic components for numerous applications of broad societal benefit, including automated construction (in which hundreds or thousands of robots fabricate large, complex structures), autonomous emergency response (in which large teams of robots find and retrieve incapacitated human survivors after a disaster), and automated environmental cleanup (in which robots secure a dangerous environment by removing debris or hazardous substances). Furthermore, distributed algorithms for multi-agent systems are of broad scientific relevance beyond the realm of cyberphysical systems. The natural world is, in its algorithmic essence, decentralized at many levels. Hence, any advancement in the understanding of how groups of individual agents collaborate to accomplish a coherent task will have broad scientific ramifications. The project has a robust educational and outreach program. One aspect is a hands-on curriculum for robotics outreach activities, called the 'Cyber-Physical Manipulation Lab.' Using a custom-designed robot platform, this educational module introduces the theory and practice of cyber-physical systems to young students to attract them to STEM subject areas at an early age. Results of the project are also incorporated into several graduate and undergraduate level courses at Rice University and Boston University.

This project establishes a new framework for the formal verification of cyber-physical systems. The framework combines the power of logical decision engines and scalable numerical methods to perform safety verification of general nonlinear hybrid systems. The key difficulty with formal verification of hybrid systems is that all scalable modern verification techniques rely heavily on the use of powerful decision procedures. For hybrid systems, one needs to reason about logic formulas over the real numbers with nonlinear functions, which has been regarded as an intractable problem. The project proposes new directions for tackling the core decision problems, with the combined power of logical and numerical algorithms. The research directly leads to the development of practical tools that will push the frontier of verification of realistic cyber-physical systems to a brand new level. This project aims at fundamental research of problems that stand at the core of the design, analysis, and implementation of reliable cyber-physical systems. It combines techniques from logic, numerical analysis, and automated reasoning, and will produce a unifying methodology that is powerful to address main challenges in this field. The techniques developed in this project will significantly enhance the complexity and reliability of the next generations of cyber-physical systems. Cyber-physical systems are ubiquitous in safety-critical applications as diverse as aerospace, automotive, civil infrastructure, energy, manufacturing, and healthcare. Malfunctioning cyber-physical systems can have catastrophic economic and societal consequences. This project will have a broad range of impact in these areas. This research aims to significantly enhance the management of complexity and reliability of the next generations of cyber-physical systems, and will broadly impact all the application areas.