Cyber-physical systems (CPS) encompass the next generation of computerized control for countless aspects of the physical world and interactions thereof. The typical engineering process for CPS reuses existing designs, models, components, and software from one version to the next. For example, in automotive engineering, it is common to reuse significant portions of existing model-year vehicle designs when developing the next model-year vehicle, and such practices are common across CPS industries, from aerospace to biomedical. While reuse drastically enhances efficiency and productivity, it leads to the possibility of introducing unintended mismatches between subcomponents' specifications. For example, a 2011 US National Highway Traffic Safety Administration (NHTSA) recall of over 1.5 million model-year 2005-2010 vehicles was due to the upgrade of a physical transmission component that was not appropriately addressed in software. A mismatch between cyber and physical specifications may occur when a software or hardware upgrade (in effect, a cyber or physical specification change) is not addressed by an update (in effect, a matching specification change) in the other domain. This research will develop new techniques and software tools to detect automatically if cyber-physical specification mismatches exist, and then mitigate the effects of such mismatches at runtime, with the overall goal to yield more reliable and safer CPS upon which society increasingly depends. The detection and mitigation methods developed will be evaluated in an energy CPS testbed. While the evaluation testbed is in the energy domain, the methods are applicable to other CPS domains such as automotive, aerospace, and biomedical. The educational goals will bridge gaps between computer science and electrical engineering, preparing a diverse set of next-generation CPS engineers by developing education platforms to enhance CPS engineering design and verification skills. The proposed research is to develop new techniques and tools to automatically identify and mitigate the effects of cyber-physical specification mismatches. There are three major research objectives. The first objective is to identify cyber-physical specification mismatches. To identify mismatches, a detection problem will be formalized using the framework of hybrid input/output automata (HIOA). Offline algorithms will be designed to find candidate specifications from models and implementations using static and dynamic analyses, and then identify candidate mismatches. The second objective is to monitor and assure safe CPS upgrades. As modern CPS designs are complex, it may be infeasible to determine all specifications and mismatches between all subcomponents at design time. Runtime monitoring and verification methods will be developed for inferred specifications to detect mismatches at runtime. When they are identified, a runtime assurance framework building on supervisory control and the Simplex architecture will assure safe CPS runtime operation. The third objective is to evaluate safe CPS upgrades in an example CPS. The results of the other objectives and their ability to ensure safe CPS upgrades will be evaluated in an energy CPS testbed, namely an AC electrical distribution microgrid that interfaces DC-producing renewables like photovoltaics to AC.

Motivated by the fact that the 2014 Ebola outbreak is the largest in history and there is a pressing need to understand how to improve delivery of care with the right technological interventions at the right place, this Rapid Response Research is aimed at realizing a human-in-the-loop medical cyber-physical system (CPS) for monitoring patients, insuring compliance with relevant safety protocols, and collecting data for advancing multidisciplinary research on infectious disease control. The ultimate goal is to enhance safety of Ebola workers by minimizing their contact with potentially contaminated surfaces and materials through integration of methods and technologies to realize smart and connected treatment clinics. This project could impact the response to infectious disease outbreaks by augmenting existing treatment clinics with cost-effective, modular, reconfigurable and
 open-design CPS technologies. The project will train a new cadre of engineering students, researchers and innovators to be 
sensitive to societal needs and national priorities by involving K-Gray, undergraduate and graduate students in all aspects of the project, especially at the co-ideation and co-design stages. The project will bring together a multidisciplinary team of engineers, scientists, technologists, medical experts, and humanitarian aid workers to develop holistic solutions to infectious disease control. The broader impacts also include operational cost savings in treatment clinics by reducing the need and use of the personal protective equipment and preserve resources such as water by reducing consumption. In order to prevent, detect and respond to current Ebola outbreak and future similar infectious disease outbreaks, this research plan has the following interconnected aims: (1) contribute new knowledge, methods, and tools to better understand the operational procedures in an infectious disease treatment clinic, (2) design, implement and validate a treatment ward augmented with a medical CPS for patient monitoring, (3) apply intuitive control interfaces and data visualization tools for practical human-robot interaction, (4) realize traded, coordinated and collaborative shared control techniques for safe and effective mobile robot navigation inside a treatment facility, (5) assess acceptability and effectiveness of the technology among health care workers and patients. The team will develop a self-contained, modular and reconfigurable system composed of a connected sensor network for patient monitoring and a mobile robot platform for telemedicine that will primarily focus on the interoperability and integration of existing standardized 
hardware and software systems to realize a testbed for verification and validation of a medical CPS. Medical, emergency response and humanitarian aid experts will be engaged to critically assess user-experiences and acceptability among medical staff to develop pathways for fielding the system in a treatment clinic. This RAPID project will lead the way in designing the next generation of human-in-the-loop medical CPS for empowering health care workers worldwide in treating patients during infectious disease outbreaks.

The automotive industry finds itself at a cross-roads. Current advances in MEMS sensor technology, the emergence of embedded control software, the rapid progress in computer technology, digital image processing, machine learning and control algorithms, along with an ever increasing investment in vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) technologies, are about to revolutionize the way we use vehicles and commute in everyday life. Automotive active safety systems, in particular, have been used with enormous success in the past 50 years and have helped keep traffic accidents in check. Still, more than 30,000 deaths and 2,000,000 injuries occur each year in the US alone, and many more worldwide. The impact of traffic accidents on the economy is estimated to be as high as $300B/yr in the US alone. Further improvement in terms of driving safety (and comfort) necessitates that the next generation of active safety systems are more proactive (as opposed to reactive) and can comprehend and interpret driver intent. Future active safety systems will have to account for the diversity of drivers' skills, the behavior of drivers in traffic, and the overall traffic conditions. This research aims at improving the current capabilities of automotive active safety control systems (ASCS) by taking into account the interactions between the driver, the vehicle, the ASCS and the environment. Beyond solving a fundamental problem in automotive industry, this research will have ramifications in other cyber-physical domains, where humans manually control vehicles or equipment including: flying, operation of heavy machinery, mining, tele-robotics, and robotic medicine. Making autonomous/automated systems that feel and behave "naturally" to human operators is not always easy. As these systems and machines participate more in everyday interactions with humans, the need to make them operate in a predictable manner is more urgent than ever. To achieve the goals of the proposed research, this project will use the estimation of the driver's cognitive state to adapt the ASCS accordingly, in order to achieve a seamless operation with the driver. Specifically, new methodologies will be developed to infer long-term and short-term behavior of drivers via the use of Bayesian networks and neuromorphic algorithms to estimate the driver's skills and current state of attention from eye movement data, together with dynamic motion cues obtained from steering and pedal inputs. This information will be injected into the ASCS operation in order to enhance its performance by taking advantage of recent results from the theory of adaptive and real-time, model-predictive optimal control. The correct level of autonomy and workload distribution between the driver and ASCS will ensure that no conflicts arise between the driver and the control system, and the safety and passenger comfort are not compromised. A comprehensive plan will be used to test and validate the developed theory by collecting measurements from several human subjects while operating a virtual reality-driving simulator.

This project addresses urgent challenges in high confidence validation and verification of automotive vehicles due to on-going and anticipated introduction of advanced, connected and autonomous vehicles into mass production. Since such vehicles operate across both physical and cyber domains, faults can occur in traditional physical components, in cyber components (i.e., algorithms, processors, networks, etc.), or in both. Thus, advanced vehicles need to be tested for both physical and cyber-related fault conditions. The goal of this project is to develop theory, methods, and novel tools for generating and optimizing test trajectories and data inputs that can uncover both physical and cyber faults of future automotive vehicles. The level of vehicle reliability and safety achieved for current vehicles is remarkable considering their mass production, low cost, and wide range of operating conditions. If successful, the research advances made in this project will enable achieving similar levels of reliability and safety for future vehicles relying on advanced driver assistance technologies, connectivity and autonomy. The project will advance the field of cyber-physical systems, in general, and their lifecycle management, in particular. The validation and verification theory and methodology for cyberphysical systems will be expanded for uncovering anomalies and faults, especially using comprehensive case-based and optimization-based techniques for test scenario generation. The theoretical advances and case studies will contribute to the state-of-the-art in optimal control theory, game theory, information theory, data collection and processing, autonomous and connected vehicles, and automotive control. Sampling-based vehicle data acquisition and vehicle-aware data management strategies will be developed which can be applied more broadly, e.g., to cloud-based vehicle prognostics / conditional maintenance and mobile health-monitoring devices. Finally, approaches for efficient on-board data collection and aggregation will be implemented in a Cyber-physical system (CPS) Black Box prototype. The development of a vehicle-aware data management system (VDMS) will be pursued, leading to optimized use of data mining and compression inside the CPS Black Box to aggressively reduce the communication and computational costs. Synergistically with theoretical and methodological advances, automotive case studies will be undertaken with both realistic simulations and real experiments in collaboration with an industrial partner (AVL).