In the United States, there is still a great disparity in medical care and most profoundly for emergency care, where limited facilities and remote location play a central role. Based on the Wessels Living History Farm report, the doctor to patient ratio in the United States is 30 to 10,000 in large metropolitan areas, only 5 to 10,000 in most rural areas; and the highest death rates are often found in the most rural counties. For emergency patient care, time to definitive treatment is critical. However, deciding the most effective care for an acute patient requires knowledge and experience. Though medical best practice guidelines exist and are in hospital handbooks, they are often lengthy and difficult to apply clinically. The challenges are exaggerated for doctors in rural areas and emergency medical technicians (EMT) during patient transport. This project's solution to transform emergency care at rural hospitals is to use innovative CPS technologies to help hospitals to improve their adherence to medical best practice. The key to assist medical staff with different levels of experience and skills to adhere to medical best practice is to transform required processes described in medical texts to an executable, adaptive, and distributed medical best practice guidance (EMBG) system. Compared to the computerized sepsis best practice protocol, the EMBG system faces a much bigger challenge as it has to adapt the best practice across rural hospitals, ambulances and center hospitals with different levels of staff expertise and equipment capabilities. Using a Global Positioning System analogy, a GPS leads drivers with different route familiarity to their destination through an optimal route based on the drivers' preferences, the EMBG system leads medical personnel to follow the best medical guideline path to provide emergency care and minimize the time to definitive treatment for acute patients. The project makes the following contributions: 1) The codification of complex medical knowledge is an important advancement in knowledge capture and representation; 2) Pathophysiological model driven communication in high speed ambulance advances life critical communication technology; and 3) Reduced complexity software architectures designed for formal verification bridges the gap between formal method research and system engineering.

The objective of this work is to generate new fundamental science that enables the operation of cyber-physical systems through complex environments. Predicting how a system will behave in the future requires more computing power if that system is complex. Navigating through environments with many obstacles could require significant computing time, which may delay the issue of decisions that have to be made by the on-board algorithms. Fortunately, systems do not always need the most accurate model to predict their behavior. This project develops new theory for deciding between the best model to use when making a decision in real time. The approach involves switching between different predictive models of the system, depending on the computational burden of the associated controller, and the accuracy that the predictive model provides. These tools will pave the way for more kinds of aircraft to navigate closely and safely with one another through the National Air Space (NAS), including Unmanned Air Systems (UAS). The results from this project will enable more accurate and faster trajectory synthesis for controllers with nonlinear plants, or nonlinear constraints that encode obstacles. The approach utilizes hybrid control to switch between models whose accuracy is normalized by their computational burden of predictive control methods. This synergistic approach enables computationally-aware cyber-physical systems (CPSs), in which model accuracy can be jointly considered with computational requirements. The project advances the knowledge on modeling, analysis, and design of CPSs that utilize predictive methods for trajectory synthesis under constraints in real-time cyber-physical systems. 
 The results will include methods for the design of algorithms that adapt to the computational limitations of autonomous and semi-autonomous systems while satisfying stringent timing and safety requirements. With these methods come new tools to account for computational capabilities in real-time, and new hybrid feedback algorithms and prediction schemes that exploit computational capabilities to arrive at more accurate predictions within the time constraints. The algorithms will be modeled in terms of hybrid dynamical systems, to guarantee dynamical properties of interest. The problem space will draw from models of UAS in the NAS.

Cells, to carry out many important functions, employ an elaborate transport network with bio-molecular components forming roadways as well as vehicles. The transport is achieved with remarkable robustness under a very uncertain environment. The main goal of this proposal is to understand how biology achieves such functionality and leveraging the knowledge toward realizing effective engineered transport mechanisms for micron sized cargo. The realization of a robust infrastructure that enables simultaneous transport of many micron and smaller sized particles will have a transformative impact on a vast range of areas such as medicine, drug development, electronics, and bio-materials. A key challenge here is to probe the mechanisms often at the nanometer scale as the bio-molecular components are at tens of nanometer scale. The main tools for addressing these challenges come from an engineering perspective that is guided by existing insights from biology. The proposal will bring together researchers from engineering and biology and it provides an integrated environment for students. Moreover, it is known that an impaired transport mechanism can underlie many neurodegenerative maladies, and as the research here pertains to studying intracellular transport, discoveries hold the potential for shedding light on what causes the impaired transport. Robust infrastructure that enables simultaneous transport of many micron and smaller sized particles will have a transformative impact on a vast range of areas such as medicine, drug development, electronics, and bio-materials. Daunting challenges from the underlying highly uncertain and complex environments impede enabling robust and efficient transport systems at micro-scale. Motivated by transport in biological cells, this work proposes a robust and efficient engineered infrastructure for transporting micron/molecular scale cargo using biological constructs. For probing and manipulating the transport network, the proposal envisions strategies for coarse and fine resolution objectives at the global and local scales respectively. At the fine scale of monitoring and control, scarce and expensive physical resources such as high resolution sensors have to be shared for interrogation/control of multiple carriers. In this proposal, the principles for joint control, sensor allocation and scheduling of resources to achieve enhanced performance objectives of a high resolution probing tool, will be developed. A modern control perspective forms an essential strategy for managing multiple objectives. At the global scale, entire traffic will be monitored to arrive at real-time and off-line inferences on traffic modalities. Associated principles for dynamically identifying and tracking clusters of carriers and their importance will be built. This categorization of physical elements and their importance will determine the dynamic allocation of computational resources. Associated study of trade-offs will guide a combined strategy for allocation of computational resources and gathering of information on physical elements. Methods based on the reconstruction of graph topologies for reaching inferences that are suited to dynamically related time trajectories for the transportation infrastructure will be developed. The research proposed is transformative as it will enable a new transport paradigm at the cellular scale, which will also provide unique insights into intracellular transport where it will be possible to investigate multiple factors under the same experimental conditions.

Additive Manufacturing holds the promise of revolutionizing manufacturing. One important trend is the emergence of cyber additive manufacturing communities for innovative design and fabrication. However, due to variations in materials and processes, design and computational algorithms currently have limited adaptability and scalability across different additive manufacturing systems. This award will establish the scientific foundation and engineering principles needed to achieve adaptability, extensibility, and system scalability in cyber-physical additive manufacturing systems, resulting in high efficiency and accuracy fabrication. The research will facilitate the evolution of existing isolated and loosely-connected additive manufacturing facilities into fully functioning cyber-physical additive manufacturing systems with increased capabilities. The application-based, smart interfacing infrastructure will complement existing cyber additive communities and enhance partnerships between academia, industry, and the general public. The research will contribute to the technology and engineering of Cyber-physical Systems and the economic competitiveness of US manufacturing. This interdisciplinary research will generate new curricular materials and help educate a new generation of cybermanufacturing workforce. The research will establish smart and dynamic system calibration methods and algorithms through deep learning that will enable high-confidence and interoperable cyber-physical additive manufacturing systems. The dynamic calibration and re-calibration algorithms will provide a smart interfacing layer of infrastructure between design models and physical additive manufacturing systems. Specific research tasks include: (1) Establishing smart and fast calibration algorithms to make physical additive manufacturing machines adaptable to design models; (2) Deriving prescriptive compensation algorithms to achieve extensible design models; (3) Dynamic recalibration through deep learning for improved predictive modeling and compensation; and (4) Developing a smart calibration server and APP prototype test bed for scalable additive cyberinfractures.

Strategic decision-making for physical-world infrastructures is rapidly transitioning toward a pervasively cyber-enabled paradigm, in which human stakeholders and automation leverage the cyber-infrastructure at large (including on-line data sources, cloud computing, and handheld devices). This changing paradigm is leading to tight coupling of the cyber- infrastructure with multiple physical- world infrastructures, including air transportation and electric power systems. These management-coupled cyber- and physical- infrastructures (MCCPIs) are subject to complex threats from natural and sentient adversaries, which can enact complex propagative impacts across networked physical-, cyber-, and human elements. We propose here to develop a modeling framework and tool suite for threat assessment for MCCPIs. The proposed modeling framework for MCCPIs has three aspects: 1) a tractable moment-linear modeling paradigm for the hybrid, stochastic, and multi-layer dynamics of MCCPIs; 2) models for sentient and natural adversaries, that capture their measurement and actuation capabilities in the cyber- and physical- worlds, intelligence, and trust-level; and 3) formal definitions for information security and vulnerability. The attendant tool suite will provide situational awareness of the propagative impacts of threats. Specifically, three functionalities termed Target, Feature, and Defend will be developed, which exploit topological characteristics of an MCCPI to evaluate and mitigate threat impacts. We will then pursue analyses that tie special infrastructure-network features to security/vulnerability. As a central case study, the framework and tools will be used for threat assessment and risk analysis of strategic air traffic management. Three canonical types of threats will be addressed: environmental-to-physical threats, cyber-physical co-threats, and human-in-the-loop threats. This case study will include development and deployment of software decision aids for managing man-made disturbances to the air traffic system.

The project will produce breakthroughs in the science of human-machine interaction and will produce lasting impacts on exercise machine technologies. The proposed Cyber-Enabled Exercise Machines (CEEMs) adapt to their users, seeking to maximize the effectiveness of exercise while guaranteeing safety. CEEMs measure and process biomechanical variables and generate adjustments to its own resistance, and generate cues to be followed by the exerciser. CEEMs are reconfigurable by software, which permits a wide range of exercises with the same hardware. Two prototype machines will be field-tested with the student-athlete population and used to validate project goals. The prototypes will be a valuable instrument for dissemination and outreach, as well as for student engagement. The outcomes of this research have repercussions beyond athletic conditioning: the same foundations and methodologies can be followed to design machines for rehabilitation, exercise countermeasure devices for astronauts, and custom exercise devices for the elderly and persons with disabilities. Thus, the project has the potential to improve health of society members at various levels. This research will contribute to the foundations of cyber-physical system science in the following aspects: biomechanical modeling and real-time musculoskeletal state estimation; estimation theory and unscented H-infinity estimation; control theory and human-machine interaction dynamics, and micro-evolutionary optimization for real-time systems. The proposed Cyber-Enabled Exercise Machines (CEEMs) are highly reconfigurable devices which adapt to the user in pursuit of an optimization objective, namely maximal activation of target muscle groups. Machine adaptation occurs through port impedance modulation, and optimal cues are generated for the exerciser to follow. The goals of the project are threefold: i) development of foundational cyber-physical science and technology in the field of human-machine systems; ii) development of new approaches to modeling, design, control and optimization of advanced exercise machines, and iii) application of the above results to develop two custom-built CEEMs: a rowing ergometer and a 2-degree-of-freedom resistance machine.

In the United States, there is still a great disparity in medical care and most profoundly for emergency care, where limited facilities and remote location play a central role. Based on the Wessels Living History Farm report, the doctor to patient ratio in the United States is 30 to 10,000 in large metropolitan areas, only 5 to 10,000 in most rural areas; and the highest death rates are often found in the most rural counties. For emergency patient care, time to definitive treatment is critical. However, deciding the most effective care for an acute patient requires knowledge and experience. Though medical best practice guidelines exist and are in hospital handbooks, they are often lengthy and difficult to apply clinically. The challenges are exaggerated for doctors in rural areas and emergency medical technicians (EMT) during patient transport. This project's solution to transform emergency care at rural hospitals is to use innovative CPS technologies to help hospitals to improve their adherence to medical best practice. The key to assist medical staff with different levels of experience and skills to adhere to medical best practice is to transform required processes described in medical texts to an executable, adaptive, and distributed medical best practice guidance (EMBG) system. Compared to the computerized sepsis best practice protocol, the EMBG system faces a much bigger challenge as it has to adapt the best practice across rural hospitals, ambulances and center hospitals with different levels of staff expertise and equipment capabilities. Using a Global Positioning System analogy, a GPS leads drivers with different route familiarity to their destination through an optimal route based on the drivers' preferences, the EMBG system leads medical personnel to follow the best medical guideline path to provide emergency care and minimize the time to definitive treatment for acute patients. The project makes the following contributions: 1) The codification of complex medical knowledge is an important advancement in knowledge capture and representation; 2) Pathophysiological model driven communication in high speed ambulance advances life critical communication technology; and 3) Reduced complexity software architectures designed for formal verification bridges the gap between formal method research and system engineering.

Parking can take up a significant amount of the trip costs (time and money) in urban travel. As such, it can considerably influence travelers' choices of modes, locations, and time of travel. The advent of smart sensors, wireless communications, social media and big data analytics offers a unique opportunity to tap parking's influence on travel to make the transportation system more efficient, cleaner, and more resilient. A cyber-physical social system for parking is proposed to realize parking's potential in achieving the above goals. This cyber-physical system consists of smart parking sensors, a parking and traffic data repository, parking management systems, and dynamic traffic flow control. If successful, the results of the investigation will create a new paradigm for managing parking to reduce traffic congestion, emissions and fuel consumption and to enhance system resilience. These results will be disseminated broadly through publications, workshops and seminars. The research will provide interdisciplinary training to both graduate and undergraduate students. The results of this research also fills a void in our graduate transportation curriculum in which parking management gets little coverage. The investigators will organize an online short training course in Coursera and National Highway Institute to bring results to a broader audience. The investigators will also collaborate with Carnegie Museum of Natural History to develop an online digital map and related educational programs, which will be presented in the museum galleries during public events. Technically, new theories, algorithms and systems for efficient management of transportation infrastructure through parking will be developed in this research, leveraging cutting-edge sensing technology, communication technology, big data analytics and feedback control. The research probes massive individualized and infrastructure based traffic and parking data to gain a deeper understanding of travel and parking behavior, and develops a novel reservoir-based network flow model that lays the foundation for modeling the complex interactions between parking and traffic flow in large-scale transportation networks. The theory will be investigated at different levels of granularity to reveal how parking information and pricing mechanisms affect network flow in a competitive market of private and public parking. In addition, this research proposes closed-loop control mechanisms to enhance mobility and sustainability of urban networks. Prices, access and information of publicly owned on-street and off-street parking are dynamically controlled to: a) change day-to-day behavior of all commuters through day-to-day travel experience and/or online information systems; b) change travel behavior of a fraction of adaptive travelers on the fly who are aware of time-of-day parking information and comply to the recommendations; and c) influence the market prices of privately owned parking areas through a competitive parking market.

Cities provide ready and efficient access to facilities and amenities through shared civil infrastructures such as transportation and healthcare. Making such critical infrastructures resilient to sudden changes, e.g., caused by large-scale disasters, requires careful management of limited and varying resources. The rapidly growing big data from both physical sensors and social media in real-time suggest an unprecedented opportunity for information technology to enable increasing efficiency and effectiveness of adaptive resource management techniques in response to sharp changes in supply and/or demand on critical infrastructures. Within the general areas of resilient infrastructures and big data, this project will focus on the integration of heterogeneous Big Data and real-time analytics that will improve the adaptive management of resources when critical infrastructures are under stress. The integration of heterogeneous data sources is essential because many kinds of physical sensors and social media provide useful information on various critical infrastructures, particularly when they are under stress. This Research Coordination Network (RCN) will promote meetings and activities that stimulate and enable new research on integration of heterogeneous physical sensor data and social media for real-time big data analytics in support of resilient critical infrastructures such as transportation and healthcare in smart cities. As first example, the RCN will support participation from young faculty attending the Early Career Investigators' Workshop on Cyber-Physical Systems in Smart Cities (ECI-CPS) at CPSweek (April of each year) and young faculty attending the Workshop on Big Data Analytics for Cyber-physical Systems (BDACPS). As a second example, the RCN will support contributions to a Special Track on Big Data Analytics for Resilient Infrastructures at the IEEE Big Data Congress. As a third example, the RCN will support participation in International meetings organized by other countries, e.g., Japan's Big Data program by Japan Science and Technology Agency (JST). The project will also maintain a repository of research resources. Concretely, the RCN will actively collect and make readily available public data sets (e.g., physical and social sensor data) and software tools (e.g., to support real-time big data analytics). The technologies and tools that arise from RCN-enabled research will be applied to socially and economically impactful areas such as reducing congestion and personalized healthcare in smart cities.

This project aims to accelerate the deployment of security measures for cyber-physical systems (CPSs). A framework is proposed that combines anomaly identification approaches, which emphasizes on the development of decentralized cyber-attack monitoring and diagnostic-like components, with robust control countermeasure to improve reliability and maintain system functionality. Within this framework, the investigators will (1) implement hybrid observers and active attack detection methods exploiting system vulnerabilities; and (2) develop and integrate cyber-attack control countermeasure at the physical system level to guarantee functionality and resiliency in the presence of identified and unidentified threats. Specifically, this project focuses on applications to connected vehicle (CV) systems where vehicles are capable of sharing information via dedicated short range communication network, with the goal of improving fuel efficiency and avoiding collision. The project's final objective would be to create a cyber-secure vehicle connectivity paradigm that incorporates cyber-attack detection algorithms and executes integrated fault-tolerant countermeasures at the vehicle level to support vehicle system resiliency and accelerate the future commercialization of automated vehicles. The research solutions of this project will impact safety, security and reliability of networked CPSs by helping accelerate the adoption of threat identification and attack resilient control countermeasures at the system and network level. The specific application to connected and automated vehicles should lead to a future market acceptance of these vehicle technologies with a potential improvement in traffic conditions, vehicle and personal safety, and energy consumption. This project involves interdisciplinary research in cyber security for the development of more secure, scalable and reliable future networked CPSs. It proposes to conduct fundamental research on a model-based computational strategy that includes: 1) implement advanced threat models in a hybrid systems framework; 2) identify system and communication vulnerabilities especially in the dedicated short range communication network (DSRC) for CVs; 3) derive hybrid observer based cyber-attack detection algorithms based on stochastic quantized models and event triggered estimation; 4) establish active attack detection methods based on system vulnerabilities; 5) develop control counter measures for each CPS based on game theory and robust control methods; 6) derive control algorithms against malicious agents in the CV to avoid vehicle collisions; 7) develop computationally fast and distributed algorithms for the above six objectives; and 8) evaluate through simulation and experimental validation the capabilities and impact on the vehicle of the proposed strategies.