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 Future of Work at the Human-Technology Frontier (FW-HTF) is one of 10 new Big Ideas for Future Investment announced by the National Science Foundation. The FW-HTF cross-directorate program aims to respond to the challenges and opportunities of the changing landscape of jobs and work by supporting convergent research. This award fulfills part of that aim. Effective decision making by power grid operators in extreme events (e.g., Hurricane Maria in Puerto Rico, the Ukraine cyber attack) depends on two factors: operator knowledge acquired through training and experience, and appropriate decision support tools. Decision making in electric grid operation during extreme adverse events directly impacts the life of citizens. This project will augment the cognitive performance of human operators with new, human-focused decision support tools and better, data-driven training for managing the grid especially under highly disruptive conditions. The development of new generation of tools for online knowledge fusion, event detection, cyber-physical-human analysis in operational environment can be applied during extreme events and provide energy to critical facilities like hospitals, city halls and essential infrastructure to keep citizens safe and avoid economic loss for the Nation. Higher performance of operators will improve worker quality of life and will enhance the economic and social well-being of the country. The project's training objectives will leverage existing educational efforts and outreach activities and we will publicize the multidisciplinary outcomes through multiple venues. The proposed project will integrate principles from cognitive neuroscience, artificial intelligence, machine learning, data science, cybersecurity, and power engineering to augment power grid operators for better performance. Two key parameters influencing human performance from the dynamic attentional control (DAC) framework are working memory (WM) capacity, the ability to maintain information in the focus of attention, and cognitive flexibility (CF), the ability to use feedback to redirect decision making given fast changing system scenarios. The project will achieve its goals through analyzing WM and CF and performance of power grid operators during extreme events; augmenting cognitive performance through advanced machine learning based decision support tools and adaptive human-machine system; and developing theory-driven training simulators for advancing cognitive performance of human operators for enhanced grid resilience. A new set of algorithms have been proposed for data-driven event detection, anomaly flag processing, root cause analysis and decision support using Tree Augmented naive Bayesian Net (TAN) structure, Minimum Weighted Spanning Tree (MWST) using the Mutual Information (MI) metric, and unsupervised learning improved for online learning and decision making. Additionally, visualization tools have been proposed using cognitive factor analysis and human error analysis. We propose a training process driven by cognitive and physiometric analysis and inspired by our experience in operators training in multiple domain: the power grid, aircraft and spacecraft flight simulators. A systematic approach for human operator decision making is proposed using quantifiable human and engineering analysis indices for power grid resiliency.

This project addresses timing attacks in cyber-physical systems, where attackers attempt to compromise the system functionality by changing the timing of computation and communication operations. Timing attacks could be particularly destructive for cyber-physical systems because the correctness of system functionality is affected not only by the data values of operations but also significantly by at what time operations are conducted. The discoveries and methodologies developed in this project will provide fundamental advances in addressing timing attacks, and lead to the design and implementation of more secure cyber-physical systems in a number of key sectors, including automotive and transportation systems, industrial automation, and robotics. In addition to disseminate the research results through publications and workshops, the PIs will collaborate with industry partners on transitioning the research findings into practice. The PIs will also integrate the research into the curriculum at UCR and leverage it for K-12 education through the use of Lego Mindstorm platforms. The project will build a framework for identifying, analyzing and protecting cyber-physical systems against timing attacks. Building the framework consists of three closely-related research thrusts: 1) Investigate potential timing-based attack surface, and further analyze what types and patterns of timing variations the attacks may cause and how attackers may try to hide the traces of such attacks. 2) Based on the identified attack surface and strategies, analyze how timing changes caused by these attacks may affect the overall system properties, in particular safety, stability and performance. 3) Develop control-based and cyber-security defense strategies against timing attacks. This includes run-time security detectors and mitigation/adaptation strategies across control layer and embedded system layer, as well as design-time mechanisms to provide systems that are resilient to timing attacks. This project will focus on vehicle networks and multi-agent robotic systems as main application domains.

Good algorithmic foundations for flight planning on the scale required for managing dense urban drone traffic we can expect to see in the future are currently still missing. This project provides prototype algorithms for managing this dense drone traffic. The project develops a concept for a coordination system that is able to find collision-free paths for a large number of flying unmanned air vehicles of different size and capability. It uses a hierarchical approach, combining centralized and local coordination, to manage complexity for a large-scale problem. The approach developed here can scale up to handle thousands of drones and lead to conflict free flight. It demonstrates the concept using mixed-reality simulations and using existing helicopter-like robots on a smaller scale. Current multi-robot trajectory-planning algorithms typically operate on a single level (which limits their scalability) and assume holonomic robots that can hover motionlessly (which limits their applicability). The core of the project is the development of a novel hierarchical system that addresses these limitations, combining centralized methods with a divide-and-conquer approach. The hierarchical approach allows the system to negotiate collision-free trajectories on a local level, while ensuring that robots complete their tasks on the global level. Additional research integrates several speed-up techniques into the hierarchical system and generalizes its functionality, for example, to accommodate robots of different priorities (such as drones that deliver blood to hospitals). The research involves not only graduate but also undergraduate students and trains them in cross-disciplinary research.

Complex engineered systems that can adapt to their environments while maintaining safety guarantees are crucial in many applications including Internet-of-Things, transportation, and electric power systems. The primary objective of this project is to develop a scalable design methodology to control very large collections of systems to achieve common objectives despite cyber and physical constraints. As an application, the electric load control problem, in which the goal is to coordinate the power consumption of thousands of small electric loads like air conditioners and refrigerators to help the grid balance supply and demand without inconveniencing electricity consumers and while respecting the physical limitations of the power distribution network, will be considered. The research results will support the integration of more wind and solar power, improving the grid's environmental and health impacts. Education and outreach activities will involve K-12, undergraduate, and graduate students along with stakeholders from local power companies. The key characteristics of the problems considered are a large number of dynamically almost decoupled systems. Each system has their local requirements and constraints and they are coupled through requirements about their collective behavior. A bi-level control architecture will be developed that can handle soft performance requirements and allow adaptability at the upper-level, and that guarantees the satisfaction of hard safety requirements at the lower-level. The lower-level will exploit structural properties symmetries of the systems and requirements, in particular, permutation invariance, to enable highly scalable synthesis methods to ensure safety. The upper-level will leverage adaptation/learning to improve system performance when control inputs are overridden for the purpose of safety.

This research examines the scientific foundations for modeling security and privacy trade-offs in cyber-physical systems, focusing in particular on settings where privacy-protection technologies might be abused by malicious parties to hide their attacks. The goal is to provide both security and privacy guarantees for a variety of cyber-physical systems including intelligent transportation systems, smart energy, and autonomous vehicles. Privacy and security in cyber-physical systems have been studied independently before, but often they have not been addressed jointly. This project will study privacy-protection mechanisms such as differential privacy, and explore how using such mechanisms can affect the state-of-art integrity and attack-detection mechanisms. The project will also develop novel defenses including: 1) Identifying fundamental trade-offs between privacy and security based theoretical analyses of privacy, control theory, and optimization methods, with applications such as traffic-density estimation and smart grids; 2) incorporating game-theoretic considerations in analyzing adversarial strategies; and 3) Proposing new privacy-preserving techniques applicable in cyber-physical systems and beyond.

This project tackles the following question: "Can a network of mutually-distrusting devices perform resilient inference and computation while detecting anomalous behaviors despite heterogeneity in the types of data they sense, the networking technologies they use and their computational capabilities?" The context is the increasingly pervasive Internet of Things (IoT) with low-power end users or sensors relying on edge devices to process their data, and possibly the cloud. However, IoT brings forth a unique challenge, namely, the extreme heterogeneity at multiple levels: data sensed, communication technologies used (WiFi, Bluetooth, Zigbee), and computational capabilities, making it particularly vulnerable to security threats. The goal of this project is to develop a resilient IoT system and applications, with a focus on distributed inference and computing in the presence of threats, from injection of anomalous data to impersonation of the sensors themselves. The system will be demonstrated at scale through a heterogeneous and sensor-rich campus-scale IoT deployment. The proposed testbed offers a rich platform to engage Masters and undergraduate students as well as high-schoolers through outreach programs at the Carnegie Mellon University, e.g., Engineering@CMU, SPARK Saturday, and Project Ignite. Specifically, the project aims to develop novel methodological foundations and a cross-layer system design for secure distributed computing and inference and anomaly detection in the IoT. The proposed approach exploits heterogeneous sensing data at the end-user agents and their interaction with edge devices, to provide resilience to broad classes of Byzantine adversarial scenarios and Sybil attacks. The proposed distributed algorithms yield guarantees on attaining desired computation and inference objectives under broad conditions on the data and sensing models and inter-agent connectivity. To defend against Sybil attacks that violate standard assumptions for Byzantine fault tolerance, the project aims to develop a technology-agnostic wireless fingerprinting based solution to detect anomalous devices and transmissions. The proposed solution involves a novel design of a deep neural network to extract wireless fingerprints cutting across radio technologies.

Due to low operating cost and patient safety, ultrasound is widely accepted as one of the best forms of medical imaging compared to similar technologies, such as Computer Tomography (CT) scans or Magnetic Resonance Imaging (MRI). Still, there can be large variability in image quality obtained by different experts imaging the same patient, which can affect successful diagnosis and patient treatment. This problem becomes even more pronounced across patients. Consequently, to decrease this variability this project will develop imaging techniques that are not passive but are based on real-time ultrasound beam control and adaptation, while facilitating best use of operator expertise to obtain the most informative images. Such new active ultrasound systems, where expert users with varying levels of training interact with a smart ultrasound device to improve medical imaging and facilitate diagnosis, will provide significant performance gains compared to present systems that are only manually controlled. This project will also have a significant societal impact in accurate, safe, and cost-effective diagnosis of many medical conditions, such as cancers or liver fibrosis. For instance, the use of such systems for breast cancer diagnosis will significantly reduce the number of unnecessary biopsies, which currently cost more than $1 billion annually in the US alone. At the same time this technology can enable a variety of other imaging applications that rely on different forms of ultrasound, such as mapping of the heart chambers using Doppler ultrasound or identifying the mechanical properties of materials in structures for failure prognosis. Specifically, the goal of this project is the development of an active ultrasound system where user expertise is employed to refine the control process, while autonomous elasticity (or viscoelasticity) mapping improves image quality and allows human operator to best use their skills for both optimization and diagnosis. The project's research products include: (i) data fusion techniques for ultrasound elastography; (ii) methods for interactive ultrasound elastography; and (iii) framework for safe and efficient device implementation. The ultrasound system will be validated on a test-bed based on suitable laboratory phantoms and real-time control of existing ultrasound devices. Investigators will focus on the unique aspects of this novel paradigm that, compared to existing methods, include: (1) new active, user-machine, imaging techniques improving on the characterization of the mechanical properties of tissue; and (2) the systematic transition of algorithms and user interfaces to embedded computers for safe execution by the device. This requires overcoming intellectual challenges related to the integration of visco-elastography mapping and human-on-the-loop ultrasound control, as well as synthesis of new theoretical results drawing from computational mechanics, controls and estimation, and embedded systems design. The project also has extensive education and outreach components, including curriculum development focused on design of safety-critical medical cyber-physical systems that exhibit highly dynamical system behaviors and plant uncertainty, human interactions, and the need for real-time implementation. The outreach component of this project will also improve the pre-college students' awareness of the potential and attractiveness of a research and engineering career.

This research examines the scientific foundations for modeling security and privacy trade-offs in cyber-physical systems, focusing in particular on settings where privacy-protection technologies might be abused by malicious parties to hide their attacks. The goal is to provide both security and privacy guarantees for a variety of cyber-physical systems including intelligent transportation systems, smart energy, and autonomous vehicles. Privacy and security in cyber-physical systems have been studied independently before, but often they have not been addressed jointly. This project will study privacy-protection mechanisms such as differential privacy, and explore how using such mechanisms can affect the state-of-art integrity and attack-detection mechanisms. The project will also develop novel defenses including: 1) Identifying fundamental trade-offs between privacy and security based theoretical analyses of privacy, control theory, and optimization methods, with applications such as traffic-density estimation and smart grids; 2) incorporating game-theoretic considerations in analyzing adversarial strategies; and 3) Proposing new privacy-preserving techniques applicable in cyber-physical systems and beyond.

Nowadays, anyone can buy and put together sensors, actuators, and computation components, but typically only highly trained engineers are able to compose systems that can autonomously perform complex tasks. This project makes the design of cyber-physical systems (CPS) accessible to anyone by creating computational tools that enable people to choose a set of building blocks and define what a system should do. The tools then automatically create a simple and easy to understand description of how to assemble the components and provide the control needed to accomplish the task. If the task cannot be done with a single system, the tools provide either multiple systems that need to be assembled and/or explanations as to why the task cannot be done, for example due to physical constraints. The project includes designing a competition to accelerate the development of design tools, and mentoring of students from underrepresented groups. Inspired by advances in program synthesis, control synthesis and modular CPS, this project (i) defines formal specifications and synthesis processes for CPS whose task requires motion in the physical environment, and (ii) creates automated design tools that synthesize both the structure and control of the CPS and that guarantee either full or partial task satisfaction. The formalisms and tools are based on the Syntax-Guided Synthesis (SyGuS) paradigm where the design space is reduced by considering additional structure and leverages computational methods from satisfiability-modulo-theories (SMT) solvers to program synthesis tools, inverse kinematics solvers, motion planners and design optimization. The tools are evaluated on two physical and two simulated platforms.