Processors in cyber-physical systems are increasingly being used in applications where they must operate in harsh ambient conditions and a computational workload which can lead to high chip temperatures. Examples include cars, robots, aircraft and spacecraft. High operating temperatures accelerate the aging of the chips, thus increasing transient and permanent failure rates. Current ways to deal with this mostly turn off the processor core or drastically slow it down when some part of it is seen to exceed a given temperature threshold. However, this pass/fail approach ignores the fact that (a) processors experience accelerated aging due to high temperatures, even if these are below the threshold, and (b) while deadlines are a constraint for real-time tasks to keep the controlled plant in the allowed state space, the actual controller response times that will increase if the voltage or frequency is lowered (to cool down the chip) are what determine the controlled plant performance. Existing approaches also fail to exploit the tradeoff between controller reliability (affected by its temperature history) and the performance of the plant. This project addresses these issues. Load-shaping algorithms are being devised to manage thermal stresses while ensuring appropriate levels of control quality. Such actions include task migration, changing execution speed, selecting an alternative algorithm or software implementation of control functions, and terminating prematurely optional portions of iterative tasks. Validation platforms for this project include automobiles and unmanned aerial vehicles. These platforms have been chosen based on both their importance to society and the significant technical challenges they pose. With CPS becoming ever more important in our lives and businesses, this project which will make CPS controllers more reliable and/or economical has broad potential social and economic impacts. Collaboration with General Motors promotes transition of the new technology to industry. The project includes activities to introduce students to thermal control in computing, in courses spanning high-school, undergraduate and graduate curricula.

The problem of controlling biomechatronic systems, such as multiarticulating prosthetic hands, involves unique challenges in the science and engineering of Cyber Physical Systems (CPS), requiring integration between computational systems for recognizing human functional activity and intent and controlling prosthetic devices to interact with the physical world. Research on this problem has been limited by the difficulties in noninvasively acquiring robust biosignals that allow intuitive and reliable control of multiple degrees of freedom (DoF). The objective of this research is to investigate a new sensing paradigm based on ultrasonic imaging of dynamic muscle activity. The synergistic research plan will integrate novel imaging technologies, new computational methods for activity recognition and learning, and high-performance embedded computing to enable robust and intuitive control of dexterous prosthetic hands with multiple DoF. The interdisciplinary research team involves collaboration between biomedical engineers, electrical engineers and computer scientists. The specific aims are to: (1) research and develop spatio-temporal image analysis and pattern recognition algorithms to learn and predict different dexterous tasks based on sonographic patterns of muscle activity (2) develop a wearable image-based biosignal sensing system by integrating multiple ultrasound imaging sensors with a low-power heterogeneous multicore embedded processor and (3) perform experiments to evaluate the real-time control of a prosthetic hand. The proposed research methods are broadly applicable to assistive technologies where physical systems, computational frameworks and low-power embedded computing serve to augment human activities or to replace lost functionality. The research will advance CPS science and engineering through integration of portable sensors for image-based sensing of complex adaptive physical phenomena such as dynamic neuromuscular activity, and real-time sophisticated image understanding algorithms to interpret such phenomena running on low-power high performance embedded systems. The technological advances would enable practical wearable image-based biosensing, with applications in healthcare, and the computational methods would be broadly applicable to problems involving activity recognition from spatiotemporal image data, such as surveillance. This research will have societal impacts as well as train students in interdisciplinary methods relevant to CPS. About 1.6 million Americans live with amputations that significantly affect activities of daily living. The proposed project has the long-term potential to significantly improve functionality of upper extremity prostheses, improve quality of life of amputees, and increase the acceptance of prosthetic limbs. This research could also facilitate intelligent assistive devices for more targeted neurorehabilitation of stroke victims. This project will provide immersive interdisciplinary CPS-relevant training for graduate and undergraduate students to integrate computational methods with imaging, processor architectures, human functional activity and artificial devices for solving challenging public health problems. A strong emphasis will be placed on involving undergraduate students in research as part of structured programs at our institution. The research team will involve students with disabilities in research activities by leveraging an ongoing NSF-funded project. Bioengineering training activities will be part of a newly developed undergraduate curriculum and a graduate curriculum under development. The synergistic research plan has been designed to advance CPS science and engineering through the development of new computational methods for dynamic activity recognition and learning from image sequences, development of novel wearable imaging technologies including high-performance embedded computing, and real-time control of a physical system. The specific aims are to: (1) Research and develop spatio-temporal image analysis and pattern recognition algorithms to learn and predict different dexterous tasks based on sonographic patterns of muscle activity. The first aim has three subtasks designed to collect, analyze and understand image sequences associated with functional tasks. (2) Develop a wearable image-based biosignal sensing system by integrating multiple ultrasound imaging sensors with a low-power heterogeneous multicore embedded processor. The second aim has two subtasks designed to integrate wearable imaging sensors with a real-time computational platform. (3) Perform experiments to evaluate the real-time control of a prosthetic hand. The third aim will integrate the wearable image acquisition system developed in Aim 2, and the image understanding algorithms developed in Aim 1, for real-time evaluation of the control of a prosthetic hand interacting with a virtual reality environment. Successful completion of these aims will result in a real-time system that acquires image data from complex neuromuscular activity, decodes activity intent from spatiotemporal image data using computational algorithms, and controls a prosthetic limb in a virtual reality environment in real time. Once developed and validated, this system can be the starting point for developing a new class of sophisticated control algorithms for intuitive control of advanced prosthetic limbs, new assistive technologies for neurorehabilitation, and wearable real-time imaging systems for smart health applications.

Reliable operation of cyber-physical systems (CPS) of societal importance such as Smart Electric Grids is critical for the seamless functioning of a vibrant economy. Sustained power outages can lead to major disruptions over large areas costing millions of dollars. Efficient computational techniques and tools that curtail such systematic failures by performing fault diagnosis and prognostics are therefore necessary. The Smart Electric Grid is a CPS: it consists of networks of physical components (including generation, transmission, and distribution facilities) interfaced with cyber components (such as intelligent sensors, communication networks, and control software). This grant provides funding to develop new methods to build models for the smart grid representing the failure dependencies in the physical and cyber components. The models will be used to build an integrated system-wide solution for diagnosing faults and predicting future failure propagations that can account for existing protection mechanisms. The original contribution of this work will be in the integrated modeling of failures on multiple levels in a large distributed cyber-physical system and the development of novel, hierarchical, robust, online algorithms for diagnostics and prognostics. If successful, the model-based fault diagnostics and prognostics techniques will improve the effectiveness of isolating failures in large systems by identifying impending failure propagations and determining the time to critical failures that will increase system reliability and reduce the losses accrued due to failures. This work will bridge the gap between fault management approaches used in computer science and power engineering that are needed as the grid becomes smarter, more complex, and more data intensive. Outcomes of this project will include modeling and run-time software prototypes, research publications, and experimental results in collaborations with industry partners that will be made available to the scientific community.

Reliable operation of cyber-physical systems (CPS) of societal importance such as Smart Electric Grids is critical for the seamless functioning of a vibrant economy. Sustained power outages can lead to major disruptions over large areas costing millions of dollars. Efficient computational techniques and tools that curtail such systematic failures by performing fault diagnosis and prognostics are therefore necessary. The Smart Electric Grid is a CPS: it consists of networks of physical components (including generation, transmission, and distribution facilities) interfaced with cyber components (such as intelligent sensors, communication networks, and control software). This grant provides funding to develop new methods to build models for the smart grid representing the failure dependencies in the physical and cyber components. The models will be used to build an integrated system-wide solution for diagnosing faults and predicting future failure propagations that can account for existing protection mechanisms. The original contribution of this work will be in the integrated modeling of failures on multiple levels in a large distributed cyber-physical system and the development of novel, hierarchical, robust, online algorithms for diagnostics and prognostics. If successful, the model-based fault diagnostics and prognostics techniques will improve the effectiveness of isolating failures in large systems by identifying impending failure propagations and determining the time to critical failures that will increase system reliability and reduce the losses accrued due to failures. This work will bridge the gap between fault management approaches used in computer science and power engineering that are needed as the grid becomes smarter, more complex, and more data intensive. Outcomes of this project will include modeling and run-time software prototypes, research publications, and experimental results in collaborations with industry partners that will be made available to the scientific community.

The objective of this proposal is to develop a distributed algorithmic framework, supported by a highly fault-tolerant software system, for executing critical transmission-level operations of the North American power grid using gigantic volumes of Synchrophasor data. As the number of Phasor Measurement Units (PMU) increases to more than thousands in the next 4-5 years, it is rather intuitive that the current state-of-the-art centralized communication and information processing architecture of Wide-Area Measurement System (WAMS) will no longer be sustainable under such data-explosion, and a completely distributed cyber-physical architecture will need to be developed. The North American Synchrophasor Initiative (NASPI) is currently addressing this architectural aspect by developing new communication and computing protocols through NASPI-net and Phasor Gateway. However, very little attention has been paid so far to perhaps the most critical consequence of this envisioned distributed architecture "namely", distributed algorithms, and their relevant middleware. Our primary task, therefore, will be to develop parallel computational methods for solving real-time wide-area monitoring and control problems with analytical investigation of their stability, convergence and robustness properties, followed by their implementation and testing against extraneous malicious attacks using our WAMS-RTDS testbed at NC State. In particular, we will address three critical research problems "namely" distributed wide-area oscillation monitoring, transient stability assessment, and voltage stability monitoring. The intellectual merit of this research will be in establishing an extremely timely application area of the PMU technology through its integration with distributed computing and optimal control. It will illustrate how ideas from advanced ideas from numerical methods and distributed optimization can be combined into power system monitoring and control applications, and how they can be implemented via fault-tolerant computing to maintain grid stability in face of catastrophic cyber and physical disturbances. The broader impact of this project will be in providing a much-needed application of CPS engineering to advance emerging research on PMU-integrated next-generation smart grids. Research results will be broadcast through journal publications, jointly organized graduate courses between NC State and University of Illinois Urbana Champagne, conference tutorials and workshops. Undergraduate research for minority engineering students will be promoted via the FREEDM Systems Center, summer internships via Information Trust Institute (UIUC) and RENCI, and middle/high-school student mentoring through the NCSU Science House program.

Accurate and reliable knowledge of time is fundamental to cyber-physical systems for sensing, control, performance, and energy efficient integration of computing and communications. This statement underlies the proposal. Emerging CPS applications depend on precise knowledge of time to infer location and control communication. There is a diversity of semantics used to describe time, and quality of time varies as we move up and down the system stack. System designs tend to overcompensate for these uncertainties and the result is systems that may be over designed, inefficient, and fragile. The intellectual merit derives from the new and fundamental concept of time and the holistic measure of quality of time (QoT) that captures metrics including resolution, accuracy, and stability. The proposal builds a system stack ("ROSELINE") that enables new ways for clock hardware, operating system, network services, and applications to learn, maintain and exchange information about time, influence component behavior, and robustly adapt to dynamic QoT requirements, as well as to benign and adversarial changes in operating conditions. Application areas that will benefit from Quality of Time will include: smart grad, networked and coordinated control of aerospace systems, underwater sensing, and industrial automation. The broader impact of the proposal is due to the foundational nature of the work which builds a robust and tunable quality of time that can be applied across a broad spectrum of applications that pervade modern life. The proposal will also provide valuable opportunities to integrate research and education in graduate, undergraduate, and K-12 classrooms. There will be extensive outreach through publications, open sourcing of software, and participation in activities such as the Los Angeles Computing Circle for pre-college students.

In telerobotic applications, human operators interact with robots through a computer network. This project is developing tools to prevent security threats in telerobotics, by monitoring and detecting malicious activities and correcting for them. To develop tools to prevent and mitigate security threats against telerobotic systems, this project adapts cybersecurity methods and extends them to cyber-physical systems. Knowledge about physical constraints and interactions between the cyber and physical components of the system are leveraged for security. A monitoring system is developed which collects operator commands and robot feedback information to perform real-time verification of the operator. Timely and reliable detection of any discrepancy between real and spoofed operator movements enables quick detection of adversarial activities. The results are evaluated on the UW-developed RAVEN surgical robot. This project brings together research in robotics, computer and network security, control theory and machine learning, in order to gain better understanding of complex teleoperated robotic systems and to engineer telerobotic systems that provide strict safety, security and privacy guarantees. The results are relevant and applicable to a wide range of applications, including telerobotic surgery, search and rescue missions, military operations, underwater infrastructure and repair, cleanup and repair in hazardous environments, mining, as well as manipulation/inspections of objects in low earth orbit. The project algorithms, software and hardware are being made available to the non-profit cyber-physical research community. Graduate and undergraduate students are being trained in cyber-physical systems security topics, and K-12, community college students and under-represented minority students are being engaged.

The objective of this proposal is to develop a distributed algorithmic framework, supported by a highly fault-tolerant software system, for executing critical transmission-level operations of the North American power grid using gigantic volumes of Synchrophasor data. As the number of Phasor Measurement Units (PMU) increases to more than thousands in the next 4-5 years, it is rather intuitive that the current state-of-the-art centralized communication and information processing architecture of Wide-Area Measurement System (WAMS) will no longer be sustainable under such data-explosion, and a completely distributed cyber-physical architecture will need to be developed. The North American Synchrophasor Initiative (NASPI) is currently addressing this architectural aspect by developing new communication and computing protocols through NASPI-net and Phasor Gateway. However, very little attention has been paid so far to perhaps the most critical consequence of this envisioned distributed architecture "namely", distributed algorithms, and their relevant middleware. Our primary task, therefore, will be to develop parallel computational methods for solving real-time wide-area monitoring and control problems with analytical investigation of their stability, convergence and robustness properties, followed by their implementation and testing against extraneous malicious attacks using our WAMS-RTDS testbed at NC State. In particular, we will address three critical research problems "namely" distributed wide-area oscillation monitoring, transient stability assessment, and voltage stability monitoring. The intellectual merit of this research will be in establishing an extremely timely application area of the PMU technology through its integration with distributed computing and optimal control. It will illustrate how ideas from advanced ideas from numerical methods and distributed optimization can be combined into power system monitoring and control applications, and how they can be implemented via fault-tolerant computing to maintain grid stability in face of catastrophic cyber and physical disturbances. The broader impact of this project will be in providing a much-needed application of CPS engineering to advance emerging research on PMU-integrated next-generation smart grids. Research results will be broadcast through journal publications, jointly organized graduate courses between NC State and University of Illinois Urbana Champagne, conference tutorials and workshops. Undergraduate research for minority engineering students will be promoted via the FREEDM Systems Center, summer internships via Information Trust Institute (UIUC) and RENCI, and middle/high-school student mentoring through the NCSU Science House program.

This grant provides funding for the formulation of a data model, and trajectory planning platform and methodology to execute a fully digital 3D, 5-axis machining capability. Research will be performed on methods for utilizing multiple Graphical Processor Units (GPUs), which are readily available, parallel digital processing hardware, in these calculations. The methodology will be implemented in the context of an existing advanced computational framework that has tools for voxelization, variable resolution digital modeling, and parallel computing, integrating the fields of manufacturing and computer science. Experiments involving 5-axis machining will be executed to validate the methodology. Components will be machined and inspected on a coordinate measurement machine to verify that the target geometry has been achieved. If successful, this work will bring classical subtractive manufacturing back into the arsenal of rapid prototyping, providing users of typical CNC machine tools with the ability to rapidly determine if a part can be produced on a specific machine and machine the part. Having such a design and analysis tool will help to reduce the cost, improve the quality and allow rapid deployment of new innovations in components that require machining. This work will contribute to variable resolution digital representations to be employed in next generation digital manufacturing systems. It will also combine state-of-the-art concepts in computing and manufacturing to realize a completely new a cyber-physical approach to manufacturing.

This project will result in fundamental physical and algorithmic building blocks of a novel cyber-physical for a two-way communication platform between handlers and working dogs designed to enable accurate training and control in open environments (eg, disaster response, emergency medical intervention). Miniaturized sensor packages will be developed to enable non- or minimally-invasive monitoring of dogs' positions and physiology. Activity recognition algorithms will be developed to blend data from multiple sensors. The algorithms will dynamically determine position and behavior from time series of inertial and physiological measurements. Using contextual information about task performance, the algorithms will provide duty-cycling information to reduce sensor power consumption while increasing sensing specificity. The resulting technologies will be a platform for implementation of communication. Strong interactions among computer science, electrical engineering, and veterinary science support this project. Work at the interface between electrical engineering and computer science will enable increased power efficiency and specificity of sensing in the detectors; work at the interface of electrical engineering and veterinary behavior will enable novel physiological sensing packages to be developed which measure behavioral signals in real time; Project outcomes will enable significant advances in how humans interact with both cyber and physical agents, including getting clearer pictures of behavior through real time physiological monitoring. Students are part of the project and multidisciplinary training will help to provide development of the Cyber-Physical Systems pipeline. Project outreach efforts will include working with middle school children, especially women and under-represented minorities, presentations in public museums that will promote public engagement and appreciation of the contribution of cyber-physical systems to daily lives. The goal of each outreach activity is to encourage both interest and excitement for STEM topics, demonstrating how computer science and engineering can lead to effective and engaging cyber-physical systems.