Robotic manipulation and automation systems have received a lot of attention in the past few years and have demonstrated promising performance in various applications spanning smart manufacturing, remote surgery, and home automation. These advances have been partly due to advanced perception capabilities (using vision and haptics) and new learning models and algorithms for manipulation and control. However, state-of-the-art cyber-physical systems remain limited in their sensing and perception to a direct line of sight and direct contact with the objects they need to perceive. The goal of this project is to design, build, and evaluate a cyber-physical system that can sense, perceive, learn, and manipulate far beyond what is feasible using existing systems. To do so, the research will explore the terahertz band, which offers a new sensing dimension by inferring the inherent material properties of objects via wireless terahertz signals and without direct contact. This project will also explore radio-frequency signals that can traverse occlusions. Building on these emerging sensing modalities, the core of the project focuses on developing full-spectrum perception, control, learning, and manipulation tasks. The success of this project will result in CPS system architectures with unprecedented capabilities, enabling fundamentally new opportunities to make robotic manipulation more efficient and allowing robots to perform new complex tasks that have not been possible before.

This project provides a workshop ?Cyber Physical Systems (CPS) Rising Stars? to be held at the University of Virginia. The purpose of the workshop is to identify and mentor outstanding Ph.D. students and postdocs who are interested in pursuing academic careers in CPS areas. This workshop gives participants a chance to gain insights about navigating the early stages of careers in academia, as well as provide networking opportunities with faculty and peers, opening the door for on-going collaboration and professional support for years to come. The workshop includes a program committee comprised of faculty with extensive background in CPS who will select students for the workshop. One workshop aims to increase representation and diversity in CPS, by encouraging applications from women,underrepresented minorities (URM), and persons with disabilities. A specific effort is also being made to recruit workshop participants who may be at non-R1 schools. This will be the second CPS Rising Stars Workshop. This workshop will be modeled after the first including a variety of sessions designed to build a cadre of researchers in CPS and foster future collaborations amongst the students in the field. The first Rising Star workshop was successful in recruiting a high percentage of URM students. This workshop will retain this recruiting focus in addition to paying special attention to selection of non-R1 candidates.

Connected Automated Vehicle (CAV) applications are expected to transform the transportation landscape and address some of the pressing safety and efficiency issues. While advances in communication and computing technologies enable the concept of CAVs, the coupling of application, control and communication components of such systems and interference from human actors, pose significant challenges to designing systems that are safe and reliable beyond prototype environments. Realizing CAV applications, in particular in situations where humans may partly remain in the loop, requires addressing uncertainties that arise from human input. Large scale deployment of CAVs will also require addressing challenges in coordination of actions among CAVs and with human operated systems. To address these challenges, this project develops a novel model-based stochastic hybrid systems (SHS)-theoretic approach that relies on describing and communicating behaviors of actors in the system in the form of evolving SHS using Bayesian learning. The models are then utilized in a stochastic model predictive control (SMPC) framework for optimal coordination of actions. The proposed research will provide wide-ranging societal benefits through three major impact areas: first, by advancing research in stochastic communication-aware control design for hybrid systems; second, through the development of new models and advanced controllers to address the emerging challenges of coordinating mixed systems of automated and manned vehicles, hence opening new vistas in other areas involving general multi-agent systems; and third, through educational and outreach activities that are natural extensions of this multidisciplinary research. This project is also the first fruits of a recent National Science Foundation/Deutsche Forschungs Gesellschaft (NSF/DFG) collaboration on cyber-physical systems (CPS). Through this collaboration, NSF funds the US component (University of Central Florida and University of Georgia) while the German partners (University of Technology and University of Koblenz-Landau) are funded by DFG.

Large-scale systems with societal relevance, such as power generation systems, are increasingly able to leverage new technologies to mitigate their environmental impact, e.g., by harvesting energy from renewable sources. This NSF CPS project aims to investigate methods and computational tools to design a new user-centric paradigm for energy apportionment and distribution and, more broadly, for trustworthy utility services. In this paradigm, distributed networked systems will assist the end users of electricity in scheduling and apportioning their consumption. Further, they will enable local and national utility managers to optimize the use of green energy sources while mitigating the effects of intermittence, promote fairness, equity, and affordability. This project pursues a tractable approach to address the challenges of modeling and designing these large-scale, mixed-autonomy, multi-agent CPSs. The intellectual merits include new scalable methods, algorithms, and tools for the design of distributed decision-making strategies and system architectures that can assist the end users in meeting their goals while guaranteeing compliance with the fairness, reliability, and physical constraints of the design. The broader impacts include enabling the automated design of distributed CPSs that coordinate their decision-making in many applications, from robotic swarms to smart manufacturing and smart cities. The research outcomes will also be used in K-12 and undergraduate STEM outreach efforts.

Machine learning has found many successes in modern commercial application domains like computer vision, speech analysis, and natural language processing. However, its broader use in critical infrastructure cyber-physical systems (CI-CPS), such as, energy, water, transportation, and oil and natural gas systems, has been far less than ideal. This is mainly due to concerns with the reliability of existing machine learning techniques and the lack of explainability of the learned models. Moreover, CI-CPS often borrow techniques directly from commercial applications that fail to consider physical and topological constraints inherent in these systems. Security of machine learning has been extensively studied recently, revealing vulnerabilities of machine learning models and the effectiveness in deviating learning outcomes by polluting the model input. This is especially devastating in CI-CPS where learning is used for safety-critical operations and such deviation can cause irreversible harm to people and physical assets. Secure machine learning that models unique CI-CPS constraints is thus a much needed research area and is the focus of this project. This proposal intersects three fields - security, machine learning, and CI-CPS - to enhance the safety and resiliency of essential infrastructures in modern society. We use two CI-CPS, power systems and transportation systems, as target application domains to illustrate the general applicability of the proposed approach. The proposed work is carried out by four research tasks. First, the project will devise a suitable threat model under which adversarial machine learning attacks, ConAML, are developed subject to CI-CPS constraints. Second, the project will propose a mitigation method for ConAML attacks by introducing random input padding in both training and inference. Third, the project will propose a new ?data-representation-model-task? association framework that realizes secure constrained machine learning from ground up, by designing a variation Dirichlet-network that bridges the input data with machine learning models in the representation space instead of the raw data space. Lastly, the project team will apply the proposed secure constrained machine learning to electric load forecasting and traffic forecasting, implement these applications in testbeds, and evaluate their security and performance under ConAML attacks. The proposed research seeks to improve the security, reliability and resiliency of CI-CPS. It contributes to the knowledge base of secure machine learning for CI-CPS, and applies to all safety-critical large interconnected CPS. The multi-disciplinary nature of the proposed work lends itself to cross-disciplinary education and training of future scientists and engineers.

Cognitive workload refers to the level of mental effort put forth by an individual in response to a cognitive task. Unfortunately, no technology currently exists that can monitor an individual?s levels of cognitive workload in real-world environments using a seamless, reliable, and low-cost approach. We propose to fill this gap by using a novel magnetocardiography (MCG) system worn upon the subject?s chest to allow the sensor to collect the magnetic fields that are naturally emanated by the heart and associated with brain activity. This science is anticipated to greatly accelerate progress in such diverse disciplines as pediatric concussion recovery, pilot training, improved user-machine interfaces, injury prevention in construction environments, increased human performance in risky missions, and improved education outcomes. In addition to advances in basic science, the proposed research is expected to be of significant interest to students and the public. Through targeting interdisciplinary education and diverse recruitment, we intend to expose new audiences to STEM concepts via workshops and family-friendly outings. The proposed MCG sensor is smartly integrated in a Cyber-Physical System (CPS) with two inter-connected loops: (a) a human-in-the-loop that addresses changes in the thresholds of different cognitive states as a function of time, and (b) a non-human-in-the-loop that adapts the system?s algorithmic and hardware components for high-accuracy classification of cognitive workload with minimum resource usage. Our goals are to: (1) Build a knowledgebase concerning the impact of hardware/algorithmic advances upon MCG sensor performance in real-world settings. (2) Explore the classification of cognitive workload from MCG data and close the loop with the wearer for dynamic calibrations that address the time-varying thresholds of cognitive states. (3) Ensure operability in dynamic real-world settings and close the loop between the cyber and physical sides for minimal resource usage. (4) Validate the CPS within the framework of measuring cognitive workload for children with concussion. Without loss of generality, we select this population given the immense clinical potential: the effects of cognitive activity on pediatric concussion recovery are currently unknown, largely due to the difficulties in quantifying cognitive activity workload.

This project explores new mathematical techniques that provide a scientific basis to understand the fundamental properties of Cyber-Physical Systems (CPS) controlled by Artificial Intelligence (AI) and guide their design. From simple logical constructs to complex deep neural network models, AI agents are increasingly controlling physical/mechanical systems. Self-driving cars, drones, and smart cities are just examples of AI-controlled CPS. However, regardless of the explosion in the use of AI within a multitude of CPS domains, the safety and reliability of these AI-controlled CPS is still an under-studied problem. This project includes activities integrated with education, so as to explore how learning through counterexamples works for AI, and to help with critical thinking skills for young students.

The goal of this project is the advancement of machine learning dynamic models and real-time control systems for human cyber-physical balance systems. Ranging from biped walkers and human bicycle riding to human-controlled helicopters, human cyber-physical balance systems maintain challenging tasks of simultaneously trajectory-tracking and unstable platforms balancing. Although many physical models were developed in past decades, it is still challenging to safely and effectively operate these human-in-the-loop balance machines in highly variable, uncertain environments. This project will develop machine learning-based mathematical models and robust control strategies for human cyber-physical balance systems. The researchers will also develop a number of integrated research and education programs to attract students from underrepresented groups into engineering and involve undergraduate students into research. Human cyber-physical balance systems involve human movements as physical and forceful interactions with unstable, underactuated platforms. It is challenging to capture and control physical human-machine or human-robot interactions in complex, uncertain environments. This project will focus on: (1) development of machine learning-based models and characterization for human cyber-physical balance systems; (2) development of new hardware/software co-design accelerated learning-based real-time control to handle human cyber-physical balance system dynamics in highly variable, uncertain environments; and (3) robotic testbeds development, experimental validation and performance evaluation. The integration of data-driven model and learning-based control strategies, along with the hardware/software co-design enabled real-time implementation, provides new perspectives on performance enhancement of safety-critical or mission-critical cyber-physical systems in dynamic, uncertain environments.

Artificial Intelligence (AI) has shown superior performance in enhancing driving safety in advanced driver-assistance systems (ADAS). State-of-the-art deep neural networks (DNNs) achieve high accuracy at the expense of increased model complexity, which raises the computation burden of onboard processing units of vehicles for ADAS inference tasks. The primary goal of this project is to develop innovative collaborative AI inference strategies with the emerging edge computing paradigm. The strategies can adaptively adjust cooperative inference techniques for best utilizing available computation and communication resources and ultimately enable high-accuracy and real-time inference. The project will inspire greater collaborations between experts in wireless communication, edge computing, computer vision, autonomous driving testbed development, and automotive manufacturing, and facilitate AI applications in a variety of IoT systems. The educational testbed developed from this project can be integrated into courses to provide hands-on experiences. This project will benefit undergraduate, master, and Ph.D. programs and increase under-represented groups? engagement by leveraging the existing diversity-related outreach efforts. A multi-disciplinary team with complementary expertise from Rowan University, Temple University, Stony Brook University, and Kettering University is assembled to pursue a coordinated study of collaborative AI inference. The PIs explore integrative research to enable deep learning technologies in resource-constrained ADAS for high-accuracy and real-time inference. Theory-wise, the PIs plan to take advantage of the observation that DNNs can be decomposed into a set of fine-grained components to allow distributed AI inference on both the vehicle and edge server sides for inference acceleration. Application-wise, the PIs plan to design novel DNN models which are optimized for the cooperative AI inference paradigm. Testbed-wise, a vehicle edge computing platform with V2X communication and edge computing capability will be developed at Kettering University GM Mobility Research Center. The cooperative AI inference system will be implemented, and the research findings will be validated on realistic vehicular edge computing environments thoroughly. The data, software, and educational testbeds developed from this project will be widely disseminated. Domain experts in autonomous driving testbed development, intelligent transportation systems, and automotive manufacturing will be engaged in project-related issues to ensure relevant challenges in this project are impactful for real-world applications.

This Faculty Early Career Development Program (CAREER) award will contribute to the advancement of national prosperity and economic welfare by researching systems that improve access to manufacturing services. Wearable electronics are widely used in health monitoring and wearable computing and there is a compelling need for comfort, biocompatibility, and easy operation. Recent progress in smart fabrics, textiles, and garments and the associated manufacturing technologies provides opportunities for next-generation wearable electronic devices that are fabricated on cloth. Automatic embroidery manufacturing is now an accessible tool for individuals and entrepreneurs. Embroidery offers great potential for electronic design due to its flexibility in transferring a desired pattern to fabric substrates. This project aims to establish a cloud manufacturing framework that integrates electronics and design-to-manufacturing translation in a system that can be used by customers, manufacturers, design experts, and developers to design and produce embroidered wearable electronics. In addition, this project also aims to broaden participation from K-12, undergraduate, and graduate students, to provide rich multidisciplinary classroom and non-classroom experiences for all levels of students, and to inspire student interest in STEM careers.