The safety and performance of cyber-physical systems (CPS) depend crucially on control and scheduling decisions that often are fixed at design time, which significantly restricts the conditions under which a system can operate both safely and with suitable performance. Going beyond prior work that has explored different control and scheduling adaptations in individual system designs, this project will conduct more general and in-depth investigations, into how cyber-physical systems? control and scheduling can be co-designed to adapt jointly, automatically, dynamically, safely, and effectively even in response to rapid, large, and diverse changes in: (1) the system's controlled behavior; (2) its environment; (3) its physical components; and (4) its platform software and hardware. Our project will immerse multiple graduate students in cross-disciplinary research, with extensive education, training, and mentoring spanning computer science, control theory, natural hazards engineering, structural engineering, mechanical engineering, and computer engineering. We will also involve undergraduate students via summer REU supplements and in-semester mentored independent study projects for academic credit, and will leverage our existing initiatives and relationships with partner organizations for K-12 outreach. As we have done in each of our previous collaborations, our multi-university team will recruit, mentor, and retain participants from groups traditionally under-represented in science and technology fields, leveraging effective and established outreach programs at our institutions.

Microbial pathogen transmission in buildings is an urgent public health concern. The pandemic of coronavirus disease 2019 (COVID-19) adds to the urgency of developing effective means to reduce pathogen transmission in public buildings with minimal disruptions in building functions. With the ultimate goal to develop healthy buildings that minimize risks of infectious diseases, this project will develop smart control strategies for buildings and assistive robots to mitigate pathogen transmission and occupant exposure. New techniques will be developed to monitor and predict pathogen spreading, automate building ventilation, enable intelligent recognition of contaminated objects, and perform precision disinfection to reduce pathogen transmission through air circulation and surface contacts. Findings from this project will also guide occupants and facility managers to develop and implement effective behavioral interventions and hygiene practices. If successful, this research will revolutionize the control of built environments to enable protection against infectious diseases, which will have vast public health and economic benefits to the nation. This project will also create new and unique opportunities to stimulate the academic interests of students and support the development of next-generation workforce adequately equipped with interdisciplinary computing and engineering skills needed to address challenges facing the nation.

The proposed research focuses on harnessing the growing fitness spaces in support of form, performance and injury prevention in exercise, therapy, and rehabilitation. The ability to monitor body dynamics and to provide real-time feedback is instrumental in fitness training and injury prevention. Motivated by the lack of fitness training models that understand the key factors related to human motion in the cyber realm, this project aims to leverage the cyber-physical components of fitness monitoring to track and encourage proper movement during strength training. The proposed framework distills body dynamics into form and performance measures that can assist application developers with the design of novel fitness and movement-based applications. This project will create knowledge that can be ported across the fitness, therapy and rehabilitation domains and will have a broad impact in the health community. For example, the findings of this project can potentially help devise a training regimen for breast cancer and other post-surgery rehabilitation patients.

Motivated by the rising caregiver burden and challenges in remote health behavior monitoring, the proposed research will enable effective assistive interventions in response to dynamically changing health behaviors for target populations. To be effective and impactful, assistive mechanisms need to capture and respond to the subtle and changing context of the human. Human behaviors, however, are challenging to learn due to their complexity and the constantly changing physical, social, and environmental context. Recently, wearables have emerged to fill this gap as users are adopting a variety of devices to help them monitor health related parameters. Given their ubiquity, wearables are positioned ideally to deliver persuasive content aimed at improving users? health outcomes. However, there is a need for a holistic approach to infer human health behaviors, even as the user's context and the devices measuring their behavior vary over time. The proposed research has the potential to transform human health outcomes by capturing and responding to fine-grained behavioral information continuously, inexpensively, and unobtrusively. This human-in-the-loop system will facilitate rapid development of Health applications by providing the foundations for using adaptive and personalized interventions for diverse health populations to enable assistive care for all.

Advances in artificial intelligence (AI) make it clear that intelligent systems will account for the next leap in scientific progress to enable a myriad of future applications that improve the quality of life, contribute to the economy, and enhance societal resilience to a broad spectrum of disruptions. Yet, advances in AI come at a considerable resource costs. To reduce the cost of AI, this project takes inspiration from biological systems. It is well-known that a key bottleneck in AI is the perception subsystem. It is the part that allows AI to perceive and understand its surroundings. Humans are very good at understanding what's critical in their environment and the human perceptual system automatically focuses limited cognitive resources on those elements of the scene that matter most, saving a significant amount of ?brain processing power?. Current AI pipelines do not have a similar mechanism, resulting in significantly higher resource costs. The project refactors data analytics and machine intelligence pipelines to allow for better prioritization of external stimuli leveraging and significantly extending advances in scheduling previously developed in the real-time systems research community. The refactored AI pipeline will improve the efficiency and efficacy of AI-enabled systems, allowing them to be safer and more responsive, while at the same time significantly lowering their cost. If successful, the project will help bring machine intelligence solutions to the benefit of all society. This is achieved through interactions between research, education, and outreach, as well as integration of multiple scientific communities, including (i) researchers on embedded computing who offer platforms and schedulers, (ii) researchers on IoT and networking, and (iii) researchers on intelligent applications and application domain experts. The work is an example of cyber-physical computing research, where a new generation of digital algorithms learn to exploit a better understanding of physical systems in order to improve societal outcomes.

Modern society relies heavily on systems that operate within strict timing requirements such as in engine control units in automobiles, aircraft avionics and navigation systems, programmable logic controllers in manufacturing plants, industrial control systems in the electricity sector, and many hundreds of others. The recent advent of autonomous cars, drones and internet-of-things (IoT) further expands the reach of these "real-time systems". The limitations of such devices viz., small computing power, less memory, limited battery power, has serious consequences for security, specifically, they become much harder to protect and defend. This research develops systematic security mechanisms for real-time embedded systems in critical applications to control what can be observed about them.

Computing systems that engage people physically with high degrees of autonomy need to provide rigorous guarantees of safety. Formal methods can been used on such systems to provide mathematical proofs to ensure correct behavior. However, machine learning and data-driven approaches are now an indispensable part of autonomous-systems design, and their reliance on highly nonlinear continuous functions and probabilistic reasoning has largely been at odds with the logical and symbolic-analysis frameworks in formal methods. As a result, the lack of formal assurance has become the key bottleneck that impedes the wider deployment and adoption of autonomous systems. This project targets this open challenge by developing formal synthesis and verification techniques for learning-based and data-driven control and planning methods for autonomous systems.

There is no question that indoor environments are often uncomfortable or unhealthy for occupants. This is an even more critical issue in healthcare facilities, where patients may experience the stressful effects of poor thermal, luminous, and acoustic environments more acutely. With complementary expertise from engineering and psychology, the proposed research is focused on creating a human-on-the-loop, responsive indoor environmental system with the potential to offer better quality of care in hospitals. The outputs of this project will have profound societal impacts on the wellbeing of both healthy individuals and on recovering sick individuals. Research outcomes will enable real time human-built environment interaction to minimize stress and optimize performance in any built environment, and ultimately lead towards economic benefits achieved through wellness and higher productivity. Improved indoor environmental quality in hospital settings will improve patient healing, which is an important societal benefit. Similar strategies can be used for educational facilities, and office buildings. This research encourages Broadening Participation through inclusion of individuals from underrepresented groups (female and Latinx Co-PIs), female and minority students, and a minority serving lead institution from an EPSCoR state. Results will be disseminated broadly through scientific publications and seminars, and K-12 outreach, including STEM competitions, and summer programs.

This project aims to create a cyber physical system for remotely controlling cellular processes in real time and leverage the biomedical potential of synthetic biology and microrobotics to create pancreatic tissue. With 114,000 people currently on the waitlist for a lifesaving organ transplant in the United States alone, the ability to directly produce patient-compatible organs, obviating the need for animal and clinical studies can revolutionize personalized medicine. Tissues in the human body such as liver, kidney, and pancreatic islets comprise cells arranged in complex patterns spanning both 2D and 3D structures. However, scaffold- and microgel-based tissue engineering approaches along with 3D bioprinting are often unable to create these complex 3D structures. In this project, the team focuses on the pancreas, which has a unique anatomical structure composed of the regular arrangement of circular cell clusters called islets. The proposed research aims at overcoming the hurdle of recreating these spatial patterns in vitro by developing a cyber physical process by which swarms of microrobots will be steered in 3D to regulate the differentiation of genetically engineered stem cells and drive these cells into forming desired pancreatic tissue. The broader impacts of this line of work are significant because it is a key first step in the synthesis of new, or the repair of ailing, human organs, providing for interactive behavior between computer controlled microrobots and genetically programmed stem cells. Manufacturing living tissue is revolutionary as it could act as a bridge between preclinical and clinical trials, to ensure better drug testing models and develop more personalized precision medicine. For pancreatic components, in particular, generating human organoids compliant with pharmaceutical standards is an exceptional challenge, and current methods are laborious, time-consuming, expensive, and irreproducible, which has caused industry to shy away from this organ. The education and outreach activities that complement the research component of this project address the need to increase underrepresented minorities (that is, women and under-served populations) in problem-solving research careers, like Engineering in K-12.

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).

Today's increasingly sensorized agricultural farms are composed of sensors and drones generating copious volumes of data. Two trends in computation have catalyzed the "Internet-of-Small-Things", or IoST, in relation to digital and sustainable agriculture. First, the availability of inexpensive sensors that can withstand the rigors of agriculture. Second, the development of approximation algorithms for on-device computation of data analytics algorithms. In parallel, some demanding algorithms can be opportunistically offloaded to edge devices or to the cloud. There is an increasing trend to leverage the data from these "small" sensor nodes to actuate dependable, prompt, and resilient actions. Dependable means the algorithms need to deal with missing or corrupted data, network disruption, and node failures. Prompt refers to low-latency decisions, which are at par with the needs of the farmers or digital agriculture providers. The proposed project, Sirius, brings together IoST with machine learning (ML), and creates a compute fabric that is adaptive to the cyber and the physical conditions, and provides prompt actuation, resilient to noisy sensor nodes and communication channels.