This Cyber-Physical Systems (CPS) grant will support research that will contribute new knowledge related to emerging monitoring and control techniques of the growth of nanomaterials, which are crucial for applications such as new types of batteries and photovoltaic devices, because precise structuring of matter is essential to realize the desired charge, mass, and energy flow patterns that underpin energy conversion and storage. With the fast arrival of tremendous amount of data produced by dynamic nanoscale imaging, the National Nanotechnology Initiative has identified the lack of in-process monitoring and control as a grand challenge impeding the design and discovery of new materials, because "existing methods are time-consuming, expensive, and require high-tech infrastructure and high skill levels to perform." This grant supports a multidisciplinary team, comprising experts from data science, control, circuit design, and material sciences, aiming to tackle this challenge by designing a cyber-physical system that can reliably convert dynamic imaging data to machine intelligible information for process monitoring and control. The results from this research will benefit nanomaterial discovery and pave a path to scalable production. The multidisciplinary approach will help broaden participation of underrepresented groups in research and positively impact science and engineering education.

Autonomous systems such as drones and self-driving cars are quickly entering human-dominated fields and becoming tangible technologies that will impact the human experience. However, as these systems share space and operate among humans, safety and reliability of autonomous systems become primary concerns. An important challenge for safety and reliability in autonomous systems is coping with uncertainty. This project focuses on three important forms of uncertainty: (1) noisy data from sensors, (2) asynchrony of distributed computation, and (3) heuristic computation of decision-making software. They bring various challenges for developing and validating software modules of autonomous systems.

Society depends on the interconnection of systems including hardware and software. They make up the built environment and the infrastructure that we depend upon. Today?s systems are subject to an increasing number of hazards and disasters both natural or manmade often leading to failures that have major impact on society. We want to know how to design systems to avoid such failures and how to bounce back quickly if such failures occur.
The workshop will reflect on the current state-of-art and state-of-practice for the above two questions. It will then bring out the research and the translational challenges to make our infrastructures truly resilient. This workshop will be hosted in a hybrid mode, with both in-person and virtual participation. The workshop will bring together external thought and action leaders in the area of resilient systems, drawn from universities, federal laboratories, and commercial organizations and providing multi-disciplinary and convergent perspectives. The workshop will be broad-based considering areas of resilient and adaptive cyberinfrastructures, resilient cyber-physical systems, and scientific foundations of resilient socio-technical systems. The workshop will be hosted by Purdue?s Center for Resilient Infrastructures, Systems, and Processes and will address three broad technical themes. An objective is to develop technology for research concepts suitable for a near-term (1-5 year) and mid-term (5-10 years) considering theoretical and practical advancements in technology.

As technology advances and an increasing number of devices enter our homes and workplace, humans have become an integral component of cyber-physical systems (CPS). One of the grand challenges of cyber-physical human systems (CPHS) is how to design autonomous systems where human-system collaboration is optimized through improved understanding of human behavior. A new frontier within this landscape is afforded by the advent of low-cost, low-power millimeter-wave radio frequency (RF) transceivers, which can be exploited almost anywhere as part of the Internet-of-Things, smart environments, and personal devices. RF sensors provide a unique, information rich dataset of high-resolution measurements of distance, direction-of-arrival, and micro-Doppler signature in a non-contact, non-intrusive fashion in most weather conditions and in the dark. This CAREER project aims to pave the way for new and innovative RF-enabled CPHS applications in service of society and a better quality-of-life by transforming current fixed-transmission RF sensors into intelligent devices that can autonomously respond to human and environmental dynamics to optimize CPHS performance. Due to the burgeoning commercial sector utilizing radar across a variety of fields, such as transportation, health and human-computer interaction, this project features integrated academic preparation for multi-disciplinary, convergence research at both undergraduate and graduate levels to educate a new generation of engineers with experience in RF sensing, machine learning, signal processing and CPHS applications. Through K-12 outreach activities and recruiting at local historically black colleges and universities (HBCUs), this project will enrich and motivate students to study STEM fields, laying the foundations for a diverse and globally competitive STEM workforce for the future.

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.