Conference: Proposed Workshop on CPS Rising Stars
John Stankovic
Lead PI:
John Stankovic
Co-PI:
Abstract
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.
Performance Period: 04/01/2023 - 03/31/2024
Institution: University of Virginia
Sponsor: National Science Foundation
Award Number: 2317388
Project URL
Project URL
CPS: DFG Joint: Medium: Collaborative Research: Perceptive Stochastic Coordination in Mass Platoons of Automated Vehicles
Lead PI:
Yaser Fallah
Abstract

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.

Performance Period: 01/01/2020 - 12/31/2023
Institution: University of Central Florida
Sponsor: NSF
Award Number: 1932037
Collaborative Research: CPS: Medium: ASTrA: Automated Synthesis for Trustworthy Autonomous Utility Services
Lead PI:
Yasser Shoukry
Abstract

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.

Performance Period: 04/01/2022 - 03/31/2025
Institution: University of California-Irvine
Sponsor: NSF
Award Number: 2139781
CPS: Medium: Secure Constrained Machine Learning for Critical Infrastructure CPS
Lead PI:
Jinyuan Stella Sun
Co-PI:
Abstract
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.
Performance Period: 02/01/2021 - 01/31/2024
Institution: University of Tennessee Knoxville
Sponsor: National Science Foundation
Award Number: 2038922
Collaborative Research: Cognitive Workload Classification in Dynamic Real-World Environments: A MagnetoCardioGraphy Approach
Lead PI:
Jingzhen Yang
Abstract
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.
Performance Period: 10/01/2023 - 09/30/2026
Institution: The Research Institute at Nationwide Children's Hospital
Sponsor: National Science Foundation
Award Number: 2320491
CAREER: Decision Procedures for High-Assurance, AI-Controlled, Cyber-Physical Systems
Lead PI:
Yasser Shoukry
Abstract

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.

Performance Period: 10/01/2019 - 04/30/2024
Institution: University of California-Irvine
Sponsor: NSF
Award Number: 2002405
CPS: Small: Real-Time Machine Learning-based Control of Human Cyber-Physical Balance Systems
Jingang Yi
Lead PI:
Jingang Yi
Co-PI:
Abstract
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.
Performance Period: 10/01/2019 - 08/31/2024
Institution: Rutgers University
Sponsor: National Science Foundation
Award Number: 1932370
Collaborative Research: CPS: Medium: RUI: Cooperative AI Inferencein Vehicular Edge Networks for Advanced Driver-Assistance Systems
Lead PI:
Jie Wu
Abstract
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.
Performance Period: 10/01/2021 - 09/30/2024
Institution: Temple University
Sponsor: National Science Foundation
Award Number: 2128378
CAREER: System-on-Cloth: A Cloud Manufacturing Framework for Embroidered Wearable Electronics
Lead PI:
Sarah Sun
Abstract

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.

Performance Period: 10/01/2021 - 07/31/2024
Institution: University of Virginia Main Campus
Sponsor: NSF
Award Number: 2222110
CAREER:Formal Synthesis of Provably Correct Cyber-Physical Defense with Asymmetric Information
Lead PI:
Jie Fu
Abstract
For mission-critical Cyber-physical systems (CPSs), it is crucial to ensure these systems behave correctly while interacting with open, dynamic, and uncertain environments. Synthesizing CPSs with assurance is a daunting task: On the one hand, the interconnected networks, sensors, and (semi-) autonomous systems introduce unprecedented vulnerabilities to both cyber- and physical spaces; On the other hand, purposeful attacks may aim to compromise more complex system properties beyond traditional stability and safety.
Performance Period: 06/01/2022 - 05/31/2027
Institution: University of Florida
Sponsor: National Science Foundation
Award Number: 2144113
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