While Volatile Organic Compounds (VOC) and ammonia have a place in our daily lives, their leakage into the environment is harmful to human health. In order to prevent and detect gaseous leaks of harmful VOCs, a cyber-physical system (CPS) comprised of ordinary people or first responders is proposed. This CPS uses small, low-cost sensors coupled to smart phones or mobile devices with the necessary computation and communication capabilities.

Visual identification of structural flaws is quite valuable not only to predict an imminent collapse of a bridge, but also to determine effective precautionary measures and repairs.

Contracts have been proposed as a formal mechanism to support compositional design first for complex software (e.g., see [1] and references therein) and later for system design (e.g., see [2] and references therein).

The overall goal of this project is to develop theory, methods and tools necessary to take advantage of data in the design, certification, manufacturing and operation of cyberphysical systems. The central question we ask is “How can we, in a data-rich world, design and operate cyberphysical systems differently?” Data-driven techniques will help transform the design process of cyberphysical systems into one in which data and models—and human designers and operators—continuously and fluently interact.

The recent increase in the variety and usage of wearable sensing systems allows for the continuous monitoring of health and wellness of users. The output of these systems enable individuals to make changes to their personal routines in order to minimize exposures to pollutants and maintain healthy levels of exercise. Furthermore, medical practitioners are using these systems to monitor proper activity levels for rehabilitation purposes and to monitor threatening conditions such as heart arrhythmias.

The overarching research goal of this project is to design a new comprehensive methodology for cyber-security monitoring and mitigation in systems with a multitude of dynamical devices that are prone to cyberattacks. To demonstrate the performance of our proposed algorithms, we study their application for an electric power distribution system as a critical cyberinfrastructure, which includes substations, feeder devices, and smart meters.

The timely and accurate detection and identification of faults in various mechanical structures during their operations can play a vitally important role.  Currently, however, there exist major challenges and barriers.  Because the mechanical structures are continuous with infinitely many possible scenarios/patterns of faults that have extremely small characteristic lengths, the sensory system needs to rely on the usage of high-frequency response signals.