Calibration, or accuracy control, of cyber-physical additive manufacturing systems relies on predictive models for geometric shape deformation. However, learning predictive models is made difficult by the wide variety of possible process conditions and shapes. In addition, resource constraints limit the manufacture of test shapes, which further impedes learning of deformation models for new shape varieties. A methodology that can make full use of data collected on different shapes and reduce the haphazard aspect of traditional learning techniques is necessary in this context.

This project addresses timing attacks in cyber-physical systems, where attackers attempt to compromise the system functionality by changing the timing of computation and communication operations. Timing attacks could be particularly destructive for cyber-physical systems because the correctness of system functionality is affected not only by the data values of operations but

This project aims to develop innovative design automation methodologies and algorithms for software synthesis of cyber-physical systems (CPS), which have applications in key sectors such as automotive, aerospace, healthcare, and industrial automation. Software has become critical and drives future innovations for many CPS, however faces significant challenges in its development, in particular regarding the formulation, exploration and validation of timing constraints.

This project aims at accelerating the deployment of security measures for cyber-physical systems (CPSs) by proposing a framework that combines anomaly identification approaches, which emphasizes on the development of decentralized cyber-attack monitoring and diagnostic-like components, with robust control countermeasure to improve reliability and maintain system functionality. One of the main challenges for cyber physical systems is the security of transmitted data over the communication network.

Start Date: September 1, 2011 The goal of this project is to integrate digital microfluidics systems with thin-film photodetectors in the top plate to realize biochemical target sensing using fluorescence. System control, adaptation, and reconfiguration through software will lead to a general-purpose lab-on-chip computing platform, in the same way as programmable computing devices allow multifunctional capabilities via software on a hardware platform. This level of integration, decision, and controlled reconfigurability will be a significant step forward in clinical diagnostics.

The purpose of this research is to develop optimization and control techniques and integrate them with real-time simulation models to achieve load balancing in complex networks.  Our application case is the regional freight system.  Freight moves on rail and road networks which are also shared by passengers.  These networks today work independently, even though they are highly interdependent, and the result is inefficiencies in the form of congestion, pollution, and excess fuel consumption.

The objective of this research is to create tools to manage uncertainty in the design and certification process of safety-critical aviation systems.  The research focuses on three innovative ideas to support this objective. First, probabilistic techniques will be introduced to specify system-level requirements and bound the performance of dynamical components.  These will reduce the design costs associated with complex aviation systems consisting of tightly integrated components produced by many independent engineering organizations.

Cyber-physical systems (CPSs) have become pervasive in the modern society, enabling transformative applications in the transportation, healthcare and energy sectors. However, the reliable development of CPSs remains an outstanding challenge. At the design level, hybrid systems theory provides a rich set of techniques and tools for ensuring correctness of high level functional properties such as safety and liveness. Current analysis techniques at the implementation level focus primarily on detecting low level runtime errors such as buffer overflows and divide by zero.

The vision of this work is to unite experts in granular mechanics, optimal control, and learning theory in order to define a methodology for advancing cyber-physical systems (CPS) involving a tight coupling of the physical with the cyber through dynamic interactions that must be learned online. The proposed work will advance the science of cyber-physical systems by more explicitly tying sensing, perception, and computing to the optimization and control of physical systems whose properties are variable and uncertain.