Recent years have seen medical devices go from being monolithic to a collection of integrated systems. Modern medical device systems have thus become a distinct class of cyber-physical systems called Medical Cyber Physical Systems (MCPS), featuring complex and close interaction of sophisticated treatment algorithms with the physical aspects of the system, and especially the patient whose safety is of the utmost concern. The goal of this project is to develop a new paradigm for the design and implementation of safe, secure, and reliable MCPS, which includes:
Telerobotic systems, such as those used in rescue operations, remotely-operated vehicles or the next-generation robotic surgery, human operators interact with robots through a communi- cation network.
The objective of this study is to develop a high-performance and robust neural-machine interface (NMI) for artificial legs, which can accurately and reliably identify user intent in real-time.
Cyber-Physical Systems (CPS) are deployed in a wide variety of safety critical applications from avionics, medical, and automotive domains. For these applications, it is essential to create a precise specification and formally verify that the implementation behaves as specified. The formal verification of these systems presents a wide variety of challenges. Models of these systems must represent the physical world, analog sensors and actuators, computer hardware and software, networks, and feedback control.
Motivation: Energy infrastructure is a critical underpinning of modern society. To ensure its reliable operation, a nation--wide or continent--wide situational awareness system is essential to provide high--resolution understanding of the system dynamics such that proper actions can be taken in real--time in response to power system disturbances and to avoid cascading blackouts. The power grid represents a typical highly dynamic cyber--physical system (CPS).
Our overarching goal is to develop a framework for design automation of cyber-physical systems that augment human-in-the-loop inference and interaction by complex systems operating at the interface of computation and physical environment.
The objective of this research is to develop new principles for creating and comparing models of skilled human activities, and to apply those models to systems for teaching, training and assistance of humans performing these activities. The models investigated will include both hybrid systems and language-based models. The research will focus on modeling surgical manipulations during robotic minimally invasive surgery. Models for expert performance of surgical tasks will be derived from recorded motion and video data.
Fault tolerance is vital to ensuring the integrity and availability of safety critical systems. Current solutions are based almost exclusively on physical redundancy at all levels of the design. The use of physical redundancy, however, dramatically increases system size, complexity, weight, and power consumption.
Motivation: Reliable and resilient operation of cyber-physical systems (CPS) of societal importance such as Smart Electric Grids is critical. Efficient models and tools to perform timely fault diagnostics and prognostics are needed for curtailing systemic failures such as power blackouts. Varying system state caused by the fluctuating power consumption, dynamic control actions, physical component degradation, and interactions with possible software anomalies make the failure analysis, prediction, and mitigation difficult.
