Abstract: Today's computer systems are mostly built with volatile memory components such as flip- flops, SRAM, and SDRAM, except for secondary non-volatile storage. As a result, traditional computer systems quickly lose information stored in memory if the power supply is interrupted even for a short period of time. Unfortunately, such volatile devices present significant limitations for computations on embedded cyber-physical systems that have to rely on unreliable power sources.

Abstract: Analytically predicting the behavior of physical systems is generally not possible. For example, the three dimensional nature of physical systems makes it provably impossible to express closed-­‐form analytical solutions even simple systems. This limitation makes experimentation the primary modality for designing new cyber-­‐physical systems.

Abstract: The objective of this research is to develop methods for the operation and design of cyber physical systems in general, and energy efficient buildings in particular. The approach is to use an integrated framework: create models of complex systems from data; then design the associated sensing-communication-computation-control system; and finally create distributed estimation and control algorithms, along with execution platforms to implement these algorithms.

Abstract: Project Description

Abstract: The American Society of Civil Engineers’ recent report on US infrastructure estimates that a $2.2 trillion investment would be needed to bring them up to par. An estimated 30% of the nation’s 10,000 steel railroad bridges and 600,000 highway bridges are either structurally deficient or functionally obsolete or both. Currently, most bridge inspections are visual only, which has been shown to be ineffective in identifying problems. Acoustic Emission (AE) testing is a comprehensive and effective way of detecting structural damage.

Abstract: This project is developing methods for human context awareness in smart buildings, aging-in-place, smart authorization, and emergency response. We enable this using inference using radio frequency (RF) sensing networks, in which channel measurements are made by deployed wireless networking devices. In RF sensing networks, the network is the sensor. We are developing methods to learn human context for smart facilities and elder care, including:

Abstract: Body sensor networks (BSN) are emerging cyber-physical systems that promise to improve the quality of life through improved health, augmented sensing and actuation for the disabled, independent living for the elderly, and reduced healthcare costs. However, the physical nature of BSNs introduces several new challenges. The human body, especially in the context of medical conditions, is a highly dynamic and unpredictable physical environment that creates constantly changing demands on sensing, actuation, and quality of service (QoS).

Abstract: A hybrid system is a dynamical model that describes the coupled evolution of both continuous‐ valued variables and discrete patterns. A prime example of such a system is a power electronic circuit, where the semiconductor transistors behave as ideal switches whose switching actions effectively change the circuit topology (i.e., the discrete pattern) that in turn defines the dynamics of currents and voltages (i.e., the continuous variables) and hence the switching actions.

Abstract: 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:

Abstract: 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.