Abstract:  Reinforcement Learning (RL) is a paradigm for learning decision- making tasks that could enable cyber-physical systems (CPS) to learn and adapt to situations on-line. For an RL algorithm to be practical for CPS control tasks, it must learn in very few samples, while continually taking actions in real-time. In addition, the algorithm must learn effi- ciently in the face of noise, sensor/actuator delays and continuous state features. We describe TEXPLORE, a model-based RL method that ad- dresses these issues.

Abstract: The interdisciplinary eldertech team at the University of Missouri is dedicated to developing and evaluating technology to keep older adults functioning at high levels and living independently. We are leveraging ongoing research at a unique local eldercare facility (TigerPlace) to study active sensing and fusion using vision and acoustic sensors for the continuous assessment of a resident’s risk of falling as well as the reliable detection of falls in the home environment.

Abstract: The goal of the proposed research is to identify ways to inexpensively provide specific information about energy consumption in buildings and facilitate conservation. Signal processing, machine learning, and data fusion techniques will be developed to extract actionable information from whole-building power meters and other available sensors.

Abstract: Robotic devices are excellent candidates for delivering repetitive and intensive practice that can restore functional use of the upper limbs, even years after a stroke. Rehabilitation of the wrist and hand in particular are critical for recovery of function, since hands are the primary interface with the world. However, robotic devices that focus on hand rehabilitation are limited due to excessive cost, complexity, or limited functionality.

Abstract: This project strikes a balance between performance considerations and power consumption in cyber-­‐physical systems through algorithms that switch among different modes of operation (e.g., low-­‐power/high-­‐power, on/off, or mobile/static) in response to environmental conditions. The computational contribution is a  hybrid optimal control framework that is connected to a number of relevant target applications where physical modeling, control design, and software architectures all constitute important components.

Abstract: An important area of cyber-physical systems research is the development of smart ground transportation systems due to their potentially significant impact on safety, the economy, and the environment. While technologies for smart transportation systems have advanced significantly over the last decade, there remain several challenges for the development of transportation systems that are collision free.

Abstract: Towards the development of robust cyber-physical systems, this project has developed solutions for planning and coordinating the motions of individual and networks of autonomous systems. This work is inspired by related challenges and techniques in algorithmic motion planning, multi-agent systems, sensor networks, learning and control. The focus of the project has been on the following tasks during the period 2012-2013:

Abstract: 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.

Abstract: This project addresses the management of the air traffic system, a cyber-physical sys- tem where the need for a tight connection between the computational algorithms and the physical system is critical to safe, reliable and efficient performance. Indeed, the lack of this tight connection is one of the reasons current systems are overwhelmed by the ever increasing traffic and suffer when there is any deviation from the expected (e.g., changing weather).