This position paper discusses limitations of the current automotive transportation active safety systems. A system approach can address all levels (the driver, the vehicle,and the traffic) of interconnection between machine, computer and human by leading to incorporating interactions and heterogeneity of different physical layers in a unified framework. The resulting analytical and computational infrastructure, with applications in crash avoidance and traffic flow management, is then discussed.
This project aims to develop a new computing device where non-volatile elements based on flash (floating gate) transistors are pervasively used in all levels of the memory hierarchy to enable almost instantaneous check pointing and recovery of program state not subject to the data bus bandwidth limit. Effectively, this new system allows its power source to be cut off at any time, and yet resumes regular operation without loss of information when the power comes back.
The objective of this research is to develop a trustworthy and high-performance neural-machine interface (NMI) that accurately interprets the user’s intended movements in real-time for neural control of artificial legs.
The hypothesis of this research is that a successful cyber-physical system will need to be a learning agent, learning the properties of its sensors, effectors, and environment from its own experience, and adapting over time. Inspired by human developmental learning, we believe that foundational concepts such as Space, Object, Action, etc., are essential for such a learning agent to abstract and control the complexity of its world. To bridge the gap between continuous interaction with the physical environment, and discrete symbolic descriptions th
