The terms denote engineering domains that have high CPS content.
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Abstract:
People with upper extremity disabilities are gaining increased independence through the use of assistive robotic arms, but performing tasks that require many small precise movements remains difficult. In fact, a confounding factor is that the more severe a person's motor impairment, the more limited are the control interfaces available to them to operate the system.
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This project develops an integrated design and simulation environment for the creation of miniature capsule robots (MCRs). An MCR is a biocompatible Cyber-Physical System (CPS) designed to operate in the human body to accomplish diagnostic or therapeutic tasks (e.g., colonoscopy, abdominal surgery, etc.). A typical MCR has to fulfill three main constraints: safety, low power operation and small size. Advances in miniaturization of electronic devices have made MCRs a reality.
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The objective of this research is to develop the theory, hardware and computational infrastructure that will enable automatically transforming user-defined, high-level tasks into correct, low-level perception informed control and configurations for modular robots.
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During the development process of CPS, an analysis of whether the system operates safely in its target environment is of utmost importance. For many applications of CPS research such as the transportation industry, this implies interconnected research in formal verification of CPS with research on knowledge representation and reasoning in multi-agent systems. The need for such research has become tragically clear in transportation accidents, one notorious case being the Air France 447 flight incident.
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In this project, we are working on the broad problem of maximizing the penetration of renewables in smart grid. To that end, we are developing strategies for optimal demand response, operation, and real-time pricing. In particular, we propose theory for optimal operation for the independent system operator (ISO), and for the load serving entity (LSE).
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Despite many advances in vehicle automation, much remains to be done: the best autonomous vehicle today still lags behind human drivers, and connected vehicle (V2V) and infrastructure (V2I) standards are only just emerging. In order for such cyber-physical systems to fully realize their potential, they must be capable of exploiting one of the richest and most complex abilities of humans, which we take for granted: seeing and understanding the visual world.
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Team decision problems are a static simplification of decentralized Markov decision problems, and form a fundamental building block of decentralized control problems. An early paper by Radner showed that, in the special case when all random variables are Gaussian and the cost function is quadratic, there exist optimal policies that are linear functions. However, Tsitsiklis and Athans proved that the general case is NP-hard.
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Many safety-critical cyber-physical systems rely on advanced sensing capabilities to react to changing environmental conditions. However, cost-effective deployments of such capabilities have remained elusive. Such deployments will require software infrastructure that enables multiple sensor-processing streams to be multiplexed onto a common hardware platform at reasonable cost, as well as tools and methods for validating that required processing rates can be maintained.
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This poster presents progress of the synergistic framework and algorithms development for a group sensors to collaborate in disaster scenarios. A number of fundamental research results were obtained on scene understanding in a network of visual sensors. These were experimentally evaluated, and datasets and results from these evaluations have been released to the community.
