Carnegie Mellon University
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Submitted by tague on Tue, 01/09/2018 - 5:31pm
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Submitted by Andre Platzer on Tue, 01/09/2018 - 5:04pm
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Submitted by Joao Hespanha on Tue, 01/09/2018 - 3:24pm
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Submitted by Brenna on Tue, 01/09/2018 - 2:26pm
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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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Achieving security in Cyber-Physical Systems (CPS) has become a critical problem. Due to their ubiquity and the essential role they play in modern societies, CPS such as the smart grid, transportation networks, medical systems, and water distribution systems are a target for malicious attackers. While significant efforts are being made to achieve resilience in CPS, the field of CPS security as a whole suffers from fragmentation. In particular, there is often a sharp divide between mainstream security and privacy and tradition system theoretic techniques.
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The advanced development of cyber-physical systems at the sub-millimeter scale will have a large impact on a diverse set of potential applications. For example, targeted drug delivery or materials conveyance for micro-scale construction are two application areas that will greatly benefit from reliable microsystems. However, conventional actuator, sensor, and computational units are generally not available at extremely small scales.
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World population is projected to reach 9.6 billion by 2050, and yields of most staple crops are not increasing at a fast enough rate to meet the corresponding nutritional needs. The overall goal of this research is to create new CPS science and technology for adaptive data acquisition for high throughput plant phenotyping, which will accelerate breeding progress for high yield food crops. Currently, the primary bottleneck in identifying high-yielding crops is the limited number of plants a breeder can evaluate in a growing season.
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This project addresses the foundational problem of knowledge and limits of knowledge within cyber-physical systems (CPS). A single system observes its environment through sensors and interacts through actuators. Neither is perfect. Thus, the CPS's internal view of the world is blurry and its actions are imprecise. CPS are still analyzed with methods that do not distinguish between truth in the world and an internal view thereof, resulting in a mismatch between the behavior of theoretical models and their real-world counterparts.
