The formalization of system engineering models and approaches.
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Submitted by alfaruque on Fri, 11/04/2016 - 6:01pm
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Sanjit A. Seshia is a Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley. He received an M.S. and Ph.D. in Computer Science from Carnegie Mellon University, and a B. Tech. in Computer Science and Engineering from the Indian Institute of Technology, Bombay. His research interests are in dependable computing and computational logic, with a current focus on applying automated formal methods to problems in cyber-physical systems, computer security, electronic design automation, and synthetic biology. His Ph.D.
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Large scale deployment of connected and automated vehicles is impeded by significant technical and scientific gaps, especially when it comes to achieving real-time and high accuracy situational awareness for cooperating vehicles. This CAREER project aims at closing these gaps through developing fundamental information networking methodologies for coordinated control of automated systems. These methodologies are based on the innovative concept of modeled knowledge propagation. The approach is to utilize the novel concepts of model communication and its derived multi-resolution networking.
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The objective of this proposal is to exploit an early concept of a flexible, low-cost, and drone-carried broadband long-distance communication infrastructure and investigates its capability for immediate smart-city application in emergency response.
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The goal of this project is to create an integrative framework for the design of coupled biological and robotic systems that accommodates system uncertainties and competing objectives in a rigorous, holistic, and effective manner. The design principles are developed using a concrete, end-to-end application of tracking and modeling fish movement with a network of gliding robotic fish.
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In recent years, we have seen a number of successful cyber-attacks against high-profile targets, which have demonstrated that resourceful and determined attackers can penetrate even highly secure systems. In light of these attacks, it becomes apparent that defenders of cyber-physical systems cannot focus solely on preventing attackers from penetrating their systems. Instead, they must also prepare to promptly detect and mitigate security breaches, thereby limiting the impact of successful attacks.
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Data-driven intelligence is an essential foundation for physical systems in transportation safety and efficiency, area surveillance and security, as well as environmental sustainability. This project develops new computer system infrastructure and algorithms for self-sustainable data-driven systems in the field.
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The goal of this project is to establish a theoretical and empirical foundation for secured and efficient energy resource management in the smart grid - a typical energy-based cyber-physical system and the future critical energy infrastructure for the nation. In this study, we focus on the detection threats and propose a Gaussian-Mixture Model-based Detection (GMMD) scheme to mitigate data integrity attacks.
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There is an increased research trend towards the application of distributed control algorithms for network power system control. We analyze the vulnerability of these distributed control algorithms to a potential attack on the communication network. We show that that the decentralized load-side control algorithm for frequency regulation in power system is fragile to communication channel uncertainty. We also propose an optimization-based framework for the design of distributed load-side control algorithm robust to communication channel uncertainty.
