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Fabio Pasqualetti

Researcher at University of California, Riverside

Publications -  218
Citations -  11425

Fabio Pasqualetti is an academic researcher from University of California, Riverside. The author has contributed to research in topics: Controllability & Computer science. The author has an hindex of 40, co-authored 195 publications receiving 9039 citations. Previous affiliations of Fabio Pasqualetti include University of Pennsylvania & University of California, Santa Barbara.

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Attack Detection and Identification in Cyber-Physical Systems

TL;DR: In this article, a mathematical framework for cyber-physical systems, attacks, and monitors is proposed, and fundamental monitoring limitations from both system-theoretic and graph-based perspectives are characterized.
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Attack Detection and Identification in Cyber-Physical Systems -- Part I: Models and Fundamental Limitations

TL;DR: This paper proposes a mathematical framework for cyber-physical systems, attacks, and monitors, and describes fundamental monitoring limitations from system-theoretic and graph- theoretic perspectives and designs centralized and distributed attack detection and identification monitors.
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Controllability of structural brain networks.

TL;DR: Tools from control and network theories are used to offer a mechanistic explanation for how the brain moves between cognitive states drawn from the network organization of white matter microstructure and suggest that densely connected areas facilitate the movement of the brain to many easily reachable states.
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Controllability Metrics, Limitations and Algorithms for Complex Networks

TL;DR: A metric is proposed to quantify the difficulty of the control problem as a function of the required control energy, bounds are derived based on the system dynamics to characterize the tradeoff between the control energy and the number of control nodes, and an open-loop control strategy with performance guarantees is proposed.
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Consensus Computation in Unreliable Networks: A System Theoretic Approach

TL;DR: In this article, the authors address the problem of ensuring trustworthy computation in a linear consensus network, where the authors model misbehaviors as unknown and unmeasurable inputs affecting the network, and cast the misbehavior detection and identification problem into an unknown-input system theoretic framework.