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Xenofon Koutsoukos

Researcher at Vanderbilt University

Publications -  418
Citations -  9420

Xenofon Koutsoukos is an academic researcher from Vanderbilt University. The author has contributed to research in topics: Cyber-physical system & Hybrid system. The author has an hindex of 45, co-authored 390 publications receiving 8146 citations. Previous affiliations of Xenofon Koutsoukos include PARC & University of Notre Dame.

Papers
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Journal ArticleDOI

Resilient Asymptotic Consensus in Robust Networks

TL;DR: This paper designs a consensus protocol based on local information that is resilient to worst-case security breaches, assuming the compromised nodes have full knowledge of the network and the intentions of the other nodes, and develops a novel graph-theoretic property referred to as network robustness.
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Local Causal and Markov Blanket Induction for Causal Discovery and Feature Selection for Classification Part I: Algorithms and Empirical Evaluation

TL;DR: It is found that non-causal feature selection methods cannot be interpreted causally even when they achieve excellent predictivity, so only local causal techniques should be used when insight into causal structure is sought.
Book ChapterDOI

A survey on localization for mobile wireless sensor networks

TL;DR: This paper provides taxonomies for mobile wireless sensors and localization, including common architectures, measurement techniques, and localization algorithms, and concludes with a description of real-world mobile sensor applications that require position estimation.
Journal ArticleDOI

Supervisory control of hybrid systems

TL;DR: The supervisory control of hybrid systems is introduced and discussed at length and the interaction between the continuous and discrete dynamics is highlighted, which is the cornerstone of any hybrid system study.
Journal ArticleDOI

Toward a Science of Cyber–Physical System Integration

TL;DR: A passivity-based design approach that decouples stability from timing uncertainties caused by networking and computation is presented, and cross-domain abstractions that provide effective solution for model-based fully automated software synthesis and high-fidelity performance analysis are described.