Mengran Xue

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Motivated by the increasing need for developing automated decision-support tools for cyber-physical networks subject to uncertainties, we have been pursuing development of a new control-theoretic framework for network security and vulnerability. In this paper, we build on the proposed framework to put forth concrete definitions for security and (dually)(More)
— We take a structural approach to the problem of designing the edge weights in an undirected graph subject to an upper bound on their total, so as to maximize the algebraic connectivity. Specifically, we first characterize the eigenvector(s) associated with the algebraic connectivity at the optimum, using optimization machinery together with eigenvalue(More)
A graph-theoretic analysis of state inference for a class of network synchronization (or diffusive) processes is pursued. Precisely, estimation is studied for a nonrandom initial condition of a canonical synchronization dynamic defined on a graph, from noisy observations at a single network node. By characterizing the maximum-likelihood estimation of the(More)
The purpose of this chapter is to (1) introduce notions of security for the physical dynamics of complex cyber-physical networks and (2) provide a tutorial on control-theoretic tools for network inference that are promising for evaluation of such dynamic notions of security. Classically, computer scientists and infrastructure network engineers have(More)
Motivated by both distributed-computation and decentralized-control applications, we study distributed linear iterative algorithms with memory. Specifically, we show that the system of linear equations Gx = b can be solved through a distributed linear iteration for arbitrary invertible G, using only a single memory element at each processor. Further, we(More)
— Increasingly, teams of self-coordinating autonomous vehicles are being used in lieu of manned transport in hostile environments, and hence characterizing the security and robustness of these autonomous vehicle networks (AVNs) from uncertainty and adversarial conduct is becoming paramount. Using a canonical double-integrator network model, we study(More)
The genetic algorithms are powerful for optimization, and have been successfully applied to controller design. However, most existing works are based on simulations and little works are available in the literature on on-line control applications. In this paper, a special feature in selecting the population of the genes is proposed, such that the genetic(More)