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Xiaofeng Liao
Researcher at Chongqing University
Publications - 496
Citations - 18524
Xiaofeng Liao is an academic researcher from Chongqing University. The author has contributed to research in topics: Exponential stability & Artificial neural network. The author has an hindex of 67, co-authored 457 publications receiving 16381 citations. Previous affiliations of Xiaofeng Liao include Chongqing University of Posts and Telecommunications & University of Electronic Science and Technology of China.
Papers
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A new chaos-based fast image encryption algorithm
TL;DR: A fast image encryption algorithm with combined permutation and diffusion is proposed and an efficient method for generating pseudorandom numbers from spatiotemporal chaos is suggested, which further increases the encryption speed.
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Event-Triggering Sampling Based Leader-Following Consensus in Second-Order Multi-Agent Systems
TL;DR: The problem of second-order leader-following consensus by a novel distributed event-triggered sampling scheme in which agents exchange information via a limited communication medium is studied and it is shown that the inter-event intervals are lower bounded by a strictly positive constant, which excludes the Zeno-behavior before the consensus is achieved.
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Delay-dependent exponential stability analysis of delayed neural networks: an LMI approach
TL;DR: The results obtained in this paper provide one more set of easily verified guidelines for determining the exponentially stability of delayed neural networks, which are less conservative and less restrictive than the ones reported so far in the literature.
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An image encryption approach based on chaotic maps
TL;DR: This paper improves the properties of confusion and diffusion in terms of discrete exponential chaotic maps, and design a key scheme for the resistance to statistic attack, differential attack and grey code attack.
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LMI-based approach for asymptotically stability analysis of delayed neural networks
TL;DR: In this paper, the authors derived sufficient conditions for asymptotic stability of neural networks with constant or time-varying delays, based on the Lyapunov-Krasovskii stability theory for functional differential equations and the linear matrix inequality approach.