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Choy Heng Lai

Researcher at National University of Singapore

Publications -  157
Citations -  3501

Choy Heng Lai is an academic researcher from National University of Singapore. The author has contributed to research in topics: Synchronization (computer science) & Complex network. The author has an hindex of 30, co-authored 157 publications receiving 3278 citations. Previous affiliations of Choy Heng Lai include Yale-NUS College.

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Model-based detector and extraction of weak signal frequencies from chaotic data.

TL;DR: This work introduces and investigates a signal detection algorithm for which chaos theory, nonlinear dynamical reconstruction techniques, neural networks, and time-frequency analysis are put together in a synergistic manner, and demonstrates that weak signals hidden beneath the noise floor can be detected by using a model-based detector.
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Amplification of weak signals and stochastic resonance via on-off intermittency with symmetry breaking.

TL;DR: It is demonstrated the possibility of observing multiplicative noise(chaos)-induced amplification of weak signal and stochastic resonance via on-off intermittency with symmetry breaking in a general class of symmetrical systems.
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Evolution of functional subnetworks in complex systems

TL;DR: It is found that during the process of system evolution, the network is gradually stabilized into a particular form in which the attractive (repulsive) subnetwork consists only of the intralinks (interlinks).
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Evolutionary Subnetworks in Complex Systems

TL;DR: In this paper, a model of coupled oscillators is proposed to investigate how functional subnetworks are evolved and developed according to the network structure and dynamics, and a new algorithm for network partition which is distinguished by the convenient operation and fast computing speed.
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Multiple effects of gradient coupling on network synchronization.

TL;DR: It is demonstrated that, for a typical complex network, there could be an optimal gradient where the maximum network synchronizability is achieved, and that, comparing with sparse homogeneous networks, dense heterogeneous networks suffer less from network breaking and, consequently, benefit more from large gradient in improving synchronization.