Publications

In this paper, we prove that the Chen system with a set of chaotic parameters is not smoothly equivalent to the Lorenz system with any parameters.

This paper is motivated by the following two related problems in complex networks: (i) control of cascading failures and (ii) mitigation of traffic congestion. Both problems are of significant recent interest as they address, respectively, the security of and efficient information transmission on complex networks. Taking into account typical features of load distribution and weights in real-world networks, we have discovered an optimal solution to both problems. In particular, we shall provide numerical evidence and theoretical analysis that, by choosing a proper weighting parameter, a maximum level of robustness against cascades and traffic congestion can be achieved, which practically rids the network of occurrences of the catastrophic dynamics.

A voice-over-Internet protocol technique with a new hierarchical data security protection (HDSP) scheme using a secret chaotic bit sequence has been recently proposed. Some insecure properties of the HDSP scheme are pointed out and then used to develop known/chosen-plaintext attacks. The main findings are: given n known plaintexts, about (100−(50/2n))% of secret chaotic bits can be uniquely determined; given only one specially-chosen plaintext, all secret chaotic bits can be uniquely derived; and the secret key can be derived with practically small computational complexity when only one plaintext is known (or chosen). These facts reveal that HDSP is very weak against known/chosen-plaintext attacks. Experiments are given to show the feasibility of the proposed attacks. It is also found that the security of HDSP against the brute-force attack is not practically strong. Some countermeasures are discussed for enhancing the security of HDSP and several basic principles are suggested for the design of a secure encryption scheme.

No abstract is provided for this article.

This paper investigates the evolution of cooperation and the emergence of hierarchical leadership structure in random regular graphs. It is found that there exist different learning patterns between cooperators and defectors, and cooperators are able to attract more followers and hence more likely to become leaders. Hence, the heterogeneous distributions of reputation and leadership can emerge from homogeneous random graphs. The important directed game-learning skeleton is then studied, revealing some important structural properties, such as the heavy-tailed degree distribution and the positive in-in degree correlation.

A time-delayed chaos control method based on repetitive learning is proposed. The integration of the repetitive learning control principle and the time delayed chaos control technique enables adaptive learning of appropriate control actions from learning cycles. In contrast to conventional repetitive learning control, no exact knowledge (analytic representation) of the target periodic orbits is needed, except for the time delay constant, which can be identified via either experiments or adaptive learning methods. To facilitate the discussion, the typical chaotic Duffing system is used as an example for illustration of the general methodology. Simulation result is provided to show the effectiveness of the proposed approach.

The local stochastic stability of nonlinear complex networks is studied, subject to stochastic perturbations to the coupling strengths and stochastic parametric excitations to the nodes. The complex network is first linearized at its trivial solution and then the linearized network is reduced to N independent subsystems by using a suitable linear transformation, where N is the size of the network. The largest Lyapunov exponent for each subsystem is then calculated and all the approximate analytical solutions are evaluated for some specific cases. It is found that the largest Lyapunov exponent among all subsystems is the one associated with the subsystem that has the largest or the smallest eigenvalue of the configuration matrix of the network. Finally, an example is given to demonstrate the validity and accuracy of the theoretical analysis.

An improved multi-carrier <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula> -ary differential chaos shift keying (MM-DCSK) system is presented, where differential modulation and demodulation are carried out across multiple carriers in the frequency domain, so channel estimation is not needed. For one data frame transmission, only one non-information-bearing reference sub-carrier signal is required, and the reference of each information-bearing sub-carrier is its previous sub-carrier signal, thus high energy efficiency is attained. If channel response changes during a frame period, the time diversity is achieved. In addition, the peak-to-average power ratio (PAPR) is considered, and it is found that adjacent symbols with a large Euclidean distance achieve a low PAPR. Accordingly, a low-complexity PAPR reduction algorithm is proposed based on symbol-interleaving with only one inverse fast Fourier transform processor. The simulation results demonstrate that the system with the proposed algorithm dramatically reduces the PAPR. Analytical bit-error-rate expressions are derived and verified by simulations over additive white Gaussian noise and multi-path fading channels.

The issue of opinion sharing and formation has received considerable attention in the academic literature, and a few models have been proposed to study this problem. However, existing models are limited to the interactions among nearest neighbors, ignoring those second, third, and higher-order neighbors, despite the fact that higher-order interactions occur frequently in real social networks. In this paper, we develop a new model for opinion dynamics by incorporating long-range interactions based on higher-order random walks. We prove that the model converges to a fixed opinion vector, which may differ greatly from those models without higher-order interactions. Since direct computation of the equilibrium opinion is computationally expensive, which involves the operations of huge-scale matrix multiplication and inversion, we design a theoretically convergence-guaranteed estimation algorithm that approximates the equilibrium opinion vector nearly linearly in both space and time with respect to the number of edges in the graph. We conduct extensive experiments on various social networks, demonstrating that the new algorithm is both highly efficient and effective.