Haipeng Peng

Synchronization control of memristor-based recurrent neural networks with perturbations.

In this paper, the synchronization control of memristor-based recurrent neural networks with impulsive perturbations or boundary perturbations is studied. We find that the memristive connection weights have a certain relationship with the stability of the system. Some criteria are obtained to guarantee that memristive neural networks have strong noise tolerance capability. Two kinds of controllers are designed so that the memristive neural networks with perturbations can converge to the equilibrium points, which evoke humans memory patterns. The analysis in this paper employs the differential inclusions theory and the Lyapunov functional method. Numerical examples are given to show the effectiveness of our results.

Multiple routes transmitted epidemics on multiplex networks

Abstract This letter investigates the multiple routes transmitted epidemic process on multiplex networks. We propose detailed theoretical analysis that allows us to accurately calculate the epidemic threshold and outbreak size. It is found that the epidemic can spread across the multiplex network even if all the network layers are well below their respective epidemic thresholds. Strong positive degree–degree correlation of nodes in multiplex network could lead to a much lower epidemic threshold and a relatively smaller outbreak size. However, the average similarity of neighbors from different layers of nodes has no obvious effect on the epidemic threshold and outbreak size.

Robust and Efficient Biometrics Based Password Authentication Scheme for Telecare Medicine Information Systems Using Extended Chaotic Maps

The Telecare Medicine Information Systems (TMISs) provide an efficient communicating platform supporting the patients access health-care delivery services via internet or mobile networks. Authentication becomes an essential need when a remote patient logins into the telecare server. Recently, many extended chaotic maps based authentication schemes using smart cards for TMISs have been proposed. Li et al. proposed a secure smart cards based authentication scheme for TMISs using extended chaotic maps based on Lee’s and Jiang et al.’s scheme. In this study, we show that Li et al.’s scheme has still some weaknesses such as violation the session key security, vulnerability to user impersonation attack and lack of local verification. To conquer these flaws, we propose a chaotic maps and smart cards based password authentication scheme by applying biometrics technique and hash function operations. Through the informal and formal security analyses, we demonstrate that our scheme is resilient possible known attacks including the attacks found in Li et al.’s scheme. As compared with the previous authentication schemes, the proposed scheme is more secure and efficient and hence more practical for telemedical environments.

An Enhanced Biometric-Based Authentication Scheme for Telecare Medicine Information Systems Using Elliptic Curve Cryptosystem

The telecare medical information systems (TMISs) enable patients to conveniently enjoy telecare services at home. The protection of patient’s privacy is a key issue due to the openness of communication environment. Authentication as a typical approach is adopted to guarantee confidential and authorized interaction between the patient and remote server. In order to achieve the goals, numerous remote authentication schemes based on cryptography have been presented. Recently, Arshad et al.(J Med Syst 38(12): 2014) presented a secure and efficient three-factor authenticated key exchange scheme to remedy the weaknesses of Tan et al.’s scheme (J Med Syst 38(3): 2014). In this paper, we found that once a successful off-line password attack that results in an adversary could impersonate any user of the system in Arshad et al.’s scheme. In order to thwart these security attacks, an enhanced biometric and smart card based remote authentication scheme for TMISs is proposed. In addition, the BAN logic is applied to demonstrate the completeness of the enhanced scheme. Security and performance analyses show that our enhanced scheme satisfies more security properties and less computational cost compared with previously proposed schemes.

Chaos–order transition in foraging behavior of ants

Significance We have studied the foraging behavior of group animals that live in fixed colonies (especially ants) as an important problem in ecology. Building on former findings on deterministic chaotic activities of single ants, we uncovered that the transition from chaotic to periodic regimes results from an optimization scheme of the self-organization of such an animal colony. We found that an effective foraging of ants mainly depends on their nest as well as their physical abilities and knowledge due to experience. As an important outcome, the foraging behavior of ants is not represented by random, but rather by deterministic walks, in a random environment: Ants use their intelligence and experience to navigate. The study of the foraging behavior of group animals (especially ants) is of practical ecological importance, but it also contributes to the development of widely applicable optimization problem-solving techniques. Biologists have discovered that single ants exhibit low-dimensional deterministic-chaotic activities. However, the influences of the nest, ants’ physical abilities, and ants’ knowledge (or experience) on foraging behavior have received relatively little attention in studies of the collective behavior of ants. This paper provides new insights into basic mechanisms of effective foraging for social insects or group animals that have a home. We propose that the whole foraging process of ants is controlled by three successive strategies: hunting, homing, and path building. A mathematical model is developed to study this complex scheme. We show that the transition from chaotic to periodic regimes observed in our model results from an optimization scheme for group animals with a home. According to our investigation, the behavior of such insects is not represented by random but rather deterministic walks (as generated by deterministic dynamical systems, e.g., by maps) in a random environment: the animals use their intelligence and experience to guide them. The more knowledge an ant has, the higher its foraging efficiency is. When young insects join the collective to forage with old and middle-aged ants, it benefits the whole colony in the long run. The resulting strategy can even be optimal.

Robustness of Interrelated Traffic Networks to Cascading Failures

The vulnerability to real-life networks against small initial attacks has been one of outstanding challenges in the study of interrelated networks. We study cascading failures in two interrelated networks S and B composed from dependency chains and connectivity links respectively. This work proposes a realistic model for cascading failures based on the redistribution of traffic flow. We study the Barabási-Albert networks (BA) and Erdős-Rényi graphs (ER) with such structure, and found that the efficiency sharply decreases with increasing percentages of the dependency nodes for removing a node randomly. Furthermore, we study the robustness of interrelated traffic networks, especially the subway and bus network in Beijing. By analyzing different attacking strategies, we uncover that the efficiency of the city traffic system has a non-equilibrium phase transition at low capacity of the networks. This explains why the pressure of the traffic overload is relaxed by singly increasing the number of small buses during rush hours. We also found that the increment of some buses may release traffic jam caused by removing a node of the bus network randomly if the damage is limited. However, the efficiencies to transfer people flow will sharper increase when the capacity of the subway network αS > α0.

Anti-synchronization Control of Memristive Neural Networks with Multiple Proportional Delays

This paper investigates anti-synchronization control of memristive neural networks with multiple proportional delays. Here, we first study the proportional delay, which is a kind of unbounded time-varying delay in the memristive neural networks, by using the differential inclusion theory to handle the memristive neural networks with discontinuous right-hand side. In particular, several new criteria ensuring anti-synchronization of memristive neural networks with multiple proportional delays are presented. In addition, the new proposed criteria are easy to verify and less conservative than earlier publications about anti-synchronization control of memristive neural networks. Finally, two numerical examples are given to show the effectiveness of our results.

Novel way to research nonlinear feedback shift register

In this paper, we regard the nonlinear feedback shift register (NLFSR) as a special Boolean network, and use semi-tensor product of matrices and matrix expression of logic to convert the dynamic equations of NLFSR into an equivalent algebraic equation. Based on them, we propose some novel and generalized techniques to study NLFSR. First, a general method is presented to solve an open problem of how to obtain the properties (the number of fixed points and the cycles with different lengths) of the state sequences produced by a given NLFSR, i.e., the analysis of a given NLFSR. We then show how to construct all

A lightweight ID based authentication and key agreement protocol for multiserver architecture

2^{2^n - (l - n)} /2^{2^n - l}