One-to-Many Chaotic Synchronization with Application in Wireless Sensor Network

Jin, Leisheng; Zhang, Yan; Li, Lijie

doi:10.1109/lcomm.2013.081313.131169

Cited by 12 publications

(8 citation statements)

References 20 publications

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“…Moreover, it can offer a low synchronization error, especially for longer sequences. The above-mentioned features make DCS an appealing candidate for use in chaos-based Code Division Multiple Access (CDMA) systems [64], encrypted data transmission [65], [66], Ultra-Reliable Low Latency Communications (URLLC) [67], Industrial Internet of Things (IIoT) [67], and Wireless Sensor Networks (WSNs) [68], [69].…”

Section: Contributionsmentioning

confidence: 99%

Deep Chaos Synchronization

Mobini

Kaddoum

2020

IEEE Open J. Commun. Soc.

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In this study, we address the problem of chaotic synchronization over a noisy channel by introducing a novel Deep Chaos Synchronization (DCS) system using a Convolutional Neural Network (CNN). Conventional Deep Learning (DL) based communication strategies are extremely powerful but training on large data sets is usually a difficult and time-consuming procedure. To tackle this challenge, DCS does not require prior information or large data sets. In addition, we provide a novel Recurrent Neural Network (RNN)-based chaotic synchronization system for comparative analysis. The results show that the proposed DCS architecture is competitive with RNN-based synchronization in terms of robustness against noise, convergence, and training. Hence, with these features, the DCS scheme will open the door for a new class of modulator schemes and meet the robustness against noise, convergence, and training requirements of the Ultra Reliable Low Latency Communications (URLLC) and Industrial Internet of Things (IIoT).

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Section: Contributionsmentioning

confidence: 99%

Deep Chaos Synchronization

Mobini

Kaddoum

2020

IEEE Open J. Commun. Soc.

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“…When a = 1.4 and b = 0.3, system (8) is chaotic system, and the chaotic attractor of the 2-D generalized Henon map with initial valuables x 1 (0) = 0.5 and x 2 (0) = 0.6 is shown in Fig.1. In addition, the linear section and the nonlinear section of map (8) are given by…”

Section: Examples Based On P-m Synchronizationmentioning

confidence: 99%

“…The 2-D generalized Henon map and the 3-D generalized Henon map are adapted to P-M Synchronization. The master system is map (8), and the slave system is map (11).…”

Section: B Synchronization Of Chaotic Maps With Different Dimensionsmentioning

confidence: 99%

A New Matrix Projective Synchronization and Its Application in Secure Communication

Yan

Ding

2019

IEEE Access

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In this paper, a new matrix projective synchronization for chaotic (hyperchaotic) maps is proposed. The novel scheme called P-M synchronization is presented in this paper, since it combines two matrix projective synchronization schemes (one is based on the invertible matrix P, and the other is based on matrix M). Compared to the regular matrix projection synchronization, the required matrix of the proposed scheme don't change with different master systems. Under the framework of classical Lyapunov stability theory, a state feedback controller is selected to realize global synchronization. In addition, simulation results are reported, to highlight the capabilities of the P-M synchronization proposed in this paper. Finally, a speech secure communication scheme based on the P-M synchronization is implemented, implying the P-M synchronization can be applied into secure communication filed.

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“…Ever since the classic effort by Pecora and Carroll [1], the synchronization in chaotic systems became an active research area because of its useful applications [2][3][4][5]. Different synchronization schemes for chaotic systems were studied and investigated [6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Parameter Identification and Hybrid Synchronization in an Array of Coupled Chaotic Systems with Ring Connection: An Adaptive Integral Sliding Mode Approach

Siddique

Rehman

2018

Mathematical Problems in Engineering

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This article presents an adaptive integral sliding mode control (SMC) design method for parameter identification and hybrid synchronization of chaotic systems connected in ring topology. To employ the adaptive integral sliding mode control, the error system is transformed into a special structure containing nominal part and some unknown terms. The unknown terms are computed adaptively. Then the error system is stabilized using integral sliding mode control. The controller of the error system is created that contains both the nominal control and the compensator control. The adapted laws and compensator controller are derived using Lyapunov stability theory. The effectiveness of the proposed technique is validated through numerical examples.

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One-to-Many Chaotic Synchronization with Application in Wireless Sensor Network

Cited by 12 publications

References 20 publications

Deep Chaos Synchronization

Deep Chaos Synchronization

A New Matrix Projective Synchronization and Its Application in Secure Communication

Parameter Identification and Hybrid Synchronization in an Array of Coupled Chaotic Systems with Ring Connection: An Adaptive Integral Sliding Mode Approach

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