A SC–CNN-BASED CHAOTIC MASKING SYSTEM WITH FEEDBACK

A secure transmission application of the State Controlled Cellular Neural Network (SC–CNN)-based circuit is presented. Since the SC–CNN-based circuit has feedback connections between the cells, it is potentially very suitable for realizing a chaotic masking system with feedback algorithm. So, we have constructed a chaotic masking system with feedback using the SC–CNN-based circuit. PSpice simulation experiments verify that the proposed SC–CNN-based secure communication system exhibits a good performance for a wide range of amplitude and spectral characteristics of the information signal.

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Synchronization and multichannel secure communication of quantum cellular neural network

Proceeding of the 11th World Congress on Intelligent Control and Automation ◽

10.1109/wcica.2014.7053391 ◽

2014 ◽

Cited By ~ 1

Author(s):

Sen Wang ◽

Li Cai ◽

Nan Zhang ◽

Chao Wen Feng ◽

Xia Kuo Yang

Keyword(s):

Neural Network ◽

Secure Communication ◽

Cellular Neural Network

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Secure Communication Based on Tracking Control of Quantum Cellular Neural Network

2008 7th World Congress on Intelligent Control and Automation ◽

10.1109/wcica.2008.4594189 ◽

2008 ◽

Cited By ~ 2

Author(s):

Sen Wang ◽

Li Cai ◽

Qiang Kang ◽

Qin Li ◽

Gang Wu

Keyword(s):

Neural Network ◽

Tracking Control ◽

Secure Communication ◽

Cellular Neural Network

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EXPERIMENTAL CONFIRMATION OF SC-CNN-BASED CHAOTIC MASKING SYSTEM

International Journal of Bifurcation and Chaos ◽

10.1142/s0218127405014441 ◽

2005 ◽

Vol 15 (12) ◽

pp. 4013-4018 ◽

Cited By ~ 3

Author(s):

ENIS GÜNAY ◽

MUSTAFA ALÇI

Keyword(s):

Neural Network ◽

Cellular Neural Network ◽

Experimental Results ◽

Experimental Confirmation ◽

Feedback Algorithm

In this study experimental results of State Controlled Cellular Neural Network (SC-CNN)-based chaotic masking system are presented. By means of this study, the chaotic masking system with feedback algorithm in SC-CNN-based circuit is experimentally proved.

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New chaotic memristive cellular neural network and its application in secure communication system

Chaos Solitons & Fractals ◽

10.1016/j.chaos.2020.110316 ◽

2020 ◽

Vol 141 ◽

pp. 110316

Author(s):

Chunbo Xiu ◽

Ruxia Zhou ◽

Yuxia Liu

Keyword(s):

Neural Network ◽

Communication System ◽

Secure Communication ◽

Cellular Neural Network

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A secure communication scheme based on cellular neural network

1997 IEEE International Conference on Intelligent Processing Systems (Cat. No.97TH8335) ◽

10.1109/icips.1997.672837 ◽

2002 ◽

Cited By ~ 19

Author(s):

Zhang Yifeng ◽

He Zhengya

Keyword(s):

Neural Network ◽

Secure Communication ◽

Cellular Neural Network ◽

Communication Scheme

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Secure Transmission of Analog Information using Chaos

Chaos Synchronization and Cryptography for Secure Communications - Advances in Information Security, Privacy, and Ethics ◽

10.4018/978-1-61520-737-4.ch015 ◽

2011 ◽

pp. 337-360 ◽

Cited By ~ 1

Author(s):

A.S. Dmitriev ◽

E.V. Efremova ◽

L.V. Kuzmin ◽

A.N. Miliou ◽

A.I. Panas ◽

...

Keyword(s):

Secure Communication ◽

Communication Systems ◽

Signal Transmission ◽

Wireless Communication Systems ◽

Practical Realization ◽

Chaotic Response ◽

Information Signal ◽

Transmission Quality ◽

Secure Transmission

In this work the authors present a thorough experimental study of a practical realization of a complex analog signal transmission system using dynamic chaos. It is demonstrated that the chaotic synchronous response could be used as a basis for the design of secure communication channels. The results presented in this work confirm the possibility of secure wireless communications in RF band, while they allow the authors to analyze in detail the restrictions and problems connected with the quality of synchronization of the transmitter and the receiver of the wireless communication systems. The effect of the perturbing factors on the transmission quality is investigated theoretically. It is shown that the main reason of the transmission’s quality degradation is the chaotic response desynchronization associated with the phenomenon of “on-off” intermittency. It is found that under the effect of the perturbing factors, the level of information signal fed to the transmitter must be increased in order to obtain qualitative information transmission. However, in order to provide secure communication, one must decrease the information signal level. A compromise on these contradictory requirements provides an improvement of the quality of the synchronous chaotic response in the receiver.

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Dynamical Behavior of Three-Order Cellular Neural Network with Application in Image Secure Communication

2007 International Conference on Computational Intelligence and Security (CIS 2007) ◽

10.1109/cis.2007.54 ◽

2007 ◽

Author(s):

Fei Xiang ◽

Huijuan Xiao ◽

Shuisheng Qiu ◽

Chengliang Deng

Keyword(s):

Neural Network ◽

Secure Communication ◽

Dynamical Behavior ◽

Cellular Neural Network

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Industrial Computerized Tomography Images Segmentation Based on Cellular Neural Networks

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.66-68.2228 ◽

2011 ◽

Vol 66-68 ◽

pp. 2228-2235

Author(s):

Chang Jiang Liu ◽

Xu Lin Wu

Keyword(s):

Neural Network ◽

Computerized Tomography ◽

Batch Process ◽

Cellular Neural Network ◽

Gray Level ◽

Simulation Experiments ◽

Edge Information ◽

Binary Images ◽

Industrial Computerized Tomography ◽

Gray Level Images

In this paper, we aimed at segmenting industrial computerized tomography images grounded in edge information. Most researchers focused on the edges of binary images using cellular neural network. We have expanded the scope to gray level images. Thus two groups of cellular neural network were designed to obtain closed edges. One was used to convert the gray level images to binary ones, the other to extract edges. In addition, we combined with contrastenhancement. Subsequently, tracing the obtained edges accomplished segmentation. Simulation experiments on a series of engine images demonstrate our methods can not only batch process threshold segmentation without choice threshold, but also extract the fine edges .A comparison to state-of-the-art methods show ours are easy to follow with good results.

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