Enis Günay

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.

MLC CIRCUIT IN THE FRAME OF CNN

A CNN-based nonautonomous chaotic oscillator circuit design is presented. Murali–Lakshmanan–Chua circuit, known as MLC circuit, is modeled by using CNN cells. The circuit implementation is supported by an eigenvalue study of the introduced system. The proposed model gives an alternative to MLC circuit with inductorless RC-based circuit realization.

A NEW AUTONOMOUS CHAOS GENERATOR FROM STATE CONTROLLED-CELLULAR NEURAL NETWORKS

In this study, a new autonomous chaos generator is presented via State Controlled-Cellular Neural Networks (SC-CNNs). From the numerical and circuit simulations, it has been shown that the proposed SC-CNN consisting of three cells can be easily used in chaotic applications as a chaos generator. After presenting the double scroll generation, the presented SC-CNN system is used either in multi-scroll chaotic attractor generation by adding a trigonometric function generator and or to construct a SC-CNN based chaotic masking system with feedback algorithm.

EXPERIMENTAL CONFIRMATION OF SC-CNN-BASED CHAOTIC MASKING SYSTEM

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.

A NEW WAY OF GENERATING N-SCROLL ATTRACTORS VIA TRIGONOMETRIC FUNCTION

In this work, a way of generating n-scroll attractors in State Controlled-Cellular Neural Network (SC-CNN) using a trigonometric function is introduced. In spite of the studies on generation of n-scrolls cited in literature, there is no need to add break points in the nonlinear function of the system. Also, wide number of scrolls can be generated by modifying only one variable.

n-DOUBLE SCROLLS IN SC-CNN CIRCUIT VIA DIODE-BASED PWL FUNCTION

In this paper n-double scroll generating via diode-based PieceWise-Linear (PWL) circuit in State Controlled Cellular Neural Network (SC-CNN) is presented. It has been shown that by using simple diode-based configurations; alternative nonlinear circuit configurations for chaotic circuits and PWL-based systems can be used in the generation of n-double scrolls. With this study, while the analysis of the nonlinear block in the SC-CNN-based circuit is simplified, the implementation cost of the circuit is also reduced. Pspice simulations are proved with experimental studies.

AN EXPERIMENTAL STUDY ON CHAOTIC DYNAMICS OF CFOA-BASED SC-CNN CIRCUIT

In this paper, an experimental implementation of State Controlled Cellular Neural Network (SC-CNN) circuit using Current Feedback Op Amp (CFOA) is presented and its chaotic dynamics including high frequency performance are investigated by laboratory experiments. Depending on its significant advantages over the conventional voltage op amps (VOAs), without imposing any restrictions, the CFOAs have been used instead of the VOAs in SC-CNN circuit. Experimental results have shown that the proposed implementation has a capacity of higher frequency operation.

TWO IMPULSIVE SYNCHRONIZATION STUDIES USING SC-CNN-BASED CIRCUIT AND CHUA'S CIRCUIT

The impulsive synchronization method has been applied to several well-known chaotic circuits and systems such as Chuas circuit, Lorenz system and hyperchaotic circuit in the literature. In this paper, we also present two impulsive synchronization studies using SC-CNN-based circuit and Chuas circuit. In the first study, we have investigated the impulsive synchronization between two SC-CNN-based circuits. Pspice simulation results show that two SC-CNN-based circuits can be synchronized impulsively via x1 and x2 cell dynamics for different impulse width and impulse period values. And in the second study, we have investigated the impulsive synchronization between SC-CNN-based circuit and Chuas circuit. Pspice simulation results verify that two chaotic circuits, which have identical dynamical systems via appropriate parameter transformations but having quite different hardware implementations, can be synchronized impulsively for different impulse width and impulse period values.

Clonal selection algorithm based cloning template learning for edge detection in digital images with CNN

A cellular neural network (CNN) based edge detector optimized by clonal selection algorithm is presented. Cloning templates of the proposed CNN is adaptively tuned by using simple training images. The performance of the proposed edge detector is evaluated on different test images and compared with popular edge detectors from the literature. Simulation results indicate that the proposed CNN operator outperforms competing edge detectors and offers superior performance in edge detection in digital images.

Determining clonning templates of CNN via moga: Edge detection

Recent scientific and technological developments allow a dynamical evaluation method, named Cellular Neural Networks (CNNs), and enable to use it in various areas mostly in image processing. It seems to be an optimization problem to determine the cloning templates as network parameters that obtain the desired output in CNNs. In this work, to achieve edge detection in colored images via CNNs, Multi Object Genetic Algorithm (MOGA) is used to determine the cloning templates. Two different methods are used to obtain edge detection in colored images and results are compared with classical edge detection techniques.