• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.2
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• pp.154-162
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• 2003

In this paper, Associative Memory with Cellular Neural Networks classifies and recognizes image patterns as an operator applied to image process. CNN processes nonlinear data in real-time like neural networks, and made by cell which communicates with each other directly through its neighbor cells as the Cellular Automata does. It is applied to the optimization problem, associative memory, pattern recognition, and computer vision. Image processing with CNN is appropriate to 2-D images, because each cell which corresponds to each pixel in the image is simultaneously processed in parallel. This paper shows the method for designing the structure of associative memory based on CNN and getting output image by choosing the most appropriate weight pattern among the whole learned weight pattern memories. Each template represents weight values between cells and updates them by learning. Hebbian rule is used for learning template weights and LMS algorithm is used for classification.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2002.11a
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• pp.899-902
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• 2002

In this paper, we introduce a hyper-chaos synchronization method using State-Controlled Cellular Neural Network(SC-CNN). We make a hyper-chaos circuit using SC-CNN with the n-double scroll. A hyper-chaos circuit is created by applying identical n-double scrolls with weak coupled method, to each cell. Hyper-chaos synchronization was achieved using drive response synchronization between the transmitter and receiver about each state variable in the SC-CNN.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2003.10a
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• pp.416-419
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• 2003

The cellular neural networks have the circuit structure that differs from the form of general neural network. It consists of an array of the same cell which is a simple processing element, and each of the cells has local connectivity and space invariant template property. In this paper, time-multiplex image processing technique is applied for processing large images using small size CNN cell block, and we simulate the edge detection of a large image using the simulator implemented with a c program and matlab model. A 5$\times$5 CNN hardware and pre post processor is also implemented and is under test.

• Journal of KIISE:Software and Applications
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• v.27 no.5
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• pp.463-472
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• 2000

In this paper, we consider the problem of realizing associative memories via cellular neural network(CNN). After introducing qualitative properties of the CNN model, we formulate the synthesis of CNN that can store given binary vectors with optimal performance as a constrained optimization problem. Next, we observe that this problem's constraints can be transformed into simple inequalities involving linear matrix inequalities(LMIs). Finally, we reformulate the synthesis problem as a generalized eigenvalue problem(GEVP), which can be efficiently solved by recently developed interior point methods. Proposed method can be applied to both space varying template CNNs and space-invariant template CNNs. The validity of the proposed approach is illustrated by design examples.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.3
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• pp.310-315
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• 2003

In this paper, We introduce an embedding driven synchronization method using SC-CNN(State-Controlled Cellular Neural Network) which has the purpose to secure communication method through the embedding driven synchronization method in the SC-CNN. we proposed new embedding driven synchronization that this method is only using one state variable compare to the general driven synchronization methods which is using all state variables. In this paper, We achieved the usage of embedding driven synchronization and we also applied it to secure communication.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.2
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• pp.365-370
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• 2004

In this paper, complexity secure communication was presented. The complexity circuit is used to State-Controlled Cellular Neural Network(SC-CNN). We make a complexity circuit using SC-CNN with the N-double scroll. A complexity circuit is created by applying identical n-double scrolls with coupled method, to each cell. complexity synchronization was achieved using drive response synchronization between the transmitter and receiver about each state in the SC-CNN. From the result of the recovery signal through the demodulation method in the receiver. We shown that recovery quality in the receiver is the similar to other secure communication methods.

• Proceedings of the IEEK Conference
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• 2000.07a
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• pp.50-53
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• 2000

It is effectively removed noise in the image using FCNN(Fuzzy Cellular Neural Network) applying fuzzy theory to CNN(Cellular Neural Network) structure and HD(Hausdorff Distance) commonly used measures for object matching. HD calculates the distance between two point set of pixels in two-dimensional binary images without establishing correspondence. Also, this method is proposed in order to improve the operation speed. In this paper, $\alpha$-LTSHD(Least Trimmed Square HD) operator applying $\alpha$-Trimmed to LTSHD, one field of HD, is applied to FCNN structure, and it is proposed as the modified method in order to remove noise in the image. Also, it is made a comparison with the other filters by using MSE and SNR after removing noise using the FCNNS which are applied $\alpha$-LTSHD operator through the computer simulation. In a result, FCNN performance which is applied the proposed $\alpha$-LTSHD demonstrated the superiority to the other filters in the noise removal.

• Journal of information and communication convergence engineering
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• v.1 no.4
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• pp.217-222
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• 2003

In this paper, we introduce a hyper-chaos synchronization method using hyper-chaos circuit consist of State-Controlled Cellular Neural Network (SC-CNN). We make a hyper-chaos circuit using SC-CNN with the n-double scroll. A hyper-chaos circuit is created by applying identical n-double scroll or non-identical n-double scroll and Chua's oscillator with weak coupled method to each cell. Hyper-chaos synchronization was achieved using GS(Generalized Synchronization) method between the transmitter and receiver about each state variable in the SC-CNN.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.6 no.7
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• pp.1064-1068
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• 2002

In this paper, we introduce a hyper-chaos synchronization method using State-Controlled Cellular Neural Network(SC-CNN). We make a hyper-chaos circuit using SC-CNN with the n-double scroll. A hyper-chaos circuit is created by applying identical n-double scrolls with weak coupled method, to each cell. Hyper-chaos synchronization was achieved using drive response synchronization between the transmitter and receiver about each state in the SC-CNN. From the result of the recovery signal through the demodulation method in the receiver, We shown that recovery quality of state variable $$\chi$_3$ is superior to that of ${$\chi$_2}, {$\chi$_1}$ in secure communication.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.504-507
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• 2003

In this paper, we introduce a new hyper-chaos synchronization method called embedding synchronization using hyper-chaos consist of State-Controlled Cellular Neural Network (SC-CNN). We make a hyper-chaos circuit using SC-CNN with the n-double scroll. A hyper-chaos circuit is created by applying identical n-double scroll with weak coupled method to each cell. Hyper-chaos synchronization was achieved using embedding synchronization between the transmitter and receiver about each state variable in the SC-CNN.