• Title, Summary, Keyword: Cellular neural network (CNN)

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Modular Cellular Neural Network Structure for Wave-Computing-Based Image Processing

  • Karami, Mojtaba;Safabakhsh, Reza;Rahmati, Mohammad
    • ETRI Journal
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    • v.35 no.2
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    • pp.207-217
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    • 2013
  • This paper introduces the modular cellular neural network (CNN), which is a new CNN structure constructed from nine one-layer modules with intercellular interactions between different modules. The new network is suitable for implementing many image processing operations. Inputting an image into the modules results in nine outputs. The topographic characteristic of the cell interactions allows the outputs to introduce new properties for image processing tasks. The stability of the system is proven and the performance is evaluated in several image processing applications. Experiment results on texture segmentation show the power of the proposed structure. The performance of the structure in a real edge detection application using the Berkeley dataset BSDS300 is also evaluated.

A Study on the Number Recognition using Cellular Neural Network (Cellular Neural Network을 이용한 숫자인식에 관한 연구)

  • 전흥우;김명관;정금섭
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.6 no.6
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    • pp.819-826
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    • 2002
  • Cellular neural networks(CNN) are neural networks that have locally connected characteristics and real-time image processing. Locally connected characteristics are suitable for VLSI implementation. It also has applications in such areas as image processing and pattern recognition. In this thesis cellular neural networks are used for feature detection in number recognition at the stage of re-processing. The four or six directional shadow detectors are used in numbers recognition. At the stage of classification, this result of feature detection was simulated by using a multi-layer back Propagation neural network. The experiments indicate that the CNN feature detectors capture good features for number recognition tasks.

A Design of a Cellular Neural Network for the Real Image Processing (실영상처리를 위한 셀룰러 신경망 설계)

  • Kim Seung-Soo;Jeon Heung-Woo
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.10 no.2
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    • pp.283-290
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    • 2006
  • The cellular neural networks have the structure that 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 properties. So, it has a very suitable structure for the hardware implementation. But, it is impossible to have a one-to-one mapping between the CNN hardware processors and the pixels of the practical large image. In this paper, a $5{\times}5$ CNN hardware processor with pipeline input and output that can be applied to the time-multiplexing processing scheme, which processes the large image with a small CNN cell block, is designed. the operation of the implemented $5{\times}5$ CNN hardware processor is verified from the edge detection and the shadow detection experimentations.

ANALOG COMPUTING FOR A NEW NUCLEAR REACTOR DYNAMIC MODEL BASED ON A TIME-DEPENDENT SECOND ORDER FORM OF THE NEUTRON TRANSPORT EQUATION

  • Pirouzmand, Ahmad;Hadad, Kamal;Suh, Kune Y.
    • Nuclear Engineering and Technology
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    • v.43 no.3
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    • pp.243-256
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    • 2011
  • This paper considers the concept of analog computing based on a cellular neural network (CNN) paradigm to simulate nuclear reactor dynamics using a time-dependent second order form of the neutron transport equation. Instead of solving nuclear reactor dynamic equations numerically, which is time-consuming and suffers from such weaknesses as vulnerability to transient phenomena, accumulation of round-off errors and floating-point overflows, use is made of a new method based on a cellular neural network. The state-of-the-art shows the CNN as being an alternative solution to the conventional numerical computation method. Indeed CNN is an analog computing paradigm that performs ultra-fast calculations and provides accurate results. In this study use is made of the CNN model to simulate the space-time response of scalar flux distribution in steady state and transient conditions. The CNN model also is used to simulate step perturbation in the core. The accuracy and capability of the CNN model are examined in 2D Cartesian geometry for two fixed source problems, a mini-BWR assembly, and a TWIGL Seed/Blanket problem. We also use the CNN model concurrently for a typical small PWR assembly to simulate the effect of temperature feedback, poisons, and control rods on the scalar flux distribution.

A study on Generalized Synchronization in the State-Controlled Cellular Neural Network(SC-CNN)

  • Rae Youngchul;Kim Yi-gon;Tinduka Mathias
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.5 no.4
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    • pp.291-296
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    • 2005
  • In this paper, we introduce a generalized synchronization method and secure communication in the State-Controlled Cellular Neural Network (SC-CNN). We make a SC-CNN using the n-double scroll. A SC-CNN 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. SC-CNN synchronization was achieved using GS(Generalized Synchronization) method between the transmitter and receiver about each state variable in the SC-CNN. In order to secure communication, we have synthesizing the desired information with a SC-CNN circuit by adding the information signal to the hyper-chaos signal using the SC-CNN in the transmitter. And then, transmitting the synthesized signal to the ideal channel, we confirm secure communication by separating the information signal and the SC-CNN signal in the receiver.

Synchronization Method of Coupling Coefficient of Linear and Nonlinear in SC-CNN(State-Controlled Cellular Neural Network) (SC-CNN(State-Controlled Cellular Neural Network)에서 선형과 비선형 결합 계수에 의한 동기화 기법)

  • Bae, Young-Chul
    • The Journal of the Korea institute of electronic communication sciences
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    • v.7 no.1
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    • pp.91-96
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    • 2012
  • Recently, the research of security and its related problems has been received great interested. The research for hper-chaos systems and its synchronization are actively processing as one of method to apply to secure and cryptography communication. In this paper, we propose the synchronization method by coupling coefficient of linear and nonlinear in order to accomplish the synchronization of hyper-chaos system that organized by SC-CNN(State-Controlled Cellular Neural Network). We also verify and confirm the result of synchronization between entire transmitter and receiver, and each subsystem in transmitter and receiver through the phase portrait and difference of time-series by the computer simulation.

Potential Anomaly Separation and Archeological Site Localization Using Genetically Trained Multi-level Cellular Neural Networks

  • Bilgili, Erdem;Goknar, I. Cem;Albora, Ali Muhittin;Ucan, Osman Nuri
    • ETRI Journal
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    • v.27 no.3
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    • pp.294-303
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    • 2005
  • In this paper, a supervised algorithm for the evaluation of geophysical sites using a multi-level cellular neural network (ML-CNN) is introduced, developed, and applied to real data. ML-CNN is a stochastic image processing technique based on template optimization using neighborhood relationships of the pixels. The separation/enhancement and border detection performance of the proposed method is evaluated by various interesting real applications. A genetic algorithm is used in the optimization of CNN templates. The first application is concerned with the separation of potential field data of the Dumluca chromite region, which is one of the rich reserves of Turkey; in this context, the classical approach to the gravity anomaly separation method is one of the main problems in geophysics. The other application is the border detection of archeological ruins of the Hittite Empire in Turkey. The Hittite civilization sites located at the Sivas-Altinyayla region of Turkey are among the most important archeological sites in history, one reason among others being that written documentation was first produced by this civilization.

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Fast Pattern Classification with the Multi-layer Cellular Nonlinear Networks (CNN) (다층 셀룰라 비선형 회로망(CNN)을 이용한 고속 패턴 분류)

  • 오태완;이혜정;손홍락;김형석
    • The Transactions of the Korean Institute of Electrical Engineers D
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    • v.52 no.9
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    • pp.540-546
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    • 2003
  • A fast pattern classification algorithm with Cellular Nonlinear Network-based dynamic programming is proposed. The Cellular Nonlinear Networks is an analog parallel processing architecture and the dynamic programing is an efficient computation algorithm for optimization problem. Combining merits of these two technologies, fast pattern classification with optimization is formed. On such CNN-based dynamic programming, if exemplars and test patterns are presented as the goals and the start positions, respectively, the optimal paths from test patterns to their closest exemplars are found. Such paths are utilized as aggregating keys for the classification. The algorithm is similar to the conventional neural network-based method in the use of the exemplar patterns but quite different in the use of the most likely path finding of the dynamic programming. The pattern classification is performed well regardless of degree of the nonlinearity in class borders.

A study on FCNN structure based on a α-LTSHD for an effective image processing (효과적인 영상처리를 위한 α-LTSHD 기반의 FCNN 구조 연구)

  • Byun, Oh-Sung;Moon, Sung-Ryong
    • Journal of Korean Institute of Intelligent Systems
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    • v.12 no.5
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    • pp.467-472
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    • 2002
  • In this paper, we propose a Fuzzy Cellular Neural Network(FCNN) that is based on a-Least Trimmed Square Hausdorff distance(a-LTSHD) which applies Hausdorff distance(HD) to the FCNN structure in order to remove the impulse noise of images effectively and also improve the speed of operation. FCNN incorporates Fuzzy set theory to Cellular Neural Network(CNN) structure and HD is used as a scale which computes the distance between set or two pixels in binary images without confrontation of the feature object. This method has been widely used with the adjustment of the object. For performance evaluation, our proposed method is analyzed in comparison with the conventional FCNN, with the Opening-Closing(OC) method, and the LTSHD based FCNN by using Mean Square Error(MSE) and Signal to Noise Ratio(SNR). As a result, the performance of our proposed network structure is found to be superior to the other algorithms in the removal of impulse noise.

The Synchronization in Hyper-Chaos

  • Youngchul Bae;Kim, Juwan;Kim, Yigon
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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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.

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