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Stable cellular neural networks with binary outputs implement a non-linear mapping between sets of input and output images. Such a mapping is studied in detail. We prove two theorems: the first one yields a sufficient condition in...
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Stable cellular neural networks with binary outputs implement a non-linear mapping between sets of input and output images. Such a mapping is studied in detail. We prove two theorems: the first one yields a sufficient condition in order that the non-linear mapping be well-defined; the second one yields a condition, that allows to describe the mapping through a simple algorithm based on the sign of the initial derivatives. Then we enunciate two additional theorems and two corollaries, that identify the class of templates satisfying the above condition: such a class is shown to be rather large and include, as particular cases, the monotonic templates, an several kinds of non-monotonic templates. Finally, a rigorous design procedure is proposed.
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In this paper we demonstrate hyperchaotic dynamics in a very simple Cellular Neural Network (CNN) which is a one-dimensional regular array of four cells. The Lyapunov spectrum is calculated in a range of parameters, and the bifurc...
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In this paper we demonstrate hyperchaotic dynamics in a very simple Cellular Neural Network (CNN) which is a one-dimensional regular array of four cells. The Lyapunov spectrum is calculated in a range of parameters, and the bifurcation plot is presented as well.
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The synchronization is very important technology in the field of Cellular Neural Networks (CNNs) due to its various applications from the biological, environmental to communication points of view. This paper deals with the paramet...
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The synchronization is very important technology in the field of Cellular Neural Networks (CNNs) due to its various applications from the biological, environmental to communication points of view. This paper deals with the parametric synchronization phenomena in large-scaled oscillating CNN. When we determine adequate parameters for the amplitude and frequency in the large-scaled oscillating CNNs with non-oscillating cells, the CNN oscillates in various ways depending on their conditions. In this paper, we have exhibited that both multi-amplitude and multi-phase synchronization are generated by their parameters. To demonstrate them, we simulated multi-dimensional CNN of 4 × 4 (16 cells), 8×8 (64 cells) and 16 × 16 (256 cells) and checked the synchronization phenomena. Each cell has an inductor L, a capacitorC and a complex nonlinear conductance as as i = f(v). We find that the synchronization phenomena are similarly occurred from the small networks to the larger one. We give a basic theoretical viewpoint for interesting numerical experiment results for the synchronization phenomena in this paper.
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In this paper we demonstrate hyperchaotic dynamics in a new family of simple Cellular Neural Networks (CNNs) which is a one-dimensional regular array of four cells. The Lyapunov spectrum is calculated in a range of parameters, the...
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In this paper we demonstrate hyperchaotic dynamics in a new family of simple Cellular Neural Networks (CNNs) which is a one-dimensional regular array of four cells. The Lyapunov spectrum is calculated in a range of parameters, the bifurcation plots and several important phase portraits are presented as well.
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The design and implementation of a cellular neural network(CNN)architecture capable of modeling the behavior of reconfigurable cellular automata(CA)is presented in this paper. Despite the simplicity of their structure CA are capab...
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The design and implementation of a cellular neural network(CNN)architecture capable of modeling the behavior of reconfigurable cellular automata(CA)is presented in this paper. Despite the simplicity of their structure CA are capable of exhibiting extremely complex behaviors. This motivates the development of a reconfigurable CA architecture, capable of exhibiting all types of complex behaviors found in the different CA classes. However, the hardware complexity for developing such an architecture is very high and comes in direct contrast with the inherent modularity, regularity, locality and homogeneity properties of CA architectures.
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Calcium plays an important role in many cellular functions. This investigation of its role in nervous system behavior uses imaging techniques, confocal microscopy, and the VolVis volume visualization system. The use of VolVis prov...
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Calcium plays an important role in many cellular functions. This investigation of its role in nervous system behavior uses imaging techniques, confocal microscopy, and the VolVis volume visualization system. The use of VolVis provided substantial assistance in the study of cellular calcium dynamics and in monitoring neural network activity. It yielded geometric details that facilitated our understanding of the behavior of calcium indicators inside nerve cells and led to a new view of the calcium permeability of the nuclear envelope. It has also produced anatomical details that significantly facilitated the development of a technique to directly visualize the activity of neuron populations while simultaneously observing vertebrate behavior.
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Some novel CNN analogic algorithms are proposed, which are useful in the context of textile industry. They concern the detection of stains and defects, and the recognition of the labels on a cloth. Copyright
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A Cellular Neural Network (CNN) is a neural network model linked only to neighborhoods and which is suitable for image processing, such as noise reduction and edge detection. A Small World Cellular Neural Network (SWCNN) is an ext...
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A Cellular Neural Network (CNN) is a neural network model linked only to neighborhoods and which is suitable for image processing, such as noise reduction and edge detection. A Small World Cellular Neural Network (SWCNN) is an extended CNN to which has been added a small world link, which is a global short-cut. The SWCNN has better performance than the CNN. One of the weaknesses of the SWCNN has low fault tolerance. If the the neuron is failed, the SWCNN shows lower fault tolerance than the CNN. Previously, we proposed TMR and Reliability Counter (RC) for fault tolerance the SWCNN. In this paper, we propose the Stateful Reliability Counter (Stateful RC) method to improve tolerance. The Stateful RC has a failure state of the last histrory. The Stateful RC for TMR has higher fault tolerant than TMR and RC in the low repaire rate.
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In this paper, the possible presence of complex dynamics in nearly-symmetric standard Cellular Neural Networks (CNNs), is investigated. A one-parameter family of fourth-order CNNs is presented, which exhibits a cascade of period-d...
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In this paper, the possible presence of complex dynamics in nearly-symmetric standard Cellular Neural Networks (CNNs), is investigated. A one-parameter family of fourth-order CNNs is presented, which exhibits a cascade of period-doubling bifurcations leading to the birth of a complex attractor, close to some nominal symmetric CNN. Different from previous work on this topic, the bifurcations and complex dynamics are obtained for small relative errors with respect to the nominal interconnections. The fourth-order CNNs have negative (inhibitory) interconnections between distinct neurons, and are designed by a variant of a technique proposed by Smale to embed a given dynamical system within a competitive dynamical system of larger order.
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