In this paper, Cellular Neural Networks using genetic algorithm (GA-CNNs) are designed for CMOS image noise reduction. Cellular Neural Networks (CNNs) could be an efficient way to apply to the image processing technique, since CNNs have high-speed parallel signal processing characteristics. Adaptive CNNs structure is designed for the reduction of Photon Shot Noise (PSN) changed according to the average number of photons, and the design of templates for adaptive CNNs is based on the genetic algorithm using real numbers. These templates are optimized to suppress PSN in corrupted images. The simulation results show that the adaptive GA-CNNs more efficiently reduce PSN than do the other noise reduction methods and can be used as a high-quality and low-cost noise reduction filter for PSN. The proposed method is designed for real-time implementation. Therefore, it can be used as a noise reduction filter for many commercial applications. The simulation results also show the feasibility to design the CNNs template for a variety of problems based on the statistical image model.

A fast time-domain simulation technique of plane circuits via two-layer Cellular Neural Network (CNN)-based modeling, which is necessary for power/signal integrity evaluation in VLSIs, printed circuit boards, and packages, is presented. Using the new notation expressed by the two-layer CNN, 1,553 times faster simulation is achieved, compared with Berkeley SPICE (ngspice). In CNN community, CNNs are generally simulated by explicit numerical integration such as the forward Euler and Runge-Kutta methods. However, since the two-layer CNN is a stiff circuit, we cannot analyze it by using an explicit numerical integration method. Hence, to analyze the two-layer CNN and reduce the computational cost, the leapfrog method is introduced. This procedure would open an application of CNN to electronic design automation area.

The watershed transform has been used as a powerful morphological segmentation tool in a variety of image processing applications. This is because it gives a good segmentation result if a topographical relief and markers are suitably chosen for different type of images. This paper proposes a parallel implementation of the watershed transform on the cellular neural network (CNN) universal machine, called cellular watersheds. Owing to its fine grain architecture, the watershed transform can be parallelized using local information. Our parallel implementation is based on a simulated immersion process. To evaluate our implementation, we have experimented on the CNN universal chip, ACE16k, for synthetic and real images.

The lifting scheme is an efficient and flexible method for the construction of linear and nonlinear wavelet transforms. In this paper, a novel lossless image coding technique based on the lifting scheme using discrete-time cellular neural networks (DT-CNNs) is proposed. In our proposed method, the image is interpolated by using the nonlinear interpolative dynamics of DT-CNN, and since the output function of DT-CNN works as a multi-level quantization function, our method composes the integer lifting scheme for lossless image coding. Moreover, the nonlinear interpolative dynamics by A-template is used effectively compared with conventional CNN image coding methods using only B-template. The experimental results show a better coding performance compared with the conventional lifting methods using linear filters.

In this letter, our proposed approach exploits the use of original and simplest Cellular Neural Network (CNN) for 2D Doubly Complementary (DC) Infinite Impulse Response (IIR) filter banks design. The properties of feedback and feedforward templates are studied for this purpose. Through some examples it is shown how generalizations of these templates can be used for DC IIR filter banks design. We modify Lagrangian function which is used for optimizing a low-pass filter design considering the constraint for stability of CNN. The brief conclusions with design examples that illustrate the proposed method and an image enhancement and restoration applications of designed filter banks are presented.

The cooperative and competitive network suitable for circuit realization is presented, based on the network proposed by Amari and Arbib. To ensure WTA process, the output function of the original network is replaced with the piecewise linear function and supplying the inputs as pulse waveforms is obtained. In the SPICE simulations, it is confirmed that the network constructed by operational amplifiers attains WTA process, even if the scale of the network becomes large.

This paper gives a new sufficient condition for cellular neural networks with delay (DCNNs) to be completely stable. The result is a generalization of two existing stability conditions for DCNNs, and also contains a complete stability condition for standard CNNs as a special case. Our new sufficient condition does not require the uniqueness of equilibrium point of DCNNs and is independent of the length of delay.

This paper investigates the behavior of one-dimensional discrete-time binary cellular neural networks with both the A- and B-templates and gives the necessary and sufficient conditions for the above network to be stable for unspecified fixed boundaries.

Cellular Neural Networks (CNNs) have been developed as a high-speed parallel signal-processing platform. In this paper, a generalized two-layer cellular neural network model is proposed for image processing, in which two templates are introduced between the two layers. We found from the simulations that the two-layer CNNs efficiently behave compared to the single-layer CNNs for the many applications of image processing. For examples, simulation problems such as linearly non-separable task--logic XOR, center point detection and object separation, etc. can be efficiently solved with the two-layer CNNs. The stability problems of the two-layer CNNs with symmetric and/or special coupling templates are also discussed based on the Lyapunov function technique. Its equilibrium points are found from the trajectories in a phase plane, whose results agree with those from simulations.

This paper describes the implementation of a proposed image filter into a Discrete-Time Cellular Neural Network (DT-CNN). The three stages that compose the filter are described, showing that the resultant filter is capable of (1) erasing or detecting several concentric shapes simultaneously, (2) thresholding and (3) thinning of gray-scale images. Because the DT-CNN has to fill certain conditions for this filter to be implemented, it becomes a modified version of a DT-CNN. Those conditions are described and also experimental results are clearly shown.

The robustness of a template set for cellular neural networks (CNNs) is crucial for applications of VLSI CNN chips. Whereas the problem of designing any, possibly very sensitive, templates for a given task is fairly easy to solve, it is computationally expensive to find optimal solutions. For the class of bipolar CNNs, we propose an analytical approach to derive the optimally robust template set from any correctly operating template. Furthermore, our method yields a theoretical upper bound for the robustness of the CNN task.

Template learning has potential application in several areas of Cellular Neural Network research, including texture recognition, pattern detection and so on. In this letter, a recently-developed algorithm called Adaptive Simulated Annealing is investigated for learning CNN templates, as a superior alternative to the Genetic Algorithm.

Recently, the spatio-temporal filter using linear analog Cellular Neural Network (CNN), called CNN filter array, has been proposed for the purpose of dynamic image processing. In this paper, we propose a design method of descrete-time cellular neural network filter which selectively extracts the particular moving object from other moving objects and noise. The CNN filter array forms a spatio-temporal filter by arranging cells with a same function. Each of these cells is a simple linear analog temporal filter whose input is the weighted sum of its neighborhood inputs and outputs and each cell corresponds to each pixel. The CNN filter is formed by new model of discrete time CNN, and the filter parameters are determined by applying backpropagation algorithm in place of the analytic method. Since the number of connections between neurons in the CNN-type filter is relatively few, the required computation in the learning phase is reasonable amount. Further, the output S/N ratio is improved by introducing nonlinear element. That is, if the ratio of output to imput is smaller than a certain value, the output signal is treated as a noise signal and ought to be rejected. Through some examples, it is shown that the target object is enhanced in the noisy environment.

In this article, a new type of diffusion template and an analogic CNN algorithm using this diffusion template for detecting some lung cancer symptoms in X-ray films are proposed. The performance of the diffusion template is investigated and our CNN algorithm is verified to detect some key lung cancer symptoms, successfully.

When N oscillators are coupled by one resistor, we can see N-phase oscillation, because the system tends to minimize the current through the coupling resistor. Moreover, when the hard oscillators are coupled, we can see N, N - 1, , 3, 2-phase oscillation and get much more phase states. In this study, the two types of coupled oscillators networks with third and fifth-power nonlinear characteristics are proposed. One network has two-dimensional hexagonal structure and the other has two-dimensional lattice structure. In the hexagonal circuit, adjacent three oscillators are coupled by one coupling resistor. On the other hand, in the lattice circuit, four oscillators are coupled by one coupling resistor. In this paper we confirm the phenomena seen in the proposed networks by circuit experiments and numerical calculations. In the system with third-power nonlinear characteristics, we can see the phase patterns based on 3-phase oscillation in the hexagonal circuit, and based on anti-phase oscillation in lattice circuit. In the system with fifth-power nonlinear characteristics, we can see the phase patterns based on 3-phase and anti-phase oscillation in both hexagonal and lattice circuits. In particular, in these networks, we can see not only the synchronization based on 3-phase and anti-phase oscillation but the synchronization which is not based on 3-phase and anti-phase oscillation.

In this paper, after the introduction of the definition of State Controlled Cellular Neural Networks (SC-CNNs), it is shown that they are able to generate complex dynamics of circuits showing strange behaviour. Theoretical propoitions are presented to fix the templates of the SC-CNNs in such a way as to exactly match the dynamic behaviour of the circuits considered. The easy and cheap implementation of the proposed SC-CNN devices is illustrated and a gallery of experimentally obtained strange attractors are shown to confirm the practical suitability of the outlined strategy.

Various applications of cellular neural network (CNN) are reported such as a feature extraction of the patterns, an extraction of the edges or corners of a figure, noise exclusion, searching in maze and so forth. In this paper, we propose a cellular neural network whose each cell has more than two output levels. By using the output function which has several saturated levels, each cell turns to have several output states. The multiple-valued CNN enhances its associative memory function so as to express various kinds of aspects. We report an application of the enhanced asscociative memory function to a diagnosis of the liver troubles.

In this article, a new analogic CNN algorithm to extract features of postage stamps in gray-scale images Is introduced. The Gradient Controlled Diffusion method plays an important role in the approach. In our algorithm, it is used for smoothing and separating Arabic figures drawn with a color which is similar to the background color. We extract Arabic figures in postage stamps by combining Gradient Controlled Diffusion with nearest neighbor linear CNN template and logic operations. Applying the feature extraction algorithm to different test images it has been verified that it is also effective in complex segmentation problems

In this study, we discuss a discrete-time cellular neural network (DTCNN) and its applications including convergence property and stability. Two theorems about the convergence condition of nonreciprocal non-uniform DTCNNs are described, which cover those of reciprocal one as a special case. Thus, it can be applied to wide classes of image processings, such as associative memories, multiple visual patterns recognition and others. Our DTCNN realized by the software simulation can largely reduce the computational time compared to the continuous-time CNN.

Cellular Neural Networks (CNNs) are nonlinear dynamic array processors with mainly local interconnections. In most of the applications, the local interconnection pattern, called cloning template, is translation invariant. In this paper, an optimal ring-coding method for rotation invariant description of given set of objects, is introduced. The design methodology of the templates based on the ring-codes and the synthesis of CNN analogic algorithms to detect standing and moving objects in a rotationally invariant way, discussed in detail. It is shown that the algorithms can be implemented using the CNN Universal Machine, the recently invented analogic visual microprocessor. The estimated time performance and the parallel detecting capability is emphasized, the limitations are also thoroughly investigated.