In this study, multimode chaos observed from two coupled chaotic oscillators with hard nonlinearities is investigated. At first, a simple chaotic oscillator with hard nonlinearities is realized. It is confirmed that in this chaotic oscillator the origin is always asymptotically stable and that the solution, which is excited by giving relatively large initial conditions, undergoes period-doubling bifurcations and bifurcates to chaos. Next, the coexistence of four different modes of oscillations are observed from two coupled chaotic oscillators with hard nonlinearities by both of circuit experiments and computer calculations. One of the modes of oscillation is a nonresonant double-mode oscillation and this oscillation is stably generated even in the case that oscillation is chaotic. Namely, for this oscillation mode, chaotic oscillation and periodic oscillation can be simultaneously excited. This phenomenon has not been reported yet, and we name this phenomenon as double-mode chaos. Finally, the beat frequency of the double-mode chaos is confirmed to be changed by varying the value of the coupling capacitor.

In this paper, we propose a novel SPICE oriented steady-state analysis of nonlinear circuits based on the circuit partition technique. Namely, a given circuit is partitioned into the linear and nonlinear subnetworks by the application of the substitution theorem. Each subnetwork is solved using SPICE simulator by the different techniques of AC analysis and transient analysis, respectively, whose steady-state reponse is found by an iteration method. The novel points of our algorithm are as follows: Once the linear subnetworks are solved by AC analysis, each subnetwork is replaced by a simple equivalent RL or RC circuit at each frequency component. On the other hand, the reponse of nonlinear subnetworks are solved by transient analysis. If we assume that the sensitivity circuit is approximated at the DC operational point, the variational value will be also calculated from a simple RL ro RC circuit. Thus, our method is very simple and can be also applied to large scale circuits, effciently. To improve the convergency, we introduce a compensation technique which is usefully applied to stiff circuits containing components such as diodes and transistors.

In this study, some oscillators with different oscillation frequencies, N - 1 oscillators have the same oscillation frequency and only the Nth oscillator has different frequency, coupled by a resistor are investigated. At first we consider nonresonance. By carrying out circuit experiments and computer calculations, we observe that oscillation of the Nth oscillator stops in some range of the frequency ratio and that others are synchronized as if the Nth oscillator does not exist. These phenomena are also analyzed theoretically by using the averaging method. Secondly, we investigate the resonance region where the fiequency ratio is nearly equal to 1. For this region we can observe interesting double-mode oscillation, that is, synchronization of envelopes of the double-mode oscillation and change of oscillation amplitude of the Nth oscillator.

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

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, we investigate bifurcation phenomena ovserved from two autonomous three-dimensional chaotic circuits coupled by an inductor. Two types of synchronization modes are ovserved in this coupled system, i.e., in-phase synchronization and anti-phase synchronization. For the purpose of detailed analysis, we consider the case that the diodes in the subcircuits are assumed to operate as ideal switches. In this case Poincare map is derived as a three-dimensional map, and Lyapunov exponents can be calculated by using exact solutions. Various bifurcation phenomena related with chaos synchronization are clarified. We confirm that various bifurcation phenomena are observed from circuit experiments.

In this study, we show how changing a frequency in one of N chaotic circuits coupled by a resistor effects our system by means of both circuit experiment and computer calculation. In these N chaotic circuits, N-1 circuits are completely identical, and the remaining one has altered the value of the oscillation frequency. It is found that for the case of N = 3 when a value of a coupling resistor is gradually increased, only one circuit with different frequency exhibits bifurcation phenomena including inverse period-doubling bifurcation, and for larger value of coupling resistor, the chaotic circuit with different frequency suddenly stops oscillating and the remaining two chaotic circuits exhibit completely anti-phase synchronization.

Nonuniform transmission lines are crucial in integrated circuits and printed circuit boards, because these circuits have complex geometries and layout between the multi layers, and most of the transmission lines possess nonuniform characteristics. In this article, an efficient numerical method for analyzing nonuniform transmission lines has been presented by using the Chebyshev expansion method and moment techniques. Efficiency on computational cost is demonstrated by numerical example.

There have been many investigations of mutual synchronization of oscillators. In this article, N oscillators with the same natural frequencies mutually coupled by one resistor are analyzed. In this system, various synchronization phenomena can be observed because the system tends to minimize the current through the coupling resistor. When the nonlinear characteristics are third-power, we can observe N-phase oscillation, and this system can take (N 1)! phase states. When the nonlinear characteristics are fifth-power, we can observe (N 1),(N 2)3 and 2-phase oscillations as well as N-phase oscillations and we can get much more phase states from this system than that of the system with third-power nonlinear characteristics. Because of their coupling structure and huge number of steady states of the system, our system would be a structural element of cellular neural networks. In this study, it is confirmed that our systems can stably take huge number of phase states by theoretical analysis, computer calculations and circuit experiments.

This paper introduces an interactive expression editing system that allows users to design facial expressions easily. Currently, popular example-based methods construct face models based on the examples of target face. The shortcoming of these methods is that they cannot create expressions for novel faces: target faces not previously recorded in the database. We propose a solution to overcome this limitation. We present an interactive facial-geometric-feature animation system for generating expressions of novel faces. Our system is easy to use. By click-dragging control points on the target face, on the computer screen display, unique expressions are generated automatically. To guarantee natural animation results, our animation model employs prior knowledge based on various individuals' expressions. One model prior is learned from motion vector fields to guarantee effective facial motions. Another, different, model prior is learned from facial shape space to ensure the result has a real facial shape. Interactive animation problem is formulated in a maximum a posterior (MAP) framework to search for optimal results by combining the priors with user-defined constraints. We give an extension of the Motion Propagation (MP) algorithm to infer facial motions for novel target faces from a subset of the control points. Experimental results on different facial animations demonstrate the effectiveness of the proposed method. Moreover, one application of our system is exhibited in this paper, where users create expressions for facial sketches interactively.

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.

Cell assembly is one of explanations of information processing in the brain, in which an information is represented by a firing space pattern of a group of plural neurons. On the other hand, effectiveness of neural network has been confirmed in pattern recognition, system control, signal processing, and so on, since the back propagation learning was proposed. In this study, we propose a new network structure with affordable neurons in the hidden layer of the feedforward neural network. Computer simulated results show that the proposed network exhibits a good performance for the back propagation learning. Furthermore, we confirm the proposed network has a good generalization ability.

The Self-Organizing Map (SOM) is an unsupervised neural network introduced in the 80's by Teuvo Kohonen. In this paper, we propose a method of simultaneously using two kinds of SOM whose features are different (the nSOM method). Namely, one is distributed in the area at which input data are concentrated, and the other self-organizes the whole of the input space. The competing behavior of the two kinds of SOM for nonuniform input data is investigated. Furthermore, we show its application to clustering and confirm its efficiency by comparing with the k-means method.

In the real world, it is not always true that neighboring houses are physically adjacent or close to each other. in other words, "neighbors" are not always "true neighbors." In this study, we propose a new Self-Organizing Map (SOM) algorithm, SOM with False-Neighbor degree between neurons (called FN-SOM). The behavior of FN-SOM is investigated with learning for various input data. We confirm that FN-SOM can obtain a more effective map reflecting the distribution state of input data than the conventional SOM and Growing Grid.

Since we can accumulate a large amount of data including useless information in recent years, it is important to investigate various extraction method of clusters from data including much noises. The Self-Organizing Map (SOM) has attracted attention for clustering nowadays. In this study, we propose a method of using plural SOMs (TSOM: Tentacled SOM) for effective data extraction. TSOM consists of two kinds of SOM whose features are different, namely, one self-organizes the area where input data are concentrated, and the other self-organizes the whole of the input space. Each SOM of TSOM can catch the information of other SOMs existing in its neighborhood and self-organizes with the competing and accommodating behaviors. We apply TSOM to data extraction from input data including much noise, and can confirm that TSOM successfully extracts only clusters even in the case that we do not know the number of clusters in advance.

Glial cells include several types of cells such as astrocytes, and oligodendrocytes apart from the neurons in the brain. In particular, astrocytes are known to be important in higher brain function and are therefore sometimes simply called glial cells. An astrocyte can transmit signals to other astrocytes and neurons using ion concentrations. Thus, we expect that the functions of an astrocyte can be applied to an artificial neural network. In this study, we propose a multi-layer perceptron (MLP) with a pulse glial chain. The proposed MLP contains glia (astrocytes) in a hidden layer. The glia are connected to neurons and are excited by the outputs of the neurons. The excited glia generate pulses that affect the excitation thresholds of the neurons and their neighboring glia. The glial network provides a type of positional relationship between the neurons in the hidden layer, which can enhance the performance of MLP learning. We confirm through computer simulations that the proposed MLP has better learning performance than a conventional MLP.

In this paper, we propose a new algorithm for calculating the exact poles of the admittance matrix of RLCG interconnects. After choosing dominant poles and corresponding residues, each element of the exact admittance matrix is approximated by partial fraction. A procedure to obtain the residues that guarantee the passivity is also provided, based on experimental studies. In the procedure the residues are calculated by using the least squares method so that the partial fraction matches each element of the exact admittance matrix in the frequency-domain. From the partial fraction representation, the asymptotic equivalent circuit models which can be easily simulated with SPICE are synthesized. It is shown that an efficient model-order reduction is possible for short-length interconnects.

Durability describes the ability of a device to operate properly in imperfect conditions. We have recently proposed a novel neural network structure called an "Affordable Neural Network" (AfNN), in which affordable neurons of the hidden layer are considered as the elements responsible for the robustness property as is observed in human brain function. Whereas earlier we have shown that AfNNs can still generalize and learn, here we show that these networks are robust against damages occurring after the learning process has terminated. The results support the view that AfNNs embody the important feature of durability. In our contribution, we investigate the durability of the AfNN when some of the neurons in the hidden layer are damaged after the learning process.

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.

Analysis of frequency-dependent lossy transmission lines is very important for designing the high-speed VLSI, MCM and PCB. The frequency-dependent parameters are always obtained as tabulated data. In this paper, a new curve fitting technique of the tabulated data for the moment matching technique in the interconnect analysis is presented. This method based on Chebyshev interpolation enhances the efficiency of the moment matching technique.