Norio Akamatsu

Chaotically transitional phenomena in the forced negative-resistance oscillator

This paper deals with chaotically transitional phenomena which occur In the forced negative-resistance oscillator. Experimental studies using analog and digital computers have been carried out. The difference between the almost periodic oscillations and the chaotically transitional processes is clarified. Various strange attractors representing chaotically transitional processes and their average power spectra are given. They are discussed in detail and compared with the results obtained in the forced oscillatory systems.

An intrinsic mechanism for the oscillatory contraction of muscle

A new model based on the theory of dynamical systems is proposed for the intrinsic random or pscudo-random mechanism underlying certain types of muscular tremor. The active length-tension curve of the individual sarcomere, in conjunction with the passive length-tension relation is a map from length to tension with an observed time delay between length change and resulting tension change. The passive length tension relation is assumed to instantaneously relate this tension change back to a change in length. The stability properties of this iterated interval map are investigated by means of computer simulation and computation of the Lyapunov exponent and the bifurcation tree. The resulting analysis is related to experimental tremor data in the literature in terms of period doubling, bifurcation points, and “chaotic” behavior. The model appears to have its most fruitful application in understanding the insect type and isometric mammalian types of tremor.

Recognition of EMG signal patterns by neural networks

The paper tries to recognize EMG signals by using neural networks. The electrodes under the dry state are attached to wrists and then EMG is measured. These EMG signals are classified into seven categories, such as neutral, up and down, right and left, wrist to inside, wrist to outside by using a neural network. The neural network learns FFT spectra to classify them. Moreover, we perform the principal component analysis using the simple principal component analysis before we perform recognition experiments. It is shown that our approach is effective to classify the EMG signals by means of computer simulations.

Face recognition using genetic algorithm based template matching

We present a face recognition method using template matching. Template matching is performed with the help of a genetic algorithm to automatically test several positions around the target and also adjust the size of the template as the matching process progresses. We use two kinds of templates for each face. One is based on edge detection and the other depends on the YIQ color information from the face. Our template is a T-shaped region symmetrical between the eyes, covering both eyes, the nose and mouth. Our features of interest to achieve face recognition are therefore the eyes, nose and the mouth. We ignore the shape of the face so as to have a small template for faster matching and also because the effect of the shape does not result in a significant increase in the overall final accuracy. We conducted a simulation experiment to verify our idea and also did a comparative experiment using a distance measure face recognition method.

Face detection based on skin color information in visual scenes by neural networks

A method to examine whether or not human faces are included in the images and to detect their position by using the technique of skin color region extraction is presented. In this technique, the skin color which is a main feature of faces is detected, a binary image composed of skin color parts and background one is constructed from an original image using a neural network which learns color information, and then the skin color parts of some sizes are regarded as face candidates. Thus search regions are limited within the skin color parts. Therefore, an improvement in the detection speed is achieved. These face candidates are examined using a neural network which learns the features of faces, and estimates whether or not the original image includes the faces. From results of computer simulations, a search rate of 83.3 % accuracy was achieved from 15 sheets, each having from 1 to 3 faces. The sizes and positions of faces were chosen as randomly as possible. There was no search of other objects other than faces.

License plate detection system by using threshold function and improved template matching method

License plate recognition is very important in an automobile society. However, it is very difficult to do it, because a background and a car surface color can be similar to that of the license plate. Furthermore, detection of cars moving at a very high-speed is difficult to be done. We propose a new method to extract a car license plate automatically by using a genetic algorithm (GA). By using GA, the most likely plate colors are decided under various light conditions. First, the average brightness Y values of images are calculated. Next, relationship between the Y value and the most likely plate color thresholds (upper and lower bounds) are obtained by GA to estimate threshold equations by using the RLS algorithm. Finally, in order to show the effectiveness of the proposed method, we show simulation examples by using real images.

License Plate Detection Using Hereditary Threshold Determine Method

License plate recognition is very important in an automobile society. Also in it, since plate detection has big influence on subsequent number recognition, it is very important. However, it is very difficult to do it, because a background and a body color of cars are similar to that of the license plate. In this paper, we propose a new thresholds determination method in the various background by using the real-coded genetic algorithm (RGA). By using RGA, the most likely plate colors are decided under various light conditions. First, the average brightness Y values of images are calculated. Next, relationship between the Y value and the most likely plate color thresholds (upper and lower bounds)are obtained by RGA to estimate thresholds function by using the recursive least squares (RLS) algorithm. Finally, in order to show the effectiveness of the proposed method, we show simulation examples by using real images.

Feature analysis for the EMG signals based on the class distance

In this paper, a feature vector is extracted from an electromyography (EMG) signal at a wrist, and the EMG signals based on 7 motions are recognized. In order to perform good pattern recognition, it is desirable that the distance in feature vector between classes is far, and that the variance in a class is small. In consideration of these, important frequency bands of EMG signals are selected by using a genetic algorithm. We use the selected frequency band to perform the recognition experiment of EMG signal by a neural network. Finally, the effectiveness of this method is demonstrated by means of computer simulations.

Design and evaluation of neural networks for coin recognition by using GA and SA

In this paper, we propose a method to design a neural network (NN) by using a genetic algorithm (GA) and simulated annealing (SA). And also, in order to demonstrate the effectiveness of the proposed scheme, we apply the proposed scheme to a coin recognition example. In general, as a problem becomes complex and large-scale, the number of operations increases and hardware implementation to real systems (coin recognition machines) using NNs becomes difficult. Therefore, we propose the method which makes a small-sized NN system to achieve a cost reduction and to simplify hardware implementation to the real machines. The coin images used in this paper were taken by a cheap scanner. Then they are not perfect, but a part of the coin image could be used in computer simulations. Input signals, which are Fourier spectra, are learned by a three-layered NN. The inputs to NN are selected by using GA with SA to make a small-sized NN. Simulation results show that the proposed scheme is effective to find a small number of input signals for coin recognition.

Development of speedy and high-sensitivity pen system for writing pressure and writer identification

This paper describes a pen input system which was developed to obtain subtle writing pressure data. When the writing pressure is small, a conventional electronic pen cannot collate writing pressure data sufficiently. However, the developed pen can collate sufficient data with precision. The developed pen can manipulate any high-speed signatures by means of high-speed writing pressure processing. Further, in the pen input system, writing pressure data can be obtained by using only the pen, without any tablet. The writing pressure information obtained by the developed pen input system is analyzed and applied to writer identification.