Conceptual graph formalism is a knowledge representation language in AI based on a graphical form of logic. Although logic is the basis of the conceptual graph theory, there is a strongly felt absence of a formal treatment of conceptual graphs as a logic programming language. In this paper, we develop the notion of a conceptual graph program as a kind of graph-based order-sorted logic program. First, we define the syntax of the conceptual graph program by specifying its major syntactic elements. Then, we develop a kind of model theoretic semantics and fixpoint semantics of the conceptual graph program. Finally, we show that the two types of semantics coincide for the conceptual graph programs.

This paper presents a simple and efficient method for estimation of parameters useful for textured image analysis. On the basia of a 2-D Wold-like decomposition of homogenenous random fields, the texture field can be decomposed into a sum of two mutually orthogonal components: a deterministic component and an indeterministic component. The spectral density function (SDF) of the former is a sum of 1-D or 2-D delta functions. The 2-D autocorrelation function (ACF) of the latter is fitted to the assumed anisotropic ACF that has an elliptical contour. The parameters representing the ellipse and those representing the delta functions can be used to detect rotation angles and scaling factors of test textures. Specially, rotation and scaling invariant parameters, which are applicable to the classification of rotated and scaled textured images, can be estimated by combining these parameters. That is, a test texture can be correctly classified even if it is rotated and scaled. Several computer experiments on natural textures show the effectiveness of this method.

The dynamics of an artificial neural network derived from a biological system, and its two applications to engineering problems are examined. The model has a multi-layer structure simulating the primary and secondary components in the olfactory system. The basic element in each layer is an oscillator which simulates the interactions between excitatory and inhibitory local neuron populations. Chaotic dynamics emerges from interactions within and between the layers, which are connected to each other by feedforward and feedback lines with distributed delays. A set of electroencephalogram (EEG) obtained from mammalian olfactory system yields aperiodic oscillation with 1/f characteristics in its FFT power spectrum. The EEG also reveals abrupt state transitions between a basal and an activated state. The activated state with each inhalation consists of a burst of oscillation at a common time-varying instantaneous frequency that is spatially amplitude-modulated (AM). The spatial pattern of the activated state seems to represent the class of the input ot the system, which simulates the input from sensory receptors. The KIII model of the olfactory system yields sustained aperiodic oscillation with "1/f" spectrum by adjustment of its parameters. Input in the form of a spatially distributed step funciton induces a state transition to an activated state. This property gives the model its utility in pattern classification. Four different methods (SD, RMS, PCA and FFT) were applied to extract AM patterns of the common output wave forms of the model. The pattern classification capability of the model was evaluated, and synchronization of the output wave form was shown to be crucial in PCA and FFT methods. This synchronization has also been suggested to have an important role in biological systems related to the information extraction by spatiotemporal integration of the output of a transmitting area of cortex by a receiving area.

We reported that a competitive learning neural network had the ability of self-organization in the classification of questionnaire survey data. In this letter, its self-organized learning was evaluated by means of mutual information. Mutual information may be useful to find efficently the network which can give optimal classification.

This paper describes a method for screening psychiatric patients based on a questionnaire consisting of simple yes/no questions regarding to physical, mental conditions and subjective symptoms which is provided at their first visit to the hospital. The analysis of the questionnaire is important to understand patients' background. One hundred filled out questionnaires were utilized for constructing and evaluating a pseude Bayesian classifier which classifies patients into three categories i.e. Schizophrenic, emotional and neurotic disorders with average correct prediction rate of 73.3%. The rate was 16.6% higher than the result given by experienced medical doctors and the method will be a useful mean for automatic screening of the psychiatric patients.

In this paper we introduce a new feature called stroke index for document image analysis. It is based on the minimum covering run expression method (MCR). This stroke index is a function of the number of horizontal and vertical runs in the original image and of number of runs by the MCR expression. As document images may present a variety of patterns such as graph, text or picture, it is necessary for image understanding to classify these different patterns into categories beforehand. Here we show how one could use this stroke index for such applications as classification or segmentation. It also gives an insight on the possibility of stroke extraction from document images in addition to classifying different patterns in a compound image.

In this paper, a design of TCM signals for Middleton's class-A impulsive noise environment is investigated. The error event characteristics under the impulsive noise is investigated, and it is shown that the length of the signal sequence is more important than Euclidean distance between the signal sequences. Following this fact, we introduce the shortest error event path length as a measure of the signal design. In order to make this value large, increasing of states of convolutional codes is employed, and the performance improvement achieved by this method is evaluated. Numerical results show the great improvement of the error performance and conclude that the shortest error event path length is a good measure in the design of TCM signals under impulsive noise environment. Moreover, the capacity of class-A impulsive noise channel is evaluated, and the required signal sets expansion rates to obtain the achievable coding gain is discussed.

This paper describes an advanced rule-embedded neural network (RENN+) that has an extended framework for achieving a very tight integration of learning-based neural networks and rule-bases of existing if-then rules. The RENN+ is effective in pattern recognition with ill-posed conditions. It is basically composed of several component RENNs and an output RENN, which are three-layer back-propagation (BP) networks except for the input layer. Each RENN can be pre-organized by embedding the if-then rules through translation of the rules into logic functions in a disjunctive normal form, and can be trainded to acquire adaptive rules as required. A weight-modification-reduced learning algorithm (WMR) capable of standard regularization is used for the post-training to suppress excessive modification of the weights for the embedded rules. To estimate the effectiveness of the proposed RENN+, it was used for pattern recognition in a radar system for detection of buried pipes. This trial showed that a RENN+ with two component RENNs had good recognition capability, whereas a conventional BP network was ineffective.

In this paper a method of recognizing waveform based on the Discrete Wavelet Transform (DWT) presented by us is applied to detecting the K-complex in human's EEG which is a slow wave overridden by fast rhythms (called as spindle). The features of K-complex are extracted in terms of three parameters: the local maxima of the wavelet transform modulus, average slope and the number of DWT coefficients in a wave. The 4th order B-spline wavelet is selected as the wavelet basis. Two channels at different resolutions are used to detect slow wave and sleep spindle contained in the K-complex. According to the principle of the minimum distance classification the classifiers are designed in order to decide the thresholds of recognition criteria. The EEG signal containing K-complexes elicited by sound stimuli is used as pattern to train the classifiers. Compared with traditional method of waveform recognition in time domain, this method has the advantage of automatically classifying duration ranks of various waves with different frequencies. Hence, it specially is suitable to recognition of signals which are the superimposition of waves with different frequencies. The experimental results of detection of K-complexes indicate that the method is effective.

The double-stage threshold-type foreground-background congestion control for the common-store queueing system with multiple nonpreemptive priority classes is proposed to improve the transient performance, where the numbers of accepted priority packets in both foreground and background stores are controlled under the double-stage threshold-type scheduling. In the double-stage threshold-type congestion control, the background store is used for any priority packets, and some parts of the background store are reserved for lower-priority packets to accommodate more lower-priority packets in the background store, whereas some parts of the foreground store are reserved for higher-priority packets to avoid the priority deadlock. First, we derive the general set of coupled differential equations describing the system-state, and the expressions for mean system occupancy, throughput and loss probability. Second, the transient behavior of system performance is evaluated from the time-dependent state probabilities by using the Runge-Kutta procedure. It is shown that when the particular traffic class becomes overloaded, high throughputs and low loss probabilities of other priority classes can be obtained.

We modified the adaptive fuzzy classification algorithm (AFC), which allows fuzzy clusters to grow to meet the demands of a given task during training. Every fuzzy cluster is defined by a reference vector and a fuzzy cluster radius, and it is represented as a shape of hypersphere in pattern space. Any pattern class is identified by overlapping plural hyperspherical fuzzy clusters so that it is possible to approximate complex decision boundaries among pattern classes. The modified AFC was applied to recognize handwritten digits, and performances were shown compared with other neural networks.

Contextual classification of multispectral image data in remote sensing is discussed and concretely two improved contextual classifiers are proposed. The first is the extended adaptive classifier which partitions an image successively into homogeneously distributed square regions and applies a collective classification decision to each region. The second is the accelerated probabilistic relaxation which updates a classification result fast by adopting a pixelwise stopping rule. The evaluation experiment with a pseudo LANDSAT multispectral image shows that the proposed methods give higher classification accuracies than the compound decision method known as a standard contextual classifier.

We apply neural networks to implement a line shape recognition/classification system. The purpose of employing neural networks is to eliminate target-specific algorithms from the system and to simplify the system. The system needs only to be trained by samples. The shapes are captured by the following operations. Lines to be processed are segmented at inflection points. Each segment is extended from both ends of it in a certain percentage. The shape of each extended segment is captured as an approximate curvature. Curvature sequence is normalized by size in order to get a scale-invariant measure. Feeding this normalized curvature date to a neural network leads to position-, rotation-, and scale-invariant line shape recognition. According to our experiments, almost 100% recognition rates are achieved against 5% random modification and 50%-200% scaling. The experimental results show that our method is effective. In addition, since this method captures shape locally, partial lines (caused by overlapping etc.) can also be recognized.

This paper proposes a new method of extracting feature attentive regions in a learnt multi-layer neural network. We difine a function which calculates the degree of dependence of an output unit on an inpur unit. The value of this function can be used to investigate whether a learnt network detects the feature regions in the training patterns. Three computer simulations are presented: (1) investigation of the basic characteristic of this function; (2) application of our method to a simpie pattern classification task; (3) application of our method to a large scale pattern classfication task.

The main problem in statistical pattern recognition is to design a classifier. Many researchers point out that a finite number of training samples causes the practical difficulties and constraints in designing a classifier. However, very little is known about the performance of a classifier in small training sample size situations. In this paper, we compare the classification performance of the well-known classifiers (k-NN, Parzen, Fisher's linear, Quadratic, Modified quadratic, Euclidean distance classifiers) when the number of training samples is small.

A power converter with a new piezoelectric transformer is presented. The piezoelectric transformer, made of lead titanate solid solution ceramic, is operated with a thickness extensional vibration mode. This transformer can operate at high frequency, over several megahertz, with about 90% high efficiency. The resonant frequency for the transformer is 2 MHz. The power converter with the transformer applies the theory for a class-E switching converter using an electromagnetic transformer. Maximum output power was obtained when the switching frequency was slightly higher than the resonant frequency. 4.4 W output power was successfully obtained with 52% efficiency at 2.1 MHz switching frequency.

Naturalness of expressions reflects various pragmatic factors in addition to grammatical factors. In this paper, we discuss relations between expressions and two pragmatic factors: a point fo view of speaker and a hierarchical relation among participants. Degree of empathy" and class" is used to express these pragmatic factors as one-dimensional notion. Then inequalities and equalities of them become conditions for selecting natural expressions. The authors of this paper formulate conditions as principles about lexical and syntactical constraints, and have implemented a sentence generation grammar using the unification grammar formalism.

In the ISAR (Inverse Synthetic Aperture Radar), when a target is to be recognized by use of the radar image produced from the radar echoes, it is important first to estimate the scale of the target. To estimate the scale, the rotating motion of the target must be estimated. This paper describes a method for estimating the scale of the target from the information on the radar image by converting the target figure into a simple model and estimating the rotating motion of the target.

A method for the recognition of Arabic printed scripts entered from an image scanner is presented. The method uses the Hough transformation (HT) to extract features, Dynamic programming (DP) matching technique, and a topological classifier to recognize the characters. A process of characters recognition is further divided into four parts: preprocessing, segmentation of a word into characters, features extraction, and characters identification. The preprocessing consists of the following steps: smoothing to remove noise, baseline drift correction by using HT, and lines separation by making an horizontal projection profile. After preprocessing, Arabic printed words are segmented into characters by analysing the vertical and the horizontal projection profiles using a threshold. The character or stroke obtained from the segmentation process is normalized in size, then thinned to provide it skeleton from which features are extracted. As in the procedure of straight lines detection, a threshold is applied to every cell and those cells whose count is greater than the threshold are selected. The coordinates (R, θ) of the selected cells are the extracted features. Next, characters are classified in two steps: In the first one, the character main body is classified using DP matching technique, and features selected in the HT space. In the second one, simple topological features extracted from the geometry of the stress marks are used by the topological classifier to completely recognize the characters. The topological features used to classify each type of the stress mark are the width, the height, and the number of black pixels of the stress marks. Knowing both the main group of the character body and the type of the stress mark (if any), the character is completely identified.

We show that, if NP language L has an invulnerable generator and if L has an honest verifier standard statistical ZKIP, then L has a 5 move statistical ZKIP. Our class of languages involves random self reducible languages because they have standard perfect ZKIPs. We show another class of languages (class K) which have standard perfect ZKIPs. Blum numbers and a set of graphs with odd automorphism belong to this class. Therefore, languages in class K have 5 move statistical ZKIPs if they have invulnerable generators.