The meaning of analogical reasoning in locally stratified logic programs are described by generalized stable model (GSM) semantics. Although studies on the theoretical aspects of analogical reasoning have recently been on the increase, there have been few attempts to give declarative semantics for analogical reasoning. This paper takes notice of the fact that GSM semantics gives meaning to the effect that the negated predicates represent exceptional cases. We define predicates that denote unusual cases regarding analogical reasoning; for example, ab(x)p(x)g(x), where p(s), q(s), p(t) are given. We also add rules with negated occurrences of such predicates into the original program. In this way, analogical models for original programs are given in the form of GSMs of extended programs. A proof procedure for this semantics is presented. The main objective of this paper is not to construct a practical analogical reasoning system, but rather to present a framework for analyzing characteristics of analogical reasoning.

We introduce a novel neural network called the T-Model and investigates the learning ability of the T-Model neural network. A learning algorithm based on the least mean square (LMS) algorithm is used to train the T-Model and produces a very good result for the T-Model network. We present simulation results on several practical problems to illustrate the efficiency of the learning techniques. As a result, the T-Model network learns successfully, but the Hopfield model fails to and the T-Model learns much more effectively and more quickly than a multi-layer network.

Syntax-directed editors have several advantages in editing programs because programming is guided by the syntax and free from syntax errors. Nevertheless, they are less popular than text editiors. One of the reason is that they force a priori specified editing structures on the user and do not allow him to use his own structure. ACE (Algorithmically Customizable syntax-directed Editor) provides a solution for this problem by using a technique of machine learning; ACE has a special function of customizing the grammar algorithmically and interactively based on the learning method for grammars from examples and queries. The grammar used in the editor is customized through interaction with the user so that the user can edit his program in a more familiar structure. The customizing function has been implemented based on the methods for learning of context-free grammars from structural examples, for which the correctness and the efficiency are proved formally. This guarantees the soundness and the efficiency of customization. Furthermore, ACE can be used as an algorithmic and interactive tool to design grammars, which is required for several purposes such as compiler design and pretty-printer design.

In general, a two-dimensional display is defined by two orthogonal unit vectors. In developing the display, discriminant analysis has a shortcoming that the extracted axes are not orthogonal in general. First, in order to overcome the shortcoming, we propose discriminant analysis which provides an orthonormal system in the transformed space. The transformation preserves the discriminatory ability in terms of the Fisher criterion. Second, we present a necessary and sufficient condition that discriminant analysis in the original space provides an orthonormal system. Finally, we investigate the relationship between orthogonal discriminant analysis and the Karhunen-Loeve expansion in the original space.

This paper presents a system that automatically refines the theory expressed in the function-free first-order logic. Our system can efficiently correct multiple faults in both the concept and subconcepts of the theory, given only the classified examples of the concept. It can refine larger classes of theory than existing systems can since it has overcome many of their limitations. Our system is based on a new combination of an inductive and an explanation-based learning algorithms, which we call the biggest-first multiple-example EBL (BM-EBL). From a learning perspective, our system is an improvement over the FOIL learning system in that our system can accept a theory as well as examples. An experiment shows that when our system is given a theory that has the classification error rate as high as 50%, it can still learn faster and with more accuracy than when it is not given any theory.

A lossless image compression method based on two-dimensional (2D) linear prediction with variable coefficients is proposed. This method employs a space varying autoregressive (AR) model. To achieve a higher compression ratio, the method introduces new ideas in three points: the level conversion, the fast recursive parameter estimation, and the switching method for coding table. The level conversion prevents an AR model from predicting gray-level which does not exist in an image. The fast recursive parameter estimation algorithm proposed here calculates varying coefficients of linear prediction at each pixel in shorter time than conventional one. For encoding, the mean square error between the predicted value and the true one is calculated in the local area. This value is used to switch the coding table at each pixel to adapt it to the local statistical characteristics of an image. By applying the proposed method to "Girl" and "Couple" of IEEE monochromatic standard images, the compression ratios of 100 : 46 and 100 : 44 have been achieved, respectively. These results are superior to the best results (100 : 61 and 100 : 57) obtained by the approach under JPEG recommendations.

In this paper, we consider the polynomial time inferability from positive data for unions of two tree pattern languages. A tree pattern is a structured pattern known as a term in logic programming, and a tree pattern language is the set of all ground instances of a tree pattern. We present a polynomial time algorithm to find a minimal union of two tree pattern languages containing given examples. Our algorithm can be considered as a natural extension of Plotkin's least generalization algorithm, which finds a minimal single tree pattern language. By using this algorithm, we can realize a consistent and conservative polynomial time inference machine that identifies unions of two tree pattern languages from positive data in the limit.

The optimal coding strategy for signal detection in the correlated gaussian noise is established for the distributed sensors system with essentially zero transmission rate constraint. Specifically, we are able to obtain the same performance as in the situation of no restriction on rate from each sensor terminal to the fusion center. This simple result contrasts with the previous ad hoc studies containing many unnatural assumptions such as the independence of noises contaminating received signal at each sensor. For the design of optimal coder, we can use the classical Levinson-Wiggins-Robinson fast algorithm for block Toeplitz matrix to evaluate the necessary weight vector for the maximum-likelihood detection.

Novel, very small-size multilayer MMIC's using miniature microstrip lines on a thin dielectric film, as well as the features of the multilayer structure, are presented. Very narrow-width thin-film transmission lines, meander-like configurations, line crossovers, and vertical connections, which are effective for significant chip-size reduction and flexible layout, are realized and utilized in a 2.5-3 µmN-layer dielectric film structure. 180-degree and 90-degree hybrids and umltiport Wilkinson dividers, which are implemented in small areas of 0.1 mm2 and 1.7 mm2, are presented. Furthermore, layout flexibility in the multilayer structure is demonstrated by implementing distributed amplifiers into the layers.

A dual transformation incorporating the frequency-dependent scaling factor with the impedance dimension is proposed to synthesize the current-mode counterpart of a voltage-mode original. A general class of current-mode active-RC biquadratic filters and a switched-capacitor low-pass biquad are derived to demonstrate the synthesis procedure. Their simulation and test results show that the current transfer functions are the same as the voltage transfer functions of the originals, and thus confirm the validity of the procedure. The dual trasformation described herein is general in that with the scaling factor chosen appropriately it can meet a wide variety of circuit transformation, and thus useful also for circuit classification and identification.

A study on the limitation of optical communication systems has received much attention. A method to overcome the standard quantum limit is to apply non-standard quantum state, especially squeezed state. However, the advantage of the non-standard quantum state is degraded by the transmission energy loss. To cope with this problem, we have proposed a concept of the received quantum state control (RQSC), but the realization has some difficulties. In this paper, we propose a new system to realize the received quantum state control system, employing injection locked laser (ILL) system. Then we show that our new system can overcome the standard quantum limit.

This paper treats the synthesis of immittance floatator using nullors. Eight sets of circuit equations for realizing immittance floatators and their nullor (nullator-norator) representations are given. By replacing nullors with active elements such as biporlar junction transistors (BJTs), current conveyors (CCIIs), operational amplifiers (OAs) and operational transconductance amplifiers (OTAs), the immittance floatators can be derived. The development is important because it enables one to convert the present wealth of knowledge concerning grounded immittance simulation networks into floating immittance simulation networks. Using immittance floatators, we can obtain not only the floating form of 1-port but also that of 2-port networks. Novel circuits use solely minus-type norators. Using one-type (minus- or plus-type) norators greatly simplifies the simulation circuit. In the case of an immittance floatator using CCIIs as the active elements, the effects of nonideal CCIIs and sensitivities are given. Many circuits can be systematically derived using nullor technique.

This paper extends an earlier study on the T-Model neural network to its collective computational properties. We present arguments that it is necessary to use the half-interconnected T-Model networks rather than the fully-interconnected Hopfield model networks. The T-Model has been generated in response to a number of observed weaknesses in the Hopfield model. This paper identities these problems and show how the T-Model overcomes them. The T-Model network is essentially a feedforward network which does not produce a local minimum for computations. A concept for understanding the dynamics of the T-Model neural circuit is presented and its performance is also compared with the Hopfield model. The T-Model neural circuit is implemented and tested with standard CMOS technology. Simulations and experiments show that the T-Model allows immense collective network computations and does not produce a local minimum. High densities comparable to that of the Hopfield model implementations have also been achieved.

This paper proposes parallel VLSI processors for robotics based on multiple processing elements organized around multiple bus interconnection networks. The advantages of multiple bus interconnection networks are generality, simplicity of implementation and capability of parallel communications between processing elements, therefore it is considered to be suitable for parallel VLSI systems. We also propose the optimal scheduling formulated in an integer programming problem to minimize the delay time of the parallel VLSI processors.

The F-matrix expressions of inverted-L-type four-terminal networks, each involving an element with the power-law conductivity σ(ω)ωa (0a1) connected to a resistance R, an inductance L or a capacitance C, were derived using the standard procedures of Laplace transformation, indicating that the exponents of the complex angular frequency s, so far limited to the integers for the transmission circuits with finite elements, can be extended to the real numbers. The responses to a step voltage calculated show hysteretic behavior reflecting the resistance-capacitance ambivalent nature of the power-law conductivity.

This paper deals with an adaptive credit assignment algorithm to select strategies having higher capabilities in the learning classifier system (LCS) based upon the genetic algorithm (GA). We emulate a kind of prizes and incentives employed in the economies with imperfect information. The compensation scheme provides an automatic adjustment in response to the changes in the environment, and a comfortable guideline to incorporate the constraints. The learning process in the LCS based on the GA is realized by combining a pair of most capable strategies (called classifiers) represented as the production rules to replace another less capable strategy in the similar manner to the genetic operation on chromosomes in organisms. In the conventional scheme of the learning classifier system, the capability s(k, t) (called strength) of a strategy k at time t is measured by only the suitableness to sense and recognize the environment. But, we also define and utilize the prizes and incentives obtained by employing the strategy, so as to increase s(k, t) if the classifier provide good rules, and some amount is subtracted if the classifier k violate the constraints. The new algorithm is applied to the portfolio management. As the simulation result shows, the net return of the portfolio management system surpasses the average return obtained in the American securities market. The result of the illustrative example is compared to the same system composed of the neural networks, and related problems are discussed.

Threre are two types of nonlinear associative silicon retinas. One is a sparse Hopfield type neural network which is called a H-type retina and the other is its dual network which is called a DH-type retina. The input information sequences of H-type and HD-type retinas are given by nodes and links as voltages and currents respectively. The error correcting capacity (minimum basin of attraction) of H-type and DH-type retinas is decided by the minimum numbers of links of cutset and loop respectively. The operation principle of the regeneration is based on the voltage or current distribution of the neural field. The most important nonlinear operation in the retinas is a dynamic quantization to decide the binary value of each neuron output from the neighbor value. Also, the edge is emphasized by a line-process. The rates of compression of H-type and DH-type retinas used in the simulation are 1/8 and (2/3) (1/8) respectively, where 2/3 and 1/8 mean rates of the structural and binarizational compression respectively. We could have interesting and significant simulation results enough to make a chip.

This paper presents a destributed algorithm that uses weak copy consistency to create mutual exclusion in a distributed computer system. The weak copy consistency is deduced from the uncertainty of state which occurs due to the finite and unpredictable communication delays in a distributed environment. Also the method correlates outdated state information to current state. The average number of messages to enter critical section in the algorithm is n/2 to n messages where n is the number of sites. We show that the algorithm achieves mutual exclusion and the fairness and liveness of the algorithm is proven. We study the performance of the algorithm by simulation technique.

High-speed digital land mobile communications suffer from frequency-selective fading due to a long delay difference. Several techniques have been proposed to overcome the multipath propagation problem. Among them, an adaptive array antenna is suitable for very high-speed transmission because it can suppress the multipath signal of a long delay difference significantly. This paper describes the LMS adaptive array antenna for frequency-selective fading reduction and a new diversity technique. First, we propose a method to generate a reference signal in the LMS adaptive array. At the beginning of communication, we use training codes for the reference signal, which are known at a receiver. After the training period, we use detected codes for the reference signal. We can generate the reference signal modulating a carrier at the receiver by those codes. The carrier is oscillated independently of the incident signal. Then, the carrier frequency of the reference signal is in general different from that of the incident signal. However, the LMS adaptive array works in such a way that the carrier frequency of the array output coincides with that of the reference signal. Namely, the frequency difference does not affect the performance of the LMS adaptive array. Computer simulations show the proper behavior of the LMS adaptive array with the above reference signal generator. Moreover, we present a new multipath diversity technique using the LMS adaptive array. The LMS adaptive array reduces the frequency-selective fading by suppressing the multipath components. This means that the transmitted power is not used sufficiently. We propose a multiple beam antenna with the LMS adaptive array. Each antenna pattern receives one of the multipath components, and we combine them adjusting the timing. Then, we realize the multipath diversity. In addition to the multipath fading reduction, we can improve a signal-to-noise ratio by the diversity technique.

The energy distribution and emission efficiency of electrons emitted from a superconductor-insulator-superconductor (SIS) junction have been investigated by numerical calculation adopting the free electron model. The emission efficiency of an SIS junction cold cathode was found to be about 0.3% of tunneling current flowing to the SIS junction when the energy gap voltage of superconductor was 20 meV, the work function of counter electrode 1 eV, the bias voltage 0.96 V, the thickness of the counter electrode 100 , the electric field strength between the plate and the counter electrode 106 V/m, and the relaxation time 0.01 ps. It is clear that the SIS junction cold cathode can emit electrons with sharper energy distributions at much the same efficiency as compared with a metal-insulator-metal (MIM) junction cold cathode.