By a measure we mean a function µ from {0, 1}* (the set of all binary sequences) to real numbers such that µ(x)0 and µ({0, 1}*). A malign measure is a measure such that if an input x in {0, 1}n (the set of all binary sequences of length n) is selected with the probability µ(x)/µ ({0, 1}n) then the worst-case computation time tWOA (n) and the average-case computation time tav,µA(n) of an algorithm A for inputs of length n are functions of n of the same order for any algorithm A. Li and Vitányi found that measures that are known as a priori measures are malign. We prove that a priori" -ness and malignness are different in one strong sense.

Algorithms for computing channel capacity have been proposed by many researchers. Recently, one of the authors proposed an efficient algorithm using Newton's method. Since this algorithm has local quadratic convergence, it is advantageous when we want to obtain a numerical solution with high accuracy. In this letter, it is shown that this algorithm can be extended to the algorithm for computing the constrained capacity, i.e., the capacity of discrete memoryless channels with linear constraints. The global convergence of the extended algorithm is proved, and its effectiveness is verified by numerical examples.

This paper describes the fundamental principle of novel push-push oscillators using hairpin-shaped split-ring resonators and their application to voltage controlled and injection locked oscillators for frequency synthesizers. The experimental results make it clear that the synthesizer systems discussed here have the advantages of high frequency operation, compact size and low power consumption. Experimental work has been carried out in the L band, but these systems can be applied to much higher frequencies.

In using a natural language database interface (NLI) to access the contents of a databese, the user queries may contain terms that do not appear at all in both the NLI lexicon and the database. A friendly NLI should not reject user queries with unknown terms, but should be able to handle them, and should be able to learn new lexical items. Such capability increases the usefulness of the NLI, and allows the NLI to more cover the domain of the underlying database. Therefore, a technique to handle unknown terms is decisive in designing a friendly NLI. In this work, we discuss a method that would allow a NLI to identify the meanings of unknown database field values, and terms that are exceeding the conceptual coverage of the database, in the user queries, by engaging the user in clarification dialogues based on a database-domain hierarchy. It will be shown that the method enables the NLI lexicon to learn new lexical items at run time while the clarification dialogues, and it may provide the necessary information for generating informative answers to some particular failing user queries. Moreover, the method is an efficient means to handle queries with insufficience contextual cues. The examples throughout this work are drawn from FIFA 90, an experimental NLI to a soccer database.

A genetic method to generate a neural network which has both structure and connection weights adequate for a given task is proposed. A neural network having arbitrary connections is regarded as a virtual living thing which has genes representing its connections among neural units. Effectiveness of the network is estimated from its time sequential input and output signals. Excellent individuals, namely appropriate neural networks, are generated through generation iterations. The basic principle of the method and its applications are described. As an example of evolution from randomly generated networks to feedforward networks, an XOR problem is dealt with, and an action control problem is used for making networks containing feedback and mutual connections. The proposed method is available for designing a neural network whose adequate structure is unknown.

When a new fabrication process is set up, especially in layout design for functional cells, of practical importance is how to make the best use of layout resources so far accumulated in old fabrication processes. Usually layout data of each element are expressed mostly in terms of positional coordinate values, and hence it is extremely tedious to modify them at every change of design rules for a new fabrication technology. To cope with this difficulty, the present paper describes an automatic recycling scheme for layout resources accumulated dedicatedly for functional cell generation. The main subject of this scheme is to transform given layout data into a layout description format expressed in layout parameters. Once layout data are parameterized, layout patterns of functional cells can be reconstructed simply by tuning up parameters in accordance with a new set of design rules. A part of implementation results are also shown.

A learning procedure of a three layer neural network with limited structure, called a multi-valued neural network, is proposed. The three layer net has a single linear neuron in its output layer. All input weights of a number of hidden neurons are identical. The network takes k+1 distinct stable values, where k is the number of hidden neurons. The proposed learning procedure consists of two parts, Phase and Phase . The former is one for the learning of weights between the hidden and output layers, and the latter is one for those between the input and the hidden layers. The network is applied to classification of numerals, which shows the effectiveness of the proposed learning procedure.

This paper presents a hardware architecture design methodology for hidden markov model based recognition systems. With the aim of realizing more advanced and user-friendly systems, an effective architecture has been studied not only for decoding, but also learning to make it possible for the system to adapt itself to the user. Considering real-time decoding and the efficient learning procedures, a bi-directional ring array processor is proposed, that can handle various kinds of data and perform a large number of computations efficiently using parallel processing. With the array architecture, HMM sub-algorithms, the forward-backward and Baum-Welch algorithms for learning and the Viterbi algorithm for decoding, can be performed in a highly parallel manner. The indispensable HMM implementation techniques of scaling, smoothing, and estimation for multiple observations can be also carried out in the array without disturbing the regularity of parallel processing. Based on the array processor, we propose the configuration of a system that can realize all HMM processes including vector quantization. This paper also describes that a high PE utilization efficiency of about 70% to 90% can be achieved for a practical left-to-right type HMMs.

This paper describes new methematical tools, taken from quantum field theory (QFT), which may make it possible to characterize localized excitations (including solitons, but also including chaotic modes) generated by PDE systems. The significance to computer hardware and neurocomputing is also discussed. This mathematics--IF further developed--may also have the potential to reorganize and simplify our understanding of QFT itself--a topic of very great intellectual and practical importance. The paper concludes by describing three new possibilities for research, which will be very important to achieving these goals.

A group-based random access communication system which consists of two groups of many users is considered. The two different groups share a common random multiple access channel. Users from a group are allocated a high transmitting power level and have a high probability of correct reception among overlapping packets. We set a threshold, θ, which is such that the group with the high power level will occupy the channel if less than or equal to θ packets are transmitted from the group with the low power level. We obtain a two-dimensional Markovian model by tracing the number of backlogged users in the two groups. The two-dimensional Markov chain is shown to be not ergodic and thus the system is not stable. A two-dimensional retransmission algorithm is developed to stabilize the system and the retransmission control parameters are chosen so as to maximize the channel throughput. An equilibrium point analysis is performed by studying the drift functions of the system backlog and it is shown that there is a unique global equilibrium point. The channel capacity for the system is found to be in the range from 0.47 up to 0.53, which is a remarkable increase compared to the conventional slotted ALOHA system.

This paper proposes a feedback-loop type transmission power control (TPC) scheme coupled with first and second order prediction methods and analyzes the optimum control period and residual control error. In order to minimize residual control error, the three main factors contributing to residual control error are analyzed. First, to detect accurately up-link rain attenuation, a channel quality detection method is proposed and analyzed experimentally for puseudo-error detection. Second, rain attenuation rates in Ka band are measured and analyzed statistically. Finally, the optimum control period of the proposed TPC scheme is analyzed. The simulation results on the prototype TPC system show a maximum of 4.5 dB residual control error is achievable with an optimum control period of about 1 second to 1.5 seconds.

With the arrival of B-ISDN, point-to-point routing alone is no longer adequate. A new class of computer and video related services, such as mass mailing, TV broadcasting, teleconferencing, and video 900 service, requires the network to handle multiple destination routing (MDR). Multiple destination routing enables widespread usage of multipoint services at a lower cost than networks using point-to-point routing. With this in mind, network providers are researching more into MDR algorithms. However, the MDR problem itself is very complex. Furthermore, its optimal solution, the Steiner tree problem, is NP-complete and thus not suitable for real-time applications. Recently, various algorithms which approximate the Steiner tree problem have been proposed and, in this invited paper, we will summarize the simulation results of these algorithms. But first, we will define the MDR problem, the issues involved, and the benchmark used to compare MDR algorithms. Then, we will categorize the existing MDR algorithms into a five-level classification tree. Lastly, we will present various published results of static algorithms and our own simulation results of quasi-static algorithms.

Recently, fuzzy logic which is a kind of infinite multiple-valued logic has been studied to treat certain ambiguities, and its algebraic properties have been studied by the name of fuzzy logic functions. In order to treat modality (necessity, possibility) in fuzzy logic, which is an important concept of multiple-valued logic, the intuitionistic logical negation is required in addition to operations of fuzzy logic. Infinite multiple-valued logic functions introducing the intuitionistic logical negation into fuzzy logic functions are called Kleene-Stone logic functions, and they enable us to treat modality. The domain of modality in which Kleene-Stone logic functions can handle, however, is too limited. We will define α-KS logic functions as infinite multiple-valued logic functions using a unary operation instead of the intuitionistic logical negation of Kleene-Stone logic functions. In α-KS logic functions, modality is closer to our feelings. In this paper we will show some algebraic properties of α-KS logic functions. In particular we prove that any n-variable α-KS logic function is determined uniquely by all inputs of 7 values which are 7 specific truth values of the original infinite truth values. This means that there is a bijection between the set of α-KS logic functions and the set of 7-valued α-KS logic functions which are restriction of α-KS logic functions to 7 specific truth values. Finally, we show a necessary and sufficient condition for a 7-valued logic function to be a 7-valued α-KS logic function.

The purpose of this paper is to offer a modal logic which enables us symbolic reasoning about data, especially, fuzzy relations. For such a purpose, the present author provided some systems of modal fuzzy logic. As a continuous one of those previous works, a logic based on the graded modalities is proposed. After showing some properties of this logic, the decision procedure for this logic is given in the rectangle method.

A non-profit satellite communication network is desired to be configured by using low cost earth stations in the field of education, research and health in the Pacific region. This paper proposes the following concept as one of the tools to realize such a network: (a) A hitchhiker transponder dedicated to the network, and (b) The volunteer group prepares earth stations. A preliminary system design shows that the S band hitchhiker payload is most appropriate and has the weight of about 3kg. The feasibility of manufacturing earth stations by a volunteer group is examined through the experiment using ETS-V satellite. The parameters of the hitchhiker payload are re-examined on the basis of the experience of the experiment.

This paper proposes a superposed SSMA (Spread Spectrum Multiple Access)-QPSK (Quadrature Phase Shift Keying) signal transmission scheme over high speed QPSK signals to achieve higher frequency utilization efficiency and to facilitate lower power transmitters for SSMA-QPSK signal transmission. Experimental results show that the proposed scheme which employs the coding-rate of one-half FEC (Forward Error Correction) and a newly proposed co-channel interference cancellation scheme for SSMA-QPSK signals can transmit twenty SSMA-QPSK channels simultaneously over a nonlinearly amplified high speed QPSK signal transmission channel and achieve as ten times SSMA channels transmission as that without co-channel interference cancellation when the SSMA-QPSK signal power to the high speed QPSK signal power ratio equals -30dB. Moreover, cancellation feasibility generation of the interference signals replica through practical hardware implementation is clarified.

A new optimal nonlinear regulator design method is developed by applying a multi-layered neural network and a fixed point theorem for a nonlinear controlled system. Based on the calculus of variations and the fixed point theorem, an optimal control law containing a nonlinear mapping of the state can be derived. Because the neural network has not only a good learning ability but also an excellent nonlinear mapping ability, the neural network is used to represent the state nonlinear mapping after some learning operations and an optimal nonlinear regulator may be formed. Simulation demonstrates that the new nonlinear regulator is quite efficient and has a good robust performance as well.

The method of estimating multiple sound source locations based on a neural network algorithm and its performance are described in this paper. An evaluation function is first defined to reflect both properties of sound propagation of spherical wave front and the uniqueness of solution. A neural network is then composed to satisfy the conditions for the above evaluation function. Locations of multiple sources are given as exciting neurons. The proposed method is evaluated and compared with the deterministic method based on the Hyperbolic Method for the case of 8 sources on a square plane of 200m200m. It is found that the solutions are obtained correctly without any pseudo or dropped-out solutions. The proposed method is also applied to another case in which 54 sound sources are composed of 9 sound groups, each of which contains 6 sound sources. The proposed method is found to be effective and sufficient for practical application.

Asynchronous machines are a topic of great interest in the research area of actuators. Due to the complexity of these systems and to the required performance, the modelling and control of asynchronous machines are complex questions. Problems arise when the control goals require accurate descriptions of the electric machine or when we need to identify some electrical parameters; in the models employed it becomes very hard to take into account all the phenomena involved and therefore to make the error amplitude adequately small. Moreover, it is well known that, though an efficient control strategy requires knowledge of the flux vector, direct measurement of this quantity, using ad hoc transducers, does not represent a suitable approach, because it results in expensive machines. It is therefore necessary to perform an estimation of this vector, based on adequate dynamic non-linear models. Several different strategies have been proposed in literature to solve the items in a suitable manner. In this paper the authors propose a neural approach both to derive NARMAX models for asynchronous machines and to design non-linear observers: the need to use complex models that may be inefficient for control aims is therefore avoided. The results obtained with the strategy proposed were compared with simulations obtained by considering a classical fifth-order non-linear model.

The Consultative Committee of Space Data Systems (CCSDS) proposes a packetized telemetry scheme for the convenience of data exchange and networking in space activity. This paper describes the outline of the telemetry scheme and the on-orbit experiment which was carried out to show the applicability of the proposed CCSDS packet telemetry scheme using the Japan's satellite "Hiten" in a highly elliptical orbit. The telemetry data which are generated by the onboard instruments are packetized in Hiten, and reformed to the original data in earth stations successfully. The experimental results show that the standardized scheme is helpful for tracking cross-support between organizations, and that the concatenated code is quite effective to transmit data in a low C/N condition.