Novel circuit design techniques for bipolar and MOS four-quadrant analog multipliers operable on low supply voltage are described. There are three design techniques for multipliers operable on low supply voltage. One is the transistor-size unbalance technique. Another is the bias offset technique. A third is the multitail technique. Bipolar and MOS four-quadrant analog multipliers proposed in this paper consist of transistor-pairs with different transistor sizes (i.e. emitter areas or gate W/L values are different), transistor-pairs with the same bias offset or multitail cells (i.e. quadritail cells and an octotail cell). Several kinds of squaring circuits consisting of such transistor-pairs are applied to the multipliers when the multiplication method is based on the quarter-square technique. These multipliers all have satisfiable multiplication characteristics with four-quadrant operations in analog signal processing, whether implemented in bipolar technology or implemented in MOS technology.

Process and device technologies of CMOS devices for low-voltage operation are described. First, optimum power-supply voltage for CMOS devices is examined in detail from the viewpoints of circuit performance, device reliability and power dissipation. As a result, it is confirmed that power-supply voltage can be reduced without any speed loss of the CMOS device. Based upon theoretical understanding, the author suggests that lowering threshold voltage and reduction of junction capacitance are indispensable for CMOS devices with low-voltage supply, in order to improve the circuit performance, as expected from MOS device scaling. Process and device technologies such as Silicon On Insulator (SOI) device, low-temperature operation and CMOS Shallow Junction Well FET (CMOS-SJET) structure are reviewed for reduction of the threshold voltage and junction capacitance which lead to high-seed operation of the COMS device at low-voltage.

This paper proposes a self frequency preset (SFP) PLL synthesizer to realize a simple frequency preset PLL synthesizer with temperature-resistant and shorter frequency settling time than the conventional temperature un-compensated phase and frequency preset (PFP) PLL synthesizer. Since the proposed synthesizer employs a simple frequency locked loop (FLL) circuit to preset the output frequency at each frequency hopping period, the synthesizer eliminates the need to store f-V characteristic of the VCO in ROM. The frequency settling time of the proposed synthesizer is theoretically and experimentally analyzed. The theoretical analysis using the realistic f-V characteristic of a IF band VCO show that the frequency settling time of the proposed synthesizer is 130µs shorter than that of the conventional PFP PLL synthesizer at 40MHz hopping in the 200MHz band for all temperatures. Furthermore, the experimental results confirm that the frequency acquisition time of a prototype FLL circuit is accordant with the calculated results. Thus, the proposed SFP PLL synthesizer can achieve faster frequency settling than the conventional PFP PLL synthesizer for all temperatures and its simple configuration allows to be easily implemented with existing CMOS ASIC devices.

This paper presents Bi-CMOS current-mode multiple valued logic circuit with 1.5 V supply voltage. This circuit is composed of current mirror, threshold detector and current source. This circuit has advantages such as high accuracy, high speed, high density and low supply voltage. So, it is possible to realize high-radix multiple valued logic circuit. As an other application of the proposed circuit, a processing unit of fuzzy inference is given. This circuit operates with high speed and high accuracy. The circuit simulation of the proposed circuit has been performed using SPICE2 program.

An idea of optimal output permutation of multiple-valued sum-of-products expressions is presented. The sum-of-products involve the TSUM operator on the MIN of window literal functions. Some bounds on the maximum number of implicants needed to cover an output permuted function are clarified. One-variable output permuted functions require at most p1 implicants in their minimal sum-of-products expressions, where p is the radix. Two-variable functions with radix between three and six are analyzed. Some speculations of maximum number of the implicants could be established for functions with higher radix and more than 2-variables. The result of computer simulation shows that we can have a saving of approximately 15% on the average using permuting output values. Moreover, we demonstrate the output permutation based on the output density as a simpler method. For the permutation, some speculation is shown and the computer simulation shows a saving of approximately 10% on the average.

This paper presents an optimization method for pseudo-Kronecker expressions of p-valued input two-valued output functions by using multi-place decision diagrams for p2 and p4. A conventional method using extended truth tables requires memory of O (3n) to simplify an n-variable expression, and is only practical for functions of up to n14 variables when p2. The method presented here utilizes multi-place decision diagrams, and can optimize considerably larger problems. Experimental results for up to n39 variables are shown.

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.

In this paper, a synthesizing method is proposed for the design of discrete-time cellular neural networks for binary image processing. Based on the theory of digital-logical design paradigm of threshold logic, the template parameters of the discrete-time cellular neural network for a prescribed binary image processing problem are calculated. Application examples including edge detection, connected component detection, and hole filling are given to demonstrate the merits and limitations of the proposed method. For a given realization of the parameters of the cloning template, a guideline for the selection of the offset Ic for maximum error tolerance is also considered.

The mechanism of environment-dependent self-organization of "positional information" in a coupled nonlinear oscillator system is proposed as a new principle of realtime coordinative control in biological distributed system. By modeling the pattern formation in tactic response of Physarum plasmodium, it is shown that a global phase gradient pattern self-organized by mutual entrainment encodes not only the positional relationship between subsystems and the total system but also the relative relationship between internal state of the system and the environment.

A priori estimation is presented for a computational complexity of the homotopy method applying to a certain class of strongly monotone nonlinear equations. In the present papers, a condition is presented for a certain class of uniquely solvable equations, under which an upper bound of a computational complexity of the Newton type homotopy method can be a priori estimated. In this paper, a condition is considered in a case of linear homotopy equations including the Newton type homotopy equations. In the first place, the homotopy algorithm based on the simplified Newton method is introduced. Then by using Urabe type theorem, which gives a sufficient condition guaranteeing the convergence of the simplified Newton method, a condition is presented under which an upper bound of a computational complexity of the algorithm can be a priori estimated, when it is applied to a certain class of strongly monotone nonlinear equations. The presented condition is demonstrated by numerical experiments.

The paper deals with the level-crossing intervals of a stationary random process. Probability densities of the level-crossing intervals of a process consisting of a sine wave plus Gaussian random noise are experimentally investigated by digital simulation. The Gaussian random noise is selected to be of 7-th order Butterworth power spectrum density. The obtained probability densities appear with a number of isolated peaks much larger than the number observed on the corresponding probability densities of the Gaussian noise alone. When the sine wave frequency is greater than or approximately equal to the half of the cutoff frequency of the noise spectrum, experimental data suggest that each isolated peak has a Gaussian-like density. The mean time interval associated with each Gaussian-like density is equal to a multiple number of the period of the sine wave, while the variance is found to be fairly the same for all peaks of a same probability density. The assumption of "quasi-independence" is not valid for the level-crossing intervals of the present process.

In this paper, a new time series analysis method is proposed. The proposed method uses the exponential (EXP) model. The residual signal is assumed to be identically and independently distributed (IID). To achieve accurate and efficient estimates, the parameter of the system model is calculated by maximizing the logarithm of the likelihood of the residual signal which is assumed to be IID t-distribution. The EXP model theoretically assures the stability of the system. This model is appropriate for analyzing signals which have not only poles, but also poles and zeroes. The asymptotic efficiency of the EXP model is addressed. The optimal solution is calculated by the Newton-Raphson iteration method. Simulation results show that only a small number of iterations are necessary to reach stationary points which are always local minimum points. When the method is used to estimate the spectrum of synthetic signals, by using small α we can achieve a more accurate and efficient estimate than that with large α. To reduce the calculation burden an alternative algorithm is also proposed. In this algorithm, the estimated parameter is updated in every sampling instant using an imperfect, short-term, gradient method which is similar to the LMS algorithm.

In this article a Neural Network learning scheme is described, which is a generalization of VQ (Vector Quantization) and ART2a (a simplified version of Adaptive Resonance Theory 2). The basic differences between VQ and ART2a will be exhibited and it will be shown how these differences are covered by the generalized scheme. The generalized scheme enables a rich set of variations on VQ and ART2a. One such variation uses the expression ||I||2+||zj||2/||zj||sin(I,zj), as the distance measure between input vector I and weight vector zj. This variation tends to be more robust to noise than ART2a, as is shown by experiments we performed. These experiments use the same data-set as the ART2a experiments in Ref.(3).

The problem of local minima is inevitable when solving combinatorial optimization problems by conventional methods such as the Hopfield network, relying on the minimization of an objective function E(X). Such a problem arises from the search mechanism in which only the local information about the objective function E(X) is used. In this paper we propose a new approach called the Single Minimum Method (SMM) which uses the global information in searching for the solutions to combinatorial optimization problems. In this approach, we add a function -TS(X) to the original objective function E(X) to construct the function F(X)=E(X)-TS(X) which has only one minimum, one which can be easily found by any general gradiet method including the Hopfield network. Based on an analogy between thermodynamic systems and neural networks, it is shown that the global information about the original objective function E(X) is included in the single minimum of the function F(X) and can be used for finding the global minimum of the objective function E(X). In order to show how to apply the Single Minimum Method to a combinatorial optimization problem we give an algorithm for the TSP problem based on our method. The simulation results show that the algorithm can almost always find the shortest or near shortest paths. Finally, a modified SMM, which has some great advantages for hardware implementation, is also given.

A new computer architecture using multiwavelength optoelectronic integrated circuits (OEICs) is proposed to attack the problems caused by interconnection complexity. Multiwavelength-OEIC architecures, where various wavelengths are employed as information carriers, provide the wavelength as an extra dimension of freedom for parallel processing, so that we can perform several independent computations in parallel in a single optical module using the wavelength space. This multiplex computing" enables us to reduce the wiring area required by a network and improve their complexity. In this paper, we discuss the efficient multiplexing of Batcher's bitonic sorting networks, highly parallel computing architectures that require global interconnections inherently. A systematic multiplexing of interconnection topology is presented using a binary representation of the connectivities of interconnection paths. It is shown that the wiring area can be reduced by a factor of 1/r2 using r kinds of wavelength components.

Various models of a neuron have been proposed and many studies about them and their networks have been reported. Among these neurons, this paper describes a study about the model of a neuron providing its own feedback input and possesing a chaotic dynamics. Using a return map or a histogram of laminar length, type-I intermittency is recognized in a recurrent neuron and its network. A posibility of controlling dynamics in recurrent neural networks is also mentioned a little in this paper.

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.

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.

Practical linewidth measurement accuracy better than 0.02 µm 3 sigma that meets the production requirement for devices with sub-half micron features, was achieved in a field emission scanning electron-beam metrology system (Hitachi S-7000). In order to establish high accuracy linewidth measurement, it was found in the study that reduction of electron-beam diameter and precise control of operating conditions are significantly effective. For the purpose of reducing electron-beam diameter, a novel electron optical system was adopted to minimize the chromatic aberration which defines electron-beam profile. As a result the electron beam diameter was reduced from 20 nm to 16 nm. In order to reduce measurement uncertainties associated with actual operating conditions, a field emission electron gun geometry and an objective lens current monitor were investigated. Then the measurement uncertainties due to operating conditions was reduced from 0.016 µm to 0.004 µm.

We have proposed a new omnidirectional image sensor COPIS (COnic Projection Image Sensor) for guiding navigation of a mobile robot. Its feature is passive sensing of the omnidirectional image of the environment in real-time (at the frame rate of a TV camera) using a conic mirror. COPIS is a suitable sensor for visual navigation in real world environment with moving objects. This paper describes a method for estimating the location and the motion of the robot by detecting the azimuth of each object in the omnidirectional image. In this method, the azimuth is matched with the given environmental map. The robot can always estimate its own location and motion precisely because COPIS observes a 360 degree view around the robot even if all edges are not extracted correctly from the omnidirectional image. We also present a method to avoid collision against unknown obstacles and estimate their locations by detecting their azimuth changes while the robot is moving in the environment. Using the COPIS system, we performed several experiments in the real world.