We develop a convergence theory of the simple genetic algorithm (SGA) for two-bit problems (Type I TBP and Type II TBP). SGA consists of two operations, reproduction and crossover. These are imitations of selection and recombination in biological systems. TBP is the simplest optimization problem that is devised with an intention to deceive SGA into deviating from the maximum point. It has been believed that, empirically, SGA can deviate from the maximum point for Type II while it always converges to the maximum point for Type I. Our convergence theory is a first mathematical achievement to ensure that the belief is true. Specifically, we demonstrate the following. (a) SGA always converges to the maximum point for Type I, starting from any initial point. (b) SGA converges either to the maximum or second maximum point for Type II, depending upon its initial points. Regarding Type II, we furthermore elucidate a typical sufficient initial condition under which SGA converges either to the maximum or second maximum point. Consequently, our convergence theory establishes a solid foundation for more general GA convergence theory that is in its initial stage of research. Moreover, it can bring powerful analytical techniques back to the research of original biological systems.

This paper describes the performance analysis of a distributed antenna system which includes space and path diversity with radio channel estimation. This system is used for CDMA personal communication systems. In this paper, the performance of a diversity system is analyzed precisely considering multipath and inter-antenna interference. In a diversity system, the adaptive RAKE receiver which estimates the characteristics of a radio channel adaptively has been used for diversity combining. In the adaptive RAKE, the time-variant characteristic has been approximated by a time function. In this paper, the estimation performance of the adaptive RAKE is analyzed in cases of time functions of 0-th, first and second degrees. The performances are evaluated and compared with the differential RAKE. The adaptive RAKE is found to improve the signal quality of more than 2dB in comparison with the differential RAKE. It is also found that the optimum parameter design can be achieved flexibly for radio channel estimation by using higher degree time functions.

This paper proposes Coherent-HYBrid Direct-Sequence Fast-Frequency-Hopping (CHYB-DS-FFH) CDMA with Adaptive Interference Cancelling (AIC) for cellular mobile communications. The features of CHYB-DS-FFH are symbol-by-symbol frequency diversity and low chip-rate DS multiplexing both of which are based on a coherent FFH modulation and demodulation scheme. The combination of coherent FFH, space diversity, and AIC is very effective for reducing the performance degradation due to interference. Computer simulations demonstrate BER performance of a 2 hop 500-kHz-interval frequency hopping system using () a linear canceller or () a nonlinear canceller. Both systems employ the two branch space diversity reception of 10kb/s QPSK with FFH over a 1MHz system bandwidth. In quasi-static channels, the average BER performance is 10-2 with average Eb/N0 less than 8dB. In dynamic fading channels under full interference conditions, CHYB-DS-FFH with the linear adaptive interference canceller realizes a BER of 10-2 at the average Eb/N0 of 15dB with maximum Doppler frequency fD of 5Hz, whereas CHYB-DS-FFH with the non-linear adaptive interference canceller achieves the same BER at the average Eb/N0 of 15dB with fD, equal to 30Hz.

A receiving system suitable for multipath fading channels with co-channel interference is described. This system is equipped with both an M-sectored directional antenna and an adaptive equalizer to mitigate the influence due to multipath propagation and co-channel interference. By using directional antennas, this receiving system can separate desirable signals from undesirable signals, such as multipath signals with longer delay time and co-channel interference. It accepts multipath signals which can be equalized by maximum likelihood sequence estimation, and rejects both multipath signals with longer delay time and co-channel interference. Based on computer simulation results, the performance of the proposed receiving system is analyzed assuming simple propagation models with Rayleigh-distributed multipath signals and co-channel interference.

This paper proposes a new adaptive Interference Cancelling Equalizer (ICE) with a blind algorithm. From a received signal, ICE not only eliminates inter-symbol interference, but also cancels co-channel interference. Blind ICE can operate well even if training signals for the interference are unknown. First, training signal conditions for applying blind ICE are considered. Next, a theoretical derivation for blind ICE is developed in detail by applying the maximum likelihood estimation theory. It is shown that RLS-MLSE with diversity, which is derived for mobile radio equalizers, is also effective for blind ICE. Computer simulations demonstrate the 40kb/s QDPSK transmission performance of Blind ICE as a blind canceller with two branch diversity reception under Rayleigh fading in a single interference environment. The simulations assume synchronous training; the canceller is trained for the desired signal but not for the interference signals. Blind ICE can be successfully achieved at more than -10dB CIR values when average Eb/N0 is 15dB and a maximum Doppler frequency is 40Hz.

Myoelectric (ME) signals during dynamic movement suffer from motion arifact noise caused by mechanical friction between electrodes and the skin. It is difficult to reject artifact noises using linear filters, because the frequency components of the artifact noise include those of ME signals. This paper describes a nonlinear method of eliminating artifacts. It consists of an inverse autoregressive (AR) filter, a nonlinear filter, and an AR filter. To deal with ME signals during dynamic movement, we introduce an adaptive procedure and fuzzy rules that improve the performance of the nonlinear filter for local features. The result is the best ever reported elimination performance. This fuzzy rule based adaptive nonlinear artifact elimination filter will be useful in measurement of ME signals during dynamic movement.

This paper presents an adaptive MLSE (Maximum Likelihood Sequence Estimator) suitable for TDMA cellular systems. The proposed MLSE has two special features such as handling wide dynamic range signals without analogue gain controls and fast channel tracking capability. In order to handle wide dynamic range signals without conventional AGCs (Automatic Gain Controller), the proposed MLSE uses envelope components of received signals obtained from a non-linear log-amplifier module which has wide log-linear gain characteristics. By using digital signal processing technique, the log-converted envelope components are normalized and converted to linear values which conventional adaptive MLSEs can handle. As a channel tracking algorithm of the channel estimator, the proposed MLSE adopts a QT-LMS (Quick-Tracking Least Mean Square) algorithm, which is obtained by modifying LMS algorithm to enable a faster tracking capability. The algorithm has a fast tracking capability with low complexity and is suitable for implementation in a fixed-point digital signal processor. The performances of the MLSE have been evaluated through experiments in TDMA cellular environments with π/4-shifted QPSK, 24.3k symbol/sec. It is shown that, under conditions of 65dB amplitude variations and 80Hz Doppler frequency, the MLSE successfully achieves less than 3% B.E.R., which is required for digital cellular systems.

An adaptive array antenna can be considered as a useful tool of combating with fading in mobile communications. We can directly obtain the optimal weight coefficients without updating in temporal sampling, if the arrival angles and signal-to-noise ratio (SNR) of the desired and the undesired signals can be accurately estimated. The Maximum Entropy Method (MEM) can estimate the arrival angles, and the SNR from spatially sampled signals by an array antenna more precisely than the Discrete Fourier Transform (DFT). Therefore, this paper proposes and investigates an adaptive array antenna based on spatial spectral estimation using MEM. We call it MEM array. In order to reduce complexity for implementation, we also propose a modified algorithm using temporal updating as well. Furthermore, we propose a method of both improving estimation accuracy and reducing the number of antenna elements. In the method, the arrival angles can be approximately estimated by using temporal sampling instead of spatial sampling. Computer simulations evaluate MEM array in comparison with DFT array and LMS array, and show improvement owing to its modified algorithm and performance of the improved method.

It is an important problem whether or not we can reject the disturbances from distributed parameter circuit. In order to analyze this problem structurally, it is necessary to investigate the basic equation of distributed parameter circuit in the framework of state space. Since the basic equation has two parameters for time and space, the state value belongs to an infinite-dimensional space. In this paper, the disturbance-rejection problems with incomplete state feedback and/or incomplete state feedback and feedforward for infinite-dimensional systems are studied in the framework of geometric approach. And under certain assumptions, necessary and/or sufficient conditions for these problems to be solvable are proved.

A Direct Radiating Array Antenna (DRAA) concept has been introduced to international satellite communications in order to achieve multiple shaped beams which are electrically reconfigurable. The subject of this paper is to describe the new design method for a reconfigurable DRAA. The design procedure consists of three steps, 1) derivation of the initial array layout using Fourier transform method (FTM) , 2) array shape rearrangement, 3) optimization of the final array excitation with the modified constraint least mean square (MCLMS) algorithm. At the first step, it is necessary to derive the initial array layout for the desired shaped beam with respect to array shape, number of antenna elements, and excitation distribution. For this purpose, a new closed form solution of FTM using N-polygonal desired coverage is used. At the second step, the array shape is rearranged to fit the beam forming network (BFN) configuration which can reduce insertion loss and influence on frequency variation sensitivity. At the third step, the array excitation is optimized using MCLMS which is exploited to satisfy the power sum constraints caused by the restriction of the BFN configuration. The design method provides useful insight regarding the layout design of a DRAA with well-shaped coverages, the low insertion loss of the BFN and the high sidelobe isolation characteristic. The design of the reconfigurable DRAA with the specified multiple shaped (beams is demonstrated and compared with the experimental model.

This paper presents techniques for generating the input patterns for locating logic design errors (PLE's) by Boolean function manipulation based on binary decision diagrams (BDD's). One PLE has one Boolean variable X or and constant values. A primary output of a correct circuit takes value X, while the designed circuit takes either 0 or 1. By using PLE's, the X-algorithms locate single or multiple logic design errors in a combinational circuit. Although PLE's play the most important role in the X-algorithms, the condition under which PLE's exist has not been formalized. This paper gives a formal analysis on the existence condition of PLE's. It is shown that the condition is always satisfied by incorporating another type of PLE. From the condition, an implicit representation of PLE's is derived. In addition, two kinds of approaches are presented for generating PLE's by Boolean function manipulation based on BDD's. One is an approach for generating all the existing PLE's. The other is a heuristic approach to obtain a limited number of PLE's in a short time. Both approaches generate PLE's including don't cares. Incorporating them, a compact representation of PLE is achieved. Experimental results have shown the compactness of the proposed representations and the availability of the pattern generation techniques.

This paper describes a new method of approximating ideal Hilbert transformers by using time reversal techniques. As is well known, an ideal Hilbert transformer is not physically realizable because it is not causal. Nevertheless, it is extremely imprortant conceptually in the area of digital signal processing. In this paper, we propose a method to approximately implement such a Hilbert transformer. The method divides the impulse response of the ideal Hilbert transformer into two parts, i.e., causal and noncausal parts. Although a causal filter is physically realizable, a noncausal filter is not realizable. A noncausal filter is realized using time reversal techniques for input signals to the filter, and then the Hilbert transformer can be approximately implemented by the parallel connection of causal and noncausal filters.

In this paper, the relationship between the recursive least square (RLS) method with a U-D decomposition algorithm and ARMA lattice filter realization algorithm is presented. Both the RLS method and the lattice filter realization algorithm are used for the same applications, such as model identification, etc., therefore, it is expected that the lattice filter algorithm is in some ways related to the RLS. Though some of the proposed lattice filter algorithms have been derived by the RLS method, they do not express the relationship between RLS snd ARMA lattice filter realization algorithm. In order to describe the relation clearly, a new structure of ARMA lattice filter is proposed. Further, based on the relationship, a method of model identification with frequency weighting (MIFW), which is different from a previous method, is derived. The new MIFW method modifies the lattice parameters which are acquired without a frequency weighting and obtain the parameters of an ARMA model, which is identified with frequency weighting. The proposed MIFW method has the following restrictions: (1) The used frequency weighting is FIR filter with a low order. (2) By using the parameters of the ARMA lattice filter with ARMA (N,M) order and the frequency weighting with L order, the new ARMA parameter with the frequency weignting is with ARMA(N-L,M-L) order. By using the proposed MIFW method, the ARMA parameters estimated with the frequency weighting can be obtained without starting the computation again.

In many applications involving the processing of noisy signals, it is desired to know the noise variance. This paper proposes a new method for estimating the noise variance from the signals of autoregressive (AR) and autoregressive moving-average (ARMA) systems corrupted by additive white noise. The method proposed here uses the low-order Yule-Walker (LOYW) equations and the lattice filter (LF) algorithm for the estimation of noise variance from the noisy output measurements of AR and ARMA systems, respectively. Two techniques are proposed here: iterative technique and recursive one. The accuracy of the methods depends on SNR levels, more specifically on the inherent accuracy of the Yule-Walker and lattice filter methods for signal plus noise system. The estimated noise variance is used for the blind indentification of AR and ARMA systems. Finally, to demonstrate the effectiveness of the method proposed here many numerical results are presented.

We present distributed load balancing mechanisms implemented on multiprocessor systems for real time video encoding, which dynamically equalize load amounts among PE's to cope with extensive computing requirements. The loosely coupled multiprocessor system, e.g. a torus connected one, is treated as the objective system. Two decentralized controlled load balancicg algorithms are proposed, and mathematical analyses are provided to obtain some insights of our decentralized controlled mechanisms. We also prove the proposed algorithms are steady and effective theoretically and experimentally.

Truncated sum (TSUM for short) is useful for MV-PLA's realization. This paper introduces a new class of multiple-valued logic functions that are expressed by truncated sum, differential product (DPRODUCT for short), NOT and variables, where TSUM (x, y)min (xy, p1) and DPRODUCT (x, y)max (xy(p1), 0) is newly defined as the product that is derived by applying De Morgan's laws to TSUM. We call the functions T-functios. First, this paper clarifies that a set of T-functions is not a lattice. It clarifies that Lukasiewicz implication can be expressed by TSUM and NOT. It guarantees that a set of p-valued T-functios is not complete but complete with constants. Next, the speculations of the number of T-functions for less than ten radixes are derived. For eleven or more radix p, a speculation of the number of p-valued T-functions is shown. Moreover, it compares the T-functions with B-functions. The B-functions have been defined as the functions expressed by MAX, MIN, NOT and variables. As a result, it shows that a set of T-functions includes a set of B-functions. Finally, an inclusion relation among these functional sets and normality condition is shown.

This paper introduces some modelling methods of time-varying stochastic process and its linear/nonlinear adaptive identification. Time-varying models are often identified by using a least square criterion. However the criterion should assume a time invariant stochastic model and infinite observed data. In order to adjust these serious different assumptions, some windowing techniques are introduced. Although the windows are usually applied to a batch processing of parameter estimates, all adaptive methods should also consider them at difference point of view. In this paper, two typical windowing techniques are explained into adaptive processing. In addition to the use of windows, time-varying stochastic ARMA models are built with these criterions and windows. By using these criterions and models, this paper explains nonlinear parameter estimation and the property of estimation convergence. On these discussions, some approaches are introduced, i.e., sophisticated stochastic modelling and multi-rate processing.

Many metrics between trees have been proposed. However, there is no research on a graph metric that can be applied to molecular graphs. And most of the reports on tree metrics have dealt with rooted and ordered trees. As the first step defining a graph metric for molecular graphs, this paper proposes a tree metric between unrooted and unordered trees. This metric is based on a mapping between trees that determines a transformation from one tree to another. The metric is the minimum weight among the weights of all possible transformations. The characteristics of the mapping are investigated. A top-down computing method is proposed using the characteristics of the mapping. The time and space complexities are OT(N 2aN 2b(N 3aN 3b)) and Os(N 2aN 2b), respectively, where Na and Nb are the numbers of vertices of the two trees. If the degrees of all vertices of the trees are bounded by a constant, the time complexity of the method is O (N 3aN 3b). The computing time to obtain the distance between a pair of molecular graphs using a computer (SUN SparcStation ELC) is 0.51 seconds on average for all the pairs of 111 molecular graphs that have 12.0 atoms on average. This methic can be applied to the clustering of molecular graphs.

This paper discusses the asynchronous and synchronous parallel derivation of languages based on standard formal grammars. Some of the synchronous languages defined in this paper are essentially equivalent to the languages of E0L and EIL systems. Languages with restrictions on the number of parallel derivation steps are difined so that a t-time language is the set of strings w derived in t(w) or less parallel derivatio steps, where t(n) is an integer function. the properties of asynchronous derivation are generally discussed to clarify their conditions so that the derivation results are independent of the order in which productions are applied. It is shown that: (1) Any context sensitive grammar (CSG) G can be transformed into a CSG G such that the language generated by synchronous derivation in G is equal to that generated by asynchronous derivation in G , and vice versa; (2) Any regular language is a log-time context free language (CFL); (3) The class of CFLs is incomparable with that of log-time CSLs; and (4) If there is a bounded cellular automaton recognizing any language L in time T(n), then L is an O(T(n))-time CSL.

Reduction of the complexity of the NLMS algorithm has received attention in the area of adaptive filtering. A processing cost reduction method, in which the component of the weight vector is updated when the absolute value of the sample is greater than or equal to the average of the absolute values of the input samples, has been proposed. The convergence analysis of the processing cost reduction method has been derived from a low-pass filter expression. However, in this analysis the effect of the weignt vector components whose adaptations are skipped is not considered in terms of the direction of the gradient estimation vector. In this paper, we use an arbitrary value instead of the average of the absolute values of the input samples as a threshold level, and we derive the convergence characteristics of the processing cost reduction method with arbitrary threshold level for zero-mean white Gaussian samples. From the analytical results, it is shown that the range of the gain constant to insure convergence and the misadjustment are independent of the threshold level. Moreover, it is shown that the convergence rate is a function of the threshold level as well as the gain constant. When the gain constant is small, the processing cost is reduced by using a large threshold level without a large degradation of the convergence rate.