In an earlier paper, we have shown that each section of the L-section minimal trellis diagram for a linear block code consists of parallel and structurally identical (except branch labels) subgraphs without cross connections. These parallel subgraphs are called parallel components of the section. In this paper, it is shown that if the sets of path label sequences of two parallel components have a common sequence, then the parallel components have the same branch labels, and the number of parallel components with the same branch labels in each section and the detail structure of each parallel component up to its branch labels are analyzed and expressed in terms of the dimensions of specific linear codes related to the given code. As an example, the 2i-section minimal trellis diagram for a Reed-Muller code is analyzed. Complexity measures of soft-decision maximum likelihood decoding for binary linear block codes are also discussed.

Finite state translation systems (fsts') are a widely studied computational model in the area of tree automata theory. In this paper, the string generating capacities of fsts' and their subclasses are studied. First, it is shown that the class of string languages generated by deterministic fsts' equals to that of parallel multiple context-free grammars, which are an extension of context-free grammars. As a corollary, it can be concluded that the recognition problem for a deterministic fsts is solvable in O(ne1)-time, where n is the length of an input word and e is a constant called the degree of the deterministic fsts'. In contrast to the latter fact, it is also shown that nondeterministic monadic fsts' with state-bound 2 can generate an NP-complete language.

The studies on the biological effects of ELF electric fields conducted in Japan are reviewed. Among international studies, they are characterized as the studies from the viewpoint of bioengineering. In early studies, the safety standard of high voltage transmission lines was determined by a distinct biological effect, i.e., the sensation of the spark discharge caused by electrostatic induction. In numerical analysis, the field coupling to both animal and human bodies became well understood. Some new measurement techniques were developed which enabled us to evaluate the field exposure on a human body. A system was developed to realize the chronic exposure of an electric field on mice and cats. An optical telemetry technique was developed to measure the physiological response of an animal when it was exposed to an electric field. An ion-current shuttle box was developed to investigate the behavioral change of a rat when it was exposed to an ion-current as well as an electric field. In animal experiments, a mechanism of sensing the field was investigated. The cause of the seasonal change of field sensitivity was found. In cases of chronic exposure, suppression of growth was suspected. In shuttle box studies, an avoidance behavior from an ion-current was quantified. To find whether there are any adverse or beneficial effects of the field exposure on human beings, further study is required to clarify the mechanisms of the biological effects.

C-V and I-V characteristics of an n-MOSFET with Si-implanted gate-SiO2 of 50 nm are analyzed by using a test device with large equal channel width and length of 100 µm, and discussed for realizing a large hysteresis window of threshold voltage. Interface trap densities change logarithmically from 31010 to 11012cm2eV1 as the Si-dose at 25 keV increases from zero to 31016cm2. Threshold-voltage changes caused by 25 keV implantaions are as high as 0.2 V. Effective mobilities (subthreshold swings) change from 600 (0.10) to 100 cm2/Vs (0.26 V/decade) as the Si-dose increases from 0 to 31016 cm2 at 25 keV, and both parameters are related with the change of interface trap densities. There is a close relationship between the hysteresis windows of gate current and threshold voltage, and the largest threshold voltage window in a low gate voltage region is obtained for the MOSFET with Si-implantation at 25 keV/31016 cm2.

This paper describes blue second harmonic generation (SHG) by an organic crystal of 2-furyl methacrylic anhydride (FMA). It has short cut-off wavelength of 380 nm and SHG coefficients at 1064 nm. d3324 pm/V and d3116 pm/V. In 900 nm region 90-degree phase-matched blue SHG is observed using a Ti: Sapphire laser as a fundamental source. This crystal is not hygroscopic and does not exhibit sublimation at room temperature. Fine polishing is also possible.

The performance of selection diversity combined with decision feedback equalizer for reception of TDMA carriers is investigated in this paper. The second generation digital land mobile communication systems standardized in the U.S., Japan, and Europe employ TDMA carriers at transmission bit rates up to several hundreds kbit/s. In order to provide higher quality of mobile communications services to the user with employing TDMA carriers, the systems would require both diversity and equalization techniques to combat attenuation of received signal power level due to Rayleigh fading and intersymbol interference resulting from time-variant multipath fading, respectively. This paper proposes a novel integration method of selection diversity and decision feedback equalization techniques which provides the better bit error rate performance than that for the conventional selection diversity method with decision feedback equalizer. The feature of proposed method is that selection diversity and decision feedback equalization techniques are integrated so as to interwork each other. We call the proposed method by the Decision Feedback Diversity with Decision Feedback Equalizer. The detailed algorithm of the proposed method is first presented, and then the system parameters for the method are evaluated based on the computer simulation results. Finally the computer simulation results for the performance of the proposed method are presented and compared to those for the conventional Selection Diversity with Decision Feedback Equalizer and the conventional Dual Diversity Combining and Equalization method under the typical mobile radio environments, in order to demonstrate the validity of the proposed method.

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 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.

An adaptive method analyzing analytic speech signals is proposed in this paper. The method decreases the errors of finite precision on calculation in a method with real coefficients. It is shown from the results of experiments that the proposed method is more useful than adaptive methods with real coefficients.

We discuss estimation error as a basic problem in formant estimation in the analysis of speech of very short-time duration in the glottal closure of the vowel. We also show in our simulation that good estimation of the first formant is almost impossible with the ordinary method using a waveform cutting. We propose a new method in which the cut waveform, as a discontinuous function of finite time, is mapped to a continuous function defined in the whole time domain; and we show that using this method, the estimation accuracy for low frequency formants can be greatly improved.

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.

Persistent data structures, introduced by Sarnak and Tarjan, have been found especially useful in designing geometric algorithms. In this paper, we present a persistent form of binary-binary search tree, and then apply this data structure to solve various geometric searching problems, such as, three dimensional ray-shooting, hidden surface removal, polygonal point enclosure searching and so on. In all applications, we are able to either improve existing bounds or establish new bounds.

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.

This article deals with the binary neural network with negative self-feedback connections as a method for solving combinational optimization problems. Although the binary neural network has a high convergence speed, it hardly searches out the optimum solution, because the neuron is selected randomly at each state update. In thie article, an improvement using the negative self-feedback is proposed. First it is shown that the negative self-feedback can make some local minimums be unstable. Second a selection rule is proposed and its property is analyzed in detail. In the binary neural network with negative self-feedback, this selection rule is effective to escape a local minimum. In order to comfirm the effectiveness of this selection rule, some computer simulations are carried out for the N-Queens problem. For N=256, the network is not caught in any local minimum and provides the optimum solution within 2654 steps (about 10 minutes).

This report describes 4-2 compressors composed of Complementary Pass-Transistor Logic (CPL). We will show that circuit designs of the 4-2 compressors can be optimized for high speed and small size using only exclusive-OR's and multiplexers. According to a circuit simulation with 0.8µm CMOS device parameters, the maximum propagation delay and the average power consumption per unit adder are 1.32 ns and 11.6 pJ, respectively.

This paper uses both network analysis and experiments to confirm that the neural network learning algorithm that minimizes output variation (BPV) provides much more robustness than back-propagation (BP) or BP with noise-modified training samples (BPN). Network analysis clarifies the relationship between sample displacement and what and how the network learns. Sample displacement generates variation in the output of the output units in the output layer. The output variation model introduces two types of deformation error, both of which modify the mean square error. We propose a new error which combines the two types of deformation error. The network analysis using this new error considers that BPV learns two types of training samples where the modification is either towards or away from the category mean, which is defined as the center of sample distribution. The magnitude of modification depends on the position of the training sample in the sample distribution and the degree of leaning completion. The conclusions is that BPV learns samples modified towards to the category mean more stronger than those modified away from the category mean, namely it achieves nonuniform learning. Another conclusion is that BPN learns from uniformly modified samples. The conjecture that BPV is much more robust than the other two algorithms is made. Experiments that evaluate robustness are performed from two kinds of viewpoints: overall robustness and specific robustness. Benchmark studies using distorted handprinted Kanji character patterns examine overall robustness and two specifically modified samples (noise-modified samples and directionally-modified samples) examine specific robustness. Both sets of studies confirm the superiority of BPV and the accuracy of the conjecture.

Analysis of satellite images requires classificatio of image objects. Since different categories may have almost the same brightness or feature in high dimensional remote sensing data, many object categories overlap with each other. How to segment the object categories accurately is still an open question. It is widely recognized that the assumptions required by many classification methods (maximum likelihood estimation, etc.) are suspect for textural features based on image pixel brightness. We propose an image feature based neural network approach for the segmentation of AVHRR images. The learning algoriothm is a modified backpropagation with gain and weight decay, since feedforward networks using the backpropagation algorithm have been generally successful and enjoy wide popularity. Destructive algorithms that adapt the neural architecture during the training have been developed. The classification accuracy of 100% is reached for a validation data set. Classification result is compared with that of Kohonen's LVQ and basic backpropagation algorithm based pixel-by-pixel method. Visual investigation of the result images shows that our method can not only distinguish the categories with similar signatures very well, but also is robustic to noise.

This paper proposes an approach for approximately realizing nonlinear mappings of interval vectors by interval neural networks. Interval neural networks in this paper are characterized by interval weights and interval biases. This means that the weights and biases are given by intervals instead of real numbers. First, an architecture of interval neural networks is proposed for dealing with interval input vectors. Interval neural networks with the proposed architecture map interval input vectors to interval output vectors by interval arithmetic. Some characteristic features of the nonlinear mappings realized by the interval neural networks are described. Next, a learning algorithm is derived. In the derived learning algorithm, training data are the pairs of interval input vectors and interval target vectors. Last, using a numerical example, the proposed approach is illustrated and compared with other approaches based on the standard back-propagation neural networks with real number weights.

In this paper, we show that the neural network can approximate the chaotic behavior in nonlinear dynamical system by experimental study. Chaotic neural activities have been reported in many respects including neural network field. On the contrary, can the neural network learn the chaotic behavior? There have been explored the neural network architecture for predicting successive elements of a sequence. Also there have been several studies related to learning algorithms for general recurrent neural networks. But they often require complicated procedure in time calculation. We use simple standard backpropagation for a kind of simple recurrent neural network. Two types of chaotic system, differential equation and difference equation, are examined to compare characteristics. In the experiments, Lorenz equation is used as an example of differential equation. One-dimensional logistic equation and Henon equation are used as examples of difference equation. As a result, we show the approximation ability of chaotic dynamics in difference equation, which is logistic equation and Henon equation, by neural network. To indicate the chaotic state, we use Lyapunov exponent which represents chaotic activity.

This paper presents a linear time algorithm for testing whether or not there is a path ,vm> of an undiercted tree T (|V(T)|n) that coincides with a string ss1sm (i.e., label(v1)label(vm)s1sm). Since any path of the tree is allowed, linear time substring matching algorithms can not be directly applied and a new method is developed. In the algorithm, O(n/m) vertices are selected from V(T) such that any path pf length more than m 2 must contain at least one of the selected vertices. A search is performed using the selected vertices as 'bases' and two tables of size O(m) are constructed for each of the selected vertices. A suffix tree, which is a well-known-data structure in string matching, is used effectively in the algorithm. From each of the selected vertices, a search is performed with traversing the suffix tree associated with s. Although the size of the alphabet is assumed to be bounded by a constant in this paper, the algorithm can be applied to the case of unbounded alphabets by increasing the time complexity to O(n log m).