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.

A new timing driven placement method based on the fuzzy theory is proposed. In this method, the longest path delay, the chip area and the wire length can be simultaneously minimized. Introducing the probability measures of fuzzy events, falling down into the optimal solutions can be avoided.

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.

Extended form of interpolatory approximation is presented for tne n-dimensional (n-D) signals whose generalized spectrums have weighted norms smaller than a given positive number. The presented approximation has the minimum measure of approximation error among all the linear and the nonlinear approximations using the same generalized sample values.

An optical array imaging system is presented with basic experimental results. First, a remote object is illuminated with laser light at an angle and the reflected light is detected with an array sensor after interfering it with the reference light. This process is repeated by changing the illumination angle to collect a set of fringe patterns, which are A/D converted and stored in a harddisk in a computer. Then, the data are processed on a computer, first, to estimate the complex-amplitude object wave fields, second, to derive the eigenvector with the maximum eigenvalue for the correlation of the estimated object fields, and finally, to form an image of the object. The derivation of the eigenvector follows an iterative algorithm, which can be interpreted as the process of repeating backward wave propagation of the field between the two apertures illuminating and detecting laser light. The eigenvector field can be expected to backpropagate to focus at a point on the object with the maximum coefficient of reflection, so that a beam-steering operation is applied to the eigenvector to form an image of the object. The method uses only the information of the array data and the lateral spacings of the receiving array (CCD) elements. Hence, the method can give good images of objects even if the reference light is uncollimated with an unknown distorted wavefront, and even if the illuminating angles are imprecise in three dimensions. Basic experimental results clearly show the usefulness of the method.

One of the problems with subband image coding is the increase in image sizes caused by filtering. To solve this, it has been proposed to process the filtering by transforming input sequence into a periodic one. Then filtering is implemented by circular convolution. Although this technique solves the problem, there are very strong restrictions, i.e., limitation on the filter type and on the filter bank structure. In this paper, development of this technique is presented. Consequently, any type of linear phase FIR filter and any structure of filter bank can be used.

This paper describes the application of a neural network to the optimal routing problem in broadband multimedia networks, where the objective is to maximize network utilization while considering the performance required for each call. In a multimedia environment, the performance required for each call is different, and an optimal path must be found whenever a call arrives. A neural network is appropriate for the computation of an optimal path, as it provides real-time solutions to difficult optimization problems. We formulated optimal routing based on the Hop field neural network model, and evaluated the basic behavior of neural networks. This evaluation confirmed the validity of the neural network formulation, which has a small computation time even if there are many nodes. This characteristic is especially suitable for a large-scale system. In addition, we performed a computer simulation of the proposed routing scheme and compared it with conventional alternate routing schemes. The results show the benefit of neural networks for the routing problem, as our scheme always balances the network load and attains high network utilization.

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.

A pattern is a finite string of constant symbols and variable symbols. The language of a pattern is the set of all strings obtained by substituting any nonnull constant string for each variable symbol in the pattern. The class of pattern languages was introduced by Angluin in 1979 as a concrete class which is inferable from positive data. In this paper, we consider the decision problem whether for given two patterns there is a containment relation between their languages, which was posed by Angluin and its decidability remains open. We give some sufficient conditions to make this problem decidable. We also introduce the notions of generalizations and minimal generalizations common to a set of patterns. We characterize the above open problem using the minimal generalization.

A new cleaning solution (FPM; HF-H2O2-H2O) was investigated in order to remove effectively metallic impurities on the silicon wafer surface. The removability of metallic impurities on the wafer surface and the concentrations of metallic impurities adsorbed on the wafer surface from each contaminated cleaning solution were compared between FPM and conventional cleaning solutions, such as HPM (HCl-H2O2-H2O), SPM (H2SO4-H2O2), DHF (HF-H2O) and APM (NH4OH-H2O2-H2O). This new cleaning solution had higher removability of metallic impurities than conventional ones. Adsorption of some kinds of metallic impurities onto the wafer surface was a serious problem for conventional cleaning solutions. This problem was solved by the use of FPM. FPM was important not only as a cleaning solution for metallic impurities, but also as an etchant. Furthemore, this new cleaning solution made possible to construct a simple cleaning system, because the concentrations of HF and H2O2 are good to be less than 1% for each, and it can be used at room temperature.

Thermal desorption spectroscopy (TDS) is applied to analyze the oxidation reactions of hydrogen-terminated Si(100) surfaces in both the heating and cooling processes after hydrogen desorption. The oxidation reaction of oxygen and water with a silicon surface after hydrogen desorption shows hysteresis in the heating and cooling processes. In the cooling process, oxidation finishes when the silicon surface is adequately oxidized to about a 10 thickness. Oxidation continues to occur at lower temperatures when the total volume of oxygen and water is too small to saturate the bare silicon surface. The reaction of water with silicon releases hydrogen at more than 500. Hydrogen does not adsorb on the silicon oxide surface. A trace amount of oxygen, less than 110-6 Torr, roughens the surface.

This paper proposes design schemes which obtain an efficient spare-channel assignment against single and double link failures for a self-healing network. Spare-channel design problems can be formulated as a linear-programming (LP) problem when variables are assumed to be continuous. For the problem, the proposed algorithm effectively solves a sub-set of whole constraints by making use of a maximum-flow algorithm in an iterative manner. It is shown that the maximum number of iteration times is limited by the number of links in the network. Moreover, the relation between the design function and the self-healing function is discussed. It is also shown that the cooperation of the two functions can realize more effective control in large scale networks.

This paper proposes a model for learning non-parametric densities using finite-dimensional parametric densities by applying Yamanishi's stochastic analogue of Valiant's probably approximately correct learning model to density estimation. The goal of our learning model is to find, with high probability, a good parametric approximation of the non-parametric target density with sample size and computation time polynomial in parameters of interest. We use a learning algorithm based on the minimum description length (MDL) principle and derive a new general upper bound on the rate of convergence of the MDL estimator to a true non-parametric density. On the basis of this result, we demonstrate polynomial-sample-size learnability of classes of non-parametric densities (defined under some smoothness conditions) in terms of exponential families with polynomial bases, and we prove that under some appropriate conditions, the sample complexity of learning them is bounded as O((1/ε)(2r1)/2r1n(2r1)/2r(1/ε)(1/ε)1n(1/δ) for a smoothness parameter r (a positive integer), where ε and δ are respectively accuracy and confidence parameters. Futher, we demonstrate polynomial-time learnability of classes of non-parametric densities (defined under some smoothness conditions) in terms of histogram densities with equal-length cells, and we prove that under some appropriate condition, the sample complexity of learning them is bounded as O((1/ε)3/21n3/2(1/ε)(1/ε)1n(1/δ)).

Chemical structures of native oxides formed during wet chemical treatments of silicon surfaces were investigated using X-ray Photoelectron Spectroscopy (XPS) and Fourier Transformed Infrared. Attenuated Total Reflection (FT-IR-ATR). It was found that the amounts of Si-H bonds in native oxide and at native oxide/ silicon interface are negligibly small in the case of native oxides formed in H2SO4-H2O2 solution. Based on this discovery, it was found that native oxides can be characterized by the amount of Si-H bonds in the native oxide and the combination of various wet chemical treatments with the treatment in NH4OH-H2O2-H2O solution results in the drastic decrease in the amount of Si-H bonds in the native oxides.

Multidimensional signal processing has recently been attracting attention in various fields, and has been studied theoretically. TV receives using 3-D (3-Dimensional: horizontal, vertical and temporal) processing, such as IDTV (ImproveD TV), are already available. In addition, television systems with high quality video and mostly with wide-aspect ratio are being studied worldwide. All the proposed systems adopt 3-D signal processing. 3-D processing can fully utilize the transmitted signal, and can take full advantage of the available bandwidth. This results in improved picture quality. This paper reviews the 3-D signal processing used in IDTV and EDTV (EnhanceD TV) in Japan. Video signals are analyzed in the 3-D frequency domain, and 3-D filter design is also studied.

This paper describes a super high definition (SHD) image processing system we have developed. The computing engine of this system is a parallel processing system with 128 processing elements called NOVI- HiPIPE. A new pipelined vector processor is introduced as a backend processor of each processing element in order to meet the great computing power required by SHD image processing. This pipelined vector processor can achieve 120 MFLOPS. The 128 pipelined vector processors installed in NOVI- HiPIPE yield a total system peak performance of 15 GFLOPS. The SHD image processing system consists of an SHD image scanner, and SHD image storage node, a full color printer, a film recorder, NOVI- HiPIPE, and a Super Frame Memory. The Super Frame Memory can display a ful color moving image sequence at a rate of 60 fps on a CRT monitor at a resolution of 2048 by 2048 pixels. Workstations, interconnected through an Ethernet, are used to control these units, and SHD image data can be easily transfered among the units. NOVI- HiPIPE has a frame memory which can display SHD still images on a color monitor, therefore, one processed frame can be directly displayed. We are developing SHD image processing algorithms and parallel processing methodologies using this system.

In this paper, a new description of a separable-denominator (S-D) two-dimensional (2-D) transfer matrix is proposed, and its realization is considered. Some of this problem had been considered for the transfer matrices whose elements are two-variables rational functions. We shall propose a 2-D transfer matrix whose inputs-outputs relation is represented by a ratio of two-variables polynomial matrices, and present an algorithm to obtain a 2-D state-space model from it. Next, it is shown that the description proposed in this paper is always minimally realizable. And, we shall present a method of obtaining the description proposed in this paper from a S-D 2-D rational transfer matrix.

Logarithmic number systems (LNS) provide a very fast computational method. Their exceptional speed has been demonstrated in signal processing and then in computer graphics. But the precision problem of LNS in computer graphics has not been fully examined. In this paper analysis is made for the problem of LNS in picture generation, in particular for circle drawing. Theoretical error analysis is made for the circle drawing. That is, some expressions are developed for the relative error variances. Then they are examined by simulation experiments. Some comparisons are also done with floating point arithmetic with equivalent word length and dynamic range. The results show that the theory and the experiments agree reasonably well and that the logarithmic arithmetic is superior to or at least comparable to the corresponding floating point arithmetic with equivalent word length and dynamic range. Those results are also verified by visual inspections of actually drawn circles. It also shows that the conversion error (from integer to LNS), which is inherent in computer graphics with LNS, does not make too much influence on the total computational error for circle drawing. But it shows that the square-rooting makes the larger influence.

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.

Synchrotron radiation (SR) irradiation of amorphous SiO2 (a-SiO2) induces continuous removal of the SiO2 film without the use of etching gas. The dependence of the photostimulated evaporation rate on substrate temperature and SR intensity was measured and the reaction mechanism is discussed in detail separately for surface and bulk. Using the high material selectivity of the Sr-stimulated evaporation, a sefl-aligned process to fabricate a 0.6 µm line-and-space pattern is presented. Si surface cleaning is demonstrated as an example of application of this reaction to thin native oxide film grown by wet pretreatment. Si(100)-21 and Si(111)-77 structures were observed by reflection high energy electron diffraction (RHEED) at temperatures as low as 650. The difference between a-SiO2 and native oxide on the evaporation rate is higlighted. Epitaxial Si growth using disilane (Si2H6) gas occurs selectively in the SR-irradiated region on a Si(100) surface. Using SR irradiation in an ultrahigh vacuum, followed by residual oxide reduction by disilane, is proposed as an effective cleaning method.