This paper proposes an efficient design method of three-dimensional (3-D) recursive digital filters for video signal processing via decomposition of magnitude specifications. A given magnitude specification of a 3-D digital filter is decomposed into specifications of 1-D digital filters with three different (horizontal, vertical, and temporal) directions. This decomposition can reduce design problems of 3-D digital filters to design problems of 1-D digital filters, which can be designed with ease by conventional methods. Consequently, design of 3-D digital filters can be efficiently performed without complicated tests for stability and large amount of computations. In order to process video signal in real time, the 1-D digital filters with temporal direction must be causal, which is not the case in horizontal and vertical directions. Since the proposed method can approximate negative magnitude specifications obtained by the decomposition with causal 1-D R filters, the 1-D digital filters with temporal direction can be causal. Therefore the 3-D digital filters designed by the proposed method is suitable for real time video signal processing. The designed 3-D digital filters have a parallel separable structure having high parallelism, regularity and modularity, and thus is suitable for high-speed VLSI implementation.

We introduce a novel neural network called the T-Model and investigates the learning ability of the T-Model neural network. A learning algorithm based on the least mean square (LMS) algorithm is used to train the T-Model and produces a very good result for the T-Model network. We present simulation results on several practical problems to illustrate the efficiency of the learning techniques. As a result, the T-Model network learns successfully, but the Hopfield model fails to and the T-Model learns much more effectively and more quickly than a multi-layer network.

For the development of a practical device simulation, it is necessary to solve the large sparse linear equations with a high speed computation of direct solution method. The use of parallel computation methods to solve the linear equations can reduce the CPU time greatly. The Multi Step Diakoptics (MSD) algorithm, is proposed as one of these parallel computation methods with direct solution, which is based on Diakoptics, that is, a tearing-based parallel computation method for sparse linear equations. We have applied the MSD algorithm to device simulation. This letter describes the partition and connection schedules in the MSD algorithm. The evaluation of this algorithm is done using a massively parallel computer with distributed memory (AP1000).

Plasmaless etching using ClF3 gas has been investigated on nitride films with different composition. For the sputter deposited and thermally grown silicon nitride films containing no hydrogen, the etch rate increases and the activation energy decreases with increase of the composition ratio of silicon to nitrogen between 0.75 and 1.3. This fact indicates that the etching is likely to proceed through the reaction between Si and ClF3. The native oxide on the silicon-nitride films can also be removed with ClF3 gas. Ultra-violet light irradiation from a low pressure mercury lamp remarkably accelerates the removal of the native oxide and the etch rate of the thermally grown silicon-nitride films. For the plasma deposited films, the etch rate is strongly accelerate with increasing hydrogen content in the films, but the activation energy hardly depends on the bounded hydrogen in the films, consistent with the results for Si etching.

This paper presents a system that automatically refines the theory expressed in the function-free first-order logic. Our system can efficiently correct multiple faults in both the concept and subconcepts of the theory, given only the classified examples of the concept. It can refine larger classes of theory than existing systems can since it has overcome many of their limitations. Our system is based on a new combination of an inductive and an explanation-based learning algorithms, which we call the biggest-first multiple-example EBL (BM-EBL). From a learning perspective, our system is an improvement over the FOIL learning system in that our system can accept a theory as well as examples. An experiment shows that when our system is given a theory that has the classification error rate as high as 50%, it can still learn faster and with more accuracy than when it is not given any theory.

The globalization of business where single products and services are designed, developed, and manufactured in many different countries signals a significant need for cost-effective and reliable information movement and management capabilities. Similarly, consumers are seeking technologies which will allow them to visit the Smithsonian, scan a book in the Oxford Library, and interview a Japanese monk for a school report, all from the comfort of home. A necessary ingredient for realizing this global society is a strong telecommunications infrastructure. Our paper describes some of the customer needs and technology advances that are causing a revolution in planning global telecommunications networks. We present a new telecommunications paradigm and study its impact in two key areas: multi-country network routing at both the traffic and facility levels, and global network robustness.

The meaning of analogical reasoning in locally stratified logic programs are described by generalized stable model (GSM) semantics. Although studies on the theoretical aspects of analogical reasoning have recently been on the increase, there have been few attempts to give declarative semantics for analogical reasoning. This paper takes notice of the fact that GSM semantics gives meaning to the effect that the negated predicates represent exceptional cases. We define predicates that denote unusual cases regarding analogical reasoning; for example, ab(x)p(x)g(x), where p(s), q(s), p(t) are given. We also add rules with negated occurrences of such predicates into the original program. In this way, analogical models for original programs are given in the form of GSMs of extended programs. A proof procedure for this semantics is presented. The main objective of this paper is not to construct a practical analogical reasoning system, but rather to present a framework for analyzing characteristics of analogical reasoning.

The two most important parameters in reactive ion etching process, ion bombardment energy and flux, were extracted through a simple RF waveform measurement at the excitation electrode in a conventional cathode-coupled plasma RIE system. By using the extracted plasma parameters, damage and contamination in Si substrates induced by reactive ion etching in a SiCl4 plasma were investigated. A very convenient map representation of ion energy and ion flux was introduced in understanding the etching process occurring in the RIE system.

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.

We present a successful method for designing 2-D circularly symmetric R lowpass filters with constant group delay. The procedure is based on a transformation of a 1-D prototype R filter with constant group delay, whose magnitude response is the 2-D cross-sectional response. The 2-D filter transfer function has a separable denominator and a numerator which is obtained from the prototype numerator by means of a series of McClellan transformations whose free parameters can be optimized by successive procedure. The method is illustrated by an example.

The mechanism of UV-excited dry cleaning using photoexcited chlorine radicals has been investigated for removing iron and aluminum contamination on a silicon surface. The iron and aluminum contaminants with a surface concentration of 1013 atoms/cm2 were intentionally introduced via an ammonium-hydrogenperoxide solution. The silicon etching rates from the Uv-excited dry cleaning differ depending on the contaminants. Fe and Al can be removed in the same manner. The removal of Fe and Al is highly temperature dependent, and is little affected by the silicon etching depth. Both Fe and Al on the silicon surface were completely removed by UV-excited dry cleaning at a cleaning temperature of 170, and were decreased by two orders of magnitude from the initial level when the surface was etched only 2 nm deep.

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.

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.

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.

As part of Hitachi's development of clean semiconductor processing equipment, the Fluids Modeling Group of the Mechanical Engineering Research Laboratory is developing a computer model, CONTAMINATE, for simulating contamination of wafers in chemical vapor deposition (CVD) systems. CONTAMINATE is based on a 2D implementation of the SIMPLER algorithm for simulating convection/diffusion transport processes. The new model includes modules for simulating fluid flow, heat transfer, chemical reactions, and gas-phase formation and deposition of clusters and particles. CONTAMINATE outputs property fields and estimates of various film quality indices. Using CONTAMINATE we simulated a SiH4: O2: N2 gas mixture at 300 K flowing over a wafer heated to 700 K. System pressures were varied from 1-100 torr and SiH4 pressures from 0.1 to 10 torr. Deposition characteristics are in qualitative agreement with actual systems and are summarized as follows: (1) No particles larger than 0.1µm deposited for any of the conditions tested. (2) Film damage occurred above 10 torr, but no damage occurred below 10 torr. (3) Increasing SiH4 pressure at constant system pressure eliminated particle deposition because particles grew large enought that thermophoresis blocked particle diffusion. (4) Conformal deposition of featured surfaces was achieved only at 1 torr. (5) Film thickness variation over the diameter of the wafer was 15% at 100 torr, 3% at 10 torr, and 1% at 1 torr.

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.

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.

We consider the role of equivalence queries in learning unknown concepts using membership and equivalence queries. Equivalence queries have the following two roles: (R1) indicating whether a learning algorithm has succeeded to learn the unknown concept and (R2) providing counterexamples. In this paper, we consider the learning using membership and equivalence queries but using only the (R2) part of equivalence queries. In order to gain an insight into the learning membership and equivalence queries but using only the (R2) part of equivalence queries, we define equivalence-detecting problem". Let C be a representation class which is polynomial time learnable using membership and equivalence queries. We show that if the equivalence-detecting problem for C is polynomial time solvable then C is polynomial time learnable using membership and equivalence queries without using (R1). Moreover, we show that under certain conditions, the two notions, polynomial time solvability of equivalence-detecting problem" and polynomial time learnability using membership and equivalence queries without using (R1)", are equivalent. For concrete examples, we prove that dfas are polynomial time learnable using membership and equivalence queries without using (R1) in the learning situation where the algorithm is informed the number of states of the minimum states dfa accepting the target set in advance. On the other hand, we show that the equivalence-detecting problem for dfas are not solvable in the learning situation where the algorithm can use no additional information. This result together with our main result shows that, in this learning situation, the (R1) part of equivalence queries are necessary to learn dfas using membership and equivalence queries.