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.

The blocking probabilities of n64Kb/s multi-slot calls are generally much higher than that of single slot calls. In order to improve these blocking probabilities of multi-slot calls, we propose a scheme to limit the number of time slots to be searched for lower rate calls. We analyze the performance of our scheme in a double-buffered time-space-time switching network which accommodates multi-slot calls as well as single-slot calls. The proposed method yields the reduced blocking probabilities of multi-slot calls, the increased traffic handling capacity and the reduced CPU processing load, compared with those of the conventional methods.

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.

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.

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.

The paper proposes a new multicarrier 16QAM system for high-quality and high-bit-rate transmission with high spectral efficiency in land mobile radio communications. The proposed system uses a multicarrier transmission scheme to provide immunity against frequency-selective fading distortion. It also uses pilot-symbol-aided 16QAM to increase spectral efficiency, and it combines space diversity and FEC with maximum likelihood decoding to improve the bit error rate (BER) performance. Computer simulation shows that a BER of less than 10-4 is obtained over frequency-selective fading channels with rms delay spread of less than 5.4µs. Using a bandwidth of 200kHz the proposed system can achieve high-quality transmission with a total information rate of 256kbit/s.

A design of an Ada IPC (Inter-Program Communication) interface is proposed, through which a designer of distributed systems can (a) specify arbitrary data types needed for inter-program communication and (b) use parallel programming features to build highly parallel systems; a test simulator was built for execution of the IPC interface and a multi-window system was realized as an application of the interface on the simulator; the interface was found to be useful, making description of inter-program communication simpler and easier.

Relating to the problem of suppressing the immanent redundancy contained in an image with out vitiating the quality of the resultant approximation, the interpolation of multi-dimensional signal is widely discussed. The minimization of the approximation error is one of the important problems in this field. In this paper, we establish the optimum interpolatory approximation of multi-dimensional orthogonal expansions. The proposed approximation is superior, in some sense, to all the linear and the nonlinear approximations using a wide class of measures of error and the same generalized moments of these signals. Further, in the fields of information processing, we sometimes consider the orthonormal development of an image each coefficient of which represents the principal featurr of the image. The selection of the orthonormal bases becomes important in this problem. The Fisher's criterion is a powerful tool for this class of problems called declustering. In this paper, we will make some remarks to the problem of optimizing the Fisher's criterion under the condition that the quality of the approximation is maintained.

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.

Recently, the back propagation method, which is one of the algorithms for learning neural networks, has been widely applied to various fields because of its excellent characteristics. But it has drawbacks, for example, slowness of learning speed, the possibility of falling into a local minimum and the necessity of adjusting a learning constant in every application. In this article we propose an algorithm which overcomes some of the drawbacks of the back propagation by using an improved genetic algorithm.

This paper describes a 32-bit fully asynchronous microprocessor, with 4-stage pipeline based on a RISC-like architecture. Issues relevant to the processor such as design of self-timed datapath, asynchronous controller and interconnection circuits are discussed. Simulation results are included using parameters extracted from layout, which showed about the 300 MIPS processing speed and used 71,000 transistors with 0.5 µm CMOS technology.

The Alopex algorithm is presented as a universal learning algorithm for neural networks. Alopex is a stochastic parallel process which has been previously applied in the theory of perception. It has also been applied to several nonlinear optimization problems such as the Travelling Salesman Problem. It estimates the weight changes by using only a scalar cost function which is measure of global performance. In this paper we describe the use of Alopex algorithm for solving nonlinear learning tasks by multilayer feed-forward networks. Alopex has several advantages such as, ability to escape from local minima, rapid algorithmic computation based on a scalar cost function and synchronous updation of weights. We present the results of computer simulations for several tasks, such as learning of parity, encoder problems and the MONK's problems. The learning performance as well as the generalization capacity of the Alopex algorithm are compared with those of the backpropagation procedure, and it is shown that the Alopex has specific advantages over backpropagation. An important advantage of the Alopex algorithm is its ability to extract information from noisy data. We investigate the efficacy of the algorithm for faster convergence by considering different error functions. We show that an information theoretic error measure shows better convergence characteristics. The algorithm has also been applied to more complex practical problems such as undersea target recognition from sonar returns and adaptive control of dynamical systems and the results are discussed.

New detection method of passivation defect was studied. The method was the Cu decoration method without bias (bias-free Cu decoration). As the result of comparison with conventional method, it was found that a bias-free Cu decoration method was effective, sensitive and simple. In this method, the difference of humidity resistance induced by poor passivation coverage could be evaluated.

Precise matching of the SEM (secondary electron microscope) image of the DUT (device under test) interconnection pattern with the CAD layout is required in the CAD-linked electron beam test system. We propose the point pattern matching method that utilizes a corner pattern in the CAD layout. In the method, a corner pattern which consists of a small number of pixels is derived by taking into account the design rules of VLSIs. By using the corner pattern as a template, the matching points of the template are sought in both the SEM image and CAD layout. Then, the point image obtained from the SEM image of DUT is matched with that from the CAD layout. Even if the number of points obtained in the DUT pattern is different from that in the CAD layout due to the influence of noise present in the SEM image of the DUT pattern, the point matching method would be successful. The method is applied to nonpassivated and passivated LSIs. Even for the passivated LSI where the contrast in the SEM image is mainly determined by voltage contrast, matching is successful. The computing time of the proposed method is found to be shortened by a factor of 4 to 10 compared with that in a conventional correlation coefficient method.

We describe a geometrical optics approach for the analysis of dielectric tapered waveguides. The method is based on the ray-optical treatment for wave-normal rays defined newly to waves of light in open structures. Geometrical optics fields are represented in terms of two kinds of wave-normal rays: leaky rays and guided rays. Since the behavior of these rays is different in the two regions separated at critical incidence, the geometrical optics fields have certain classes of discontinuity in a transition region between leaky and guided regions. Guided wave solutions are given as a superposition of guided rays that zigzag along the guides, all of which are totally reflected upon the interfaces. By including some leaky rays adjacent to the guided rays, we obtain more accurate guided wave solutions. Calculated results are in excellent agreement with wave optics solutions.

A problem which often arises in computer vision is that of matching corresponding points of images. In the case of object recognition, for example, the computer compares new images to templates from a library of known objects. A common way to perform this comparison is to extract feature points from the images and compare these points with the template points. Another common example is the case of motion detection, where feature points of a video image are compared to those of the previous frame. Note that in both of these example, the point correspondence is complicated by the fact that the point sets are not only randomly ordered but have also been distorted by an unknown transformation and having quite different coordinates. In the case of object recognition, there exists a transformation from the object being viewed, to its projection onto the camera's imaging plane, while in the motion detection case, this transformation represents the motion (translation and rotation) of the ofject. If the parameters of the transformation are completely unknow, then all n! permutations must be compared (n : number of feature points). For each permutation, the ensuing transformation is computed using the least-squared projection method. The exponentially large computation required for this is prohibitive. A neural computational method is propopsed to solve these combinatorial problems. This method obtains the best correspondence matching and also finds the associated transform parameters. The method was applied to two dimensional point correspondence and three-to-two dimensional correspondence. Finally, this connectionist approach extends readily to a Boltzmann machine implementation. This implementation is desirable when the transformation is unknown, as it is less sensitive to local minima regardless of initial conditions.

A procedure for preparing a cross-sectional transmission electron microscopy (TEM) micrograph of a specific area is outlined. A specific area in a specimen has been very difficult to observe with TEM, because a particular small area cannot be preselected in the conventional specimen preparation technique using mechanical polishing, dimpling and ion milling. The technique in this paper uses a focused ion beam (FIB) to fabricate a cross-sectional specimen at a desired area. The applications of this specimen preparation technique are illustrated for investigations of particles in the process of fabricating devices and degraded aluminum/aluminum vias. The specimen preparation technique using FIB is useful for observing a specific area. This technique is also useful for shortening the time of specimen preparation and observing wide areas of LSI devices.

This document describes a proposal for the implementation of a new VLSI neural network technique called Parallel Propagated Targets (PPT). This technique differs from existing techniques because all layer, within a given network, can learn simultaneously and not sequentially as with the Back Propagation algorithm. the Parallel Propagated Target algorithm uses only information local to each layer and therefore there is no backward flow of information within the network. This allows a simplification in the system design and a reduction in the complexity of implementation, as well as acheiving greater efficiency in terms of computation. Since all synapses can be calculated simultaneously it is possible using the PPT neural algorithm, to parallelly compute all layers of a multi-layered network for the first time.

Resonant slots are widely used for conventional slotted waveguide array. Reflection from each slot causes a standing wave in the waveguide and beam tilting technique is essential to suppress the reflection at the antenna input port. But the slot reflection narrows the overall frequency bandwidth and the design taking it into account is complicated. This paper proposes a reflection cancelling slot pair as an array element, which consists of two slots spaced by 1/4λg. Round trip path-length difference between them is 1/2λg and reflection waves from a pair disappear and traveling-wave excitation in the waveguide is realized. The full wave analysis reveals that mutual coupling between paired slots is large and seriously reduces the radiation from a pair. Offset arrangement of slots in a pair is recommended to decrease the mutual coupling and to realize strong coupling. In practical array design, the mutual couplings from other pairs were simulated by imposing periodic boundary conditions above the aperture. To clarify the advantages of the slot pair over a conventional resonant slot, the predicted characteristics are compared. Reflection characteristics of the array using the slot pair is excellent and a boresite beam array can be realized. In addition, a slot pair can realize stronger coupling than the conventional resonant slot, while the bandwidth of the former in terms of the aperture field phase illumination is narrower than that of the latter. These suggests that the slot pair array is much more suitable for a small array than conventional one. Finally, the predicted characteristics are confirmed by experiments.

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.