In robot vision system, enormously large computation power is required to perform three-dimensional (3-D) instrumentation and object recognition. However, many kinds of complex and irregular operations are required to make accurate 3-D instrumentation and object recognition in the conventional method for software implementation. In this paper, a VLSI-oriented Model-Based Robot Vision (MBRV) processor is proposed for high-speed and accurate 3-D instrumentation and object recognition. An input image is compared with two-dimensional (2-D) silhouette images which are generated from the 3-D object models by means of perspective projection. Because the MBRV algorithm always gives the candidates for the accurate 3-D instrumentation and object recognition result with simple and regular procedures, it is suitable for the implementation of the VLSI processor. Highly parallel architecture is employed in the VLSI processor to reduce the latency between the image acquisition and the output generation of the 3-D instrumentation and object recognition results. As a result, 3-D instrumentation and object recognition can be performed 10000 times faster than a 28.5 MIPS workstation.

The issue about document structure recognition and document understanding is today one of interesting subjects from a viewpoint of practical applications. The research objective is to extract the meaningful data from document images interpretatively and also classify them as the predefined item data automatically. In comparison with the traditional image-processing-based approaches, the knowledge-based approaches, which make use of various knowledge in order to interpret structural/constructive features of documents, have been currently investigated as more flexible and applicable methods. In this paper, we propose a totally integrated paradigm for understanding table-form documents from a viewpoint of the architectural framework.

This paper presents an overview of the SDC-I (Super Database Computer I) developed at the University of Tokyo, Japan. The purpose of the project was to build a high performance SQL server which emphasizes query processing over transaction processing. Recently relational database systems tend to be used for heavy decision support queries, which include many join, aggregation, and order-by operations. At present high-end mainframes are used for these applications requiring several hours in some cases. While the system architecture for high traffic transaction processing systems is well established, that for adhoc query processing has not yet adequately understood. SDC-I proved that a parallel machine could attain significant performance improvements over a coventional sequential machine through the exploitation of the high degree of parallelism present in relational query processing. A unique bucket spreading parallel hash join algorithm is employed in SDC, which makes the system very robust in the presense of data skew and allows SDC to attain almost linear performance scalability. SDC adopts a hybrid parallel architecture, where globally it is a shared nothing architecture, that is, modules are connected through the multistage network, but each module itself is a symmetric multiprocessor system. Although most of the hardware elements use commodity microprocessors for improved performance to cost, only the interconnection network incorporates the special function to support our parallel relational algorithm. Data movement over the memory and the network, rather than computation, is heavy for I/O intensive database processing. A dedicated software system was carefully designed for efficient data movement. The implemented prototype consists of two modules. Its hardware and software organization is described. The performance monitoring tool was developed to visualize the system activities, which showed that SDC-I works very efficiently.

In this paper, symbols and numerals in topographic maps are recognized by the multi-angled parallelism (MAP) matching method, and small dots and lines are extracted by the MAP operation method. These results are then combined to determine the value, position, and attributes of elevation marks. Also, we reconstruct three dimensional surfaces described by contours, which is difficult even for humans since the elevation symbols are sparse. In reconstruction of the surface, we define an energy function that enfores three constraints: smoothness, fit, and contour. This energy function is minimized by solving a large linear system of simultaneous equations. We describe experiments on 25,000:1 scale topographic maps of the Tsukuba area.

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.

A simple method for separating the dissipation factors associated with both conductor losses and dielectric losses of printed circuit boards in microwave frequencies is presented. This method utilizes the difference in dependence of two dissipation factors on the dimensions of bounded stripline resonators using a single printed circuit board specimen as a center strip conductor. In this method, the separation is made through a procedure involving the comparison of the measured values of the total dissipation factor with those numerically calculated for the resonators. A method, which is based on a TEM wave approximation and uses Green's function and a variational principle, is used for the numerical calculation. Both effective conductivity for three kinds of industrial copper conductor supported with a substrate of polymide film and dielectric loss tangent of the substrates are determined using this method from the values of the unloaded Q measured at the 10 GHz region. Radiation losses from the resonator affecting the accuracy of the separation are discussed, as well as the values of the effective conductivity of metals on the polyimide substrate which is calculated using the above method. The resulting values of the effective conductivity agree with those using the triplateline method within 10%.

An Ordered Binary Decision Diagram (BDD) is a graph representation of a Boolean function. According to its good properties, BDD's are widely used in various applications. In this paper, we investigate the computational complexity of basic operations on BDD's. We consider two important operations: reduction of a BDD and binary Boolean operations based on BDD's. This paper shows that both the reduction of a BDD and the binary Boolean operations based on BDD's are NC1-reducible to REACHABILITY. That is, both of the problems belong to NC2. In order to extend the results to the BDD's with output inverters, we also considered the transformations between BDD's and BDD's with output inverters. We show that both of the transformations are also NC1-reducible to REACHBILITY.

With the advent of an aging society, the incidence of Alzheimer-type dementia (hereinafter referred to as AD for convenience) has drastically increased. Compared with classic cerebrovascular dementia, AD requires different therapeutic modalities. Despite such differences, it is difficult to establish a differential diagnosis of AD and cerebrovascular dementia. In the present paper, we analyze the neuropsychological symptoms and signs associated with AD, such as visual cognitive dysfunction, with particular attention to head and eye coordination. The subjects were allowed to gaze at targets disposed 1 m away and at a visual angle of 25 and 50 in order to compare healthy volunteers and patients with senile dementias such as multi-infarct dementia (MID). As a consequence, patients with AD presented clinical manifestations not seen in patients with other senile dementias; that is, (1) an increase in stepwise eye movement, (2) anisotropy in the velocity of right-directional and left-directional eye movements, (3) a decrease in the velocity of head movements (4) incomplete gaze, and (5) decreased head share.

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.

A new efficient waveform relaxation technique based on dynamically overlapped partitioning scheme is presented. This overlapped partitioning method enables the application of waveform relaxation technique to bipolar VLSI circuits. Instead of fixed overlapping, we select the depth of overlapping dynamically based on the sensitivity criteria. By minimizing the overlapped area, we could reduce the additional computational overhead which results from overlapping the partitions. This overlapped waveform relaxation method has better convergence properties due to smaller error introduced at each step compared with standard relaxation techniques. When overlapped partitioning is used in the case of digital circuits, the waveforms obtained after first iteration are nearly accurate. Therefore, by using these waveforms as initial guess waveforms for the second iterations we can reduce Newton-Raphson iterations at each time point.

If one of the R, L, or C Parameter of an RLC parallel circuit is changed periodically, under certain conditions, an oscillation called Parametric oscillation occurs. If one of remaining circuit elements is made to change due to an external cause (e.g. an external electric or magnetic field), then the parametric oscillation will experience some modulation. This modulation process and the subsequent demodulation can be exploited to create several types of sensors. In this letter, we describe the features of a new parametric magnetic speed sensor and its application in Induction motor robust control.

This paper describes the relaxation-based algorithms with the dynamic partitioning technique for bipolar circuit analysis. In this technique, a circuit is partitioned dynamically based on the consideration of the operating region of specified bipolar devices. This technique has been used already in the waveform relaxation method. In this paper, the dynamic circuit partitioning technique is implemented in the Iterated Timing Analysis (ITA). First, the dynamic partitioning method and its validity are described. Next, the present ITA is applied to the transient simulation of several digital bipolar circuits and compared with the waveform relaxation method.

This paper presents an optimal filtering algorithm using the covariance information in linear continuous distributed parameter systems. It is assumed that the signal is observed with additive white Gaussian noise. The autocovariance function of the signal, the variance of white Gaussian noise, the observed value and the observation matrix are used in the filtering algorithm. Then, the current filter has an advantage that it can be applied to the case where a partial differential equation, which generates the signal process, is unknown.

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.

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.

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.

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.

This paper addresses the problem of estimating the parameters of multivariate ARMA processes by using higher-order statistics called cumulants. The main objective in this paper is to extend the idea of the q-slice algorithm in univariate ARMA processes to multivariate ARMA processes. It is shown for a multivariate ARMA process that the MA coefficient matrices can be estimated up to postmultiplication of a permutation matrix by using the third-order cumulants and of an extended permutation matrix by using the fourth-order cumulants. Simulation examples are included to demonstrate the effectiveness of the proposed method.

A parallel quicksort algorithm in Ada is proposed and analyzed, its computational complexities are derived, and its performance profile is determined by simulation.

The data-driven model of computation is well suited for flexible and highly parallel simulation of neural networks. First, the operational semantics of data-driven languages preserve the locality and functionality of neural networks, and naturally describe their inherent parallelism. Second, the asynchronous data-driven execution facilitates the implementation of large and scalable multiprocessor systems, which are necessary to obtain considerable degrees of simulation sppedups. In this paper, we present a dynamic data-driven multiprocessor system, and demonstrate its suitability for the paralel simulation of back propagation neural networks. Two parallel implementations are described and evaluated using an image data compression network. The system is scalable, and as a result, the performance improved proportionally with the increase in number of processors.