This paper investigates some fundamental properties of alternating one-way (or two-way) pushdown automata (pda's) with sublogarithmic space. We first show that strongly (weakly) sublogarithmic space-bounded two-way alternating pda's are more powerful than one-way alternating pda's with the same space-bound. Then, we show that weakly sublogarithmic space-bounded two-way (one-way) alternating pda's are more powerful than two-way (one-way) nondeterministic pda's and alternating pda's with only universal states using the same space, and we also show that weakly sublogarithmic space-bounded one-way nondeterministic Turing machines are incomparable with one-way alternating Turing machines with only universal states using the same space. Furthermore, we investigate several fundamental closure properties, and show that the class of languages accepted by weakly sublogarithmic space-bounded one-way alternating pda's and the class of languages accepted by sublogarithmic space-bounded two-way deterministic pda's (nondeterministic pda's, alternating pda's with only universal states) are not closed under concatenation, Kleene closure, and length preserving homomorphism. Finally, we briefly investigate a relationship between 'strongly' and 'weakly'.

An ordered binary decision diagram (OBDD) is a directed acyclic graph for representing a Boolean function. OBDDs are widely used in various areas which require Boolean function manipulation, since they can represent efficiently many practical Boolean functions and have other desirable properties. However, there is very little theoretical research on the complexity of constructing an OBDD. In this paper, we prove that the optimal variable ordering problem of a shared BDD is NP-complete, and briefly discuss the approximation hardness of this problem and related OBDD problems.

In this paper, we give an algorithm which, given a set F of at most (n - 1) - k faulty nodes, and two sets S = {s₁,..., s_k} and T = {t₁,..., t_k}, 1 k n - 1, of nonfaulty nodes in n-dimensional star graphs G_n, finds k fault-free node disjoint paths s_i t_ji, where (j₁,..., j_k) is a permutation of (1,..., k), of length at most d(G_n) + 5 in O(kn) optimal time, where d(G_n) = 3(n-1)/2 is the diameter of G_n.

This paper presents a parallel move generation of a Chess machine system for achieving the purpose of reducing the number of move generation cycles. The parallel system is composed of five move generation modules which share the move generating cycles to reduce the time of building a game tree. Simulation results show that the proposed parallel move generation architecture takes about half of the number of move generation cycles to build a game tree that is the same as the one built by a sequential move generation module.

A model for the floating point adder/subtractor which can perform rounding and addition/subtraction operations in parallel is presented. The major requirements and structure to achieve this goal are described and algebraically verified. Processing flow of the conventional floating point addition/subtraction operation consists of alignment, addition/subtraction, normalization, and rounding stages. In general, the rounding stage requires a high speed adder for increment, increasing the overall execution time and occupying a large amount of chip area. Furthermore, it accompanies additional execution time and hardware logics for renormalization stage which may occur by an overflow from the rounding operation. A floating adder/subtractor performing addition/subtraction and IEEE rounding in parallel is designed by optimizing the operational flow of floating point addition/subtraction operation. The floating point adder/subtractor presented does not require any additional execution time nor any high speed adder for rounding operation. In addition, the renormalization step is not required because the rounding step is performed prior to the normalization operation. Thus, performance improvement and cost-effective design can be achieved by this approach.

This paper proposes a mutual exclusion method that is unified for the parallel and distributed systems. The method partially serializes requests into partial queues of requests, which are next totally serialized into a main queue. A request in the main queue is authorized to enter the critical section (CS) when the request receives the privilege token from the previous request in the queue. In the distributed system of N sites that each is a parallel system, mutual exclusion is performed by cooperation of two algorithms based on the same method. The algorithm for the distributed system works on a logical network (that is a directed tree) of S ( N) sites. The algorithm for each site produces a local-main queue of requests. The chunk of requests in the local queue is concatenated at a time to the partial queue of the distributed system. The the cost of mutual exclusion -- the number of intersite messages required per CS entry -- is reduced to O(1) (between 0 and 3).

We present two efficient attribute evaluator construction methods for a wide subclass of L-attributed grammars by enumeration of attributed items during one-pass bottom-up parsing. We have already proposed a construction method of a parser/evaluator for the subclass of L-attributed grammar. However the evaluator produced by our previous method uses a great number of attributed items to evaluate all attributes of a given input string. In this paper we propose two generalized methods to reduce the number of attributed itmes used in attribute evaluation. Our methods allow us to evaluate all attributes taking advantage of the use of available lookahead information.

Recently, a number of concurrency control algorithms have been proposed for multidatabase system (MDBS) concurrency control methods (CCMs) and the most challenging issue of them has been a concern about how to ensure global serializability (GSR). In this paper, we examine two concurrency control algorithms of MDBS through simulation approach: optimistic ticket method (OTM) and global ticket method (GTM). In historical note, OTM is known to be the first practical solution, since this approach ensures GSR by way of automatically resolving indirect conflicts among global transactions without making any restrictions on local CCMs. However, OTM is expected to yield poor performance since it enforces all global transactions to take a local ticket which causes direct conflicts between them. In GTM, the global transaction manager in an MDBS assigns a global ticket to global transactions rather than accessing a local ticket as in OTM. Our experimental results showed that GTM outperforms OTM in cases that short timeout values are given. However, in case that the timeout value relatively becomes long, our results demonstrated that OTM outperforms GTM.

For the purpose of automatic speech recognition, language models (LMs) are used to predict possible succeeding words for a given partial word sequence and thereby to reduce the search space. In this paper several kinds of stochastic language models (SLMs) are evaluated-bigram, trigram, hidden Markov model (HMM), bigram-HMM, stochastic context-free grammar (SCFG) and hand-written Bunsetsu Grammar. To compare the predictive power of these SLMs, the evaluation was conducted from two points of views: (1) relationship between the number of model parameters and entropy, (2) predictive rate of succeeding part of speech (POS) and succeeding word. We propose a new type of bigram-HMM and compare it with the other models. Two kinds of approximations are tried and examined through experiments. Results based on both of English Brown-Corpus and Japanese ATR dialog database showed that the extended bigram-HMM had better performance than the others and was more suitable to be a language model.

We propose a new algorithm for minimizing the number of vertices of an approximate curve by keeping the error within a given bound (min-# problem) with the parallel-strip error criterion. The best existing algorithm which solves this problem has O (n² log n) time complexity. Our algorithm which uses the Cone Intersection Method does not have an improved time complexity, but does have a high efficiency. In particular, for practical data such as those which represent the boundaries or the skeletons of an object, the new algorithm can solve the min-# problem in nearly O(n²) time.

In this paper, we present some contributions to improve a previous work's approach presented for the segmentation of magnetic resonance images of the human brain, based on the unsupervised Hopfield neural network. We formulate the segmentation problem as a minimization of an energy function constructed with two terms, the cost-term as a sum of errors' squares, and the second term is a temporary noise added to the cost-term as an excitation to the network to escape from certain local minimums and be more close to the global minimum. Also, to ensure the convergence of the network and its utility in clinic with useful results, the minimization is achieved with a step function permitting the network to reach its stability corresponding to a local minimum close to the global minimum in a prespecified period of time. We present here our approach segmentations results of a patient data diagnosed with a metastatic tumor in the brain, and we compare them to those obtained based on, previous works using Hopfield neural networks, Boltzmann machine and the conventional ISODATA clustering technique.

PID-type controllers have been well-known and widely used in many industries. Their regulation property of those was more improved through the addition of Bang-Bang-action. In spite of the potentials of these PID-plus Bang-Bang controllers, their regulation property is still limited by the fixed window limit value that determines the control action, i. e., PID or Bang-Bang. Thus, this paper presents an approach for improving the regulation property by dynamically changing the window limit value according to the plant dynamics with Neural Network predictive model. The improved regulation property is illustrated through simulation studies for position control of DC servo-motor system in the sense of classical figures of merit such as overshoot and rise time.

In the business systems design learning environment, there may be more than one solution to any given problem. For instance, the data model will be different depending on each learner's perspective. Accordingly, group learning systems are very effective in this domain. We have developed CAMELOT (Collaborative and Multimedia Environment for Learners on Teams) [18] using the Nominal Group Technique for group problem solving. In this paper, the basic framework of the collaborative learning system and the effectiveness of collaborative learning in designing the Data Model are described. By using CAMELOT, each learner learns how to analyze through case studies and how to cooperate with his or her group in problem solving. Learners come to a deeper understanding from using CAMELOT than from studying independently because they are enabled to reach better solutions through discussion, tips from other learners, and examination of one another's works.

A novel method of multichannel surface EMG processing has been developed to compensate for the distortion in bipolar surface EMG signals due to the movement of innervation zones. The distortion of bipolar surface EMG signals was mathematically described as a filtering function. A compensating technique for such distorted bipolar surface EMG signals was developed for the brachial biceps during dynamic contractions in which the muscle length and tension change. The technique is based on multichannel surface EMG measurement, a method for estimating the movement of an innervation zone, and the inverse filtering technique. As a result, the distorted EMG signals were compensated and transformed into nearly identical waveforms, independent of the movement of the innervation zone.

In order to solve problems efficiently on a a quantum Turing machine, measurements (i. e., observations of physical systems) have been effectively used. In this paper, however, we show that the measurements executed during computation on the quantum Turing machine are not effective.

Among three factors mainly affecting the cache access time, i. e., hit access time, miss rate and miss penalty, previous approaches were focused on reducing the hit access time and miss rate. In this paper, we propose a scheme called MPC (Miss-Predicting Cache) which achives additional reduction of the average instruction cache access time through reducing the miss penalty. The MPC scheme which predicts cache miss and starts cache miss operations in advance, therefore, is supplementary to previous cache schemes targeted for reducing the miss rate and/or hit access time. Performance of the MPC scheme was evaluated using dinero, a trace-driven cache simulator, with the estimation of silicon area using 0.8 µm CMOS standard cell library.

A simple and convenient approach for a radial symmetrical point detection is proposed. In this paper the real part-only synthesis is utilized in order to make an origin symmetric pattern of the original image and to perform automatically the calculation of its autocorrelation for the detection of the symmetry center of the image.