Many rewriting systems, including those of terms, strings, graphs, and conjunction of atoms, are used throughout computer science and artificial intelligence. While the concepts of "substitutions," "places" in objects and the "replacement" of "subobjects" by other objects seems to be common to all rewriting systems, there does not exist a common foundation for such systems. At the present time, many of the theories are constructed independently, one for each kind of rewritten object. In the conventional approach, abstract rewriting systems are used to discuss common properties of all rewriting systems. However, they are too abstract to capture properties relating to substructures of objects. This paper aims to provide a first step towards a unified formalization of rewriting systems. The major problem in their formulation may be the formalization of the concept of "places". This has been solved here by employment of the concept of contexts rather than by formalization of places. Places determine subobjects from objects, while, conversely, contexts determine objects from subobjects. A class of rewriting systems, called β rewriting systems, is proposed. It is defined on axiomatically formulated base structures, called β structures, which are used to formalize the concepts of "contexts" and "replacement" common to many rewritten objects. The class of β rewriting systems includes very important systems such as semi-Thue systems and Petri Nets. Abstract rewriting systems are also a subclass of β rewriting systems.

In supervisory control, discrete event dynamic systems (DEDSs) are modeled by finite-state automata, and their behaviors described by the associated formal languages; control is exercised by a supervisor, whose control action is to enable or disable the controllable events. In this paper we present a general stability concept for DEDSs, stability in the sense of Lyapunov with resiliency, by incorporating Lyapunov stability concepts with the concept of stability in the sense of error recovery. We also provide algorithms for verifying stability and obtaining a domain of attraction. Relations between the notion of stability and the notion of fault-tolerance are addressed.

A processor allocation scheme for mesh computers greatly affects their system utilization. The performance of an allocation scheme is largely dependent on its ability to detect available submeshes. We propose a new type of submesh, called a link-disjoint submesh, for processor allocation in mesh computers. This type of submesh increases the submesh recognition capability of an allocation scheme. A link-disjoint submesh is not a contiguous submesh as in the previous scheme, but this submesh still has no common communication link with any other submesh. When wormhole routing or circuit switching is used, the communication delay caused by non-contiguous processor allocation is minor. Through simulation, the performance of our scheme is measured and compared to the previous schemes in terms of such parameters as finish time and system utilization. It is shown through simulation that the link-disjoint submesh increases the performance of an allocation scheme.

This paper presents a proportional-share CPU scheduler which can support multimedia applications in a general-purpose workstation environment. For this purpose, we have extended the stride scheduler which is designed originally for conventional tasks. New scheduling parameters are introduced to specify timing requirements of multimedia applications. Through the use of the rate regulator, the accuracy error of the scheduling is reduced to 0 (1). Separate task groups are proposed to represent both relative shares and absolute shares. The proposed scheduler is evaluated using a simulation study. The results show that the proposed scheduler achieves improved accuracy and adaptability as well as flexibility.

This paper extends left-incompatible term rewriting systems defined by Toyama et al. It is also shown that the functional strategy is normalizing in the class, where the functional strategy is the reduction strategy that finds index by some rule selection method and top-down and left-to-right lazy pattern matching method.

This paper proposes a 6.4-kbit/s extension to G.729 (conjugate structure algebraic code excited linear prediction: CS-ACELP). Each G.729 module was investigated to determine which bits could be removed without hurting the speech quality, then two coders that have different bit allocations were designed. They have two different algebraic codebooks (a 10-bit algebraic codebook that has two pulses and an 11-bit algebraic codebook that has two or three pulses). This paper also proposes a conditional orthogonalized search for a fixed codebook to improve the speech quality. The conditional orthogonalized search chooses, one of two search methods (orthogonalized or non-orthogonalized) based on the optimum pitch gain. The quality of the two coders was evaluated using objective measurements (SNR and segmental SNR) and subjective ones (mean opinion score: MOS and a pair-comparison test). The selected coder was evaluated under practical conditions. Subjective test results have indicated that the quality of the proposed coder (10-ms frame length) is equivalent to that of the 6.3-kbit/s G.723.1 coder, which has a 30-ms frame length.

We consider the situation that plural degraded images are obtained. When no prior knowledge about original images are known, these images are individually restored by an optimum restoration filter, for example, by Wiener Filter or by Projection Filter. If correlations between original images are obtained, some restoration filters based on Wiener Filter or Projection Filter are proposed. In this paper, we deal with the case that some pixels or some parts of original images overlap, and propose a restoration method using a formulae for overlapping. The method is based on Partial Projection Filter. Moreover, we confirm an efficacy of the proposed method by numerical examples.

In general cases of pattern recognition, a pattern to be recognized is first represented by a set of features and the measured values of the features are then classified. Finding features relevant to recognition is thus an important issue in recognizer design. As a fundamental design framework taht systematically enables one to realize such useful features, the Subspace Method (SM) has been extensively used in various recognition tasks. However, this promising methodological framework is still inadequate. The discriminative power of early versions was not very high. The training behavior of a recent discriminative version called the Learning Subspace Method has not been fully clarified due to its empirical definition, though its discriminative power has been improved. To alleviate this insufficiency, we propose in this paper a new discriminative SM algorithm based on the Minimum Classification Error/Generalized Probabilistic Descent method and show that the proposed algorithm achieves an optimal accurate recognition result, i.e., the (at least locally) minimum recognition error situation, in the probabilistic descent sense.

We present a method for learning classification functions from pre-classified training examples and hypotheses written roughly by experts. The goal is to produce a classification function that has higher accuracy than either the expert's hypotheses or the classification function inductively learned from the training examples alone. The key idea in our proposed approach is to let the expert's hypotheses influence the process of learning inductively from the training examples. Experimental results are presented demonstrating the power of our approach in a variety of domains.

This paper proposes high-speed similitude retrieval schemes for a viewpoint-based similarity discrimination system (VB-SDS) and presents analytical and experimental performance evaluations. The VB-SDS, which contains a huge set of semantic definitions of commonly used words and computes semantic similarity between any two words under a certain viewpoint, promises to be a very important module in analogical and case-based reasoning systems that provide solutions under uncertainty. By computing and comparing similarities for all words contained in the system, the most similar word for a given word can be retrieved under a given viewpoint. However, the time this consumes makes the VB-SDS unsuitable for inference systems. The proposed schemes reduce search space based on the upper bound of a similarity calculation function to increase retrieval speed. An analytical evaluation shows the schemes can achieve a thousand-fold speedup and confirmed through experimental results for a VB-SDS containing about 40,000 words.

This paper first investigates a relationship between inkdot-depth and inkdot-size of inkdot two-way alternating Turing machines and pushdown automata with sublogarithmic space, and shows that there exists a language accepted by a strongly loglog n space-bounded alternating pushdown automaton with inkdot-depth 1, but not accepted by any weakly o (log n) space-bounded and d (n) inkdot-size bounded alternating Turing machine, for any function d (n) such that lim_n [d (n)log n/n^1/2] = 0. In this paper, we also show that there exists an infinite space hierarchy among two-way alternating pushdown automata with sublogarithmic space.

We have developed an efficient recursive algorithm based on the interacting multiple model (IMM) for enhancing speech degraded by additive white noise. The clean speech is modeled by the hidden filter model (HFM). The simulation results shows that the proposed method offers performance gains relative to the previous one with slightly increased complexity.