Dynamic Term Rewriting Calculus is a new computation model proposed by the authors for the purpose of formal description and verification of algorithms treating Term Rewriting Systems. The computation of DTRC is basically term rewriting. The characteristic features of DTRC are dynamic change of rewriting rules during computation and hierarchical declaration of not only function symbols and variables but also rewriting rules. These features allow us to program metacomputation of TRSs in DTRC, that is , we can implement in DTRC in a natural way those algorithms which manipulate term rewriting systems as well as those procedures which verify such algorithms. In this paper, we give a formal description of DTRC. We then show some results on confluence property of DTRC.

In this paper, we propose an efficient task scheduling scheme, called CTS (Class-based Task Scheduling), to obtain high performance in terms of high system utilization and low waiting times for tasks. While a better submesh allocation scheme can improve system performance, an allocation policy alone cannot improve performance significantly. This is due to the fact that the FCFS task scheduling policy leads to large external fragmentation. The CTS strategy maintains four separate queues, one for each incoming task class. This avoids the blacking property incurred in the FCFS scheduling. To reduce the external fragmentation, a job tends to wait for an occupied submesh of the same size instead of using a new submesh in the CTS strategy. Simulation results indicate that the proposed scheduling strategy improves the performance compared to the FCFS scheduling policy by reducing the average waiting delay significantly.

Modern database systems have to support complex data objects, which appear in advanced data models such as object-oriented data models and nested relational data models. Set-valued objects are basic constructs to build complex structures in those models. Therefore, efficient processing of set-valued object retrieval (simply, set retrieval) is an important feature required of advanced database systems. Our previous work proposed a basic scheme to apply superimposed coded signature files to set retrieval and showed its potential advantages over the B-tree index based approach using a performance analysis model. Retrieval with signature files is always accompanied by mismatches called false drops, and proper control of the false drops is indispensable in the signature file design. This study intensively analyzes the false drops in set retrieval with signature files. First, schemes to use signature files are presented to process set retrieval involving "has-subset," "is-subset," "has-intersection," and "is-equal" predicates, and generic formulas estimating the false drops are derived. Then, three sets of concrete formulas are derived in three ways to estimate the false drops in the four types of set retrieval. Finally, their estimates are validated with computer simulations, and advantages and disadvantages of each set of the false drop estimation formulas are discussed. The analysis shows that proper choice of estimation formulas gives quite accurate estimates of the false drops in set retrieval with signature files.

This paper focuses on recovering from processor transient faults in pipelined multiprocessor systems. A pipelined machine may employ out of order execution and branch prediction techniques to increase performance, thus a precise computation state would not be available. We propose an efficient scheme to maintain the precise computation state in a pipelined machine. The goal of this paper is to implement checkpointing and rollback recovery utilizing the technique of precise interrupt in a pipelined system. Detailed analysis is included to demonstrate the effectiveness of this method.

This paper presents a character recognition method based on a dynamic model, which can be applied to character patterns from both on-line and off-line input. Other similar attempts simply treat on-line patterns as off-line input, while this method makes use of the on-line input's characteristics by representing the time information of handwriting in the character pattern representations. Experiments were carried out on the Hiragana character set. Without non-linear normalization, this method achieved recognition rates of 92.3% for on-line input and 89.1% for off-line input. When non-linear normalization is used, there is an increase in performance for both types of input with on-line input achieving 94.5% and off-line input achieving 94.1%. The reason for the difference in the effectiveness of non-linear normalization on off-line and on-line patterns could be that while the method used for off-line input was an established and proved one, we used our own initial attempt at non-linear normalization for the on-line patterns. If the same level of effectiveness of non-linear normalization as off-line input is achieved on the on-line input, however, the recognition rate for on-line input again improves becoming 96.3%. Since only one standard pattern was used per category for the dictionary patterns, the above results show the promise of this method. This result shows the compatibility of this method to both on-line and off-line input, as well as its effective use of on-line input's characteristics. The effectiveness of this use of the time information is shown by using an actual example. The data also shows the need for a method of non-linear normalization which is more suitable for on-line input.

This paper discusses the relationships of two important program classes of linearly recursive programs, that is, decomposable programs and rule commutative programs. We prove that the decomposable programs are always rule commutative. Furthermore, the rule commutative programs that satisfy certain conditions are decomposable. These results are meaningful for integrating the related specified optimization algorithms.

In this letter, we obtain the absolute exponential stability result of neural networks with globally Lipschitz continuous, increasing and bounded activation functions under a sufficient condition which can unify some relevant sufficient ones for absolute stability in the literature. The obtained absolute exponential stability result generalizes the existing ones about absolute stability of neural networks. Moreover, it is demonstrated, by a mathematically rigorous proof, that the network time constant is inversely proportional to the global exponential convergence rate of the network trajectories to the unique equilibrium. A numerical simulation example is also presented to illustrate the analysis results.

Event-related are the kind of signals that are time-related to a given event. In this work, we will study the effect of bias and overlapping noise on Fourier linear combiner (FLC)-based filters, and its implication on filtering event-related signals. We found that the bias alters the weights behaviour, and therefore the filter output, and we discuss solutions to the problem of spectral overlap.