A binary algorithm for solving n by n systems of integer equations

We discuss a “binary” algorithm for solving systems of linear equations with integer coefficients. So-called “binary” algorithms differ from ordinary ones in that there is no roundoff error, but only overflow, and the underlying analysis is p-adic analysis rather than conventional real analysis. The advantages of this algorithm are especially apparent when extremely large numbers are involved and no roundoff error can be tolerated.

VLSI implementation of this and other “binary” algorithms is very appealing because of the extreme regularity of the circuits involved.     

Retrieval of movie scenes by semantic matrix and automatic feature weight update

A new semantic-based video scene retrieval method is proposed in this paper. Twelve low-level features extracted from a video clip are represented in a genetic chromosome and target videos that user has in mind are retrieved by the interactive genetic algorithm through the feedback iteration. In this procedure, high-level semantic relevance between retrieved videos is accumulated with so-called semantic relevance matrix and semantic frequency matrix for each iteration, and they are combined with an automatic feature weight update scheme to retrieve more target videos at the next iteration. Experiments over 300 movie scene clips extracted from latest well-known movies, showed an user satisfaction of 0.71 at the fourth iteration for eight queries such as “gloominess”, “happiness”, “quietness”, “action”, “conversation”, “explosion”, “war”, and “car chase”.     

Some permutation routing algorithms for low-dimensional hypercubes

Oblivious permutation routing in binary d-cubes has been well studied in the literature. In a permutation routing, each node initially contains a packet with a destination such that all the 2^d destinations are distinct. Kaklamanis et al. (Math. Syst. Theory 24 (1991) 223–232) used the decomposability of hypercubes into Hamiltonian circuits to give an asymptotically optimal routing algorithm. The notion of “destination graph” was first introduced by Borodin and Hopcroft to derive lower bounds on routing algorithms. This idea was recently used by Grammatikakis et al. (Proceedings of the Advancement in Parallel Computing, Elsevier, Amsterdam, 1993) to construct many–one routing algorithms for the binary 2-cube and 3-cube. In the present paper, further theoretical development is made along this line. It is then applied to obtain algorithms for binary d-cubes with d up to 12, which compare favorably with the above-mentioned “Hamiltonian circuit” algorithm. Some results on t-nary cubes with t3 are also obtained.     

Overload effects and their prevention

The so-called “thrashing effect”, well known from virtual storage, but also reported from data-base systems and packet switching networks, has turned out to be a common phenomenon of large systems with concurrent processing. It simply means that beyond a saturation point an increase of the load (e.g. number of jobs) leads to a (sometimes sudden) decrease in performance (e.g. throughput). With growing size and complexity of computer systems and the general trend towards distribution, overload phenomena of different origin can interfere and superimpose mutually, resulting in a composite overload effect that can hardly be broken down into its constituents. Because the complexity of such systems defies detailed modeling, it is more appropriate to look at those systems in a more macroscopic, behavioral way, considering only the two externally measurable variables “load” and “throughput”. The resulting abstraction from internal details can smooth the way to a more general treatment and application. The article deals with such overload phenomena and their prevention in a general way using a control-theoretic approach. Special emphasis is placed on dynamic behavior, where load characteristics are changing with time, making feedback mechanisms necessary. The problem is approached as a dynamic optimum search problem for which different algorithms are presented and compared by simulation.     

Comparison of two inference methods for P-type fuzzy logic control through experimental investigation using a hydraulic manipulator

This paper presents a comparison of the two important inference schemes: “individual-rule-based inference” and “compositional rule of inference” as applied to fuzzy logic control, through experimental investigation. The techniques are implemented on a hydraulic manipulator of an industrial machine with P-type fuzzy control. The fuzzy logic controller is designed for automatic positioning of the cutter blade of an automated fish-cutting machine. The features of the machine, which uses hydraulic servo control for cutter positioning, are outlined. The performance of the machine under the two inference schemes is examined and contrasted. Some practical implementations of the results are indicated.     

A hybrid method for robust car plate character recognition

Image-based car plate recognition is an indispensable part of an intelligent traffic system. The quality of the images taken for car plates, especially for Chinese car plates, however, may sometimes be very poor, due to the operating conditions and distortion because of poor photographical environments. Furthermore, there exist some “similar” characters, such as “8” and “B”, “7” and “T” and so on. They are less distinguishable because of noises and/or distortions. To achieve robust and high recognition performance, in this paper, a two-stage hybrid recognition system combining statistical and structural recognition methods is proposed. Car plate images are skew corrected and normalized before recognition. In the first stage, four statistical sub-classifiers recognize the input character independently, and the recognition results are combined using the Bayes method. If the output of the first stage contains characters that belong to prescribed sets of similarity characters, structure recognition method is used to further classify these character images: they are preprocessed once more, structure features are obtained from them and these structure features are fed into a decision tree classifier. Finally, genetic algorithm is employed to achieve optimum system parameters. Experiments show that our recognition system is very efficient and robust. As part of an intelligent traffic system, the system has been in successful commercial use.     

Runge-Kutta-Nystrom triples

The embedded Runge-Kutta-Nystrom process for the numerical solution of the special second-order initial-value problem can be extended by the addition of “dense” formulae for both y and y′ which yield solutions at points within the normal step intervals. As for Runge-Kutta methods two modes of implementation are possible and it is shown that these are equivalent under certain conditions. The cases of “dense” formulae applied to RKN4(3) and RKN6(4) families are considered and test results are presented.     

Integration of neural networks and genetic algorithms for an intelligent manufacturing controller

This paper addresses the development and implementation of a “controller” for a single manufacturing machine. This prototype will serve as an important tool to study the integration of several functions and the utilization of status data to evaluate scheduling and control decision alternatives. The emphasis is on creating a prediction capability to aid in assessing the long-term system performance impact resulting from decisions made and environmental changes. This prediction capability is implemented by using neural networks, simulation, and genetic algorithms. Neural networks predict the behavior of different sequencing policies available in the system. The contribution of the genetic algorithms to the decision-making process is the development of a “new” scheduling rule based on a “building blocks” procedure initiated by the neural networks     

Applying abstraction and formal specification in numerical software design

Numerical software development tends to struggle with an increasing complexity. This is, on the one hand, due to the integration of numerical models, and on the other hand, due to change of hardware. Parallel computers seem to fulfill the need for more and more computer resources, but they are more complex to program.

The article shows how abstraction is used to combat complexity. It motivates that separating a specification, “what,” its realisation, “how,” and its implementation, “when, where,” is of vital importance in software development. The main point is that development steps and levels of abstraction are identified, such that the obtained software has a clear and natural structure.

Development steps can be cast into a formal, i.e., mathematical framework, which leads to rigourous software development. This way of development leads to accurate and unambiguous recording of development steps, which simplifies maintenance, extension and porting of software. Portability is especially important in the field of parallel computing where no universal parallel computer model exists.