Catálogo de publicaciones - revistas

<jats:p>This article reviews the available methods for automated identification of objects in digital images. The techniques are classified into groups according to the nature of the computational strategy used. Four classes are proposed: (1) the simplest strategies, which work on data appropriate for feature vector classification, (2) methods that match models to symbolic data structures for situations involving reliable data and complex models, (3) approaches that fit models to the photometry and are appropriate for noisy data and simple models, and (4) combinations of these strategies, which must be adopted in complex situations. Representative examples of various methods are summarized, and the classes of strategies with respect to their appropriateness for particular applications.</jats:p>

<jats:p>The join operation is one of the fundamental relational database query operations. It facilitates the retrieval of information from two different relations based on a Cartesian product of the two relations. The join is one of the most diffidult operations to implement efficiently, as no predefined links between relations are required to exist (as they are with network and hierarchical systems). The join is the only relational algebra operation that allows the combining of related tuples from relations on different attribute schemes. Since it is executed frequently and is expensive, much research effort has been applied to the optimization of join processing. In this paper, the different kinds of joins and the various implementation techniques are surveyed. These different methods are classified based on how they partition tuples from different relations. Some require that all tuples from one be compared to all tuples from another; other algorithms only compare some tuples from each. In addition, some techniques perform an explicit partitioning, whereas others are implicit.</jats:p>

<jats:p>Software reuse is the process of creating software systems from existing software rather than building software systems from scratch. This simple yet powerful vision was introduced in 1968. Software reuse has, however, failed to become a standard software engineering practice. In an attempt to understand why, researchers have renewed their interest in software reuse and in the obstacles to implementing it.</jats:p> <jats:p> This paper surveys the different approaches to software reuse found in the research literature. It uses a taxonomy to describe and compare the different approaches and make generalizations about the field of software reuse. The taxonomy characterizes each reuse approach in terms of its reusable <jats:italic>artifacts</jats:italic> and the way these artifacts are <jats:italic>abstracted, selected, specialized,</jats:italic> and <jats:italic>integrated</jats:italic> . </jats:p> <jats:p> Abstraction plays a central role in software reuse. Concise and expressive abstractions are essential if software artifacts are to be effectively reused. The effectiveness of a reuse technique can be evaluated in terms of <jats:italic>cognitive distance</jats:italic> —an intuitive gauge of the intellectual effort required to use the technique. Cognitive distance is reduced in two ways: (1) Higher level abstractions in a reuse technique reduce the effort required to go from the initial concept of a software system to representations in the reuse technique, and (2) automation reduces the effort required to go from abstractions in a reuse technique to an executable implementation. </jats:p> <jats:p>This survey will help answer the following questions: What is software reuse? Why reuse software? What are the different approaches to reusing software? How effective are the different approaches? What is required to implement a software reuse technology? Why is software reuse difficult? What are the open areas for research in software reuse?</jats:p>

<jats:p> This tutorial discusses Householder reduction of <jats:italic>n</jats:italic> linear equations to a triangular form which can be solved by back substitution. The main strength of the method is its unconditional numerical stability. We explain how Householder reduction can be derived from elementary-matrix algebra. The method is illustrated by a numerical example and a Pascal procedure. We assume that the reader has a general knowledge of vector and matrix algebra but is less familiar with linear transformation of a vector space.— <jats:italic>Author's Abstract</jats:italic> </jats:p>

<jats:p> Although experimental studies have been widely applied to the investigation of algorithm performance, very little attention has been given to experimental method in this area. This is unfortunate, since much can be done to improve the quality of the data obtained; often, much improvement may be needed for the data to be useful. This paper gives a tutorial discussion of two aspects of good experimental technique: the use of <jats:italic>variance reduction techniques</jats:italic> and <jats:italic>simulation speedups</jats:italic> in algorithm studies. </jats:p> <jats:p> In an illustrative study, application of variance reduction techniques produces a decrease in variance by a factor 1000 in one case, giving a dramatic improvement in the precision of experimental results. Furthermore, the complexity of the simulation program is improved from Θ <jats:italic>mn</jats:italic> /H <jats:italic> <jats:sub>n</jats:sub> </jats:italic> ) to Θ( <jats:italic>m</jats:italic> + <jats:italic>n</jats:italic> log <jats:italic>n</jats:italic> ) (where <jats:italic>m</jats:italic> is typically much larger than <jats:italic>n</jats:italic> ), giving a much faster simulation program and therefore more data per unit of computation time. The general application of variance reduction techniques is also discussed for a variety of algorithm problem domains. </jats:p>