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The connective fault tolerance of a multiple-bus system was studied using a processor-bus-link (PBL) graph. The PBL graph model characterizes the processor connectivity of multiple-bus systems with different types of faults. The design of a highly fault-tolerant network is important for the subject to minimize connectivity requirements. It provides a representation of a hypergraph implying that the analysis is useful in characterizing the node and hyperedge connectivities and the minimum cut sets.

A sparse crossbar (n, m, c)-concentrator is a bi-partite graph with n inputs and m outputs in which any c or fewer inputs can be matched with an equal number of outputs, where c is called its capacity. We present a number of new results on the crosspoint complexity of such concentrators. First, we describe a sparse crossbar (n, m, m)-concentrator whose crosspoint complexity matches Nakamura-Masson's lower bound for any given n and m. Second, we present a sparse crossbar (2m, m, m)-concentrator with crosspoint complexity also matching Nakamura-Masson's lower bound, and with fixed fan-in and nearly fixed fan-out. Third, we derive an easily computable lower bound on the crosspoint complexity of sparse crossbar (n, m, c)-concentrators. Finally, we show that this bound is attainable within a factor of two when n - m [less than or equal to] c [less than or equal to] [m./c]. Index Terms - Bipartite graph, concentrator, sparse crossbar, crosspoint complexity.

Previous research has documented the importance of constructing a good representation for understanding and solving problems. Symbolic representations (e.g., matrices and node link networks) in particular are good for conceptualizing the underlying structure of many problems. Because solvers often do not construct appropriate symbolic representations for problems, the authors examined whether solvers can transfer such representations from an example problem to a nonisomorphic target problem. They found evidence of such transfer in college students for 4 different symbolic representations; 3 were diagrammatic (matrix, hierarchy, and network representations), whereas 1 was more purely conceptual (a part-whole representation). The 4 experiments reported in this article enabled the authors to identify several key component processes of representational transfer, as well as some of the factors affecting their execution. On the basis of the results of the experiments, as well as on a comparison of the apparent requirements of representational and procedural (i.e., analogical) transfer, the authors propose a model of the component processes underlying representational transfer.

An algorithm for the detection of dominant points and for building a hierarchical approximation of a digital curve is proposed. The algorithm does not require any parameter tuning and is shown to perform well for a wide variety of shapes, including scaled and rotated ones. Dominant points are first located by a coarse-to-fine detector scheme. They constitute the vertices of a polygon closely approximating the curve. Then, a criterion of perceptual significance is used to repeatedly remove suitable vertices until a stable polygonal configuration, the contour sketch, is reached. A highly compressed hierarchical description of the shape also becomes available.

As an alternative to coordinate locations, patterns of points can be represented using interpoint distances. In certain applications, such as dimensioning and multidimensional scaling, interpoint distances have traditionally been used. In other applications, such as radar location, interpoint distance information is readily obtained. This paper utilizes kinematics, rigidity theory, and graph algorithms to determine the theoretical minimum interpoint distance information adequate for uniquely representing point patterns. It is shown that this theoretical minimum is comparable to that amount of information required by coordinate descriptions.

Currently the aspect graph is computed from the theoretical standpoint of perfect resolution in object shape, the viewpoint and the projected image. This means that the aspect graph may include details that an observer could never see in practice. Introducing the notion of scale into the aspect graph framework provides a mechanism for selecting a level of detail that is 'large enough' to merit explicit representation. This effectively allows control over the number of nodes retained in the aspect graph. This paper introduces the concept of the scale space aspect graph, defines three different interpretations of the scale dimension, and presents a detailed example for a simple class of objects, with scale defined in terms of the spatial extent of features in the image.

A study explored various graphic characteristics associated with the visual analysis of single-subject data. A sample of 20 rehabilitation therapists used visual analysis to rate 24 graphs of data sets using a traditional baseline-treatment (AB) format. Using the ntraclass correlation approach, the authors assessed the interrater reliability and found that it ranged from .52 to .66 for each graph. Data analysis revealed that the graphic characteristics of level and mean shift were associated with consistent judgments across the raters, and that changes in slope across the two phases of a graph were associated with substantial rater disagreement. The authors discuss the implications of using visual and statistical procedures to analyze single-subject data, and argue that quantitative adjuncts to visual analysis may facilitate the reliable interpretation of graphed data for a single subject. (Reprinted by permission of the publisher.)

Mass media professionals usually resort to the pie chart to represent portions of a whole because of its intuitive appeal. Practitioners occasionally use complex-shaped pies instead of circular pies in their attempt to be more creative. However, it is uncertain whether creativity is achieved without any trade-off in accuracy. A study conducted by Prabu David hinted that the use of complex shapes may result in significantly distorted data.