27,007 research outputs found

    Compact Oblivious Routing

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    Oblivious routing is an attractive paradigm for large distributed systems in which centralized control and frequent reconfigurations are infeasible or undesired (e.g., costly). Over the last almost 20 years, much progress has been made in devising oblivious routing schemes that guarantee close to optimal load and also algorithms for constructing such schemes efficiently have been designed. However, a common drawback of existing oblivious routing schemes is that they are not compact: they require large routing tables (of polynomial size), which does not scale. This paper presents the first oblivious routing scheme which guarantees close to optimal load and is compact at the same time - requiring routing tables of polylogarithmic size. Our algorithm maintains the polylogarithmic competitive ratio of existing algorithms, and is hence particularly well-suited for emerging large-scale networks

    An error-controlled methodology for approximate hierarchical symbolic analysis

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    Limitations of existing approaches for symbolic analysis of large analog circuits are discussed. To address their solution, a new methodology for hierarchical symbolic analysis is introduced. The combination of a hierarchical modeling technique and approximation strategies, comprising circuit reduction, graph-based symbolic solution of circuit equations and matrix-based error control, provides optimum results in terms of speech and quality of results.European Commission ESPRIT 21812Comisión Interministerial de Ciencia y Tecnología TIC97-058

    Point cloud segmentation using hierarchical tree for architectural models

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    Recent developments in the 3D scanning technologies have made the generation of highly accurate 3D point clouds relatively easy but the segmentation of these point clouds remains a challenging area. A number of techniques have set precedent of either planar or primitive based segmentation in literature. In this work, we present a novel and an effective primitive based point cloud segmentation algorithm. The primary focus, i.e. the main technical contribution of our method is a hierarchical tree which iteratively divides the point cloud into segments. This tree uses an exclusive energy function and a 3D convolutional neural network, HollowNets to classify the segments. We test the efficacy of our proposed approach using both real and synthetic data obtaining an accuracy greater than 90% for domes and minarets.Comment: 9 pages. 10 figures. Submitted in EuroGraphics 201
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