This paper presents an efficient and highly scalable algorithm, designed from scratch, to calculate total-viewshed in large high-resolution digital elevation models (DEMs) without restrictions as to whether or not the...
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This paper presents an efficient and highly scalable algorithm, designed from scratch, to calculate total-viewshed in large high-resolution digital elevation models (DEMs) without restrictions as to whether or not the observer is linked to the ground. The keys to the high efficiency of the proposed method are: 1) the selection of a reliable sampling to represent the subareas of study;2) the use of a compact and stable data structure to store the calculated data;and 3) the high reutilization of data and calculation between the large number of viewpoints. The obtained results demonstrate that the proposed algorithm is the fastest over the most commonly used GIS-software showing very similar numerical accuracy.
The simulative development of clothing and other textile products requires a complete set of material parameters to be provided. Currently, different simulation software providers users with different values and forma...
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The simulative development of clothing and other textile products requires a complete set of material parameters to be provided. Currently, different simulation software providers users with different values and formats for these parameters. This paper provides an overview about the most important values and proposed structures for storing both the raw data and the extracted parameters. The structure is implemented in both JSON and XML formats, allowing integration in proven formats for three-dimensional worlds such as gltf and x3d. Finally, a structure for organization of the raw data of the testing devices is described. Following this structure allows automatic processing, normalization and extraction of the parameters in short time. The goal of the paper is to simplify and unify the exchange of material parameters for textile fabrics.
Hob is a program analysis system that enables the focused application of multiple analyses to different modules in the same program. In our approach, each module encapsulates one or more data structures and uses membe...
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Hob is a program analysis system that enables the focused application of multiple analyses to different modules in the same program. In our approach, each module encapsulates one or more data structures and uses membership in abstract sets to characterize how objects participate in data structures. Each analysis verifies that the implementation of the module 1) preserves important internal data structure consistency properties and 2) correctly implements a set algebra interface that characterizes the effects of operations on the data structure. Collectively, the analyses use the set algebra to 1) characterize how objects participate in multiple data structures and to 2) enable the interanalysis communication required to verify properties that depend on multiple modules analyzed by different analyses. We implemented our system and deployed several pluggable analyses, including a flag analysis plug- in for modules in which abstract set membership is determined by a flag field in each object, a PALE shape analysis plug- in, and a theorem proving plug- in for analyzing arbitrarily complicated data structures. Our experience shows that our system can effectively 1) verify the consistency of data structures encapsulated within a single module and 2) combine analysis results from different analysis plug- ins to verify properties involving objects shared by multiple modules analyzed by different analyses.
The two-dimensional (2D) Self-Organizing Map (SOM) has a well-known "border effect". Several spherical SOMs which use lattices of the tessellated icosahedron have been proposed to solve this problem. However...
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The two-dimensional (2D) Self-Organizing Map (SOM) has a well-known "border effect". Several spherical SOMs which use lattices of the tessellated icosahedron have been proposed to solve this problem. However, existing data structures for such SOMs are either not space efficient or are time consuming when searching the neighborhood. We introduce a 2D rectangular grid data structure to store the icosahedron-based geodesic dome. Vertices relationships are maintained by their positions in the data structure rather than by immediate neighbor pointers or an adjacency list. Increasing the number of neurons can be done efficiently because the overhead caused by pointer updates is reduced. Experiments show that the spherical SOM using our data structure, called a GeoSOM, runs with comparable speed to the conventional 2D SOM. The GeoSOM also reduces data distortion due to removal of the boundaries. Furthermore, we developed an interface to project the GeoSOM onto the 2D plane using a cartographic approach, which gives users a global view of the spherical data map. Users can change the center of the 2D data map interactively. In the end, we compare the GeoSOM to the other spherical SOMs by space complexity and time complexity. (c) 2006 Elsevier Ltd. All rights reserved.
The approximate range emptiness problem requires a memory-efficient data structure D to approximately represent a set S of n distinct elements chosen from a large universe U = {0, 1, . . . , N - 1} and answer an empti...
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The approximate range emptiness problem requires a memory-efficient data structure D to approximately represent a set S of n distinct elements chosen from a large universe U = {0, 1, . . . , N - 1} and answer an emptiness query of the form "S boolean AND [a;b] = empty set?" for an interval [a;b] of length L (a, b is an element of U), with a false positive rate epsilon. The designed D for this problem can be kept in high-speed memory and quickly determine approximately whether a query interval is empty or not. Thus, it is crucial for facilitating online query processing in the information-centric Internet of Things applications, where the IoT data are continuously generated from a large number of resource-constrained sensors or readers and then are processed in networks. However, the existing works on the approximate range emptiness problem only consider the simple case when the set S is static, rendering them unsuitable for the continuously generated IoT data. In this paper, we study the approximate range emptiness problem over sliding windows in the IoT data streams, denoted by epsilon-ARESD-problem, where both insertion and deletion are allowed. We first prove that, given a sliding window size n and an interval length L, the lower bound of memory bits needed in any data structure for epsilon-ARESD-problem is n log(2)(nL/epsilon) + Theta(n). Then, a data structure is proposed and proved to be within a factor of 1.33 of the lower bound. The extensive simulation results demonstrate the advantage of the efficiency of our data structure over the baseline approach.
In the present paper, we derive an efficient data structure for the organization of the nodes in the coupled finite element/clement-free Galerkin method. With respect to its implementation, we compare various approach...
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In the present paper, we derive an efficient data structure for the organization of the nodes in the coupled finite element/clement-free Galerkin method. With respect to its implementation, we compare various approaches of recursive spatial discretizations that facilitate most flexible handling of the nodes. The goal of the paper is to refine the implementation issues of the data structure which is fundamental to the clement-free Galerkin method and thus to speed-up this otherwise computationally rather expensive meshfree method. Copyright (C) 2001 John Wiley & Sons, Ltd.
We propose a new static high-performance mesh data structure for triangle surface meshes on the GPU. Our data structure is carefully designed for parallel execution while capturing mesh locality and confining data acc...
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We propose a new static high-performance mesh data structure for triangle surface meshes on the GPU. Our data structure is carefully designed for parallel execution while capturing mesh locality and confining data access, as much as possible, within the GPU's fast "shared memory." We achieve this by subdividing the mesh into patches and representing these patches compactly using a matrix-based representation. Our patching technique is decorated with ribbons, thin mesh strips around patches that eliminate the need to communicate between different computation thread blocks, resulting in consistent high throughput. We call our data structure RXMesh: Ribbon-matriX Mesh. We hide the complexity of our data structure behind a flexible but powerful programming model that helps deliver high performance by inducing load balance even in highly irregular input meshes. We show the efficacy of our programming model on common geometry processing applications-mesh smoothing and filtering, geodesic distance, and vertex normal computation. For evaluation, we benchmark our data structure against well-optimized GPU and (single and multi-core) CPU data structures and show significant speedups.
This note describes a new data structure which records information and supports queries about elements that have been previously inserted and deleted. Both time and space parameters match those of Overmars (1981); the...
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This note describes a new data structure which records information and supports queries about elements that have been previously inserted and deleted. Both time and space parameters match those of Overmars (1981); the advantage to the structure described here is its simplicity.
We present an Array-based Half-Facet mesh data structure, or AHF, for efficient mesh query and modification operations. The AHF extends the compact array-based half-edge and half-face data structures (T. J. Alumbaugh ...
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We present an Array-based Half-Facet mesh data structure, or AHF, for efficient mesh query and modification operations. The AHF extends the compact array-based half-edge and half-face data structures (T. J. Alumbaugh and X. Jiao, Compact array-based mesh data structures, IMR, 2005) to support mixed-dimensional and non-manifold meshes. The design goals of our data structure include generality to support such meshes, efficiency of neighborhood queries and mesh modification, compactness of memory footprint, and facilitation of interoperability of mesh-based application codes. To accomplish these goals, our data structure uses sibling half-facets as a core abstraction, coupled with other explicit and implicit representations of entities. A unique feature of our data structure is a comprehensive implementation in MATLAB, which allows rapid prototyping, debugging, testing, and deployment of meshing algorithms and other mesh-based numerical methods. We have also developed a C++ implementation built on top of MOAB (T.J. Tautges, R. Meyers, and K. Merkley, MOAB: A Mesh-Oriented database, Sandia National Laboratories, 2004). We present some comparisons of the memory requirements and computational costs, and also demonstrate its effectiveness with a few sample applications.
In this paper, we consider the representation and management of an element set on which a lattice partial order relation is defined. In particular, let n be the element set size. We present an O(n root n)-space implic...
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In this paper, we consider the representation and management of an element set on which a lattice partial order relation is defined. In particular, let n be the element set size. We present an O(n root n)-space implicit data structure for performing the following set of basic operations: 1. Test the presence of an order relation between two given elements, in constant time. 2. Find a path between two elements whenever one exists, in O(l) steps, where l is the path length. 3. Compute the successors and/or predecessors set of a given element, in O(h) steps, where h is the size of the returned set. 4. Given two elements, find all elements between them, in time O(k log d), where k is the size of the returned set and d is the maximum in-degree or out-degree in the transitive reduction of the order relation. 5. Given two elements, find the least common ancestor and/or the greatest common successor in O(root n)-time. 6. Given k elements, find the least common ancestor and/or the greatest common successor in O(root n + k log n)time. (Unless stated otherwise, all logarithms are to the base 2.) The preprocessing time is O(n(2)). Focusing on the first operation, representing the building-box for all the others, we derive an overall O(n root n)-space x time bound which beats the order n(2) bottleneck representing the present complexity for this problem. Moreover, we will show that the complexity bounds for the first three operations are optimal with respect to the worst case. Additionally, a stronger result can be derived. In particular, it is possible to represent a lattice in space O(n root t), where t is the minimum number of disjoint chains which partition the element set.
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