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Power decomposition method for compression of the electric-field integral equation

Power decomposition method for compression of the electric-f...

引用

Computational Electromagnetics Workshop

作者： L. Landesa G. Gajardo-Silva J. M. Taboada Computer and Communications Sciences Department Escuela Politecnica University of Extremadura Caceres Spain

We focus on the problem of compression of farfield interactions in the matrices of the method of moments. We present a new point of view with respect to other alternatives: instead of compressing each block of the impedance matrix (corresponding to the mutual coupling between a pair of geometry groups), our hypothesis here is that this compression can be separately obtained inside of each group. In this manner each group is compressed only once, which allows us to obtain larger compression rates than the usual mutual-coupling based schemes. With this idea, we propose a recursive mechanism similar to that used in the multilevel fast multipole method, leading to a hi erarchical multilevel building of macro basis functions that finally provides a O(N logN) algorithm for computational electromagnetics. Moreover, the proposed calculation of compressed basis functions is completely independent on the excitation.

关键词： Integral equations Impedance Mutual coupling Geometry Matrix decomposition Moment methods Computational electromagnetics Acceleration Approximation algorithms partitioning algorithms

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An Approach For Edge Detection Based On Beamlet Transform

An Approach For Edge Detection Based On Beamlet Transform

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International Conference on Image and Graphics (ICIG)

作者： Xiaoming Mei Liang-pei Zhang Ping-xiang Li Wuhan University China

Edge detection is very useful and important for image processing and computer vision, as it can locate significant variations of gray images. In this paper, an algorithm based on beamlet transform is proposed to detect edges in image. Beamlets can be generated by recursive dyadic partitioning, vertex marking and connecting, the beamlet transform is the collection of all line integrals formed by viewing the image as a piecewise constant object and integrating along line segment in the beamlet dictionary, for the maximal beamlet coefficient surviving the Canny criterion, draw a line segment depicting that beamlet, all these beamlets in different scales are fused to generate an edge map at the image pixel level. The propose method can detect lines with any orientation, location and length in different scales and avoids subjective setting. Experimental results show that the proposed method can detect edges accurately even from noise image and has a better performance. It can be suited to different images processing, in practice it has surprisingly powerful and apparently unprecedented capabilities.

关键词： Image edge detection Image processing Image segmentation Detectors Computer vision Pixel Remote sensing partitioning algorithms Joining processes Dictionaries

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A novel geometric algorithm for fast wire-optimized floorplanning 03

A novel geometric algorithm for fast wire-optimized floorpla...

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IEEE International Conference on Computer-Aided Design

作者： P.G. Sassone S.K. Lim School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA

ISBN: (纸本)9781581137620

As the size and complexity of VLSI circuits increase, the need for faster floorplanning algorithms also grows. In this work we introduce Traffic, a new method for creating wire- and area-optimized floorplans. Through the use of connectivity grouping, simple geometry, and efficient data structures, Traffic achieves higher result quality than Simulated Annealing (SA) in a fraction of the time. This speed allows designers to explore a large circuit design space in a reasonable amount of time, rapidly evaluate small changes to big circuits, and quickly produce initial solutions for other floorplanning algorithms.

关键词： Traffic control partitioning algorithms Shape Data structures Circuit simulation Simulated annealing Circuit synthesis Permission Optimization methods Geometry

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Projective Clustering Ensembles

Projective Clustering Ensembles

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IEEE International Conference on Data Mining (ICDM)

作者： Francesco Gullo Carlotta Domeniconi Andrea Tagarelli DEIS Department University of Calabria Rende Cosenza Italy Department of Computer Science George Mason University Fairfax VA USA

Recent advances in data clustering concern clustering ensembles and projective clustering methods, each addressing different issues in clustering problems. In this paper, we consider for the first time the projective clustering ensemble (PCE) problem, whose main goal is to derive a proper projective consensus partition from an ensemble of projective clustering solutions. We formalize PCE as an optimization problem which does not rely on any particular clustering ensemble algorithm, and which has the ability to handle hard as well as soft data clustering, and different feature weightings. We provide two formulations for PCE, namely a two-objective and a single-objective problem, in which the object-based and feature-based representations of the ensemble solutions are taken into account differently. Experiments have demonstrated that the proposed methods for PCE show clear improvements in terms of accuracy of the output consensus partition.

关键词： Clustering algorithms Clustering methods partitioning algorithms Data mining Computer science USA Councils Computational efficiency Feature extraction

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A new algorithm for constrained finite time optimal control of hybrid systems with a linear performance index

A new algorithm for constrained finite time optimal control ...

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European Control Conference (ECC)

作者： Mato Baotić Frank J. Christophersen Manfred Morari ETH Zentrum Automatic Control Laboratory Zürich Switzerland

In this paper we present a modification of the algorithm described in [1, 2] for computing the solution to the constrained finite time optimal control problem for discrete time linear hybrid systems. As opposed to the quadratic performance index used in the original algorithm here we use a linear performance index. The algorithm combines a dynamic programming strategy with a multi-parametric linear program solver. By comparison with literature results it is shown that the algorithm presented here solves the considered class of problems in a computationally efficient way.

关键词： Heuristic algorithms Dynamic programming Optimal control Aerospace electronics partitioning algorithms Time factors Performance analysis

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Efficient and Self-Balanced ROLLUP Aggregates for Large-Scale Data Summarization

Efficient and Self-Balanced ROLLUP Aggregates for Large-Scal...

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IEEE International Conference on Services Economics (SE)

作者： Duy-Hung Phan Quang-Nhat Hoang-Xuan Matteo Dell'Amico Pietro Michiardi EURECOM

Data summarization queries that compute aggregates by grouping datasets across several dimensions are essential to help users make sense of very large datasets. In this work, we focus on ROLLUP, an important operator that has been recently added to the Hadoop MapReduce ecosystem. However, its current implementation suffers from very large communication costs, leading to inefficient executions. We thus proceed with the design of a new ROLLUP operator for high-level languages. Our operator is self-optimizing, which means that it automatically performs load-balancing and determines a suitable operating point to achieve the highest performance. We have implemented our ROLLUP operator for Apache Pig, a popular high-level language in the Hadoop ecosystem. Our experimental results, obtained on both synthetic and real datasets, indicate that our new operator outperforms the current ROLLUP implementation in Pig by at least 50%.

关键词： Aggregates Tuning Runtime Algorithm design and analysis Load modeling Clustering algorithms partitioning algorithms

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Grid-clustering: an efficient hierarchical clustering method for very large data sets

Grid-clustering: an efficient hierarchical clustering method...

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International Conference on Pattern Recognition

作者： E. Schikuta Institute of Applied Computer Science and Information Systems University of Technology Vienna Austria

Clustering is a common technique for the analysis of large images. In this paper a new approach to hierarchical clustering of very large data sets is presented. The GRIDCLUS algorithm uses a multidimensional grid data structure to organize the value space surrounding the pattern values, rather than to organize the patterns themselves. The patterns are grouped into blocks and clustered with respect to the blocks by a topological neighbor search algorithm. The runtime behavior of the algorithm outperforms all conventional hierarchical methods. A comparison of execution times to those of other commonly used clustering algorithms, and a heuristic runtime analysis are presented.

关键词： Clustering methods Clustering algorithms partitioning algorithms Multidimensional systems Data structures Runtime Computer science Information systems Heuristic algorithms Algorithm design and analysis

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Clustering RDF data using K-medoids

Clustering RDF data using K-medoids

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First International Conference of Intelligent Computing and Engineering (ICOICE)

作者： Seham A. Bamatraf Rasha A. BinThalab College of Engineering & Petroleum Hadhramout University Mukalla Yemen

ISBN: (数字)9781728144870

ISBN: (纸本)9781728144887

Semantic web is a knowledge graph formed around semantic languages to enable computers and software to understand contents on the web. The content is explicitly annotated with semantic metadata using Resource Description Framework (RDF) language. However, the main issue is how to efficiently retrieve the RDF data taking into account a wide variety semantic and syntax nature and large-scale of such data. This paper aims to introduce a novel mechanism based on K-medoids algorithm for narrowing down the contents of the Web to clusters pertaining subset of information. We integrated sequence alignment algorithms with linguistic similarity measures to build a distance matrix which is used later in K-medoids clustering algorithm. The experimental outcomes showed a promised result for accuracy and quality of clustering.

关键词： Resource description framework Clustering algorithms Semantics Syntactics Dictionaries partitioning algorithms

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Research on parallel buffer analysis with grided based HPC technology

Research on parallel buffer analysis with grided based HPC t...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Lv Pang Guoqing Li Yunxuan Yan Yan Ma Center of Earth Observation and Digital Earth Chinese Academy and Sciences Beijing China Chinese Academy and Sciences China Chinese Academy of Sciences Beijing Beijing CN

Buffer analysis is one of the most important GIS functions to take spatial analysis. Based on our experience on EO data parallel processing, we take buffer generation as an example to research how to accelerate the speed and scale of GIS operations. This paper analyzes the procedure of the original sequential algorithm, and then gives out the mechanism of the parallelization. As a result, it will be easily assembled into OGC WPS container on a grid environment as both standard Web-service and enhanced WPS service. Some interested examinations also will be involved in this work to prove such technical approach.

关键词： Geographic Information Systems Parallel algorithms Parallel processing Algorithm design and analysis Distributed computing Earth Content addressable storage Grid computing partitioning algorithms Topology

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Projection search for approximate nearest neighbor

Projection search for approximate nearest neighbor

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ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD)

作者： Cheng Feng Bo Yang School of Electronic and Information Engineering Xi'an Jiaotong University Xi'an China

Many existing approaches to speed up the nearest neighbor search are based on spatial partition trees, which search the nearest neighbor for a query sample by traversing the tree data structure in a guided depth-first manner. However, this search manner is generally observed to find the exact nearest neighbor at a early time, and spend the remaining time checking the other part of the tree without any further improvement on the accuracy. In order to avoid the massive cost of redundant tree traversal, an intuitive strategy is to narrow the search area. As is known, it is the projection values of data under splitting directions that decide the tree traversal path. Hence, utilizing the distribution of projection data, we propose a tree-based approximate nearest neighbor search algorithm in this paper. We greatly reduce the cost of tree traversal by limiting the search area within the nearby cells where the query lies in, and excluding those far apart. Furthermore, we guarantee a theoretical lower bound on the accuracy. Experiments on various real-world datasets show that our algorithm outperforms the conventional random projection tree, as well as the locality sensitive hashing for approximate nearest neighbor search.

关键词： Nearest neighbor searches Approximation algorithms partitioning algorithms Measurement Limiting Search problems Computer vision

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