A High-Efficient Algorithm for Sample-Based Synthesis of Vegetation Distribution

MRA editing destroys the geometric constraints of curves/surfaces, a high efficient algorithm called wavelet signal separation to preserve geometric constraints during wavelet transformation in MRA modeling is proposed in this paper. The algorithm divides the B-spline control points into those associated and those unassociated with the geometric constraints. Through preserving the signal information associated with the constraint control points, the geometric constraints can be directly preserved after wavelet transformation. The detailed mathematics for this approach is presented. Different examples are included to demonstrate the power of the approach.

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A high-efficient algorithm for DOA estimation

2013 10th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP) ◽

10.1109/iccwamtip.2013.6716614 ◽

2013 ◽

Author(s):

Ruoyu Jiang

Keyword(s):

Efficient Algorithm ◽

Doa Estimation ◽

High Efficient

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A High-Efficient Algorithm of Mobile Load Balancing in LTE System

2012 IEEE Vehicular Technology Conference (VTC Fall) ◽

10.1109/vtcfall.2012.6398873 ◽

2012 ◽

Cited By ~ 19

Author(s):

Ying Yang ◽

Pengfei Li ◽

Xiaohui Chen ◽

Weidong Wang

Keyword(s):

Load Balancing ◽

Efficient Algorithm ◽

High Efficient

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A High-Efficient Algorithm for Parabolic Problems with Time-Dependent Coefficients

Advances in Applied Mathematics and Mechanics ◽

10.4208/aamm.2015.m1281 ◽

2017 ◽

Vol 9 (2) ◽

pp. 501-514

Author(s):

Chuanmiao Chen ◽

Xiangqi Wang ◽

Hongling Hu

Keyword(s):

Linear Combination ◽

Efficient Algorithm ◽

Interpolation Formula ◽

Parabolic Problems ◽

Extrapolation Algorithm ◽

High Efficient ◽

The Matrix ◽

Nicolson Scheme ◽

Time Extrapolation ◽

Function Time

AbstractA high-efficient algorithm to solve Crank-Nicolson scheme for variable coefficient parabolic problems is studied in this paper, which consists of the Function Time-Extrapolation Algorithm (FTEA) and Matrix Time-Extrapolation Algorithm (MTEA). First, FTEA takes a linear combination of previous l level solutions as good initial value of Un(see Time-extrapolation algorithm (TEA) for linear parabolic problems, J. Comput. Math., 32(2) (2014), pp. 183–194), so that Conjugate Gradient (CG)-iteration counts decrease to 1/3~1/4 of direct CG. Second, MTEA uses a linear combination of exact matrix values in level L, L+s, L+2s to predict matrix values in the following s–1 levels, and the coefficients of the linear combination is deduced by the quadric interpolation formula, then fully recalculate the matrix values at time level L+3s, and continue like this iteratively. Therefore, the number of computing the full matrix decreases by a factor 1/s. Last, the MTEA is analyzed in detail and the effectiveness of new method is verified by numerical experiments.

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A high efficient algorithm of mining sequential patterns

Proceedings of the 3rd World Congress on Intelligent Control and Automation (Cat. No.00EX393) ◽

10.1109/wcica.2000.863251 ◽

2002 ◽

Author(s):

Qin Feng ◽

Yang Xue-Bing

Keyword(s):

Efficient Algorithm ◽

Sequential Patterns ◽

High Efficient

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Computational Micrograph Registration with Sieve Processes

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100164660 ◽

1996 ◽

Vol 54 ◽

pp. 440-441

Author(s):

P.J. Phillips ◽

J. Huang ◽

S. M. Dunn

Keyword(s):

Planar Graph ◽

Efficient Algorithm ◽

Spatial Organization ◽

Spatial Arrangement ◽

Stereo Image ◽

Image Model ◽

Geometric Invariants ◽

Point Set

In this paper we present an efficient algorithm for automatically finding the correspondence between pairs of stereo micrographs, the key step in forming a stereo image. The computation burden in this problem is solving for the optimal mapping and transformation between the two micrographs. In this paper, we present a sieve algorithm for efficiently estimating the transformation and correspondence.In a sieve algorithm, a sequence of stages gradually reduce the number of transformations and correspondences that need to be examined, i.e., the analogy of sieving through the set of mappings with gradually finer meshes until the answer is found. The set of sieves is derived from an image model, here a planar graph that encodes the spatial organization of the features. In the sieve algorithm, the graph represents the spatial arrangement of objects in the image. The algorithm for finding the correspondence restricts its attention to the graph, with the correspondence being found by a combination of graph matchings, point set matching and geometric invariants.

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