Parallel decomposition algorithm using decimation operation for two-dimensional discrete convolution operation

1995 ◽  
Author(s):  
Shinping R. Wang ◽  
Pepe Siy
Keyword(s):  
Decomposition Algorithm ◽  
Two Dimensional ◽  
Discrete Convolution ◽  
Convolution Operation ◽  
Parallel Decomposition

Application of improved variational mode decomposition method based on two-dimensional sparrow search algorithm in natural gas pipeline leakage signal denoising

2021 ◽  
pp. 014233122110636
Author(s):  
Dongmei Wang ◽  
Lijuan Zhu ◽  
Jikang Yue ◽  
Jingyi Lu ◽  
Gongfa Li
Keyword(s):  
Search Algorithm ◽  
Signal To Noise Ratio ◽  
Decomposition Algorithm ◽  
Signal Denoising ◽  
Two Dimensional ◽  
Variational Mode Decomposition ◽  
Denoising Method ◽  
Mode Decomposition ◽  
Penalty Factor ◽  
Whale Optimization

To eliminate noise interference in pipeline leakage detection, a signal denoising method based on an improved variational mode decomposition algorithm is proposed. This work adopts a standard variational mode decomposition algorithm with decomposition level K and the penalty factor α. The improvements consist of using a two-dimensional sparrow search algorithm to find K and α. To verify the superiority of the sparrow search algorithm to find K and α, it is compared with three earlier studies. These studies used the firefly algorithm, particle swarm optimization, and whale optimization algorithm to perform the optimization. The main result of this study is to demonstrate that the variational mode decomposition improved by sparrow search algorithm gives a much improved signal-to-noise ratio compared to the other methods. In all other respects, the results are comparable.

PARDEC: A Parallel Decomposition Algorithm for Non-Hierarchical Systems or Structures

1994 ◽  
Author(s):  
Hans A. Eschenauer ◽  
Matthias Weinert
Keyword(s):  
Parallel Computing ◽  
Shape Optimization ◽  
Optimization Problems ◽  
Computing System ◽  
Decomposition Algorithm ◽  
Hierarchical Systems ◽  
Parallel Decomposition

Abstract The present paper introduces a decomposition algorithm for non-hierarchical systems or structures. The algorithm coordinates the created subsystem optimization problems by means of an approximation strategy. It is implemented on a parallel computing system and will be verified on shape optimization problems.

scholarly journals Fast Decomposition Algorithm Based on Two-Dimensional Wavelet Transform for Image Processing of Graphic Design

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Feifei Jiang ◽  
Wenting Yao
Keyword(s):  
Image Processing ◽  
Wavelet Transform ◽  
Graphic Design ◽  
Low Frequency ◽  
Decomposition Algorithm ◽  
Feature Point ◽  
Two Dimensional ◽  
Feature Points ◽  
Long Time ◽  
Fast Decomposition

Graphic design is an important part of the design field today. In this era of information explosion, designs that can deliver information faster and more accurately are bound to gain popularity among the public. In this paper, we propose a fast decomposition algorithm image processing method based on a new transform of the wavelet transform, which mainly addresses the problems of large computation of feature points and long-time consumption of traditional image processing algorithms. Firstly, the second-order decomposition of the image is performed by wavelet function to obtain the low-frequency components of the image, and the wavelet gradient vector is used to extract feature points from the overlapping regions of the low-frequency image so that the transformation parameters of feature points can be obtained quickly under the low-frequency image to guide the feature point extraction under the high-frequency image; on this basis, an improved algorithm of image processing based on the fast decomposition algorithm of two-dimensional wavelet transform with planar design is proposed. Using the properties of one-way matching and directional consistency of feature point constraints, the mismatched point pairs are effectively eliminated to improve the feature point matching accuracy and real-time performance. Finally, the effectiveness and feasibility of the proposed method are verified by two sets of experiments.

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