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A Parallelizable Framework for Segmenting Piecewise Signals
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DOI：10.1109/ACCESS.2018.2890077.png)
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Abstract
En 中文
Piecewise signals appear in many application fields. Here, we propose a framework for segmenting such signals based on the modeling of each piece using a parametric probability distribution. The proposed framework first models the segmentation as an optimization problem with sparsity regularization. Then, an algorithm based on dynamic programming is utilized for finding the optimal solution. However, dynamic programming often suffers from a heavy computational burden. Therefore, we further show that the proposed framework is parallelizable and propose using GPU-based parallel computing to accelerate the computation. This approach is highly desirable for the analysis of large volumes of data that are ubiquitous. The experiments on both the simulated and real genomic datasets from the next-generation sequencing demonstrate an improved performance in terms of both segmentation quality and computational speed.
Keywords:
Parallel computing
piecewise distribution
segmentation algorithm
dynamic programming
next generation sequencing
Journal
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3.6
Papers:
9.8W
Citations:
29.4W
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2018 IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)
2018 IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)IF0
PREAI
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