A Parallelizable Framework for Segmenting Piecewise Signals
| العنوان: | A Parallelizable Framework for Segmenting Piecewise Signals |
|---|---|
| المؤلفون: | David Brie, Junbo Duan, Jérôme Idier, Mingxi Wan, Charles Soussen, Yu-Ping Wang |
| المساهمون: | Xi'an Jiaotong University (Xjtu), Laboratoire des signaux et systèmes (L2S), Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Centre de Recherche en Automatique de Nancy (CRAN), Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Laboratoire des Sciences du Numérique de Nantes (LS2N), IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Nantes - UFR des Sciences et des Techniques (UN UFR ST), Université de Nantes (UN)-Université de Nantes (UN)-École Centrale de Nantes (ECN)-Centre National de la Recherche Scientifique (CNRS), Tulane University |
| المصدر: | IEEE Access, Vol 7, Pp 13217-13229 (2019) IEEE Access IEEE Access, IEEE, 2019, 7, pp.13217-13229. ⟨10.1109/ACCESS.2018.2890077⟩ |
| بيانات النشر: | IEEE, 2019. |
| سنة النشر: | 2019 |
| مصطلحات موضوعية: | Parallel computing, dynamic programming, next generation sequencing, Parallelizable manifold, Optimization problem, General Computer Science, segmentation algorithm, Computer science, Parametric Probability Distribution, 020209 energy, Computation, General Engineering, 02 engineering and technology, Regularization (mathematics), Dynamic programming, 0202 electrical engineering, electronic engineering, information engineering, Piecewise, 020201 artificial intelligence & image processing, General Materials Science, Segmentation, lcsh:Electrical engineering. Electronics. Nuclear engineering, Algorithm, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, lcsh:TK1-9971, piecewise distribution |
| الوصف: | 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 which are ubiquitous. Experiments on both simulated and real genomic datasets from next generation sequencing demonstrate improved performance in terms of both segmentation quality and computational speed. |
| اللغة: | English |
| تدمد: | 2169-3536 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5e3547ec7f019cf567e2d9aaf0227c14 https://ieeexplore.ieee.org/document/8594545/ |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....5e3547ec7f019cf567e2d9aaf0227c14 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 21693536 |
|---|