Profile-Splitting Linearized Bregman Iterations for Trend Break Detection Applications
Trend break detection is a fundamental problem that materializes in many areas of applied science, where being able to identify correctly, and in a timely manner, trend breaks in a noisy signal plays a central role in the success of the application. The linearized Bregman iterations algorithm is one...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-03-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/9/3/423 |
| _version_ | 1798035112796880896 |
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| author | Gustavo Castro do Amaral Felipe Calliari Michael Lunglmayr |
| author_facet | Gustavo Castro do Amaral Felipe Calliari Michael Lunglmayr |
| author_sort | Gustavo Castro do Amaral |
| collection | DOAJ |
| description | Trend break detection is a fundamental problem that materializes in many areas of applied science, where being able to identify correctly, and in a timely manner, trend breaks in a noisy signal plays a central role in the success of the application. The linearized Bregman iterations algorithm is one of the methodologies that can solve such a problem in practical computation times with a high level of accuracy and precision. In applications such as fault detection in optical fibers, the length <i>N</i> of the dataset to be processed by the algorithm, however, may render the total processing time impracticable, since there is a quadratic increase on the latter with respect to <i>N</i>. To overcome this problem, the herewith proposed profile-splitting methodology enables blocks of data to be processed simultaneously, with significant gains in processing time and comparable performance. A thorough analysis of the efficiency of the proposed methodology stipulates optimized parameters for individual hardware units implementing the profile-splitting. These results pave the way for high performance linearized Bregman iteration algorithm hardware implementations capable of efficiently dealing with large datasets. |
| first_indexed | 2024-04-11T20:53:44Z |
| format | Article |
| id | doaj.art-6987068adb3c4538b7ae21479b3a9f28 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-04-11T20:53:44Z |
| publishDate | 2020-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-6987068adb3c4538b7ae21479b3a9f282022-12-22T04:03:45ZengMDPI AGElectronics2079-92922020-03-019342310.3390/electronics9030423electronics9030423Profile-Splitting Linearized Bregman Iterations for Trend Break Detection ApplicationsGustavo Castro do Amaral0Felipe Calliari1Michael Lunglmayr2Center for Telecommunications Studies, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro 22451-900, BrazilCenter for Telecommunications Studies, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro 22451-900, BrazilInstitute of Signal Processing, Johannes Kepler University, 4040 Linz, AustriaTrend break detection is a fundamental problem that materializes in many areas of applied science, where being able to identify correctly, and in a timely manner, trend breaks in a noisy signal plays a central role in the success of the application. The linearized Bregman iterations algorithm is one of the methodologies that can solve such a problem in practical computation times with a high level of accuracy and precision. In applications such as fault detection in optical fibers, the length <i>N</i> of the dataset to be processed by the algorithm, however, may render the total processing time impracticable, since there is a quadratic increase on the latter with respect to <i>N</i>. To overcome this problem, the herewith proposed profile-splitting methodology enables blocks of data to be processed simultaneously, with significant gains in processing time and comparable performance. A thorough analysis of the efficiency of the proposed methodology stipulates optimized parameters for individual hardware units implementing the profile-splitting. These results pave the way for high performance linearized Bregman iteration algorithm hardware implementations capable of efficiently dealing with large datasets.https://www.mdpi.com/2079-9292/9/3/423trend break detectionlinearized bregman iterationoptical time domain reflectometryfpga |
| spellingShingle | Gustavo Castro do Amaral Felipe Calliari Michael Lunglmayr Profile-Splitting Linearized Bregman Iterations for Trend Break Detection Applications Electronics trend break detection linearized bregman iteration optical time domain reflectometry fpga |
| title | Profile-Splitting Linearized Bregman Iterations for Trend Break Detection Applications |
| title_full | Profile-Splitting Linearized Bregman Iterations for Trend Break Detection Applications |
| title_fullStr | Profile-Splitting Linearized Bregman Iterations for Trend Break Detection Applications |
| title_full_unstemmed | Profile-Splitting Linearized Bregman Iterations for Trend Break Detection Applications |
| title_short | Profile-Splitting Linearized Bregman Iterations for Trend Break Detection Applications |
| title_sort | profile splitting linearized bregman iterations for trend break detection applications |
| topic | trend break detection linearized bregman iteration optical time domain reflectometry fpga |
| url | https://www.mdpi.com/2079-9292/9/3/423 |
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