Profile-splitting linearized bregman iterations for trend break detection applications

Gustavo Castro Do Amaral*, Felipe Calliari, Michael Lunglmayr

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

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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 N 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 N. 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.

Original languageEnglish
Article number423
JournalElectronics (Switzerland)
Issue number3
Publication statusPublished - 2020


  • FPGA
  • Linearized Bregman iteration
  • Optical time domain reflectometry
  • Trend break detection


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