Abstract
We investigate the detection of smooth steps in a measured signal using an algorithm based on linearized Bregman iterations (LBI). Such smooth steps occur when a trend break does not occur abruptly but gradually over multiple samples. We extend the detection algorithm by an approximate deconvolution add-on that enables reliable step detection while even allowing reducing the number of iterations of the LBI algorithm. We present simulation results in the context of fiber fault detection demonstrating the detection performance that is achievable with this combined approach, allowing reducing the required number of iterations by approximately \(40\%\).
The research reported in this paper has been partly funded by BMK, BMDW, and the State of UpperAustria in the frame of SCCH, part of the COMET Programme managed by FFG. This work is supported by: the COMET-K2 “Center for Symbiotic Mechatronics” of the Linz Center of Mechatronics (LCM), funded by the Austrian federal government and the federal state of Upper Austria. The authors would like to acknowledge the support from Brazilian agencies CNPq, Capes, and FAPERJ.
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Lunglmayr, M., Guzman, Y.G., Calliari, F., Amaral, G.C.d. (2022). Smooth Step Detection. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds) Computer Aided Systems Theory – EUROCAST 2022. EUROCAST 2022. Lecture Notes in Computer Science, vol 13789. Springer, Cham. https://doi.org/10.1007/978-3-031-25312-6_35
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