KEYWORDS: Acoustics, Single mode fibers, Signal to noise ratio, Sensors, Optical sensing, Optical amplifiers, Machine learning, Data modeling, Continuous wave operation, Signal detection
Red palm weevil (RPW) is a harmful pest that has wiped out many palm plantations worldwide. Early detection of RPW is difficult, especially on large plantations. Here, we report on combining fiber–optic distributed acoustic sensing (DAS) and machine learning to detect weevil larvae less than three weeks old, in a controlled environment. In particular, we use the temporal and spectral data provided by a fiber–optic DAS system to train a convolutional neural network (CNN), which distinguishes “healthy” and “infested” signals with a classification accuracy higher than 97%. Additionally, a rigorous machine learning classification approach is introduced to improve the false alarm performance metric by >20%.
Optical-time-domain-reflectometer (OTDR) suffers from the existence of dead-zones along a deployed fiber under test (FUT). Within a dead-zone, OTDR typically fails to provide any reliable diagnostic information. We here use a fewmode fiber (FMF) to completely cancel the OTDR dead-zone produced by the front facet reflection of the FUT. In particular, we launch the optical pulses in the form of the LP01 mode into the FMF, and meanwhile we record the Rayleigh signal from the higher-order modes. The developed system successfully monitors the amplitude and frequency of a vibration event produced by a piezoelectric transducer (PZT) located within the dead-zone.
We have proposed and experimentally demonstrated for the first time a Brillouin optical time-domain analyzer (BOTDA) assisted by Block-Matching and 3D filtering (BM3D) image denoising technique. BM3D uses the spatial-domain non-local principle to improve the denoising in the transform domain, thus it makes the degradation of measurement accuracy/experimental spatial resolution small when compared with non-local means (NLM) and wavelet denoising (WD). Moreover, we extend the BM3D image denoising to video denoising (Video-BM3D, VBM3D), in order to accurately measure the slowly varying temperature in long-distance BOTDA. VBM3D uses both the spatial and temporal correlations of the data for denoising, thus it can significantly reduce the noise and make the measured values close to the real temperature even if it is temporally changing. In this talk, we will review our work and discuss some future perspectives.
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