Hollow-core fibers (HCF) are increasingly being studied and evaluated for telecommunication, as well as sensing application. They seem to have many advantages compare to standard single-mode fibers (SMF), especially in the area of the cyber-security of optical fiber infrastructures and also information they carrying. In our research we mainly focus on cyber-security issues, especially acoustic sensing. In the paper we evaluate sensitivity of negative curve HCF using a Mach–Zehnder interferometry (MZI). Results are compared with SMF (G.657 in this case). Both fibers were in primary coating with similar length. Sensitivity measurements were conducted within the controlled environment of an anechoic chamber. Results show that both fibers are sensitive to acoustic vibration and with post-processing method based on de-noising of the measured signal, the sensitivity can be improved. The clarity of the captured speech signal was assessed using the Speech Transmission Index for Public Address (STIPA).
The paper introduces an innovative object classification method for urban environments, employing distributed acoustic sensing (DAS) to address the complexities of urban landscapes. Utilizing omnipresent optical telecommunication cables, our approach involves a modified convolutional neural network (CNN) with transfer learning, achieving up to 85% accuracy. This method reuses most of the original network for feature extraction, with a final layer customized for new urban datasets – initially trained at the Brno University of Technology and then adapted to city center data. The model effectively identifies urban elements like vehicles and pedestrians, showcasing the potential of DAS for real-time classification in urban management and planning.
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