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Design Fiber Performance Monitoring Tool (FPMT) for Online Remote Fiber Line Performance Detection
Faculty
Engineering
Year:
2022
Type of Publication:
ZU Hosted
Pages:
Authors:
Azhar Ahmed Hamdy Abdelsatar
Staff Zu Site
Abstract In Staff Site
Journal:
Electronics MDPI
Volume:
Keywords :
Design Fiber Performance Monitoring Tool (FPMT)
Abstract:
A new technique for fiber faults events detection and monitoring in optical communication network systems is proposed. The fiber performance monitoring tool is a new proposed technique designed to detect, locate, and estimate the fiber faults without interrupting the data flow with efficient costs and to improve the availability and reliability of optical networks as it detects fiber faults remotely in real time. Instead of the traditional old method, the new proposed FPMT uses an optical time domain reflectometer to detect multiple types of fiber failures, e.g., fiber breaks, fiber end face contamination, fiber end face burning, large insertion losses on the connector and interconnection, or mismatches between two different types of fiber cables. The proposed technique methodology to detect the fiber failures depends on analyzing the feedback of the reflected signal and the pattern shape of the reflected signal over network fiber lines, supports a higher range of distance testing and performance monitoring, and can be performed inside an optical network in real time and remotely by integrating with an OSC board. The proposed technique detects fiber faults with an average accuracy of measurement up to 99.8%, the maximum distance to detect fiber line faults is up to 150 km, and it can improve the system power budget with a minimal insertion loss of 0.4 dB. The superiority of the suggested technique over real networks was verified with success by the Huawei labs’ infrastructure nodes in the simulation experiment results.
Author Related Publications
Azhar Ahmed Hamdy Abdelsatar, "Performance Study For Color Filter Array Demosaicking Methods", IEEE conference, 2007
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Azhar Ahmed Hamdy Abdelsatar, "Unsupervised Patterned Fabric Defect Detection using Texture Filtering and K-Means clustering", IEEE conference, 2017
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Azhar Ahmed Hamdy Abdelsatar, "Patterned Fabric Defect Detection System Using Near Infrared Imaging", IEEE conference, 2017
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Azhar Ahmed Hamdy Abdelsatar, "fully automated approach for patterned fabric defect detection", Egypt-Japan university for science and technology, 2016
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Azhar Ahmed Hamdy Abdelsatar, "Augmented doppler filter bank based approach for enhanced targets detection", Wydawnictwo SIGMA, 2023
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Mohammed Ayesh Muhammad Hanafi, "Compressed sensing for reliable body area propagation with efficient signal reconstruction", IEEE, 2018
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