Preparation of especially pure Ge-Se glasses via germanium monoselenide for Mid-IR fiber optics

Adopting Intelligent Modelling to Trace Fault in Underground Optical Network: A Comprehensive Survey

“…The communication system such as voice and data, deployed on fiber optics transmissions infrastructure has revolutionized the telecommunications industry and business in the few decades (Rottondi et al, 2018). Optical communication systems have also been deployed in various private and public organizations to enhance the quality of services they provide (Velmuzhov et al, 2018). Research work on fault tracing in FOC network infrastructure (Urban et al, 2013) has predominately been done by the measurement of the fault distance of the underground FOC using backscattered light principles (Thongdaeng and Worasucheep, 2016).…”

“…According to (Chen et al, 2006), faults distance in underground fiber optics networks must be identified precisely and accurately on time and spliced correctly to restore interrupted services to maintain high-level customer experience. This can only be achieved if an intelligent system (Mo et al, 2018;Shiryaev et al, 2018;Soujanya et al, 2017;Rubio-Largo et al, 2012) is incorporated in the fault tracing technique to aid in accurate prediction of the fault distance on earth. It is in this regard that this paper has proposed the use of the artificial intelligence model to supplement the functions of the conventional OTDR (Urban et al, 2013).…”

“…The intelligent model is implemented based on mathematical operations and a set of parameters required to determine the output of the expected value. Feedforward Neural Network, Radial basis function Neural Network, Kohonen Self Organizing Neural Network, Recurrent Neural Network (RNN), Convolutional Neural Network, Modular Neural Network, time delay NN, Elman and Jordan Simple RNN have been used in various computations to enhance optical network infrastructure (Shiryaev et al, 2018;Mo et al, 2018).…”

“…Supervised learning deploys techniques such as linear regression, logistic regression, decision trees, artificial neural networks, nearest neighbour models and Support Vector Machines (SVM), etc. This machine learning techniques have been used for optical network performance monitoring, in estimating the Quality of Transmission (QoT) of optical transmission systems (Caballero et al, 2012;Rottondi et al, 2018;Mo et al, 2018) used to allocate resources in data centres (Shiryaev et al, 2018). Unsupervised learning has been applied in clustering and principal component analysis techniques, recognition of modulation layout and transmission impairment mitigation.…”

“…When faults occur in underground optical transmission systems, determining the exact spot of the fault on the earth surface is extremely difficult even though the distance of the fault from the optical transmitter is known. In rare cases, optical cables are exposed when faults occur, which may be due to road construction or building projects (Velmuzhov et al, 2018). These cases present an excellent opportunity for the cable maintenance team to identify and resolve the fault (Hayford-Acquah and Asante, 2017) quickly.…”