Journal of Telecommunications and Information Technology, 2001, nr 2

kwartalni

A Review of the Teaching and Learning on Power Electronics Course

—In this review, we describe various kinds of problem and solution related teaching and learning on power electronics course all around the world. The method was used the study of literature on journal articles and proceedings published by reputable international organizations. Thirtynine papers were obtained using Boolean operators, according to the specified criteria. The results of the problems generally established that student learning motivation was low, teaching approaches that are still teacher-centered, the scope of the curriculum extends, and the physical limitations of laboratory equipment. The solutions offered are very diverse ranging from models, strategies, methods and learning techniques supported by information and communication technology

A testbed and a simulation laboratory for training engineering students in optical access network technologies

Producción CientíficaEngineering profiles focused on next-generation optical networks are gaining immense importance due to new emerging services and the amount of data expected in future network scenarios. In fact, not only are optical access networks leading to a major revolution in the network industry, but passive optical networks are the most widely deployed access networks worldwide today. This should be a strong incentive for universities to train their students in these innovative and recent technologies. In this vein, we propose the deployment of an optical communication laboratory with on-site experimental sessions in which students work with commercial equipment and realistic working environments. These working environments are necessary to train professionals in the area of optical networks. However, due to the high cost of the optical communications equipment, it is not possible to have a working place for each group and we combine these experimental sessions with some simulation sessions to complete the training. We present the design of this lab and a qualitative and quantitative study aimed at analyzing students’ experiences, the skills they have acquired, and the potential impact on their future careers. This study shows that students have a very positive perception of the lab, emphasizing that working with real equipment helps them improve technical skills and assimilate theoretical knowledge. They also point out they would like a higher number of subjects in their degrees to employ this type of lab. Finally, students perceive these sessions as very useful for their professional future.Unión Europea a través del programa INTERREG V-A España-Portugal (project 0677_DISRUPTIVE_2_E)Junta de Castilla y León (grant VA085G19)Ministerio de Ciencia, Innovación y Universidades (grants TEC2017-84423-C3-1-P and RED2018-102585-T

Telecommunications: A comparison of transmission technologies used in distance education

The purpose of this research paper will be the investigation and comparison of transmission medium technology used for telecommunication in distance education

Machine Learning-based Predictive Maintenance for Optical Networks

Optical networks provide the backbone of modern telecommunications by connecting the world faster than ever before. However, such networks are susceptible to several failures (e.g., optical fiber cuts, malfunctioning optical devices), which might result in degradation in the network operation, massive data loss, and network disruption. It is challenging to accurately and quickly detect and localize such failures due to the complexity of such networks, the time required to identify the fault and pinpoint it using conventional approaches, and the lack of proactive efficient fault management mechanisms. Therefore, it is highly beneficial to perform fault management in optical communication systems in order to reduce the mean time to repair, to meet service level agreements more easily, and to enhance the network reliability. In this thesis, the aforementioned challenges and needs are tackled by investigating the use of machine learning (ML) techniques for implementing efficient proactive fault detection, diagnosis, and localization schemes for optical communication systems. In particular, the adoption of ML methods for solving the following problems is explored: - Degradation prediction of semiconductor lasers, - Lifetime (mean time to failure) prediction of semiconductor lasers, - Remaining useful life (the length of time a machine is likely to operate before it requires repair or replacement) prediction of semiconductor lasers, - Optical fiber fault detection, localization, characterization, and identification for different optical network architectures, - Anomaly detection in optical fiber monitoring. Such ML approaches outperform the conventionally employed methods for all the investigated use cases by achieving better prediction accuracy and earlier prediction or detection capability

Mitigating Fiber Nonlinearity with Machine Learning

Nowadays, optical communication transmission is based mainly on optical fiber networks. Increasing demands for higher-capacity systems are hampered by signal distortions due to nonlinear effects of the commercial optic fibers. Different techniques have been proposed to reverse and mitigate this noise effect on the transmitted signal such as the digital backpropagation (DBP), the Volterra nonlinear compensation, the advanced modulation transmission, and perturbation pre-compensation techniques. While these techniques achieve good results they are too complicated for practical industrial implementation and add more complexity overhead on the system. This thesis is focused on investigating the merits of optical fiber mitigation using Artificial Intelligence (AI) techniques instead of analytical methods. Different AI techniques combined with perturbation-based nonlinear compensation method are used to predict the added nonlinear noise to a 16-Quadrature Amplitude Modulation (QAM) propagating signal. A MATLAB simulation program has been used to model the propagation of the signal and generate the transmitted data. The AI simulations have been employed using Python on dual-polarization single channel systems using single-stage AI techniques such as Neural Network (NN) at receiver or transmitter side and Siamese neural network (SNN), or two-stage AI techniques. In the two-stage method, different supervised classifiers have been used at the receiver side such as multi-layer perceptrons (MLP), decision tree, AdaBoosting, GBoosting, random forest, and extra trees while NN is placed at the transmitter. Additionally, different complexity reduction techniques have been applied to the proposed systems to achieve more practical performance in industrial environment applications. For the first time, a nonlinear-compensation robustness study is applied to the proposed AI techniques by detecting the performance of each technique while changing the single-mode fiber’s nonlinear coefficient value. Moreover, empirical equations are developed to represent the system’s Q-factor enhancement achieved using each of the proposed techniques as a function of the fiber nonlinear coefficient and the data features

Technology for large space systems: A special bibliography with indexes (supplement 06)

This bibliography lists 220 reports, articles and other documents introduced into the NASA scientific and technical information system between July 1, 1981 and December 31, 1981. Its purpose is to provide helpful information to the researcher, manager, and designer in technology development and mission design in the area of the Large Space Systems Technology (LSST) Program. Subject matter is grouped according to systems, interactive analysis and design, structural concepts, control systems, electronics, advanced materials, assembly concepts, propulsion, solar power satellite systems, and flight experiments