An Overview on Application of Machine Learning Techniques in Optical Networks

Today's telecommunication networks have become sources of enormous amounts of widely heterogeneous data. This information can be retrieved from network traffic traces, network alarms, signal quality indicators, users' behavioral data, etc. Advanced mathematical tools are required to extract meaningful information from these data and take decisions pertaining to the proper functioning of the networks from the network-generated data. Among these mathematical tools, Machine Learning (ML) is regarded as one of the most promising methodological approaches to perform network-data analysis and enable automated network self-configuration and fault management. The adoption of ML techniques in the field of optical communication networks is motivated by the unprecedented growth of network complexity faced by optical networks in the last few years. Such complexity increase is due to the introduction of a huge number of adjustable and interdependent system parameters (e.g., routing configurations, modulation format, symbol rate, coding schemes, etc.) that are enabled by the usage of coherent transmission/reception technologies, advanced digital signal processing and compensation of nonlinear effects in optical fiber propagation. In this paper we provide an overview of the application of ML to optical communications and networking. We classify and survey relevant literature dealing with the topic, and we also provide an introductory tutorial on ML for researchers and practitioners interested in this field. Although a good number of research papers have recently appeared, the application of ML to optical networks is still in its infancy: to stimulate further work in this area, we conclude the paper proposing new possible research directions

Roadmap on multimode photonics

Multimode devices and components have attracted considerable attention in the last years, and different research topics and themes have emerged very recently. The multimodality can be seen as an additional degree of freedom in designing devices, thus allowing for the development of more complex and sophisticated components. The propagation of different modes can be used to increase the fiber optic capacity, but also to introduce novel intermodal interactions, as well as allowing for complex manipulation of optical modes for a variety of applications. In this roadmap we would like to give to the readers a comprehensive overview of the most recent developments in the field, presenting contributions coming from different research topics, including optical fiber technologies, integrated optics, basic physics and telecommunications

Technology Directions for the 21st Century

New technologies will unleash the huge capacity of fiber-optic cable to meet growing demands for bandwidth. Companies will continue to replace private networks with public network bandwidth-on-demand. Although asynchronous transfer mode (ATM) is the transmission technology favored by many, its penetration will be slower than anticipated. Hybrid networks - e.g., a mix of ATM, frame relay, and fast Ethernet - may predominate, both as interim and long-term solutions, based on factors such as availability, interoperability, and cost. Telecommunications equipment and services prices will decrease further due to increased supply and more competition. Explosive Internet growth will continue, requiring additional backbone transmission capacity and enhanced protocols, but it is not clear who will fund the upgrade. Within ten years, space-based constellations of satellites in Low Earth orbit (LEO) will serve mobile users employing small, low-power terminals. 'Little LEO's' will provide packet transmission services and geo-position determination. 'Big LEO's' will function as global cellular telephone networks, with some planning to offer video and interactive multimedia services. Geosynchronous satellites also are proposed for mobile voice grade links and high-bandwidth services. NASA may benefit from resulting cost reductions in components, space hardware, launch services, and telecommunications services

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

ACUTA Journal of Telecommunications in Higher Education

In This Issue Supporting the Technology Enabled Classroom RUNet 2000 Design and Implementation Gourmet Dining in a Fast-Food World: Change and Challenge in 21st Century Telecommunications Organizing the Technology Leadership Function for Universities in the 21st Century Campus Reengineering at the University of Mississippi Supporting Academic Services One-to-One Deny Internet Access? When? Why? How? Institutional Excellence Award: Indiana University Bill D. Morris Award: Ruth Michalecki Columns Intervie

Methodological bases for maintaining the operational reliability of the fuel system of a long-haul passenger aircraft with two turbojet engines

Робота публікується згідно наказу ректора від 29.12.2020 р. №580/од "Про розміщення кваліфікаційних робіт вищої освіти в репозиторії НАУ" . Керівник проекту: доцент, к. т. н. Хімко Андрій Миколайович.The serviceable systems of each wide-range aircraft are an indivisible part of airplane normal operating conditions. The reliability and durability of main elements or separate units is the general task of the maintenance department of any airline or one or another operator. Normally, each modern company or operator has own Maintenance programme (MP), methods, instructions, technologies and procedures which are distinctly approved according to manufacturer's regulatory documents and consequently due to International Civil Aviation Organization (ICAO) or State Aviation Administration (SAA) requirements. To meet the needs of passengers and cargo transportation without increasing fleet of aircraft required to use modern scientific and technological progress in the field of aviation, to find out new approaches how to maintain the necessary level of profitability and adaptation all present operations according to rigid rules of aviation industry . This means you need as widely as possible to implement various improvements leading to increased performance, efficiency and safety. My diploma work is dedicated to studying the fuel system of a long-haul passenger aircraft (AC) with two turbojet engines (on the example of Boeing-777) and investigates new methodological bases to increase the operational reliability of it

Engineering derivatives from biological systems for advanced aerospace applications

The present study consisted of a literature survey, a survey of researchers, and a workshop on bionics. These tasks produced an extensive annotated bibliography of bionics research (282 citations), a directory of bionics researchers, and a workshop report on specific bionics research topics applicable to space technology. These deliverables are included as Appendix A, Appendix B, and Section 5.0, respectively. To provide organization to this highly interdisciplinary field and to serve as a guide for interested researchers, we have also prepared a taxonomy or classification of the various subelements of natural engineering systems. Finally, we have synthesized the results of the various components of this study into a discussion of the most promising opportunities for accelerated research, seeking solutions which apply engineering principles from natural systems to advanced aerospace problems. A discussion of opportunities within the areas of materials, structures, sensors, information processing, robotics, autonomous systems, life support systems, and aeronautics is given. Following the conclusions are six discipline summaries that highlight the potential benefits of research in these areas for NASA's space technology programs

Utah State Engineer, Fall 2021

Annual magazine for the College of Engineering at Utah State University.https://digitalcommons.usu.edu/engineering_magazines/1013/thumbnail.jp