Desarrollo de algoritmia para la identificación automática de espectros Raman de la misión ExoMars

Este trabajo se ha desarrollado en el marco de desarrollo del instrumento RLS, el espectrómetro Raman incluido en el rover Rosalind Franklin que la ESA lanzará a Marte en 2022 en la misión ExoMars. El objetivo principal del trabajo es el desarrollo de algoritmos y rutinas para el análisis automatizado de espectros Raman que permitan la identificación de las fases minerales presentes en dichos espectros. Este trabajo se enmarca en el desarrollo del software IDAT/SpectPro que, como ya se ha descrito, es una herramienta software desarrollada por la Universidad de Valladolid y que será utilizada durante la fase de operación del instrumento RLS en Marte para el análisis automatizado de los datos obtenidos de muestras marcianas. Las rutinas que se proponen facilitarán la tarea de interpretación de los espectros, cruzando los datos obtenidos contra una base de datos espectrales desarrollada ad-hoc para la misión. Para ello, el trabajo se ha dividido en dos tareas. 1- Por un lado, el alumno ha de familiarizarse con el tratamiento y procesamiento de espectros Raman. En base a espectros ya adquiridos, se pretende procesar un número suficiente de espectros de materiales y mezclas de materiales diferentes que permitan realizar una caracterización adecuada de las rutinas desarrolladas. De esta forma, se obtendrán capacidades de procesamiento y análisis de espectroscopia Raman, desde el tratamiento de la línea de base, detección de picos o ajustes de bandas sobre los espectros procesados. 2- Por otro lado, el alumno desarrollará una serie de algoritmos de identificación en base a parámetros espectrales (posición y altura de picos) que habrán sido previamente obtenidos mediante el análisis con SpectPro definido en el objetivo 1. Finalmente, se testeará y parametrizará el algoritmo desarrollado utilizando los espectros procesados de minerales puros y mezclas obtenidos en el laboratorio. Esto permitirá obtener una rutina de identificación optimizada para un conjunto heterogéneo de espectros.This work has been developed within the framework of the development of the RLS instrument, the Raman spectrometer included in the Rosalind Frankiln rover that ESA will launch to Mars in 2022 on the ExoMars mission. The main objective of the work is the development of algorithms and routines for the automated analysis of Raman spectra that allow the identification of the mineral phases present in these spectra. This work is part of the development of the IDAT/SpectPro software which is a software tool developed by the University of Valladolid that will be used during the operation phase of the RLS instrument on Mars for on-ground automated analysis of data obtained from Martian samples. The proposed routines will facilitate the task of interpreting the spectra, crossing the data obtained against a spectral database developed ad-hoc for the mission. To do this, the work has been divided into two tasks: 1. On one hand, the student must become familiar with the treatment and processing of Raman spectra. Based on already acquired spectra, it is intended to process a sufficient number of spectra of materials and mixtures of different materials that allow to perform an adequate characterization of the developed routines. In this way, the student will gain capabilities on the processing and analysis of Raman spectroscopy data, from baseline treatment, peak detection or band adjustments on processed spectra. 2. On the other hand, the student will develop a series of identification algorithms based on spectral parameters (position and height of peaks) that will help identify the material son a determined spectrum by comparing with the spectral database, providing the best-estimate results. Finally, the developed algorithm will be tested and parameterized by using the processed spectra of pure minerals and mixtures obtained in the laboratory. This will allow for an optimized identification routine for an heterogeneous set of spectra.Grado en Ingeniería de Tecnologías Específicas de Telecomunicació

Diseño y desarrollo de una red híbrida entre Wi-Fi mesh y 5G para entornos industriales

En los últimos años se esta viviendo una nueva revolución en la industria para dar paso a la quinta generación donde el enfoque, además de la automatización, es el ser humano. Se prevé que la implantación de la Industria 5.0 se vea acelerada por la tecnología 5G, ofreciendo una conectividad ultrarrápida y de baja latencia permitiendo, de esta manera, una transferencia de datos en tiempo real y facilitando una comunicación fluida entre máquinas, humanos y sistemas. Se espera que la combinación de Industria 5.0 y 5G fomente procesos de producción más flexibles, eficientes y colaborativos, lo que conducirá a una mayor personalización, una mayor productividad y nuevos avances en el panorama industrial. Sin embargo, la quinta generación de telecomunicaciones trae consigo un obstáculo debido al espectro de frecuencias que usa. Al ser este rango mas alto que en anteriores generaciones, la señal se ve degradada dificultando su distancia de cobertura en entornos rurales y en interiores debido a la existencia de obstáculos resultando en dificultades en las comunicaciones . Por ello, se han propuesto varias soluciones como las redes IAB que aprovecha el concepto de las redes de malla para conseguir llegar a más distancia. No obstante, estas soluciones están pensadas para entornos exteriores. En este trabajo fin de máster se propone un prototipo de red híbrida entre 5G y Wi-Fi mesh como solución aprovechando las ventajas de ambas redes consiguiendo una red más robusta en interiores. Para validar esta red se han realizado experimentos empíricos donde se ha desplegado una red 5G con una red Wi-Fi mesh como extensión. La evaluación de esta solución se ha realizado mediante el despliegue de un servidor de video en el edge de la red 5G, aprovechando el paradigma de Edge Computing, al cual los usuarios podían acceder para reproducir videos en sus dispositivos. Con esta nueva arquitectura se ha conseguido aumentar la distancia de cobertura hasta el doble utilizando los mismos recursos que la red 5G original.In recent years, a new revolution is taking place in industry to usher in the fifth generation where the focus, in addition to automation, is on humans. The implementation of Industry 5.0 is expected to be accelerated by 5G technology, offering ultra-fast, low-latency connectivity, enabling real-time data transfer and facilitating seamless communication between machines, humans and systems. The combination of Industry 5.0 and 5G is expected to foster more flexible, efficient and collaborative production processes, leading to greater personalisation, increased productivity and new developments in the industrial landscape. However, the fifth generation of telecommunications brings with it an obstacle due to the frequency spectrum it uses. As this range is higher than in previous generations, the signal is degraded, making it difficult to cover distance in rural and indoor environments due to the existence of obstacles, resulting in communication difficulties. Therefore, several solutions have been proposed such as IAB networks that take advantage of the concept of mesh networks to reach further distances. However, these solutions are intended for outdoor environments. In this master’s thesis, a prototype of a hybrid network between 5G and Wi-Fi mesh is proposed as a solution that takes advantage of the benefits of both networks to achieve a more robust network indoors. To validate this network, empirical experiments have been carried out where a 5G network has been deployed with a Wi-Fi mesh network as an extension. The evaluation of this solution has been carried out by deploying a video server on the edge of the 5G network, taking advantage of the Edge Computing paradigm, which users could access to play videos on their devices. With this new architecture, the coverage distance has been doubled using the same resources as the original 5G network.Departamento de Teoría de la Señal y Comunicaciones e Ingeniería TelemáticaMáster en Ingeniería de Telecomunicació

Empirical evaluation of 5G and Wi-Fi mesh interworking for integrated access and backhaul networking paradigm

The Fifth Generation (5G) of mobile networks and beyond have emerged with ambitions to facilitate the deployment and evolution of a wide spectrum of applications such as Industry 4.0 and 5.0 use cases. Despite this trend of increasing importance to upgrade the networked applications to the next generation, the use of 5G and beyond technologies can be a prohibitive barrier for some business sectors due to the high deployment costs that it can incur. To overcome this obstacle, more cost-effective approaches in networking are entailed. In this work, an innovative approach coupling 5G and Wi-Fi mesh networking is proposed and developed as a promising solution to extend 5G services to the indoor use case scenarios whilst being capable of keeping the capital expenditure of the network infrastructure significantly lower. In order to empirically validate and evaluate this new networking paradigm, a number of experiments have been performed over a testbed with a demanding video application as a representative use case. The experimental results prove the gained benefits from this new approach, especially, video users can be more than twice as far away without compromising the quality of the video consumption experience. Specifically, the results show that users can be 29% further away using a single router, and 100% further away if a second router is added

5G RAN service classification using long short term memory neural network

5G brings many benefits such as enlarged capacity and improved connectivity. However, it also poses challenges especially due to a significant increase in the amount of traffic on the network. This creates difficulties for operators to maintain the Quality of Service (QoS) for each of the services offered. Therefore, in order to improve the performance of such capabilities and, consequently, the experience of the users, it is necessary to identify which traffic requires more prioritisation. This would help allocate more resources to those services. This concept makes the identification and classification of traffic to gain more and more relevance and importance. In this paper, we propose a Long Short-Term Memory (LSTM) model to classify 5G Radio Access Network (RAN) behaviour into four different scenarios: streaming, video conferencing, Voice over IP (VoIP) and gaming. The results obtained show a 93% accuracy

Design and implementation of an integrated OWC and RF network slicing-based architecture over hybrid LiFi and 5 G networks

Radio frequency (RF) systems tend to become congested and overused due tothe increasing number of users, devices and the multiple technologies involvedin their deployment. This leads to the downgrading of quality of service (QoS)further caused by interference with different signals. Optical Wireless communications (OWC) are emerging as a feasible alternative as they offer unlicensed, interference-free spectrum by using the frequency range located in the visible and invisible light spectrum. Its applications can be found in various fields such as healthcare, education, finance and industry 4.0. Moreover, it enhances the security and privacy of communications. Nevertheless, the limited spectrum in OWC also requires optimised resource allocation to support the QoS of different applications or users whilst lacking established infrastructure to manage this. To address these challenges, this paper proposes a novel 5G-LiFi framework able to ensure QoS requirements by introducing network slicing in Light Fidelity (LiFi) networks integrated with 5G infrastructure. This paper has developed and deployed a 5G LiFi architecture capable of providing network slicing capabilities over the LiFisegment of the hybrid network. It allows a full control over the network trafficand tailored, improved QoS capabilities. The proposed solution has been empirically validated and evaluated in a realistic testbed employing real-world LiFi and 5G network equipment, and yielded promising results in terms of bandwidth, delay, jitter and packet loss. This work concludes that the use of heterogeneous networks integrating OWC with RF is a suitable solution and it can lead to a better use and exploitation of the different spectrums, improving the QoS offered to end-users

6G BRAINS Topology-aware Industry-Grade Network Slice Management and Orchestration

This paper describes the integration between the Open Network Automation Platform (ONAP) and UWS Slice Manager within the European project 6G Brains. The proposed solution allows for End-To-End(E2E) Network Slicing, enabling fine-grain and optimal traffic engineering of the Network components. This work’s findings ensure an E2E connection. The solution allows external services to create slices and attach them easily. The UWS Network Slice Manager allows for detailed monitoring of the network slice’s inner components. With this information, ONAP can improve the network by creating and optimising slices on demand. The validation of the integration presents the workflow to create and attach slices. These operations enable autonomous workflows for deploying E2E Services that ensure the QoS/QoE in the network