[pt] Esta dissertação está estruturada como uma coleção de cinco artigos formatados em capítulos. Os quatro primeiros artigos apresentam contribuições em modelagem e metodológicas para problemas de operação ou investimento em sistemas de potência usando arcabouço de otimização robusta adaptativa e modificações no algoritmo de geração de linhas e colunas (CCGA). O primeiro artigo aborda a programação de curto prazo com restrição de segurança, onde a resposta automática de geradores é considerada. Um modelo robusto de dois estágios é adotado, resultando em complexas instâncias de programação inteira mista, que apresentam variáveis binárias associadas às decisões de primeiro e segundo estágios. Um novo CCGA que explora a estrutura do problema é desenvolvido. O segundo artigo usa redes neurais profundas para aprender o mapeamento das demandas nodais aos pontos de ajuste dos geradores para o problema do primeiro artigo. O CCGA é usados para garantir a viabilidade da solução. Este método resulta em importantes ganhos computacionais em relação ao primeiro artigo. O terceiro artigo propõe uma abordagem adaptativa em dois estágios para um modelo robusto de programação diária no qual o conjunto de incerteza poliedral é caracterizado diretamente a partir dos dados de geração não despachável observados. O problema resultante é afeito ao CCGA. O quarto artigo propõe um modelo de dois estágios adaptativo, robusto em distribuição para expansão de transmissão, incorporando incertezas a longo e curto prazo. Um novo CCGA é desenvolvido para lidar com os subproblemas. Finalmente, sob uma perspectiva diferente e generalista, o quinto artigo investiga a adequação de prêmios de incentivo para promover inovações em aspectos teóricos e computacionais para os desafios de sistemas de potência modernos. / [en] This dissertation is structured as a collection of five papers formatted as chapters. The first four papers provide modeling and methodological contributions in scheduling or investment problems in power systems using the adaptive robust optimization framework and modifications to the column-and-constraint-generation algorithm (CCGA). The first paper addresses the security-constrained short-term scheduling problem where automatic primary response is considered. A two-stage robust model is adopted, resulting in complex mixed-integer linear instances featuring binary variables associated with first- and second-stage decisions. A new tailored CCGA which explores the structure of the problem is devised. The second paper uses deep neural networks for learning the mapping of nodal demands onto generators set point for the first paper s model. Robust-based modeling approaches and the CCGA are used to enforce feasibility for the solution. This method results in important computational gains as compared to results of the first paper. The third paper proposes an adaptive data-driven approach for a two-stage robust unit commitment model, where the polyhedral uncertainty set is characterized directly from data, through the convex hull of a set of previously observed non-dispatchable generation profiles. The resulting problem is suitable for the exact CCGA. The fourth paper proposes an adaptive two-stage distributionally robust transmission expansion model incorporating long- and short-term uncertainties. A novel extended CCGA is devised to tackle distributionally robust subproblems. Finally, under a different and higher-level perspective, the fifth paper investigates the adequacy of systematic inducement prizes for fostering innovations in theoretical and computational aspects for various modern power systems challenges.

Information Filtering and Recommender Systems have been used and has been implemented in various ways from various entities since the dawn of the Internet, and state-of-the-art approaches rely on Machine Learning and Deep Learning in order to create accurate and personalized recommendations for users in a given context. These models require big amounts of data with a variety of features such as time, location and user data in order to find correlations and patterns that other classical models such as matrix factorization and collaborative filtering cannot. This thesis researches, implements and compares a variety of models with the primary focus of Machine Learning and Deep Learning for the task of music recommendation and do so successfully by representing the task of recommendation as a multi-class extreme classification task with 100 000 distinct labels. By comparing fourteen different experiments, all implemented models successfully learn features such as time, location, user features and previous listening history in order to create context-aware personalized music predictions, and solves the cold start problem by using user demographic information, where the best model being capable of capturing the intended label in its top 100 list of recommended items for more than 1/3 of the unseen data in an offine evaluation, when evaluating on randomly selected examples from the unseen following week. / Informationsfiltrering och rekommendationssystem har använts och implementeratspå flera olika sätt från olika enheter sedan gryningen avInternet, och moderna tillvägagångssätt beror påMaskininlärrning samtDjupinlärningför att kunna skapa precisa och personliga rekommendationerför användare i en given kontext. Dessa modeller kräver data i storamängder med en varians av kännetecken såsom tid, plats och användardataför att kunna hitta korrelationer samt mönster som klassiska modellersåsom matris faktorisering samt samverkande filtrering inte kan. Dettaexamensarbete forskar, implementerar och jämför en mängd av modellermed fokus påMaskininlärning samt Djupinlärning för musikrekommendationoch gör det med succé genom att representera rekommendationsproblemetsom ett extremt multi-klass klassifikationsproblem med 100000 unika klasser att välja utav. Genom att jämföra fjorton olika experiment,så lär alla modeller sig kännetäcken såsomtid, plats, användarkänneteckenoch lyssningshistorik för att kunna skapa kontextberoendepersonaliserade musikprediktioner, och löser kallstartsproblemet genomanvändning av användares demografiska kännetäcken, där den bästa modellenklarar av att fånga målklassen i sin rekommendationslista medlängd 100 för mer än 1/3 av det osedda datat under en offline evaluering,när slumpmässigt valda exempel från den osedda kommande veckanevalueras.

The amount of data available on the web is constantly growing, and e-commerce websites are no exception. Considering the abundance of available information, finding offers for the same product in the catalogue of different retailers represents a challenge. This problem is an interesting one and addresses the needs of multiple actors. A customer is interested in finding the best deal for the product they want to buy. A retailer wants to keep up to date with the competition and adapt its pricing strategy accordingly. Various services already offer the possibility of finding duplicate products in catalogues of e-commerce retailers, but their solutions are based on matching a Global Trade Identification Number (GTIN). This strategy is limited because a GTIN may not be made publicly available by a competitor, may be different for the same product exported by the manufacturer to different markets or may not even exist for low-value products. The field of Entity Resolution (ER), a sub-branch of Natural Language Processing (NLP), focuses on solving the issue of matching duplicate database entries when a deterministic identifier is not available. We investigate various solutions from the the field and present a new model called Spring R-SupCon that focuses on low volume datasets. Our work builds upon the recently introduced model, R-SupCon, introducing a new learning scheme that improves R-SupCon’s performance by up to 74.47% F1 score, and surpasses Ditto by up 12% F1 score for low volume datasets. Moreover, our experiments show that smaller language models can be used for ER with minimal loss in performance. This has the potential to extend the adoption of Transformer-based solutions to companies and markets where datasets are difficult to create, like it is the case for the Swedish marketplace Fyndiq. / Mängden data på internet växer konstant och e-handeln är inget undantag. Konsumenter har idag många valmöjligheter varifrån de väljer att göra sina inköp från. Detta gör att det blir svårare och svårare att hitta det bästa erbjudandet. Även för återförsäljare ökar svårigheten att veta vilken konkurrent som har lägst pris. Det finns tillgängliga lösningar på detta problem men de använder produktunika identifierare såsom Global Trade Identification Number (förkortat “GTIN”). Då det finns en rad utmaningar att bara förlita sig på lösningar som baseras på GTIN behövs ett alternativt tillvägagångssätt. GTIN är exempelvis inte en offentlig information och identifieraren kan dessutom vara en annan när samma produkt erbjuds på en annan marknad. Det här projektet undersöker alternativa lösningar som inte är baserade på en deterministisk identifierare. Detta projekt förlitar sig istället på text såsom produktens namn för att fastställa matchningar mellan olika erbjudanden. En rad olika implementeringar baserade på maskininlärning och djupinlärning studeras i detta projekt. Projektet har dock ett särskilt fokus på “Transformer”-baserade språkmodeller såsom BERT. Detta projekt visar hur man generera proprietär data. Projektet föreslår även ett nytt inlärningsschema och bevisar dess fördelar. / Le volume des données qui se trouve sur l’internet est en une augmentation constante et les commerces électroniques ne font pas note discordante. Le consommateur a aujourd’hui beaucoup des options quand il decide d’où faire son achat. Trouver le meilleur prix devient de plus en plus difficile. Les entreprises qui gerent cettes plates-formes ont aussi la difficulté de savoir en tous moments lesquels de ses concurrents ont le meilleur prix. Il y-a déjà des solutions en ligne qui ont l’objectif de résoudre ce problème, mais ils utilisent un identifiant de produit unique qui s’appelle Global Trade identification number (ou GTIN). Plusieurs difficultés posent des barriers sur cette solution. Par exemple, GTIN n’est pas public peut-être, ou des GTINs différents peut-être assigne par la fabricante au même produit pour distinguer des marchés différents. Ce projet étudie des solutions alternatives qui ne sont pas basées sur avoir un identifiant unique. On discute des methods qui font la décision en fonction du nom des produits, en utilisant des algorithmes d’apprentissage automatique ou d’apprentissage en profondeur. Le projet se concentre sur des solutions avec ”Transformer” modèles de langages, comme BERT. On voit aussi comme peut-on créer un ensemble de données propriétaire pour enseigner le modèle. Finalement, une nouvelle method d’apprentissage est proposée et analysée.

Sub-optimal automated transportation control systems incur high mobility, human health and environmental costs. With society reliant on its transportation systems for the movement of individuals, goods and services, minimizing these costs benefits many. Intersection traffic signal controllers are an important element of modern transportation systems that govern how vehicles traverse road infrastructure. Many types of traffic signal controllers exist; fixed time, actuated and adaptive. Adaptive traffic signal controllers seek to minimize transportation costs through dynamic control of the intersection. However, many existing adaptive traffic signal controllers rely on heuristic or expert knowledge and were not originally designed for scalability or for transportation’s big data future. This research addresses the aforementioned challenges by developing a scalable system for adaptive traffic signal control model development using deep reinforcement learning in traffic simulation. Traffic signal control can be modelled as a sequential decision-making problem; reinforcement learning can solve sequential decision-making problems by learning an optimal policy. Deep reinforcement learning makes use of deep neural networks, powerful function approximators which benefit from large amounts of data. Distributed, parallel computing techniques are used to provide scalability, with the proposed methods validated on a simulation of the City of Luxembourg, Luxembourg, consisting of 196 intersections. This research contributes to the body of knowledge by successfully developing a scalable system for adaptive traffic signal control model development and validating it on the largest traffic microsimulator in the literature. The proposed system reduces delay, queues, vehicle stopped time and travel time compared to conventional traffic signal controllers. Findings from this research include that using reinforcement learning methods which explicitly develop the policy offers improved performance over purely value-based methods. The developed methods are expected to mitigate the problems caused by sub-optimal automated transportation signal controls systems, improving mobility and human health and reducing environmental costs. / Thesis / Doctor of Philosophy (PhD) / Inefficient transportation systems negatively impact mobility, human health and the environment. The goal of this research is to mitigate these negative impacts by improving automated transportation control systems, specifically intersection traffic signal controllers. This research presents a system for developing adaptive traffic signal controllers that can efficiently scale to the size of cities by using machine learning and parallel computation techniques. The proposed system is validated by developing adaptive traffic signal controllers for 196 intersections in a simulation of the City of Luxembourg, Luxembourg, successfully reducing delay, queues, vehicle stopped time and travel time.

<p>Convolutional neural networks (CNNs) have become important workloads due to their<br> impressive accuracy in tasks like image classification and recognition. Convolution operations<br> are compute intensive, and this cost profoundly increases with newer and better CNN models.<br> However, convolutions come with characteristics such as sparsity which can be exploited. In<br> this dissertation, we propose three different works to capture sparsity for faster performance<br> and reduced energy. </p> <p><br></p> <p>The first work is an accelerator design called <em>SparTen</em> for improving two-<br> sided sparsity (i.e, sparsity in both filters and feature maps) convolutions with fine-grained<br> sparsity. <em>SparTen</em> identifies efficient inner join as the key primitive for hardware acceleration<br> of sparse convolution. In addition, <em>SparTen</em> proposes load balancing schemes for higher<br> compute unit utilization. <em>SparTen</em> performs 4.7x, 1.8x and 3x better than dense architecture,<br> one-sided architecture and SCNN, the previous state of the art accelerator. The second work<br> <em>BARISTA</em> scales up SparTen (and SparTen like proposals) to large-scale implementation<br> with as many compute units as recent dense accelerators (e.g., Googles Tensor processing<br> unit) to achieve full speedups afforded by sparsity. However at such large scales, buffering,<br> on-chip bandwidth, and compute utilization are highly intertwined where optimizing for<br> one factor strains another and may invalidate some optimizations proposed in small-scale<br> implementations. <em>BARISTA</em> proposes novel techniques to balance the three factors in large-<br> scale accelerators. <em>BARISTA</em> performs 5.4x, 2.2x, 1.7x and 2.5x better than dense, one-<br> sided, naively scaled two-sided and an iso-area two-sided architecture, respectively. The last<br> work, <em>EUREKA</em> builds an efficient tensor core to execute dense, structured and unstructured<br> sparsity with losing efficiency. <em>EUREKA</em> achieves this by proposing novel techniques to<br> improve compute utilization by slightly tweaking operand stationarity. <em>EUREKA</em> achieves a<br> speedup of 5x, 2.5x, along with 3.2x and 1.7x energy reductions over Dense and structured<br> sparse execution respectively. <em>EUREKA</em> only incurs area and power overheads of 6% and<br> 11.5%, respectively, over Ampere</p>

No description available.

Ces dernières années, l'intelligence artificielle a été considérablement avancée et l'apprentissage en profondeur, où des réseaux de neurones profonds sont utilisés pour tenter d'imiter vaguement le cerveau humain, y a contribué de manière significative. Les réseaux de neurones profonds sont désormais capables d'obtenir un grand succès sur la base d'une grande quantité de données et de ressources de calcul suffisantes. Malgré leur succès, leur capacité à s'adapter rapidement à de nouveaux concepts, tâches et environnements est assez limitée voire inexistante. Dans cette thèse, nous nous intéressons à la façon dont les réseaux de neurones profonds peuvent s'adapter à des circonstances en constante évolution ou totalement nouvelles, de la même manière que l'intelligence humaine, et introduisons en outre des modules architecturaux adaptatifs et dynamiques ou des cadres de méta-apprentissage pour que cela se produise de manière efficace sur le plan informatique. Cette thèse consiste en une série d'études proposant des méthodes pour utiliser des calculs adaptatifs et dynamiques pour aborder les problèmes d'adaptation qui sont étudiés sous différentes perspectives telles que les adaptations au niveau de la tâche, au niveau temporel et au niveau du contexte. Dans le premier article, nous nous concentrons sur l'adaptation rapide des tâches basée sur un cadre de méta-apprentissage. Plus précisément, nous étudions l'incertitude du modèle induite par l'adaptation rapide à une nouvelle tâche avec quelques exemples. Ce problème est atténué en combinant un méta-apprentissage efficace basé sur des gradients avec une inférence variationnelle non paramétrique dans un cadre probabiliste fondé sur des principes. C'est une étape importante vers un méta-apprentissage robuste que nous développons une méthode d'apprentissage bayésienne à quelques exemples pour éviter le surapprentissage au niveau des tâches. Dans le deuxième article, nous essayons d'améliorer les performances de la prédiction de la séquence (c'est-à-dire du futur) en introduisant une prédiction du futur sauteur basée sur la taille du pas adaptatif. C'est une capacité critique pour un agent intelligent d'explorer un environnement qui permet un apprentissage efficace avec une imagination sauteur futur. Nous rendons cela possible en introduisant le modèle hiérarchique d'espace d'état récurrent (HRSSM) qui peut découvrir la structure temporelle latente (par exemple, les sous-séquences) tout en modélisant ses transitions d'état stochastiques de manière hiérarchique. Enfin, dans le dernier article, nous étudions un cadre qui peut capturer le contexte global dans les données d'image de manière adaptative et traiter davantage les données en fonction de ces informations. Nous implémentons ce cadre en extrayant des concepts visuels de haut niveau à travers des modules d'attention et en utilisant un raisonnement basé sur des graphes pour en saisir le contexte global. De plus, des transformations au niveau des caractéristiques sont utilisées pour propager le contexte global à tous les descripteurs locaux de manière adaptative. / Over the past few years, artificial intelligence has been greatly advanced, and deep learning, where deep neural networks are used to attempt to loosely emulate the human brain, has significantly contributed to it. Deep neural networks are now able to achieve great success based on a large amount of data and sufficient computational resources. Despite their success, their ability to quickly adapt to new concepts, tasks, and environments is quite limited or even non-existent. In this thesis, we are interested in how deep neural networks can become adaptive to continually changing or totally new circumstances, similarly to human intelligence, and further introduce adaptive and dynamic architectural modules or meta-learning frameworks to make it happen in computationally efficient ways. This thesis consists of a series of studies proposing methods to utilize adaptive and dynamic computations to tackle adaptation problems that are investigated from different perspectives such as task-level, temporal-level, and context-level adaptations. In the first article, we focus on task-level fast adaptation based on a meta-learning framework. More specifically, we investigate the inherent model uncertainty that is induced from quickly adapting to a new task with a few examples. This problem is alleviated by combining the efficient gradient-based meta-learning with nonparametric variational inference in a principled probabilistic framework. It is an important step towards robust meta-learning that we develop a Bayesian few-shot learning method to prevent task-level overfitting. In the second article, we attempt to improve the performance of sequence (i.e. future) prediction by introducing a jumpy future prediction that is based on the adaptive step size. It is a critical ability for an intelligent agent to explore an environment that enables efficient option-learning and jumpy future imagination. We make this possible by introducing the Hierarchical Recurrent State Space Model (HRSSM) that can discover the latent temporal structure (e.g. subsequences) while also modeling its stochastic state transitions hierarchically. Finally, in the last article, we investigate a framework that can capture the global context in image data in an adaptive way and further process the data based on that information. We implement this framework by extracting high-level visual concepts through attention modules and using graph-based reasoning to capture the global context from them. In addition, feature-wise transformations are used to propagate the global context to all local descriptors in an adaptive way.

Tesis por compendio / [ES] Edge Computing es un modelo de computación emergente basado en acercar el procesamiento a los dispositivos de captura de datos en las infraestructuras Internet of things (IoT). Edge computing mejora, entre otras cosas, los tiempos de respuesta, ahorra anchos de banda, incrementa la seguridad de los servicios y oculta las caídas transitorias de la red. Este paradigma actúa en contraposición a la ejecución de servicios en entornos cloud y es muy útil cuando se desea desarrollar soluciones de inteligencia artificial (AI) que aborden problemas en entornos de desastres naturales, como pueden ser inundaciones, incendios u otros eventos derivados del cambio climático. La cobertura de estos escenarios puede resultar especialmente difícil debido a la escasez de infraestructuras disponibles, lo que a menudo impide un análisis de los datos basado en la nube en tiempo real. Por lo tanto, es fundamental habilitar técnicas de IA que no dependan de sistemas de cómputo externos y que puedan ser embebidas en dispositivos de móviles como vehículos aéreos no tripulados (VANT), para que puedan captar y procesar información que permita inferir posibles situaciones de emergencia y determinar así el curso de acción más adecuado de manera autónoma. Históricamente, se hacía frente a este tipo de problemas utilizando los VANT como dispositivos de recogida de datos con el fin de, posteriormente, enviar esta información a la nube donde se dispone de servidores capacitados para analizar esta ingente cantidad de información. Este nuevo enfoque pretende realizar todo el procesamiento y la obtención de resultados en el VANT o en un dispositivo local complementario. Esta aproximación permite eliminar la dependencia de un centro de cómputo remoto que añade complejidad a la infraestructura y que no es una opción en escenarios específicos, donde las conexiones inalámbricas no cumplen los requisitos de transferencia de datos o son entornos en los que la información tiene que obtenerse en ese preciso momento, por requisitos de seguridad o inmediatez. Esta tesis doctoral está compuesta de tres propuestas principales. En primer lugar se plantea un sistema de despegue de enjambres de VANTs basado en el algoritmo de Kuhn Munkres que resuelve el problema de asignación en tiempo polinómico. Nuestra evaluación estudia la complejidad de despegue de grandes enjambres y analiza el coste computacional y de calidad de nuestra propuesta. La segunda propuesta es la definición de una secuencia de procesamiento de imágenes de catástrofes naturales tomadas desde drones basada en Deep learning (DL). El objetivo es reducir el número de imágenes que deben procesar los servicios de emergencias en la catástrofe natural para poder tomar acciones sobre el terreno de una manera más rápida. Por último, se utiliza un conjunto de datos de imágenes obtenidas con VANTs y relativas a diferentes inundaciones, en concreto, de la DANA de 2019, cedidas por el Ayuntamiento de San Javier, ejecutando un modelo DL de segmentación semántica que determina automáticamente las regiones más afectadas por las lluvias (zonas inundadas). Entre los resultados obtenidos se destacan los siguientes: 1- la mejora drástica del rendimiento del despegue vertical coordinado de una red de VANTs. 2- La propuesta de un modelo no supervisado para la vigilancia de zonas desconocidas representa un avance para la exploración autónoma mediante VANTs. Esto permite una visión global de una zona concreta sin realizar un estudio detallado de la misma. 3- Por último, un modelo de segmentación semántica de las zonas inundadas, desplegado para el procesamiento de imágenes en el VANTs, permite la obtención de datos de inundaciones en tiempo real (respetando la privacidad) para una reconstrucción virtual fidedigna del evento. Esta tesis ofrece una propuesta para mejorar el despegue coordinado de drones y dotar de capacidad de procesamiento de algoritmos de deep learning a dispositivos edge, más concretamente UAVs autónomos. / [CA] Edge Computing és un model de computació emergent basat a acostar el processament als dispositius de captura de dades en les infraestructures Internet of things (IoT). Edge computing millora, entre altres coses, els temps de resposta, estalvia amplades de banda, incrementa la seguretat dels serveis i oculta les caigudes transitòries de la xarxa. Aquest paradigma actua en contraposició a l'execució de serveis en entorns cloud i és molt útil quan es desitja desenvolupar solucions d'intel·ligència artificial (AI) que aborden problemes en entorns de desastres naturals, com poden ser inundacions, incendis o altres esdeveniments derivats del canvi climàtic. La cobertura d'aquests escenaris pot resultar especialment difícil a causa de l'escassetat d'infraestructures disponibles, la qual cosa sovint impedeix una anàlisi de les dades basat en el núvol en temps real. Per tant, és fonamental habilitar tècniques de IA que no depenguen de sistemes de còmput externs i que puguen ser embegudes en dispositius de mòbils com a vehicles aeris no tripulats (VANT), perquè puguen captar i processar informació per a inferir possibles situacions d'emergència i determinar així el curs d'acció més adequat de manera autònoma. Històricament, es feia front a aquesta mena de problemes utilitzant els VANT com a dispositius de recollida de dades amb la finalitat de, posteriorment, enviar aquesta informació al núvol on es disposa de servidors capacitats per a analitzar aquesta ingent quantitat d'informació. Aquest nou enfocament pretén realitzar tot el processament i l'obtenció de resultats en el VANT o en un dispositiu local complementari. Aquesta aproximació permet eliminar la dependència d'un centre de còmput remot que afig complexitat a la infraestructura i que no és una opció en escenaris específics, on les connexions sense fils no compleixen els requisits de transferència de dades o són entorns en els quals la informació ha d'obtindre's en aqueix precís moment, per requisits de seguretat o immediatesa. Aquesta tesi doctoral està composta de tres propostes principals. En primer lloc es planteja un sistema d'enlairament d'eixams de VANTs basat en l'algorisme de Kuhn Munkres que resol el problema d'assignació en temps polinòmic. La nostra avaluació estudia la complexitat d'enlairament de grans eixams i analitza el cost computacional i de qualitat de la nostra proposta. La segona proposta és la definició d'una seqüència de processament d'imatges de catàstrofes naturals preses des de drons basada en Deep learning (DL).L'objectiu és reduir el nombre d'imatges que han de processar els serveis d'emergències en la catàstrofe natural per a poder prendre accions sobre el terreny d'una manera més ràpida. Finalment, s'utilitza un conjunt de dades d'imatges obtingudes amb VANTs i relatives a diferents inundacions, en concret, de la DANA de 2019, cedides per l'Ajuntament de San Javier, executant un model DL de segmentació semàntica que determina automàticament les regions més afectades per les pluges (zones inundades). Entre els resultats obtinguts es destaquen els següents: 1- la millora dràstica del rendiment de l'enlairament vertical coordinat d'una xarxa de VANTs. 2- La proposta d'un model no supervisat per a la vigilància de zones desconegudes representa un avanç per a l'exploració autònoma mitjançant VANTs. Això permet una visió global d'una zona concreta sense realitzar un estudi detallat d'aquesta. 3- Finalment, un model de segmentació semàntica de les zones inundades, desplegat per al processament d'imatges en el VANTs, permet l'obtenció de dades d'inundacions en temps real (respectant la privacitat) per a una reconstrucció virtual fidedigna de l'esdeveniment. / [EN] Edge Computing is an emerging computing model based on bringing data processing and storage closer to the location needed to improve response times and save bandwidth. This new paradigm acts as opposed to running services in cloud environments and is very useful in developing artificial intelligence (AI) solutions that address problems in natural disaster environments, such as floods, fires, or other events of an adverse nature. Coverage of these scenarios can be particularly challenging due to the lack of available infrastructure, which often precludes real-time cloud-based data analysis. Therefore, it is critical to enable AI techniques that do not rely on external computing systems and can be embedded in mobile devices such as unmanned aerial vehicles (UAVs) so that they can capture and process information to understand their context and determine the appropriate course of action independently. Historically, this problem was addressed by using UAVs as data collection devices to send this information to the cloud, where servers can process it. This new approach aims to do all the processing and get the results on the UAV or a complementary local device. This approach eliminates the dependency on a remote computing center that adds complexity to the infrastructure and is not an option in specific scenarios where wireless connections do not meet the data transfer requirements. It is also an option in environments where the information has to be obtained at that precise moment due to security or immediacy requirements. This study consists of three main proposals. First, we propose a UAV swarm takeoff system based on the Kuhn Munkres algorithm that solves the assignment problem in polynomial time. Our evaluation studies the takeoff complexity of large swarms and analyzes our proposal's computational and quality cost. The second proposal is the definition of a Deep learning (DL) based image processing sequence for natural disaster images taken from drones to reduce the number of images processed by the first responders in the natural disaster. Finally, a dataset of images obtained with UAVs and related to different floods is used to run a semantic segmentation DL model that automatically determines the regions most affected by the rains (flooded areas). The results are 1- The drastic improvement of the performance of the coordinated vertical take-off of a network of UAVs. 2- The proposal of an unsupervised model for the surveillance of unknown areas represents a breakthrough for autonomous exploration by UAVs. This allows a global view of a specific area without performing a detailed study. 3- Finally, a semantic segmentation model of flooded areas, deployed for image processing in the UAV, allows obtaining real-time flood data (respecting privacy) for a reliable virtual reconstruction of the event. This thesis offers a proposal to improve the coordinated take-off of drones, to provide edge devices with deep learning algorithms processing capacity, more specifically autonomous UAVs, in order to develop services for the surveillance of areas affected by natural disasters such as fire detection, segmentation of flooded areas or detection of people in danger. Thanks to this research, services can be developed that enable the coordination of large arrays of drones and allow image processing without needing additional devices. This flexibility makes our approach a bet for the future and thus provides a development path for anyone interested in deploying an autonomous drone-based surveillance and actuation system. / I would like to acknowledge the project Development of High-Performance IoT Infrastructures against Climate Change based on Artificial Intelligence (GLOBALoT). Funded by Ministerio de Ciencia e Innovación (RTC2019-007159-5), of which this thesis is part. / Hernández Vicente, D. (2023). Analysis Design and Implementation of Artificial Intelligence Techniques in Edge Computing Environments [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/192605 / Compendio