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However, each of the images obtained by this technique only cover a small retinal area. Thus, ophthalmologists have to take complementary images of the eye fundus from dierent angles in order to obtain a complete visualization of patients' eye fundus. In particular, each set of images must be manually registered by a clinician, being a tedious and time-consuming process. In this work, we propose an approach based on template matching and dierential evolution to automatically register a set of OCTA images characterized by containing noise and artifacts. The proposed method is divided into three main steps. First, a preprocessing step used to extract the main vascular network is applied on every image. Then, an algorithm based on dierential evolution is run on every 2-combination of OCTA images in order to nd the best overlap between them. Finally, a greedy algorithm iteratively selects the best pairs of images (according to their tness) to create the complete mosaic. The proposed method was evaluated via the registration of several sets of OCTA images with the purpose of building their associated mosaics. Results show that our approach is robust and able to achieve a good approximation to the optimal mosaic.0Doctoral Thesis4282<a class="citation_author_name" title="Navegar por nombre de Autor de Moya García, Alejandro" href="/fez/list/author/Moya García, Alejandro/">Moya García, Alejandro</a>. (<span class="citation_date">2020</span>). <i><a class="citation_title" title="Click para ver : Automatic wide-eld registration and mosaicking of noisy OCTA images using template matching and dierential evolution" href="/fez/view/bibliuned:master-ETSInformatica-IIA-Amoya">Automatic wide-eld registration and mosaicking of noisy OCTA images using template matching and dierential evolution</a></i> Master Thesis, <span class="citation_publisher">Universidad Nacional de Educación a Distancia (España). Escuela Técnica Superior de Ingeniería Informática. Departamento de Inteligencia Artificial</span>Recordmaster TesisPublishedIngeniería InformáticaUniversidad Nacional de Educación a Distancia (España). Escuela Técnica Superior de Ingeniería Informática. Departamento de Inteligencia ArtificialMoya García, AlejandroCarmona Suárez, Enrique J.bibliuned:master-ETSInformatica-IIA-Amoyahttp://e-spacio.uned.es/fez/view/bibliuned:master-ETSInformatica-IIA-AmoyaengMoya_Garcia_Alejandro_TFM.pdfpresmd_Moya_Garcia_Alejandro_TFM.xmlbibliuned:master-ETSInformatica-IIAbibliuned:Settrabajosfindemasterbibliuned:SetopenaireMáster Universitario en Investigación en Inteligencia ArtificialSet de items trabajo fin de másterSet de openaireMoya GarcíaAcceso abierto2.381957831982019-09-19T00:00:00Z1662020-10-19T18:57:50Z2020-10-19T18:57:50ZImproving classication of pollen grain images of the POLEN23E dataset deep learningbibliuned:master-ETSInformatica-IAA-VmsevillanoIn palynology, the visual classication of pollen grains from dierent species is a hard task which is usually tackled by human operators using microscopes. Its complete automatization would save a high quantity of resources and provide valuable improvements especially for allergy-related information systems, but also for other application elds as paleoclimate reconstruction, quality control of honey-based products, collection of evi- dences in criminal investigations or fabric dating and tracking. This paper presents three state-of-the-art deep learning classication methods applied to the recently published POLEN23E image dataset. The three methods make use of convolutional neural networks: the rst one is strictly based on the idea of transfer learning, the second one is based on feature extraction and the third one represents a hybrid approach, combining transfer learning and feature extraction. The results from the three methods are indeed very good, reaching over 99% correct classication rates in the training set of images and over 96% in images not previously seen by the models where other authors reported around 70%.0Doctoral Thesis3482<a class="citation_author_name" title="Navegar por nombre de Autor de Sevillano Plaza, Victor" href="/fez/list/author/Sevillano Plaza, Victor/">Sevillano Plaza, Victor</a>. (<span class="citation_date">2019</span>). <i><a class="citation_title" title="Click para ver : Improving classication of pollen grain images of the POLEN23E dataset deep learning" href="/fez/view/bibliuned:master-ETSInformatica-IAA-Vmsevillano">Improving classication of pollen grain images of the POLEN23E dataset deep learning</a></i> Master Thesis, <span class="citation_publisher">Universidad Nacional de Educación a Distancia (España). Escuela Técnica Superior de Ingeniería Informática. Departamento de Inteligencia Artificial.</span>Recordmaster TesisPublishedIngeniería InformáticaUniversidad Nacional de Educación a Distancia (España). Escuela Técnica Superior de Ingeniería Informática. Departamento de Inteligencia Artificial.Sevillano Plaza, VictorAznarte Mellado, Jose Luisbibliuned:master-ETSInformatica-IAA-Vmsevillanohttp://e-spacio.uned.es/fez/view/bibliuned:master-ETSInformatica-IAA-VmsevillanoengSevillano_Plaza_Victor_TFM.pdfpresmd_Sevillano_Plaza_Victor_TFM.xmlbibliuned:master-ETSInformatica-IAAbibliuned:Settrabajosfindemasterbibliuned:SetopenaireMáster Universitario en I.A. Avanzada: Fundamentos, Métodos y Aplicaciones (UNED)Set de items trabajo fin de másterSet de openaireSevillano PlazaAcceso abierto2.19014431982020-09-01T00:00:00Z2862023-01-11T07:19:29Z2023-01-11T07:19:29ZAutomatic Generation of Domain Knowledge Graph for Recommendation Systems using Open Source Resourcesbibliuned:master-ETSInformatica-LSI-AsanzA group of state-of-the-art recommendation algorithms using Knowledge Graphs are RippleNet [1] or KGAT [2]. However, the main bottleneck to benchmark and work with these algorithms is that they used Microsoft Satori 1 or Freebase 2 (now Google Knowledge Graph 3) as their KG. As Satori and Google Knowledge Graph are commercial KGs and not open source, it's not possible to replicate the results found in the papers on new data, or use these solutions in real applications without paying for access. This limitation is critical in the development of this area of RS. There are researchers working on algorithms and applications, whose results cannot be replicated openly by the science community, hence, going against the scientic method. As researchers, one of our main goals is to make science accessible and replicable for all the scientic community, empowering the development of new knowledge areas. To ll this gap, this Thesis presents a system to generate domain adapted Knowledge Graphs using open source information from Wikidata. These Knowledge Graphs can be used in hybrid Recommendation Systems, that use linked knowledge on the items as side information, combining both Collaborative Filtering and Content Base Filtering strategies. The results show that the proposed system is able to create domain adapted Knowledge Graphs from open source information for recommendation datasets, and that the KGs generated are able to compete with their commercial versions. We have shown that our proposed system creates smaller KGs that are more domain adapted, that have a similar eciency in downstream tasks of Recommendation Systems than commercial KGs. This could be specially relevant in systems that require faster computational times that can be achieved with smaller KGs, as real-time systems, or to save computational cost in high-scale systems. The system ca be used as a reference to evaluate the state-of-the-art algorithms in future works in the area.0Doctoral Thesis1682<a class="citation_author_name" title="Navegar por nombre de Autor de Sanz Olive, Almudena" href="/fez/list/author/Sanz Olive, Almudena/">Sanz Olive, Almudena</a>. (<span class="citation_date">2020</span>). <i><a class="citation_title" title="Click para ver : Automatic Generation of Domain Knowledge Graph for Recommendation Systems using Open Source Resources" href="/fez/view/bibliuned:master-ETSInformatica-LSI-Asanz">Automatic Generation of Domain Knowledge Graph for Recommendation Systems using Open Source Resources</a></i> Master Thesis, <span class="citation_publisher">Universidad Nacional de Educación a Distancia (España). Escuela Técnica Superior de Ingeniería Informática. Departamento de Lenguajes y Sistemas Informáticos</span>Recordmaster TesisPublishedIngeniería AeronáuticaUniversidad Nacional de Educación a Distancia (España). Escuela Técnica Superior de Ingeniería Informática. Departamento de Lenguajes y Sistemas InformáticosSanz Olive, AlmudenaAlbornoz Cuadrado, Jorge Carrillo deGonzalez-Fierro, Miguelbibliuned:master-ETSInformatica-LSI-Asanzhttp://e-spacio.uned.es/fez/view/bibliuned:master-ETSInformatica-LSI-AsanzengSanzOlive_Almudena_TFM.pdfpresmd_SanzOlive_Almudena_TFM.xmlbibliuned:master-ETSInformatica-LSIbibliuned:Settrabajosfindemasterbibliuned:SetopenaireMáster Universitario en Lenguajes y Sistemas Informáticos (UNED)Set de items trabajo fin de másterSet de openaireSanz OliveAcceso abierto2.163317431982014-02-27T00:00:00Z1092021-10-08T23:02:31Z2021-10-08T23:02:31ZKnowledge capture and textual inferencebibliuned:master-ETSInformatica-LSI-BcabaleiroThe present and future information needs of the society rely on the ability of computers to understand and manage knowledge. The lack of this mechanism explains the problems of knowledge driven systems to effectively perform tasks as question answering and machine reading. One of the biggest bottlenecks is the automatic knowledge acquisition problem. In the actual stage of development, it seems obvious that only semisupervised or unsupervised techniques can scale to deal with large corpora of natural language like the Web. The trend has evolved from populating a predefined ontology to expressing knowledge through either unconstrained relations or propositions. The arrival of new deep language processing technologies let us think that we can annotate large collections of text with accurate predicates that can be used to extracting knowledge from text without tying it to any predefined logical schema. On the other hand, it is not clear which tasks can harness this knowledge and how it can be done. This master’s thesis proposes a new method of knowledge capture and textual inference based on three cornerstones: (1) First, we develop a procedure to turn plain text into a graph based representation taking advantage of existing tools. (2) Second, we develop a proposition extraction system. (3) Lastly, we study an unsupervised method for correction of appositive dependencies, as an example of the textual inferences that the generated proposition store enables. In addition, we generate two useful resources for future tasks of natural language processing: A corpus of 7 million documents represented as semantically enriched graphs and a proposition store of semantic classes with 8 million instances of entity-class relations.0Doctoral Thesis2342<a class="citation_author_name" title="Navegar por nombre de Autor de Cabaleiro Barciela, Bernardo" href="/fez/list/author/Cabaleiro Barciela, Bernardo/">Cabaleiro Barciela, Bernardo</a>. (<span class="citation_date">2014</span>). <i><a class="citation_title" title="Click para ver : Knowledge capture and textual inference" href="/fez/view/bibliuned:master-ETSInformatica-LSI-Bcabaleiro">Knowledge capture and textual inference</a></i> Master Thesis, <span class="citation_publisher">Universidad Nacional de Educación a Distancia (España). Escuela Técnica Superior de Ingeniería Informática. Departamento de Lenguajes y Sistemas Informáticos</span>Recordmaster TesisPublishedInformáticaUniversidad Nacional de Educación a Distancia (España). Escuela Técnica Superior de Ingeniería Informática. Departamento de Lenguajes y Sistemas InformáticosCabaleiro Barciela, BernardoPeñas Padilla, Anselmobibliuned:master-ETSInformatica-LSI-Bcabaleirohttp://e-spacio.uned.es/fez/view/bibliuned:master-ETSInformatica-LSI-BcabaleiroengCABALEIRO_BARCIELA_Bernardo_TFM.pdfpresmd_CABALEIRO_BARCIELA_Bernardo_TFM.xmlbibliuned:master-ETSInformatica-LSIbibliuned:Settrabajosfindemasterbibliuned:SetopenaireMáster Universitario en Lenguajes y Sistemas Informáticos (UNED)Set de items trabajo fin de másterSet de openaireCabaleiro BarcielaAcceso abierto2.06863731982022-09-01T00:00:00Z542023-09-16T02:45:32Z2023-09-16T02:45:32ZSelf-learning robot navigation with deep reinforcement learning techniquesbibliuned:master-ETSInformatica-IAA-BpintosThe autonomous driving has been always a challenging task. A high number of sensors mounted in the vehicle analyze the surroundings and provide to the autonomous driving algorithm useful information, such as relative distances from the vehicle to the different obstacles. Some robotic paradigms, like the reactive paradigm, uses this sensorial input to directly create an action linked to the actuators. This makes the reactivate paradigm capable to react to unpredictable scenarios with relatively low computational resources. However, they lack a robot motion planning. This can lead to longer and less comfortable trajectories with respect to the hierarchical/deliberative paradigm, which counts with a motion planning module over a predefined horizon. Although a local optimization of the robot trajectory is now possible under static scenarios, the motion planning module comes at a high cost in terms of memory and computational power. The hybrid paradigm combines the reactive and hierarchical/deliberative paradigms to solve even more complex scenarios, such as dynamic scenarios, but the memory and computational resources needed are still high. This work presents the sense-think-act-learn robotic paradigm which aims to inherit the advantages of the reactive, hierarchical/deliberative and hybrid paradigms at a reasonable computational cost. The proposed methodology makes use of reinforcement learning techniques to learn a policy by trial and error, just like the human brain works. On one hand, there is no motion planning module, so that the computational power can be limited like in the reactive paradigm. But on the other hand, a local planification and optimization of the robot trajectory takes place, like in the hierarchical/deliberative and hybrid paradigms. This planification is based on the experience stored during the learning process. Reactions to sensorial inputs are automatically learnt based on well-defined reward functions, which are directly mapped to the safety, legal, comfort and task-oriented requirements of the autonomous driving problem. Since the motion planification is based on the experience, the algorithm proposed is not bound to any embedded model of the vehicle or environment. Instead, the algorithm learns directly from the environment (real or simulated) and therefore it is not affected by uncertainties of embedded models or estimators which try to reproduce the dynamics of the vehicle or robot. Additionally, the policy is learnt automatically. The state-of-the-art algorithms invert many engineering hours to develop a policy or algorithm to fulfil all given requirements, while the method proposed in this work saves these costs and engineering time. Another interesting advantage of the proposed algorithm is the capability to adapt the logic under unknown scenarios. For that, an online learning process is implemented, but the memory and computational power required for that is high.0Doctoral Thesis2792<a class="citation_author_name" title="Navegar por nombre de Autor de Pintos Gómez de las Heras, Borja" href="/fez/list/author/Pintos Gómez de las Heras, Borja/">Pintos Gómez de las Heras, Borja</a>. (<span class="citation_date">2022</span>). <i><a class="citation_title" title="Click para ver : Self-learning robot navigation with deep reinforcement learning techniques" href="/fez/view/bibliuned:master-ETSInformatica-IAA-Bpintos">Self-learning robot navigation with deep reinforcement learning techniques</a></i> Master Thesis, <span class="citation_publisher">Universidad Nacional de Educación a Distancia (España). Escuela Técnica Superior de Ingeniería Informática. Departamento de Inteligencia Artificia</span>Recordmaster TesisPublishedIngeniería InformáticaUniversidad Nacional de Educación a Distancia (España). Escuela Técnica Superior de Ingeniería Informática. Departamento de Inteligencia ArtificiaPintos Gómez de las Heras, BorjaMartínez Tomás, RafaelCuadra Troncoso, José Manuelbibliuned:master-ETSInformatica-IAA-Bpintoshttp://e-spacio.uned.es/fez/view/bibliuned:master-ETSInformatica-IAA-BpintosengPintos_Gomez_delasHeras_Borja_TFM.pdfpresmd_Pintos_Gomez_delasHeras_Borja_TFM.xmlbibliuned:master-ETSInformatica-IAAbibliuned:Settrabajosfindemasterbibliuned:SetopenaireMáster Universitario en I.A. Avanzada: Fundamentos, Métodos y Aplicaciones (UNED)Set de items trabajo fin de másterSet de openairePintos Gómez de las HerasAcceso abierto2.0192099553255225522