Now showing items 1-20 of 354

    • A closer look at referring expressions for video object segmentation 

      Bellver Bueno, Míriam; Ventura Royo, Carles; Silberer, Carina; Kazakos, Ioannis; Torres Viñals, Jordi; Giró Nieto, Xavier (2023-01)
      Article
      Open Access
      The task of Language-guided Video Object Segmentation (LVOS) aims at generating binary masks for an object referred by a linguistic expression. When this expression unambiguously describes an object in the scene, it is ...
    • A collaborative statistical actor-critic learning approach for 6G network slicing control 

      Rezazadeh, Farhad; Chergui, Hatim; Blanco Botana, Luis; Alonso Zárate, Luis Gonzaga; Verikoukis, Christos (Institute of Electrical and Electronics Engineers (IEEE), 2021)
      Conference lecture
      Open Access
      Artificial intelligence (AI)-driven zero-touch massive network slicing is envisioned to be a disruptive technology in beyond 5G (B5G)/6G, where tenancy would be extended to the final consumer in the form of advanced digital ...
    • A comparison of deep learning methods for urban traffic forecasting using floating car data 

      Vázquez Giménez, Juan José; Arjona Martínez, Jamie; Linares Herreros, María Paz; Casanovas Garcia, Josep (Elsevier, 2020)
      Article
      Open Access
      Cities today must address the challenge of sustainable mobility, and traffic state forecasting plays a key role in mitigating traffic congestion in urban areas. For example, predicting path travel time is a crucial issue ...
    • A comparison study of U-Net based methods for brain tumor segmentation 

      Ferrando Garrido, Albert (Universitat Politècnica de Catalunya, 2023-10-19)
      Master thesis
      Open Access
      Covenantee:   Universitat de Barcelona
      The brain tumor segmentation (BraTS) Challenge is an international competition that focuses on the task of automated segmentation of the different parts of brain tumors in magnetic resonance imaging (MRI) scans. U-Net ...
    • A confusion matrix for evaluating feature attribution methods 

      Arias Duart, Anna; Mariotti, Ettore; Garcia Gasulla, Dario; Alonso Moral, Jose Maria (Institute of Electrical and Electronics Engineers (IEEE), 2023)
      Conference lecture
      Open Access
      The increasing use of deep learning models in critical areas of computer vision and the consequent need for insights into model behaviour have led to the development of numerous feature attribution methods. However, these ...
    • A dataset of microscopic peripheral blood cell images for development of automatic recognition systems 

      Acevedo, Andrea; Merino González, Anna; Alférez Baquero, Edwin Santiago; Molina Borrás, Ángel; Boldú Nebot, Laura; Rodellar Benedé, José (Elsevier, 2020-06)
      Article
      Open Access
      This article makes available a dataset that was used for the development of an automatic recognition system of peripheral blood cell images using convolutional neural networks [1]. The dataset contains a total of 17,092 ...
    • A deep learning approach for segmentation of red blood cell images and malaria detection 

      Delgado Ortet, Maria; Molina Borrás, Ángel; Alférez Baquero, Edwin Santiago; Rodellar Benedé, José; Merino González, Anna (2020-06-13)
      Article
      Open Access
      Malaria is an endemic life-threating disease caused by the unicellular protozoan parasites of the genus Plasmodium. Confirming the presence of parasites early in all malaria cases ensures species-specific antimalarial ...
    • A Deep Learning Based Approach to Automated App Testing 

      Llàcer Giner, David (Universitat Politècnica de Catalunya, 2020-09-09)
      Master thesis
      Open Access
      Mobile applications are worldwide extended. We use them for everything, from texting friends to managing our money. This boom has led to the emergence of companies dedicated exclusively to the development of mobile ...
    • A Deep Learning Based Tool For Ear Training 

      Nogales Pérez, David (Universitat Politècnica de Catalunya, 2023-05-16)
      Bachelor thesis
      Open Access
      L'objectiu principal d'aquest projecte és utilitzar tècniques d'aprenentatge profund per desenvolupar una eina capaç de generar exercicis de dictat melòdic significatius perquè els professors de música i els seus estudiants ...
    • A deep learning-based method for uncovering GPCR ligand-induced conformational states using interpretability techniques 

      Gutiérrez Mondragón, Mario Alberto; König, Caroline; Vellido Alcacena, Alfredo (Springer, 2022)
      Conference report
      Open Access
      There is increasing interest in the development of tools for investigating the protein ligand space. Understanding the underlying mechanisms of G protein-coupled receptors (GPCR) in the ligand-binding process is of particular ...
    • A deep q network-based multi-connectivity algorithm for heterogeneous 4G/5G cellular systems 

      Hernández Carlón, Juan Jesús; Pérez Romero, Jordi; Sallent Roig, Oriol; Vilà Muñoz, Irene; Casadevall Palacio, Fernando José (Springer, 2022)
      Conference report
      Restricted access - publisher's policy
      Multi-connectivity, which allows a user equipment to be simultaneously connected to multiple cells from different radio access network nodes that can be from a single or multiple radio access technologies, has emerged as ...
    • A deep Q-network-based algorithm for multi-connectivity optimization in heterogeneous cellular-networks † 

      Hernández Carlón, Juan Jesús; Pérez Romero, Jordi; Sallent Roig, Oriol; Vilà Muñoz, Irene; Casadevall Palacio, Fernando José (2022-08-01)
      Article
      Open Access
      The use of multi-connectivity has become a useful tool to manage the traffic in heterogeneous cellular network deployments, since it allows a device to be simultaneously connected to multiple cells. The proper exploitation ...
    • A differential privacy protection-based federated deep learning framework to fog-embedded architectures 

      Gutiérrez Escobar, Norma; Otero Calviño, Beatriz; Rodríguez Luna, Eva; Utrera Iglesias, Gladys Miriam; Mus León, Sergi; Canal Corretger, Ramon (Elsevier, 2024-04)
      Article
      Open Access
      Nowadays, companies collect massive quantities of data to enhance their operations, often at the expense of sharing user sensible information. This data is widely used to train Deep Learning (DL) neural networks to model, ...
    • A dual network for super-resolution and semantic segmentation of sentinel-2 imagery 

      Abadal Lloret, Sauc; Salgueiro Romero, Luis Fernando; Marcello Ruiz, Javier; Vilaplana Besler, Verónica (Multidisciplinary Digital Publishing Institute (MDPI), 2021-11-12)
      Article
      Open Access
      There is a growing interest in the development of automated data processing workflows that provide reliable, high spatial resolution land cover maps. However, high-resolution remote sensing images are not always affordable. ...
    • A novel deep learning-based diagnosis method applied to power quality disturbances 

      González Abreu, Artvin Darién; Delgado Prieto, Miquel; Osornio Rios, Roque A.; Saucedo Dorantes, Juan Jose; Romero Troncoso, René de Jesús (2021-05-02)
      Article
      Open Access
      Monitoring electrical power quality has become a priority in the industrial sector background: avoiding unwanted effects that affect the whole performance at industrial facilities is an aim. The lack of commercial equipment ...
    • A pipeline for large raw text preprocessing and model training of language models at scale 

      Armengol Estapé, Jordi (Universitat Politècnica de Catalunya, 2021-01-25)
      Master thesis
      Open Access
      Covenantee:   Universitat de Barcelona / Universitat Rovira i Virgili
      The advent of Transformer-based (i.e., based on self-attention architectures) language models has revolutionized the entire field of Natural Language Processing (NLP). Once pre-trained on large, unlabelled corpora, we can ...
    • A Preliminary study of deep learning sensor fusion for pedestrian detection 

      Chávez Plascencia, Alfredo; García Gómez, Pablo; Bernal Pérez, Eduard; Mas Giménez, Gerard de; Casas Pla, Josep Ramon; Royo Royo, Santiago (Multidisciplinary Digital Publishing Institute (MDPI), 2023-04)
      Article
      Open Access
      Most pedestrian detection methods focus on bounding boxes based on fusing RGB with lidar. These methods do not relate to how the human eye perceives objects in the real world. Furthermore, lidar and vision can have difficulty ...
    • A study of Deep Learning techniques for sequence-based problems 

      Quintana Valenzuela, Diego (Universitat Politècnica de Catalunya, 2021-10)
      Master thesis
      Restricted access - author's decision
      Transformer Networks are a new type of Deep Learning architecture first introduced in 2017. By only applying attention mechanisms, the transformer network can model relations between text sequences that outperformed other ...
    • A survey of deep learning techniques for cybersecurity in mobile networks 

      Rodríguez Luna, Eva; Otero Calviño, Beatriz; Gutiérrez Escobar, Norma; Canal Corretger, Ramon (2021-06-07)
      Article
      Open Access
      The widespread use of mobile devices, as well as the increasing popularity of mobile services has raised serious cybersecurity challenges. In the last years, the number of cyberattacks has grown dramatically, as well as ...
    • A survey of machine and deep learning methods for privacy protection in the Internet of things 

      Rodríguez Luna, Eva; Otero Calviño, Beatriz; Canal Corretger, Ramon (Multidisciplinary Digital Publishing Institute (MDPI), 2023-01-21)
      Article
      Open Access
      Recent advances in hardware and information technology have accelerated the proliferation of smart and interconnected devices facilitating the rapid development of the Internet of Things (IoT). IoT applications and services ...