Now showing items 1-20 of 298

    • 3D vehicle detection on an FPGA from LiDAR point clouds 

      García López, Javier; Agudo Martínez, Antonio; Moreno-Noguer, Francesc (Association for Computing Machinery (ACM), 2019)
      Conference report
      Open Access
      In this paper is presented a deep neural network architecture designed to run on a field-programmable gate array (FPGA) for detection vehicle on LIDAR point clouds. This works present a network based on VoxelNet adapted ...
    • 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 (2022-07-27)
      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 comprehensive review of the movement imaginary brain-computer interface methods: Challenges and future directions 

      Khademi, Sadaf; Neghabi, Mehrnoosh; Farahi, Morteza; Shirzadi, Mehdi; Marateb, Hamid Reza (Academic Press, 2022)
      Part of book or chapter of book
      Restricted access - publisher's policy
      Brain-computer interface (BCI) aims to translate human intention into a control output signal. In motor-imaginary (MI) BCI, the imagination of movement modifies the cortex brain activity. Such activities are then used in ...
    • A Convolutional Neural Network for the automatic diagnosis of collagen VI-related muscular dystrophies 

      Rodríguez Bazaga, Adrián; Roldán Molina, Mónica; Badosa Gallego, Maria del Carmen; Jiménez Mallebrera, Cecilia; Porta Pleite, Josep Maria (2019-12-01)
      Article
      Open Access
      The development of machine learning systems for the diagnosis of rare diseases is challenging, mainly due to the lack of data to study them. This paper surmounts this obstacle and presents the first Computer-Aided Diagnosis ...
    • A cross-layer review of deep learning frameworks to ease their optimization and reuse 

      Tabani, Hamid; Pujol Torramorell, Roger; Abella Ferrer, Jaume; Cazorla Almeida, Francisco Javier (Institute of Electrical and Electronics Engineers (IEEE), 2020)
      Conference report
      Open Access
      Machine learning and especially Deep Learning (DL) approaches are at the heart of many domains, from computer vision and speech processing to predicting trajectories in autonomous driving and data science. Those approaches ...
    • A deep analysis on age estimation 

      Huerta Casado, Iván; Fernandez Tena, Carles; Segura, Carlos; Hernando Pericás, Francisco Javier; Prati, Andrea (2015-12-15)
      Article
      Open Access
      The automatic estimation of age from face images is increasingly gaining attention, as it facilitates applications including advanced video surveillance, demographic statistics collection, customer profiling, or search ...
    • 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 pproach for the morphological recognition of reactive lymphocytes in patients with COVID-19 infection 

      Rodellar Benedé, José; Barrera Llanga, Kevin Iván; Alférez Baquero, Edwin Santiago; Boldú Nebot, Laura; Laguna Moreno, Javier; Molina Borrás, Ángel; Merino González, Anna (2022-05-23)
      Article
      Open Access
      Laboratory medicine plays a fundamental role in the detection, diagnosis and management of COVID-19 infection. Recent observations of the morphology of cells circulating in blood found the presence of particular reactive ...
    • A deep learning-based modeling of a 270 V -to- 28 V DC-DC converter used in more electric aircrafts 

      Rojas Dueñas, Gabriel; Riba Ruiz, Jordi-Roger; Moreno Eguilaz, Juan Manuel (Institute of Electrical and Electronics Engineers (IEEE), 2021-07-21)
      Article
      Restricted access - publisher's policy
      This paper presents a novel approach for black-box modelling of 270 V -to- 28 V DC-DC step-down converters used in more electric aircrafts (MEA). These converters normally feed constant power loads (CPL). The proposed deep ...
    • 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 novel methodology to predict regulations using deep learning 

      Mas Pujol, Sergi; Salamí San Juan, Esther; Pastor Llorens, Enric (Single European Sky ATM Research (SESAR), 2020)
      Conference report
      Open Access
      The current air traffic control system tries to allocate as many flights as possible in a scenario that is expected to be time-efficient, cost-efficient, and safe. To guaranty these safety conditions, it is performed a ...
    • 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 study of Deep Learning techniques for sequence-based problems 

      Quintana Valenzuela, Diego (Universitat Politècnica de Catalunya, 2021-10)
      Master thesis
      Open Access
      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 trainable monogenic ConvNet layer robust in front of large contrast changes in image classification 

      Moya Sánchez, Eduardo Ulises; Xambó Descamps, Sebastián; Sánchez-Pérez, Abraham; Salazar Colores, Sebastián; Cortés García, Claudio Ulises (Institute of Electrical and Electronics Engineers (IEEE), 2021-12-20)
      Article
      Open Access
      Convolutional Neural Networks (ConvNets) at present achieve remarkable performance in image classification tasks. However, current ConvNets cannot guarantee the capabilities of the mammalian visual systems such as invariance ...
    • A web scraping framework for stock price modelling using deep learning methods 

      Fibla Salgado, Aleix (Universitat Politècnica de Catalunya / Universitat de Barcelona, 2019-06)
      Bachelor thesis
      Open Access
      This work aims to shed light to the process of web scraping, emphasizing its importance in the new ’Big Data’ era with an illustrative application of such methods in financial markets.The work essentially focuses on different ...