Articles publicats Departament d'Arquitectura i Tecnologia de Computadors

Universitat de Girona Facultat de Lletres

La Universitat de Girona és una institució pública que cerca l’excel·lència en la docència i en la recerca, i que participa en el progrés i el desenvolupament de la societat, mitjançant la creació, transmissió, difusió i crítica de la ciència, la tècnica, les humanitats, les ciències socials i les arts.La Universitat de Girona està arrelada al país i a la cultura catalana i és un dels principals motors econòmics i culturals del seu entorn. Al mateix temps, expressa la vocació d’universalitat i d’obertura a totes les tradicions, avenços i cultures.La Universitat té la seva seu a la ciutat de Girona i s’integra en el sistema d’universitats públiques catalanes.


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Recent Submissions

Global and Regional Deep Learning Models for Multiple Sclerosis Stratification From MRI 

Coll, Llucia; Pareto, Deborah; Carbonell Mirabent, Pere; Cobo Calvo, Alvaro; Arrambide, Georgina; Vidal-Jordana, Angela; Comabella López, Manuel; Castilló, Joaquín; Rodríguez-Acevedo, Breogán; Zabalza, Ana; Galan, Ingrid; Midaglia, Luciana; Nos, Carlos; Auger, Cristina; Alberich, Manel; Río, Jordi; Sastre Garriga, Jaume; Oliver i Malagelada, Arnau; Montalban Gairín, Xavier; Rovira, Àlex; Tintoré, Mar; Lladó Bardera, Xavier; Tur, Carmen (2024-07)

Background: The combination of anatomical MRI and deep learning-based methods such as convolutional neural networks (CNNs) is a promising strategy to build predictive models of multiple sclerosis (MS) prognosis. However, ...

Large-scale web tracking and cookie compliance: Evaluating one million websites under GDPR with AI categorization 

Martínez Álvarez, David; Molero Grau, Aniol; Calle Ortega, Eusebi; Canals Ametller, Dolors; Jové, Albert (2025-10)

With the increasing prevalence of web-tracking technologies, including tracking cookies, pixel tracking, and browser fingerprinting techniques, there is a pressing need to analyze their impact on user privacy. Despite the ...

Evaluation of Motiv-ARCHE in the Santa Clara Museum 

González Vargas, Juan Camilo; Fabregat Gesa, Ramon; Carrillo-Ramos, Angela; Jové Lagunas, Teodor (2025-02-21)

Currently, heritage sites, such as museums, have focused on the preservation and conservation of heritage elements for present and future generations. However, when displaying their content, they often do not consider ...

Network congestion control algorithm for image transmission hri and visual light communications of an autonomous underwater vehicle for intervention 

López Barajas, Salvador; Sanz, Pedro José; Marín Prades, Raúl; Echagüe, Juan; Realpe, Sebastian (2025-01-01)

In this study, the challenge of teleoperating robots in harsh environments such as underwater or in tunnels is addressed. In these environments, wireless communication networks are prone to congestion, leading to potential ...

On Lie group IMU and linear velocity preintegration for autonomous navigation considering the Earth rotation compensation 

Carreras, Marc; Vial Serrat, Pau; Solà Ortega, Joan; Palomeras Rovira, Narcís; Carreras Pérez, Marc (2024-12-24)

Robot localization is a fundamental task in achieving true autonomy. Recently, many graph-based navigators have been proposed that combine an inertial measurement unit (IMU) with an exteroceptive sensor applying IMU ...

GNNetSlice: A GNN-based performance model to support network slicing in B5G networks 

Farreras Casamort, Miquel; Paillisse Vilanova, Jordi; Fàbrega i Soler, Lluís; Vilà Talleda, Pere (2025-02-15)

Network slicing is gaining traction in Fifth Generation (5G) deployments and Beyond 5G (B5G) designs. In a nutshell, network slicing virtualizes a single physical network into multiple virtual networks or slices, so that ...

Predicting network performance using GNNs: generalization to larger unseen networks 

Farreras Casamort, Miquel; Soto, Paola; Camelo Botero, Miguel Hernando; Fàbrega i Soler, Lluís; Vilà Talleda, Pere (2022-04-22)

Autonomous Fifth Generation (5G) and Beyond 5G (B5G) networks require modelling tools to predict the impact on the performance when new configurations and features are applied in the network. Modeling modern networks through ...

Low time complexity algorithms for path computation in Cayley Graphs 

Aguirre Guerrero, Daniela; Ducoffe, Guillaume; Fàbrega i Soler, Lluís; Vilà Talleda, Pere; Coudert, David (2019-04-30)

We study the problem of path computation in Cayley Graphs (CG) from an approach of word processing in groups. This approach consists in encoding the topological structure of CG in an automaton called Diff, then techniques ...

Going Smaller: Attention-based models for automated melanoma diagnosis 

Nazari, Sana; García Campos, Rafael (2025-02)

Computational approaches offer a valuable tool to aid with the early diagnosis of melanoma by increasing both the speed and accuracy of doctors’ decisions. The latest and best-performing approaches often rely on large ...

Autonomous Underwater Vehicle Docking Under Realistic Assumptions Using Deep Reinforcement Learning 

Palomeras Rovira, Narcís; Ridao Rodríguez, Pere (2024-11-13)

This paper addresses the challenge of docking an Autonomous Underwater Vehicle (AUV) under realistic conditions. Traditional model-based controllers are often constrained by the complexity and variability of the ocean ...

RETRACTED: Nobel et al. Modern Subtype Classification and Outlier Detection Using the Attention Embedder to Transform Ovarian Cancer Diagnosis. Tomography 2024, 10, 105-132 

Nobel, S. M.Nuruzzaman; Swapno, S. M.Masfequier Rahman; Hossain, Md Ashraful; Safran, Mejdl; Alfarhood, Sultan; Kabir, Md Mohsin; Mridha, M. F. (2024-04-03)

This retracts the article "RETRACTED: Modern Subtype Classification and Outlier Detection Using the Attention Embedder to Transform Ovarian Cancer Diagnosis" on page 105

Improving brain atrophy quantification with deep learning from automated labels using tissue similarity priors 

Clèrigues Garcia, Albert; Valverde Valverde, Sergi; Oliver i Malagelada, Arnau; Lladó Bardera, Xavier (2024-09-01)

Brain atrophy measurements derived from magnetic resonance imaging (MRI) are a promising marker for the diagnosis and prognosis of neurodegenerative pathologies such as Alzheimer’s disease or multiple sclerosis. However, ...

SegX-Net: A novel image segmentation approach for contrail detection using deep learning 

Nobel, S. M.Nuruzzaman; Hossain, Md Ashraful; Kabir, Md Mohsin; Mridha, M. F.; Alfarhood, Sultan; Safran, Mejdl (2024-03-05)

Contrails are line-shaped clouds formed in the exhaust of aircraft engines that significantly contribute to global warming. This paper confidently proposes integrating advanced image segmentation techniques to identify and ...

Hematoma expansion prediction in intracerebral hemorrhage patients by using synthesized CT images in an end-to-end deep learning framework 

Yalcin, Cansu; Abramova, Valeriia; Terceño Izaga, Mikel; Oliver i Malagelada, Arnau; Silva Blas, Yolanda; Lladó Bardera, Xavier (2024-10-01)

Spontaneous intracerebral hemorrhage (ICH) is a type of stroke less prevalent than ischemic stroke but associated with high mortality rates. Hematoma expansion (HE) is an increase in the bleeding that affects 30%–38% of ...

Sparus Docking Station: A current aware docking station system for a non-holonomic AUV 

Esteba Masjuan, Joan; Cieśląk, Patryk; Palomeras Rovira, Narcís; Ridao Rodríguez, Pere (2024-09)

This paper presents the design and development of a funnel-shaped Sparus Docking Station intended for the non-holonomic torpedo-shaped Sparus II Autonomous Underwater Vehicle. The Sparus Docking Station is equipped with ...

Calibration of a Structured Light Imaging System in Two-Layer Flat Refractive Geometry for Underwater Imaging † 

Zoraja, Domagoj; Petković, Tomislav; Forest Collado, Josep; Pribanic, Tomislav (2023-06-08)

The development of a robust 3D imaging system for underwater applications is a crucial process in underwater imaging where the physical properties of the underwater environment make the implementation of such systems ...

Generation of a network slicing dataset: The foundations for AI-based B5G resource management 

Farreras Casamort, Miquel; Paillisse Vilanova, Jordi; Fàbrega i Soler, Lluís; Vilà Talleda, Pere (2024-08)

This paper presents a comprehensive network slicing dataset designed to empower artificial intelligence (AI), and data-based performance prediction applications, in 5G and beyond (B5G) networks. The dataset, generated ...

RETRACTED: Modern Subtype Classification and Outlier Detection Using the Attention Embedder to Transform Ovarian Cancer Diagnosis 

Nobel, S. M.Nuruzzaman; Swapno, S. M.Masfequier Rahman; Hossain, Md Ashraful; Safran, Mejdl; Alfarhood, Sultan; Kabir, Md Mohsin; Mridha, M. F. (2024-04-03)

This article has been retracted. See Tomography. 2024 Apr 3;10(4):520

Evaluating the effect of multiple sclerosis lesions on automatic brain structure segmentation 

González Villà, Sandra; Valverde Valverde, Sergi; Cabezas Grebol, Mariano; Pareto, Deborah; Vilanova, Joan Carles; Ramió i Torrentà, Lluís; Rovira, Àlex; Oliver i Malagelada, Arnau; Lladó Bardera, Xavier (2017)

In recent years, many automatic brain structure segmentation methods have been proposed. However, these methods are commonly tested with non-lesioned brains and the effect of lesions on their performance has not been ...

Marked annual coral bleaching resilience of an inshore patch reef in the Florida Keys: A nugget of hope, aberrance, or last man standing? 

Gintert, Brooke E.; Manzello, Derek P.; Enochs, Ian C.; Kolodziej, Graham; Carlton, Renée; Gleason, Arthur C. R.; Grácias, Nuno Ricardo Estrela (2018-06-01)

Annual coral bleaching events, which are predicted to occur as early as the next decade in the Florida Keys, are expected to cause catastrophic coral mortality. Despite this, there is little field data on how Caribbean ...

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