Artículos (Arquitectura y Tecnología de Computadores)

URI permanente para esta colecciónhttps://hdl.handle.net/11441/11292

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Neuromorphic hardware based on memristive nanodevices for seizure detection and recovery
(IOP Publishing, 2026-02-11) Díez-De-los-Ríos, Iván; Farsani, Javad; Ricci, Saverio; Bridarolli, Davide; Camuñas Mesa, Luis Alejandro; Subramaniyam, Narayan; Tanskanen, Jarno; Hyttinen, Jari; Ielmini, Daniele; Serrano Gotarredona, María Teresa; Linares Barranco, Bernabé; Arquitectura y Tecnología de Computadores; Instituto de Microelectrónica de Sevilla (IMSE-cnm)
During the last decades, neuromorphic engineers have developed specific hardware designed to build efficient computing systems inspired by the structure of the human brain. The emergence of nanoscale memristors provided these systems with a new component which can approximately emulate the behavior of synaptic connections, improving the capability to implement in-situ learning algorithms like spike-timing-dependent plasticity. Meanwhile, neuro-inspired biomimetic platforms have been developed to directly interface with biological neurons, allowing to record and process neural signals like local field potentials (LFP). Combining both technologies, it would be possible to implant intracraneal electroencephalography electrodes with a neuromorphic chip which could sense signals from epileptic tissues and provide stimulation to prevent seizures in a closed-loop setup. In this work, we use a neuromorphic hardware platform with memristors to process LFP activity generated by an artificial neural mass model (ANMM) of the hippocampal loop implemented on a microcontroller for real-time operation, showing that the memristor system can learn correlations between neurons to detect seizures and eventually prevent them. This closed-loop ANMM-memristor crossbar interaction demonstration paves the way for trying a similar setup, replacing the ANMM with biological epileptic tissues.
BAM-SLDK: biologically inspired attention mechanism with spiking learnable delayed kernel synapses
(IOP Publishing, 2025-06-13) Chacón Falcón, Mario; Patiño Saucedo, Alberto; Camuñas Mesa, Luis Alejandro; Serrano Gotarredona, María Teresa; Linares Barranco, Bernabé; Arquitectura y Tecnología de Computadores; Instituto de Microelectrónica de Sevilla (IMSE-cnm); Ministerio para la Transformación Digital y de la Función Pública; European Commission (EC); Ministerio de Ciencia, Innovación y Universidades (MICIU). España
Spiking neural networks are emerging as an alternative neural network model due to their biological plausibility, energy efficiency, and built-in ability to learn from temporal dynamics. However, in order to effectively process data with rich spatial and temporal dependencies, the usual static projections (feedforward and recurrent) among layers of spiking neurons fail to represent all the information needed. Inspired by how synaptic delays affect the learning process in biological neurons, in this paper, we propose a biologically inspired attention mechanism based on spiking convolutions with learnable delayed kernel synapses. The proposed model increases temporal learning ability, attending simultaneously to spatial and temporal dynamics with few parameters required. More precisely, our main technical contributions are: (1) we add kernels to the temporal dimension to enlarge the receptive field of the convolution; (2) we time kernels activations to mimic multiple delayed times; and (3) we introduce three different pruning techniques to optimize the number of delays and parameters used. Experiments show that our method surpasses conventional spiking convolutional modules and achieves state-of-the-art results. When pruning, we show that, for some datasets or pruning techniques, removing up to 80% of the initially trained delays results in minimal performance loss, effectively reducing memory consumption and parameters required. To the best of our knowledge, this is the first time that learnable delayed synapses have been included in spiking convolutional layers for neuromorphic datasets classification, unlocking a new biologically inspired attention mechanism and achieving superior performance on high temporal demanding tasks.
Perceived Quality of Service in Primary Health Care Based on Google Maps Reviews Before, During, and After the COVID-19 Pandemic: Sentiment Analysis
(JMIR Publications, 2025-09-23) Aunimo, Lili; Kudryavtsev, Dmitry; Kudryavtsev, Dmitry; Muñoz Saavedra, Luis; Romero Ternero, María del Carmen; Tecnología Electrónica; Arquitectura y Tecnología de Computadores; European Commission (EC). Fondo Europeo de Desarrollo Regional (FEDER); Ministerio de Ciencia e Innovación (MICIN). España; TEP108: Robótica y Tecnología de Computadores
Background: The COVID-19 pandemic caused many changes in primary health care systems in Europe. The fast adoption of telemedicine, the shift of health care resources to COVID-19–related tasks, and the tendency of patients to cancel their nonurgent appointments are some examples of these changes. Patient satisfaction is an important outcome of health care services, and the changes caused by COVID-19 in the system may have affected it. Google Maps reviews provide an important channel for patients to communicate about their experiences regarding the primary health care system. Objective: Drawing from research on social media data analytics and text mining, this study set out to investigate the changes in public sentiment regarding primary health care in Finland and Andalusia (Spain) before, during, and after the COVID-19 pandemic. Methods: We collected 55,043 Google Maps reviews from primary health care locations in Finland and Andalusia from January 1, 2013, to May 15, 2024. There are 604 primary health care locations in Finland and 1016 in Andalusia. The total number of Google Maps reviews collected was 12,247 for Finland and 42,796 for Andalusia. First, lexicon-based sentiment analysis using the open-source software AFINN was conducted for the Finnish- and Spanish-language datasets. Thereafter, transformer-based deep learning models for sentiment analysis were applied for both languages. The numeric user ratings and the results of the sentiment analysis were then analyzed. In addition, we conducted a word frequency analysis of the reviews. Results: There were important changes in the ratings and sentiment in the data for Andalusia. The ratings shifted from median 4 (IQR 3) before the COVID-19 pandemic to median 1 (IQR 2) during and median 1 (IQR 3) after the COVID-19 pandemic, on a scale from 1 to 5. The median of the sentiment values of the review texts shifted from neutral before the COVID-19 pandemic to –2 (IQR 0.055) or –1 (IQR 1) during and after the COVID-19 pandemic, depending on which sentiment analysis method was used. Interestingly, changes in numeric ratings and sentiment of the review texts in Finland were only minor, and the median values were the same during all 3 periods. Lexical analysis revealed changes in word frequencies across the periods, reflecting shifts in primary health care experiences during the pandemic, especially among the Spanish-language reviews. Conclusions: The change toward a more negative public discussion on primary health care in Andalusia during the COVID-19 pandemic was considerable. This can be observed both in the numeric user ratings and in the sentiment analysis of the review texts. However, the data for Finland show that the public discourse stayed mostly neutral or slightly positive. The findings have implications on the quality management procedures in primary health care and on the use of user-generated content as an additional information source.
Exploring Virtual Reality-Induced Anxiety in Iatrophobia: A Pilot Study for Future Exposure Therapy
(Institute of Electrical and Electronics Engineers, 2025-07-29) Revelo-Aguilar, Fabián; Miró Amarante, María Lourdes; Gómez Rodríguez, Francisco de Asís; Arquitectura y Tecnología de Computadores; Consorcio de Bibliotecas Universitarias de Andalucía (CBUA); Ministerio de Ciencia e Innovación (MICIN). España; European Commission (EC). Fondo Europeo de Desarrollo Regional (FEDER); TEP108: Robótica y Tecnología de Computadores
This pilot study explores the feasibility of using Virtual Reality (VR) to simulate medically related environments that elicit anxiety responses in individuals with iatrophobia—a specific phobia characterized by an intense fear of medical professionals and procedures. Rather than providing a therapeutic intervention, the aim is to validate the capacity of VR-based scenarios to induce physiological and emotional reactions associated with medical anxiety, thereby laying the groundwork for future therapeutic applications. Ten participants—five with self-reported iatrophobia and five without—were exposed to a series of VR scenarios simulating clinical settings, including a waiting room, a medical consultation, and a diagnostic procedure. The study was conducted at the Ecuadorian Institute of Social Security (IESS), Ambato Hospital, Ecuador. Physiological responses, including heart rate and electrodermal activity, were measured and analyzed using statistical methods. The results demonstrate significant differences between the experimental and control groups, with individuals with Iatrophobia exhibiting heightened anxiety responses in VR environments. Furthermore, correlation analysis reveals a strong positive association between heart rate and electrodermal response, indicating the reliability of these physiological indicators in assessing anxiety levels. The study also discusses the implications of these findings for phobia treatment and highlights future research directions, including the integration of advanced VR technologies and exploring VR’s applicability in treating other specific phobias and anxiety disorders. These findings support the use of VR for eliciting controlled emotional responses in medical contexts, which may inform the design of future exposure-based interventions for iatrophobia and other medical-related phobias.
Advancing Logic Circuits With Halide Perovskite Memristors for Next-Generation Digital Systems
(Wiley, 2025-08-27) Shooshtari, Mostafa; Kim, So Yeon; Pahlavan, Saeideh; Rivera Sierra, Gonzalo; Jiménez Través, Manuel; Serrano Gotarredona, María Teresa; Bisquert, Juan; Linares Barranco, Bernabé; Arquitectura y Tecnología de Computadores; European Research Council (ERC)
The potential of all-inorganic halide perovskite-based memristors as a solution to the limitations of traditional memory systems, particularly in the context of edge computing and next-generation digital architectures, is investigated. The rapid expansion of data-driven applications demands more efficient, secure, and scalable memory technologies, prompting this exploration of memristors for their unique resistance-switching properties. The research aims to address the challenges of data security and processing efficiency by integrating memristors into logic circuits, enabling both memory and logic operations within a single device. The study is structured around the experimental fabrication and characterization of Cs3Bi2I6Br3 perovskite memristors. A simple solution-processed spin coating method with antisolvent-assisted crystallization was employed to fabricate the memristor devices. The experimental characterization of memristors, including X-ray diffraction (XRD) analysis and electrical measurements, confirmed their structural integrity and memristive behavior, with distinct hysteresis loops indicative of nonvolatile memory properties. To analyze the behavior of the memristors in electronic circuits, a Verilog-A mathematical model was developed, and simulations were conducted using the Cadence Virtuoso Electronic Design Automation (EDA) suite. The Verilog-A model demonstrates strong agreement with measured results and validates the device's hysteresis behavior. Key findings demonstrate that metal halide perovskite (MHP) memristors exhibit excellent switching characteristics, repeatability, and integration potential with complementary metal-oxide-semiconductor (CMOS) technology. These properties make them suitable for implementing various logic gates, such as IMPLY, AND, and OR gates, as well as more complex digital circuits like multiplexers and full adders. The results highlight the feasibility of using these memristors for in-memory computing, where both data storage and processing occur within the memory cells, significantly enhancing computing efficiency and security. The study concludes that MHP-based memristors offer a promising path toward more compact, energy-efficient, and secure computing architectures.
Explainable Deep Learning System for Custom Report Generation in Breast Cancer Histology
(Springer, 2025-09-24) Anguita-Molina, Miguel Ángel; Civit Masot, Javier; Muñoz Saavedra, Luis; Polo-Rodríguez, Aurora; Domínguez Morales, Manuel Jesús; Arquitectura y Tecnología de Computadores; Universidad de Sevilla; TEP108: Robótica y Tecnología de Computadores
Breast cancer is the most lethal type of cancer among women, one of the causes can be due to the lack of professionals to evaluate the results of medical images in time (Sharafaddini et al. Multimed Tools Appl, 1–112 2024). This problem is even greater in developing countries. In recent years, diagnostic tools based on artificial intelligence techniques have been developed to improve diagnosis time and results. In this work, we present a system that analyzes histopathological images obtained from breast tissue biopsies to design a classification system that distinguishes between benign and malignant tissue. To demonstrate that the proposed work is robust, multiple alternatives and combinations are studied to obtain the best cases. Finally, we compare the proposed approach with previous works. Furthermore, the developed system integrates explainable artificial intelligence techniques to produce a report to the physician, including a heat map with the areas the system has determined to be essential for classification.
PaGER-Sync ADICVIDEO: Player affective gaming experience & responses - synchronized ADICVIDEO dataset
(Elsevier, 2025-12) Civit Masot, Javier; Luna Perejón, Francisco; Muñoz Saavedra, Luis; Civit Masot, Miguel; Domínguez Morales, Manuel Jesús; Miró Amarante, María Lourdes; Arquitectura y Tecnología de Computadores; Ministerio de Ciencia, Innovación y Universidades (MICIU). España; European Commission (EC). Fondo Europeo de Desarrollo Regional (FEDER); TEP108: Robótica y Tecnología de Computadores
The PaGER-Sync ADICVIDEO dataset is a multimodal, temporally synchronized repository of physiological and facial expression data recorded during controlled, immersive video game sessions designed to simulate realistic home gaming environments. It integrates biosignals from the Empatica E4 wristband —including Electrodermal Activity (EDA), Blood Volume Pulse (BVP), and Skin Temperature (TEMP) — with facial expression features extracted from video recordings using FaceReader software. Additionally, the dataset includes scores from pre-session psychometric questionnaires (Gaming Addiction Scale, Scale of Positive and Negative Experience, Emotion Regulation Questionnaire) and demographic gender data, providing psychological and individual difference context. A summary file detailing the two strongest emotions expressed by each participant with their respective percentages is included. A total of 25 participants played three commercial video games (Tetris, Sonic Racing, and Fall Guys) under controlled conditions, while their physiological responses were continuously recorded and their facial expressions captured on video for subsequent analysis. All data streams were precisely aligned using a common video-based timestamp, enabling frame-level synchronization across modalities, and the data were segmented by game. The dataset supports a wide range of research applications in affective computing, human-computer interaction, and behavioral analysis, and is particularly well-suited for the development and evaluation of multimodal affect detection models, as well as for exploring the interplay between psychological traits and real-time emotional responses.
An explainable ensemble for diabetic retinopathy grading with a novel confidence quality factor and configurable heatmaps
(Springer, 2026-02-05) Civit Masot, Javier; Luna Perejón, Francisco; Muñoz Saavedra, Luis; Rodríguez Corral, José María; Domínguez Morales, Manuel Jesús; Civit Breu, Antón; Arquitectura y Tecnología de Computadores; European Union (UE); TEP108: Robótica y Tecnología de Computadores
While current artificial intelligence (AI) tools aid in detecting diabetic retinopathy (DR), they face significant challenge that limit their clinical utility. Most are restricted to binary (referable vs. non-referable) screening and operate as “black boxes,” lacking the detailed, transparent explanations required for diagnostic confidence. This study addresses these gaps by introducing a novel, explainable ensemble-based approach for detailed DR grading. Our system utilizes a parallel ensemble of two efficient deep learning networks, EfficientNetV2 and ConvNeXt, to perform a full five-class international clinical diabetic retinopathy (ICDR) classification. The proposed model achieves state-of-the-art performance, with 96.7% accuracy and an Area Under the Curve (AUC) over 96% for all classes on a public dataset. More importantly, it provides a comprehensive diagnostic report designed to enhance clinical trust and utility. This report features multiple, configurable superimposed heatmaps, two probability-ordered diagnostic suggestions, and a novel quality factor that estimates the confidence of the prediction. By offering richer, more transparent, and interactive explanations, our system moves beyond simple screening to function as a valuable diagnostic assistance tool for ophthalmologists and other healthcare professionals.
Application of modular and sparse complex networks in enhancing connectivity patterns of liquid state machines
(Elsevier, 2025-02) Motaghian, Farideh; Nazari, Soheila; Jafari, Reza; Domínguez Morales, Juan Pedro; Arquitectura y Tecnología de Computadores; TEP108: Robótica y Tecnología de Computadores
Different neurons in biological brain systems can self-organize to create distinct neural circuits that enable a range of cognitive activities. Spiking neural networks (SNNs), which have higher biological and processing capacity than traditional neural networks, are one field of investigation for brain-like computing. A neural computational model with a recurrent network structure based on SNN is a liquid state machine (LSM). This research proposes a novel LSM structure, where the output layer comprises classification pyramid neurons, the intermediate layer is the liquid layer, and the input layer is generated from the retina model. In this research, the liquid layer is considered a modular complex network. The number of clusters in the liquid layer corresponds to the number of hidden patterns in the data, thus increasing the classification accuracy in the data. As this network is sparse, the computational time can be reduced, and the network learns faster than a fully connected network. Using this concept, we can expand the interior of the liquid layer in the LSM into some clusters rather than taking random connections into account as in other studies. Subsequently, an unsupervised Power-Spike Time Dependent Plasticity (Pow-STDP) learning technique is considered to optimize the synaptic connections between the liquid and output layers. The performance of the suggested LSM structure was very impressive compared to deep and spiking classification networks using three challenging datasets: MNIST, CIFAR-10, and CIFAR-100. Accuracy improvements over previous spiking networks were demonstrated by the accuracy of 98.1 % (6 training epochs), 95.4 % (6 training epochs), and 75.52 % (20 training epochs) that were obtained, respectively. The suggested network not only demonstrates more accuracy when compared to earlier spike-based learning techniques, but it also has a faster rate of convergence during the training phase. The benefits of the suggested network include unsupervised learning, minimal power consumption if used on neuromorphic devices, higher classification accuracy, and lower training epochs (higher training speed).
Rapid learning with phase-change memory-based in-memory computing through learning-to-learn
(Nature Research, 2025-02-01) Ortner, Thomas; Petschenig, Horst; Vasilopoulos, Athanasios; Renner, Ronald; Brglez, Špela; Limbacher, Thomas; Piñero, Enrique; Linares Barranco, Alejandro; Pantazi, Angeliki; Legenstein, Robert; Arquitectura y Tecnología de Computadores; TEP108: Robótica y Tecnología de Computadores
There is a growing demand for low-power, autonomously learning artificial intelligence (AI) systems that can be applied at the edge and rapidly adapt to the specific situation at deployment site. However, current AI models struggle in such scenarios, often requiring extensive fine-tuning, computational resources, and data. In contrast, humans can effortlessly adjust to new tasks by transferring knowledge from related ones. The concept of learning-to-learn (L2L) mimics this process and enables AI models to rapidly adapt with only little computational effort and data. In-memory computing neuromorphic hardware (NMHW) is inspired by the brain’s operating principles and mimics its physical co-location of memory and compute. In this work, we pair L2L with in-memory computing NMHW based on phase-change memory devices to build efficient AI models that can rapidly adapt to new tasks. We demonstrate the versatility of our approach in two scenarios: a convolutional neural network performing image classification and a biologically-inspired spiking neural network generating motor commands for a real robotic arm. Both models rapidly learn with few parameter updates. Deployed on the NMHW, they perform on-par with their software equivalents. Moreover, meta-training of these models can be performed in software with high-precision, alleviating the need for accurate hardware models.
Construction of a Spike-Based Memory Using Neural-Like Logic Gates Based on Spiking Neural Networks on SpiNNaker
(Institute of Electrical and Electronics Engineers, 2023) Ayuso Martínez, Álvaro; Casanueva Morato, Daniel; Domínguez Morales, Juan Pedro; Jiménez Fernández, Ángel Francisco; Jiménez Moreno, Gabriel; Arquitectura y Tecnología de Computadores; Ministerio de Ciencia e Innovación (MICIN). España; TEP108: Robótica y Tecnología de Computadores
Neuromorphic engineering concentrates the efforts of a large number of researchers due to its great potential as a field of research, in a search for the exploitation of the advantages of the biological nervous system and the brain as a whole for the design of more efficient and real-time capable applications. For the development of applications as close to biology as possible, Spiking Neural Networks (SNNs) are used, which are considered biologically-plausible and constitute the third generation of Artificial Neural Networks. This work presents a spiking implementation of a memory, which is one of the most important components in computer architecture. In the process of designing this spiking memory, different intermediate components were also implemented and tested. The tests were carried out on the SpiNNaker neuromorphic platform. This work goes into the development of spiking blocks using a logic gate approach based on previous work and includes a comparison between other works in the state of the art related to spiking memories and the one proposed here. All the implemented blocks and developed tests are available in a public repository.
A bio-inspired hardware implementation of an analog spike-based hippocampus memory model
(Elsevier, 2026) Casanueva Morato, Daniel; Ayuso Martínez, Álvaro; Indiveri, Giacomo; Domínguez Morales, Juan Pedro; Jiménez Moreno, Gabriel; Arquitectura y Tecnología de Computadores; Ministerio de Ciencia e Innovación (MICIN). España; TEP108: Robótica y Tecnología de Computadores
The need for processing at the edge of the increasing amount of data that is being produced by multitudes of sensors has led to the demand for more power-efficient computational systems, by exploring alternative computing paradigms and technologies. Neuromorphic engineering is a promising approach that can address this need by developing electronic systems that faithfully emulate the computational properties of animal brains. In particular, the hippocampus stands out as one of the most relevant brain regions for implementing auto associative memories capable of learning large amounts of information quickly and recalling it efficiently. In this work, we present a computational spike-based memory model inspired by the hippocampus that takes advantage of the features of analog electronic circuits: energy efficiency, compactness, and real-time operation. This model can learn memories, recall them from a partial fragment and forget. It has been implemented as a Spiking Neural Networks directly on a mixed-signal neuromorphic chip. We describe the details of the hardware implementation and demonstrate its operation via a series of benchmark experiments, showing how this research prototype paves the way for the development of future robust and low-power mixed-signal neuromorphic processing systems.
Spike-timing-dependent plasticity and synaptic consolidation in Hfo₂ memristors for adaptive neuromorphic computing
(IOP Publishing, 2025-11-19) Shooshtar, Mostafa; Pahlavan, Saeideh; Serrano Gotarredona, María Teresa; Linares Barranco, Bernabé; Arquitectura y Tecnología de Computadores; European Union (UE). H2020; Ministerio para la Transformación Digital y de la Función Pública. España; European Commission (EC)
In this work, we demonstrate the potential of HfO₂-based memristors as artificial synapses capable of reproducing biologically plausible spike-timing-dependent plasticity (STDP). W/HfO₂/Ti/TiN devices were fabricated and characterized, exhibiting reliable bipolar resistive switching, stable endurance, and reproducible resistance states across multiple cells and devices. The excitatory postsynaptic current (EPSC) response under sequential voltage pulses revealed gradual potentiation, depression, and saturation dynamics, closely resembling long-term potentiation, long-term depression, and synaptic consolidation in biological systems. Furthermore, the memristors successfully emulated higher-order learning rules, including triplet-STDP and frequency-dependent plasticity, while maintaining robust performance under biologically realistic noise conditions, exhibiting less than ±2% variation under voltage perturbations and ±2.5% under spike-timing jitter across 25 trials. A compact physical model captured the interplay between vacancy-driven filament dynamics and time-dependent weight modulation, yielding STDP curves consistent withexperimentalobservations in neuroscience. These findings highlight HfO₂ memristors as promising candidates for neuromorphic computing, providing not only a faithful hardware realization of synaptic learning but also compatibility with large-scale, CMOS-integrated architectures for next-generation cognitive processors.
Localizing unknown nodes with an FPGA-enhanced edge computing UAV in wireless sensor networks: Implementation and evaluation
(Elsevier Science BV, 2024) Mani, Rahma; Rios-Navarro, Antonio; Sevillano Ramos, José Luis; Liouane, Noureddine; Arquitectura y Tecnología de Computadores; TEP108: Robótica y Tecnología de Computadores
Great interest is directed toward real-time applications to determine the exact location of sensor nodes deployed in an area of interest. In this paper, we present a novel approach using a combination of the Kalman filter and regularized bounding box method for localizing unknown nodes in an area using an FPGA-enhanced edge computing UAV whose trajectory is known and is represented as the position of many anchors. The UAV is equipped with a GPS system that allows it to gather location data of sensor nodes as it moves around its environment. We employ a regularized bounding box to predict the positions of the unknown nodes using regularization factors and we use the Kalman filter algorithm to smooth and improve the accuracy of the sensor nodes to be localized. In order to localize the unknown nodes, the UAV receives the number of hops from each node and uses this information as input to the localization algorithm. Furthermore, the use of an FPGA board allows for real-time processing of sensory data, enabling the UAV to make fast and accurate decisions in dynamic environments. The localization algorithm was implemented on the FPGA board “Zynq MiniZed 7007s evaluation board” using Xilinx blocks in Simulink, and the generated code was converted into VHDL using Xilinx System Generator. The algorithm was simulated and synthesized using “Vivado” software. In fact, the proposed system was evaluated by comparing the performances achieved through two different implementations: Hardware and Software implementation. In effect, the performance of FPGA hardware implementation presents a new achievement in localization due to its easy testing and fast implementation. Our results show that this approach can efficiently locate unknown nodes with good latency and high accuracy. In fact, the execution time of the FPGA-integrated algorithm is reduced by about 60 times compared to the software implementation and the power consumption is about 100 mW, which proves the suitability of FPGA for localization in WSNs, offering a promising solution for various mobile WSN applications.
Parallelization strategies for high-performance and energy-efficient epidemic spread simulations
(Elsevier Science BV, 2025) Cagigas Muñiz, Daniel; Díaz del Río, Fernando; Sevillano Ramos, José Luis; Guisado Lizar, José Luis; Arquitectura y Tecnología de Computadores; Ministerio de Ciencia e Innovación (MICIN). España; Agencia Estatal de Investigación. España
Simulation analysis of epidemic disease spread is crucial for a proper social and governmental response. Certain susceptible–infected–recovered (SIR) models based on cellular automata (CA) have proven to be effective tools for this purpose. Despite the growing interest in these simulation models, few studies have addressed computational efficiency. Many models are not parallelized and, as a result, are computationally inefficient. Moreover, computational efficiency is often solely associated with runtime, with limited attention given to energy consumption and energy-efficient software implementations. This paper presents various parallel implementations of a successful Covid-19 cellular automaton SIR model on multiprocessors and Graphics Processing Units (GPUs), significantly improving the performance of existing codes while substantially reducing energy consumption. The performance analysis of these parallel implementations demonstrates that simulations can be reduced from hours to under a second, with energy consumption reduced by more than three orders of magnitude. Additionally, the results reveal that in cases where multiple parallel multiprocessor alternatives are available, there is not always a direct correlation between the shortest execution time and the lowest energy consumption in CA simulations. This work aims to support practitioners interested in implementing or utilizing parallel, energy-efficient SIR model simulations for future epidemic outbreaks, green computing initiatives, and efficient cellular automata simulations in general.
AI in the Health Sector: Systematic Review of Key Skills for Future Health Professionals
(JMIR Publications Inc, 2025) Gazquez-García, Javier; Sánchez-Bocanegra, Carlos Luis; Sevillano Ramos, José Luis; Arquitectura y Tecnología de Computadores; Telefonica Chair on “Intelligence in Networks” of the Universidad de Sevilla; TEP108: Robótica y Tecnología de Computadores
Background: Technological advancements have significantly reshaped health care, introducing digital solutions that enhance diagnostics and patient care. Artificial intelligence (AI) stands out, offering unprecedented capabilities in data analysis, diagnostic support, and personalized medicine. However, effectively integrating AI into health care necessitates specialized competencies among professionals, an area still in its infancy in terms of comprehensive literature and formalized training programs. Objective: This systematic review aims to consolidate the essential skills and knowledge health care professionals need to integrate AI into their clinical practice effectively, according to the published literature. Methods: We conducted a systematic review, across databases PubMed, Scopus, and Web of Science, of peer-reviewed literature that directly explored the required skills for health care professionals to integrate AI into their practice, published in English or Spanish from 2018 onward. Studies that did not refer to specific skills or training in digital health were not included, discarding those that did not directly contribute to understanding the competencies necessary to integrate AI into health care practice. Bias in the examined works was evaluated following Cochrane’s domain-based recommendations. Results: The initial database search yielded a total of 2457 articles. After deleting duplicates and screening titles and abstracts, 37 articles were selected for full-text review. Out of these, only 7 met all the inclusion criteria for this systematic review. The review identified a diverse range of skills and competencies, that we categorized into 14 key areas classified based on their frequency of appearance in the selected studies, including AI fundamentals, data analytics and management, and ethical considerations. Conclusions: Despite the broadening of search criteria to capture the evolving nature of AI in health care, the review underscores a significant gap in focused studies on the required competencies. Moreover, the review highlights the critical role of regulatory bodies such as the US Food and Drug Administration in facilitating the adoption of AI technologies by establishing trust and standardizing algorithms. Key areas were identified for developing competencies among health care professionals for the implementation of AI, including: AI fundamentals knowledge (more focused on assessing the accuracy, reliability, and validity of AI algorithms than on more technical abilities such as programming or mathematics), data analysis skills (including data acquisition, cleaning, visualization, management, and governance), and ethical and legal considerations. In an AI-enhanced health care landscape, the ability to humanize patient care through effective communication is paramount. This balance ensures that while AI streamlines tasks and potentially increases patient interaction time, health care professionals maintain a focus on compassionate care, thereby leveraging AI to enhance, rather than detract from, the patient experience.
Multi-Response Optimization of Milling Parameters of AISI D2 Steel Using Response Surface Methodology and Desirability Function
(MDPI, 2025-09-13) Hernández, Luis W.; Ahmed, Yassmin Seid; Curra Sosa, Dagnier Antonio; Pérez-Rodríguez, Roberto; Arquitectura y Tecnología de Computadores
This study investigates multi-objective optimization of end-milling parameters for AISI D2 cold-worked tool steel using GC1130-coated carbide inserts under wet machining, focusing on cutting speed and feed rate per tooth values beyond manufacturer recommendations. The objective was to identify parameter settings that minimize surface roughness while maximizing cutting tool life—two performance criteria that often conflict in practice. A full-factorial design of experiments was implemented, varying the cutting speed (220–310 m/min) and feed rate (0.06–0.25 mm/tooth). Response Surface Methodology (RSM) was used to develop predictive models, and a desirability function approach (DFA) was applied to perform multi-response optimization under three weighting schemes. The statistical models showed strong reliability, with R2 values of 81.09% for surface roughness and 95.02% for tool life. The optimal settings—220 m/min cutting speed and 0.25 mm/tooth feed—resulted in a tool life of 11.03 min and surface roughness of 0.587 µm. This yielded the highest desirability index (D = 0.8706) under tool-life-prioritized weighting, outperforming other cases by up to 10.69%. These findings offer a practical balance between quality and durability, especially for applications where tool wear is a limiting factor.
Video game player profiles among university students: Impact of game preferences and academic background
(Elsevier, 2025) García Cabrera, Emilio; Luna Perejón, Francisco; Pertegal Vega, Miguel Ángel; Muñoz Saavedra, Luis; Sevillano Ramos, José Luis; Miró Amarante, María Lourdes; Psicología Evolutiva y de la Educación; Medicina Preventiva y Salud Pública; Arquitectura y Tecnología de Computadores; Ministerio de Ciencia e Innovación (MICIN). España; Agencia Estatal de Investigación. España
Video games have become a widespread cultural and economic phenomenon, with Spain ranking among the top European countries in gaming consumption. This study examines the gaming habits and preferences of 440 university students at the University of Seville, classifying player profiles based on game preferences and academic background. A cross-sectional study was conducted using an anonymous online survey, and principal component analysis identified three distinct player profiles: Competitive, Explorer, and Socializer. Findings indicate that gaming frequency varies significantly by academic discipline, with students in technical fields playing more frequently and preferring PC gaming, while those in social and health sciences favor mobile gaming. Moreover, the Explorer profile is associated with higher gaming frequency, whereas the Socializer profile is linked to lower engagement. Contrary to common concerns, gaming time does impact academic performance, particularly when exceeding five hours per day. Findings suggest that gamification in higher education should align with students’ gaming profiles to boost engagement and learning performance. While offering useful insights, the study’s cross-sectional design and selfreported data limit its scope. Longitudinal research is needed to assess long-term academic and well-being impacts.
Deep Learning-Based Assessment of Brainstem Volume Changes in Spinocerebellar Ataxia Type 2 (SCA2): A Study on Patients and Preclinical Subjects
(MDPI, 2025-09-29) Cabeza-Ruiz, Robin; Velázquez-Pérez, Luis; González-Dalmau, Evelio; Linares Barranco, Alejandro; Pérez-Rodríguez, Roberto; Arquitectura y Tecnología de Computadores; TEP108: Robótica y Tecnología de Computadores
Spinocerebellar ataxia type 2 (SCA2) is a neurodegenerative disorder marked by progressive brainstem and cerebellar atrophy, leading to gait ataxia. Quantifying this atrophy in magnetic resonance imaging (MRI) is critical for tracking disease progression in both symptomatic patients and preclinical subjects. However, manual segmentation of brainstem subregions (mesencephalon, pons, and medulla) is time-consuming and prone to human error. This work presents an automated deep learning framework to assess brainstem atrophy in SCA2. Using T1-weighted MRI scans from patients, preclinical carriers, and healthy controls, a U-shaped convolutional neural network (CNN) was trained to segment brainstem subregions and quantify volume loss. The model achieved strong agreement with manual segmentations, significantly outperforming four U-Net-based benchmarks (mean Dice scores: whole brainstem 0.96 vs. 0.93–0.95, pons 0.96 vs. 0.91–0.94, mesencephalon 0.96 vs. 0.89–0.93, and medulla 0.95 vs. 0.91–0.93). Results revealed severe atrophy in preclinical and symptomatic cohorts, with pons volumes reduced by nearly 50% compared to controls (p < 0.001). The mesencephalon and medulla showed milder degeneration, underscoring regional vulnerability differences. This automated approach enables rapid, precise assessment of brainstem atrophy, advancing early diagnosis and monitoring in SCA2.
No-Code Edge Artificial Intelligence Frameworks Comparison Using a Multi-Sensor Predictive Maintenance Dataset
(MDPI, 2005-05-26) Montes-Sánchez, Juan Manuel; Fernández Cuevas, Plácido; Luna Perejón, Francisco; Vicente Díaz, Saturnino; Jiménez Fernández, Ángel Francisco; Arquitectura y Tecnología de Computadores; Agencia Estatal de Investigación. España
Edge Computing (EC) is one of the proposed solutions to address the problems that the industry is facing when implementing Predictive Maintenance (PdM) implementations that can benefit from Edge Artificial Intelligence (Edge AI) systems. In this work, we have compared six of the most popular no-code Edge AI frameworks in the market. The comparison considers economic cost, the number of features, usability, and performance. We used a combination of the analytic hierarchy process (AHP) and the technique for order performance by similarity to the ideal solution (TOPSIS) to compare the frameworks. We consulted ten independent experts on Edge AI, four employed in industry and the other six in academia. These experts defined the importance of each criterion by deciding the weights of TOPSIS using AHP. We performed two different classification tests on each framework platform using data from a public dataset for PdM on biomedical equipment. Magnetometer data were used for test 1, and accelerometer data were used for test 2. We obtained the F1 score, flash memory, and latency metrics. There was a high level of consensus between the worlds of academia and industry when assigning the weights. Therefore, the overall comparison ranked the analyzed frameworks similarly. NanoEdgeAIStudio ranked first when considering all weights and industry only weights, and Edge Impulse was the first option when using academia only weights. In terms of performance, there is room for improvement in most frameworks, as they did not reach the metrics of the previously developed custom Edge AI solution. We identified some limitations that should be fixed to improve the comparison method in the future, like adding weights to the feature criteria or increasing the number and variety of performance tests.

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