DIA. Artículos del Departamento de Informática y Automática

A smart pen prototype with adaptive algorithms for stabilizing handwriting tremor signals in Parkinson’s disease

2025-08-06T00:00:00Z

[EN] This study presents a smart pen prototype designed to dynamically mitigate hand tremors, thereby enhancing writing quality and user comfort for individuals with conditions such as Parkinson’s disease. The device employs an accelerometer for real-time tremor detection, a microcontroller for rapid data processing, and a vibration motor to counteract tremor effects. Adaptive algorithms—including Fx-LMS, Fx-NLMS, a combined Fx-LMS/NLMS approach, RLS, and the Kalman Filter—were evaluated using signals from the NewHandPD dataset. Simulation results revealed that although the RLS algorithm achieved the lowest mean square error, the Kalman Filter converged approximately eight times faster, a finding that was confirmed through microcontroller tests and further validated on an orbital shaking table under constant and variable tremor conditions. These outcomes underscore the potential of the Kalman Filter as a non-invasive, adaptive solution for real-time tremor mitigation in assistive writing devices. Future improvements may include integrating additional sensors and further optimizing microcontroller performance to enhance overall adaptability and accuracy. [ES] Este estudio presenta un prototipo de bolígrafo inteligente diseñado para mitigar de forma dinámica los temblores de la mano, mejorando así la calidad de la escritura y la comodidad del usuario para personas que padecen enfermedades como el párkinson. El dispositivo utiliza un acelerómetro para la detección de temblores en tiempo real, un microcontrolador para el procesamiento rápido de datos y un motor de vibración para contrarrestar los efectos de los temblores. Se evaluaron algoritmos adaptativos —entre ellos, Fx-LMS, Fx-NLMS, un enfoque combinado de Fx-LMS/NLMS, RLS y el filtro de Kalman— utilizando señales del conjunto de datos NewHandPD. Los resultados de la simulación revelaron que, aunque el algoritmo RLS alcanzó el error cuadrático medio más bajo, el filtro de Kalman convergió aproximadamente ocho veces más rápido, un hallazgo que se confirmó mediante pruebas con el microcontrolador y se validó posteriormente en una mesa de agitación orbital en condiciones de temblor constantes y variables. Estos resultados subrayan el potencial del filtro de Kalman como solución adaptativa y no invasiva para la mitigación de los temblores en tiempo real en dispositivos de asistencia a la escritura. Las mejoras futuras podrían incluir la integración de sensores adicionales y una mayor optimización del rendimiento del microcontrolador para mejorar la adaptabilidad y la precisión generales.

An analytical review of optimization techniques in information retrieval for enhanced decision support

2025-11-19T00:00:00Z

[EN] As digital content continues to evolve, enhancing Information Retrieval (IR) systems is crucial to improve their performance, relevance, and ability to handle increasingly large datasets. This systematic review examines current advancements in IR optimization strategies, with a focus on the paradigm shift from traditional heuristics to AI-driven models. Following the PRISMA 2020 guidelines, an exhaustive literature search was conducted for publications between January 2013 and June 2025, employing a hybrid screening approach that combined Natural Language Processing (NLP) automated filtering with manual expert review. Our findings underscore the growing importance of hybrid models that leverage deep learning, particularly transformer architectures, for tasks such as personalization and relevance feedback, which have demonstrated significant performance improvements. However, significant challenges such as algorithmic bias, computational complexity, and domain-specificity impede wider implementation. This review provides a comprehensive roadmap of the IR optimization landscape, identifies persistent ethical challenges, and explores emerging research frontiers, such as quantum IR and generative models, thereby offering actionable insights for both researchers and practitioners in the decision analytics field. [ES] A medida que los contenidos digitales siguen evolucionando, es fundamental mejorar los sistemas de recuperación de información (IR) para aumentar su rendimiento, su relevancia y su capacidad para gestionar conjuntos de datos cada vez más voluminosos. Esta revisión sistemática analiza los avances actuales en las estrategias de optimización de la recuperación de información, centrándose en el cambio de paradigma de las heurísticas tradicionales a los modelos basados en la inteligencia artificial. Siguiendo las directrices PRISMA 2020, se llevó a cabo una búsqueda bibliográfica exhaustiva de publicaciones entre enero de 2013 y junio de 2025, empleando un enfoque de selección híbrido que combinaba el filtrado automatizado mediante el procesamiento del lenguaje natural (PLN) con la revisión manual por parte de expertos. Nuestros resultados subrayan la creciente importancia de los modelos híbridos que aprovechan el aprendizaje profundo, en particular las arquitecturas de transformadores, para tareas como la personalización y la retroalimentación de relevancia, que han demostrado mejoras significativas en el rendimiento. Sin embargo, retos importantes como el sesgo algorítmico, la complejidad computacional y la especificidad del dominio impiden una implementación más amplia. Esta revisión ofrece una hoja de ruta exhaustiva del panorama de la optimización de la búsqueda de información, identifica los retos éticos persistentes y explora las fronteras emergentes de la investigación, como la búsqueda de información cuántica y los modelos generativos, ofreciendo así conocimientos prácticos tanto para investigadores como para profesionales del campo del análisis de la toma de decisiones.

Radar Technologies for Space Debris Detection: A Bibliometric Review

2026-08-28T00:00:00Z

The proliferation of artificial objects in Earth’s orbit has transformed space debris into one of the most critical challenges to space sustainability. Radar technologies play a pivotal role in the detection, tracking, and characterization of these non-functional objects, providing the backbone for global Space Situational Awareness (SSA) and collision avoidance systems. This study presents a comprehensive bibliometric analysis of radar-based space debris detection research published between 2005 and 2025. Using data retrieved from the Scopus and Web of Science databases, a curated dataset of 557 peer-reviewed journal articles was examined. The analysis explores publication trends, institutions, countries, and journals, as well as co-citation and keyword co-occurrence networks to map the intellectual and thematic structure of the field. Results reveal a sustained annual growth rate of 3.76%, with China and the United States leading global research output. Core motor themes—such as space-based radar, synthetic aperture radar, and remote sensing—define the technological foundation of the domain, while emerging clusters reflect growing interest in artificial intelligence and active debris removal. The study highlights a high degree of collaboration among researchers but significant geographic concentration of capabilities. By unveiling the evolution, collaboration patterns, and conceptual frontiers of radar-based space debris research, this work provides a quantitative foundation to guide future investigations and policy strategies aimed at ensuring long-term orbital sustainability.

Lightweight Skin Lesion Segmentation for Edge Deployment: A Critical Review of Architectures, Accuracy Compensation, and Clinical Translation

2026-04-09T00:00:00Z

[EN]Automated skin lesion segmentation via deep learning has achieved remarkable accuracy in controlled laboratory settings, yet its clinical translation to point-of-care (PoC) edge devices remains fundamentally constrained by the computational demands of state-of-the-art architectures. This review systematically bridges the gap between algorithmic innovation and hardware-aware deployment by presenting a critical analysis spanning three interconnected dimensions: 1) architectural evolution from convolutional neural networks (CNNs) through Vision Transformers to emerging linear-complexity State Space Models (e.g., Mamba), evaluated against a unified efficiency–accuracy Pareto framework rather than isolated accuracy rankings; 2) mathematical formalisation of lightweighting-induced precision degradation and its compensation via knowledge distillation, structural reparameterisation, and frequency-domain enhancement, with explicit assessment of evidence quality and reproducibility across studies; and 3) empirical deployment profiling across heterogeneous edge platforms including NVIDIA Jetson, Intel OpenVINO, and mobile processors. Through a multi-dimensional evaluation framework integrating spatial overlap (Dice), boundary fidelity (HD95), and on-device throughput (FPS), supplemented by a meta-analysis of dataset usage biases and validation protocol inconsistencies, we establish that heterogeneous multi-paradigm fusion architectures can preserve clinical-grade segmentation quality under severe resource constraints—provided that evaluation methodologies are standardised. Critically, our quantitative analysis of 36 reviewed studies reveals that approximately 65% of reported performance figures are derived from non-standardised data splits, rendering direct cross-study comparisons statistically unreliable. We further identify critical open challenges—including cross-domain generalisation, hardware–algorithm co-design, demographic fairness, and edge-native federated learning—an...

Unified Multi-Task Learning vs. Decoupled Transformer-based Perception: A Comparative Analysis

2026-03-02T00:00:00Z

[EN]Efficient environmental perception is a cornerstone of Advanced Driver Assistance Systems (ADAS) and autonomous driving. A persistent architectural dilemma in this domain is whether to employ unified Multi-Task Learning (MTL) frameworks, which optimize computation through shared backbones, or modular multi-model pipelines, which prioritize task-specific accuracy. This paper presents a comparative analysis of these two paradigms for joint object detection and drivable area estimation. Specifically, we evaluate YOLOPX, a representative anchor-free MTL architecture, against a decoupled multi-model system that integrates RT-DETRv2 for vehicle detection and the lightweight YOLO11n-seg for drivable area segmentation on the BDD100K benchmark under identical hardware conditions. The results show that, although the MTL YOLOPX model achieves higher throughput, the decoupled system delivers substantially better detection performance, particularly in the stricter 𝑚𝐴𝑃 50:95 metric, while preserving competitive segmentation quality and maintaining real-time latency suitable for edge deployment. These findings suggest that modular designs, rather than monolithic MTL models, can offer a more favorable balance between safety-critical detection accuracy and computational efficiency for next-generation intelligent vehicles.

Mining patient data from heterogeneous sources for decision making on administration of non invasive mechanical ventilation in intensive care units.

2014-01-01T00:00:00Z

[EN]This paper addresses the problem of decision making regarding the administration of non invasive mechanical ventilation in intensive care units. The great number of factors to take into account, its heterogeneity and diverse origin make very difficult this process. In order to facilitate this task we propose the application of data mining methods to extract knowledge from the wide and complex information available. The aim is to find out the factors influencing the success/failure of NIMV and to predict the results in future patients. These methods have not been previously applied in this field in spite of the good results obtained in other medical areas. In this work a comparative study of different algorithms has been carried out using a wide spectrum of data obtained during 6 years about 389 patients that received treatment with NIMV. The results reveal that some multiclasifiers can be useful tools for helping physicians in the choice of the best action.

Machine Learning Methods for Mortality Prediction of Polytraumatized Patients in Intensive Care Units – Dealing with Imbalanced and High-Dimensional Data

2014-01-01T00:00:00Z

[EN]The aim of this study is the prediction of death of polytraumatized patients based on epidemiological, clinical and health treatment variables by means of data-mining methods. The main problems to be addressed were high dimensionality and imbalanced data. Since the techniques usually used to deal with these drawbacks, as feature selection methods and sampling strategies respectively, did not provided satisfactory results, the aim of the study was to find out the data mining algorithms showing the best behavior in this kind of scenarios. The study was carried out with data from 497 patients diagnosed with severe trauma who were hospitalized in the Intensive Care Unit (ICU) of the University Hospital of Salamanca. The results of the study reveal the better behavior of multiclassifiers as compared with simple classifiers in contexts of high dimensionality and imbalanced datasets, without the need to resort to undersampling and oversampling strategies, which can lead to the loss of valuable data and overfitting problems respectively.

Computational study of the effect of forced exercise (swim stress) on the NADPH- diaphorase activity in the supraoptic nucleus

2002-01-01T00:00:00Z

[EN]Nitric oxide synthesizing neurons of the hypothalamus show changes following a variety of experimental and pathological conditions, such as salt loading, hypotension, cholestasis, water deprivation, and several types of stress including immobilization, exposure to low temperature and changes of environment. Recently, our group has shown that forced exercise (swim stress) is an additional stressor for the nitrergic neurons located in the paraventricular nucleus of the hypothalamus. On the other hand it is currently well-known, by means of histochemical (NADPH-diaphorase), immunohistochemical and hybridization methods, that the supraoptic nucleus contains an important nitrergic population of neurons. In the present study, the effect of forced exercise on these neurons was investigated in a computational study of the NADPH-diaphorase positive population of the supraoptic nucleus. A significant increase in the number of positive neurons was observed following forced swimming, especially after 30 and 40 min. These data indicate: 1.– The influence of stress on the NADPH-diaphorase-activity in the supraoptic nucleus. 2.– The involvement of hypothalamic nitric oxide synthesizing-neurons in the response to different types of acute stressors. 3.– The excellence of a combination of morphological and computational tecniques for the detection of changes in plasticity of the hypothalamic neurons.

Predictors of the post-stroke status in the discharge from the hospital. Importance in nursing

2023-01-01T00:00:00Z

[ES]A menudo, por parte del paciente y de la familia, se solicita a los profesionales de enfermería que predigan los factores que influyen en el estado post-ictus. Se han realizado numerosos estudios para determinar los factores que influyen en el estado neurológico post-ictus en el momento del alta hospitalaria. Sin embargo, las técnicas de aprendizaje automático no se han utilizado para este propósito. Con el objetivo de obtener reglas de asociación del pronóstico neurológico, se ha llevado a cabo un doble análisis, tanto clínico como con técnicas de aprendizaje automático, de las posibles asociaciones de factores que influyen en el estado neurológico de los pacientes post-ictus. El algoritmo Apriori detectó varias reglas de asociación con alta confianza (≥ 95%), con el siguiente patrón: En pacientes en el rango de edad de 50-80 años, la asociación de un NIHSS entre 11 y 15 puntos (NIHSS intermedio/bajo), junto con la trombectomía, conduce a la recuperación ad integrum al alta. Con la técnica de remuestreo SMOTE, se alcanzó el 100% de confianza para la asociación de NIHSS elevado (>20) y afectación de las arterias carótida y basilar, con pronóstico nefasto (exitus). Estas reglas confirman, por primera vez con aprendizaje automático, la importancia de la asociación de algunos predictores, en el pronóstico post-ictus. El conocimiento por parte de las enfermeras de estas reglas puede mejorar los resultados del ictus. Adicionalmente, el papel de la enfermería en los programas de educación sobre los factores de riesgo, y pronóstico de un ictus se torna imprescindible.; [EN]Nurses are often asked to predict factors that influence post-stroke outcome by the patient and family. Many studies have been carried out in order to determine the factors that influence the neurological status of the post-stroke patient at the moment of the discharge from the hospital. However, machine learning techniques have not been used for this purpose. Therefore, with the objective of obtaining association rules of neurological prognosis, a double analysis, both clinical and with machine learning techniques of the possible associations of factors that influence the neurological status of the post-stroke patients has been carried out. The Apriori algorithm detected several association rules with high confidence (≥ 95%), from which the following pattern: In patients in the age range of 50-80 years, the association of a NIHSS between 11 and 15 points (intermediate/low NIHSS), along with thrombectomy, leads to recovery ad integrum at discharge. With the SMOTE resampling technique, the 100% confidence was reached for the association of high NIHSS (>20) and involvement of the carotid and basilar arteries, with a dire prognosis (exitus). These rules confirm, for the first time with machine learning, the importance of the association of some predictors, in the post-stroke prognosis. The knowledge by the nurses of these association rules can successfully improve stroke outcome. In addition, the role of nurses in education programs that teach knowledge of risk factors and stroke prognosis becomes essential.

Prognostic factors associated with mortality in patients with severe trauma: From prehospital care to the Intensive Care Unit

2015-01-01T00:00:00Z

[EN]Objective To identify factors related to mortality in adult trauma patients, analyzing the clinical, epidemiological and therapeutic characteristics at the pre-hospital levels, in the Emergency Care Department and in Intensive Care. Design A retrospective, longitudinal descriptive study was carried out. Statistical analysis was performed using SPSS, MultBiplot and data mining methodology. Setting Adult multiple trauma patients admitted to the Salamanca Hospital Complex (Spain) from 2006 to 2011. Main variables of interest Demographic variables, clinical, therapeutic and analytical data from the injury site to ICU admission. Evolution from ICU admission to hospital discharge. Results A total of 497 patients with a median age of 45.5 years were included. Males predominated (76.7%). The main causes of injury were traffic accidents (56.1%), precipitation (18.4%) and falls (11%). The factors with the strongest association to increased mortality risk (p < 0.05) were age > 65 years (OR 3.15), head injuries (OR 3.1), pupillary abnormalities (OR 113.88), level of consciousness according to the Glasgow Coma Scale ≤ 8 (OR 12.97), and serum lactate levels > 4 mmol/L (OR 9.7). Conclusions The main risk factors identified in relation to the prognosis of trauma patients are referred to the presence of head injuries. Less widely known statistical techniques such as data mining or MultBiplot also underscore the importance of other factors such as lactate concentration. Trauma registries help assess the healthcare provided, with a view to adopt measures for improvement.; [ES]Objetivo Identificar los factores relacionados con la mortalidad de los pacientes adultos politraumatizados, analizar las características clínicas, epidemiológicas y terapéuticas en los niveles prehospitalario, Servicio de Urgencias y Cuidados Intensivos. Diseño Estudio retrospectivo, longitudinal y descriptivo. Análisis estadístico a través del programa SPSS, MultBiplot y la metodología de minería de datos. Ámbito Pacientes adultos politraumatizados ingresados en el Complejo Hospitalario de Salamanca entre los años 2006 y 2011. Variables de interés principales Variables demográficas, clínicas, terapéuticas y analíticas desde el lugar del accidente hasta el ingreso en la UCI. Variables evolutivas durante el ingreso en la UCI y hasta el alta hospitalaria. Resultados Se incluyó a 497 pacientes, con una mediana de edad 45,5 años. Predominio de varones (76,7%). La causa principal del traumatismo fueron los accidentes de tráfico (56,1%), precipitaciones (18,4%) y caídas (11%). Los factores con mayor asociación a un incremento del riesgo de mortalidad (p < 0,05) fueron la edad > 65 años (OR 3,15), el traumatismo craneoencefálico (OR 3,1), las alteraciones pupilares (OR 113,88), el nivel de consciencia según la escala de Glasgow ≤ 8 (OR 12,97) y las cifras de lactato > 4 mmol/L (OR 9,7). Conclusiones Los principales factores de riesgo identificados en relación con el pronóstico de los pacientes politraumatizados son los relacionados con la presencia de traumatismo craneoencefálico. Mediante la utilización de distintas técnicas estadísticas menos conocidas como la minería de datos o el MultBiplot también se destaca la importancia de otros factores como el lactato. Los registros de traumatismos ayudan a conocer la asistencia sanitaria realizada para poder establecer medidas de mejora.

An Architectural Multi-Agent System for a Pavement Monitoring System with Pothole Recognition in UAV Images

2020-01-01T00:00:00Z

[EN]In recent years, maintenance work on public transport routes has drastically decreased in many countries due to difficult economic situations. The various studies that have been conducted by groups of drivers and groups related to road safety concluded that accidents are increasing due to the poor conditions of road surfaces, even affecting the condition of vehicles through costly breakdowns. Currently, the processes of detecting any type of damage to a road are carried out manually or are based on the use of a road vehicle, which incurs a high labor cost. To solve this problem, many research centers are investigating image processing techniques to identify poor-condition road areas using deep learning algorithms. The main objective of this work is to design of a distributed platform that allows the detection of damage to transport routes using drones and to provide the results of the most important classifiers. A case study is presented using a multi-agent system based on PANGEA that coordinates the different parts of the architecture using techniques based on ubiquitous computing. The results obtained by means of the customization of the You Only Look Once (YOLO) v4 classifier are promising, reaching an accuracy of more than 95%. The images used have been published in a dataset for use by the scientific community.

Intelligent sensors in assistive systems for deaf people: a comprehensive review

2024-01-01T00:00:00Z

[EN]This research aims to conduct a systematic literature review (SLR) on intelligent sensors and the Internet of Things (IoT) in assistive devices for the deaf and hard of hearing. This study analyzes the current state and promise of intelligent sensors in improving the daily lives of those with hearing impairments, addressing the critical need for improved communication and environmental interaction. We investigate the functionality, integration, and use of sensor technologies in assistive devices, assessing their impact on autonomy and quality of life. The key findings show that many sensor-based applications, including vibration detection, ambient sound recognition, and signal processing, lead to more effective and intuitive user experiences. The study emphasizes the importance of energy efficiency, cost-effectiveness, and user-centric design in developing accessible and sustainable assistive solutions. Moreover, it discusses the challenges and future directions in scaling these technologies for widespread adoption, considering the varying needs and preferences of the end-users. Finally, the study advocates for continual innovation and interdisciplinary collaboration in advancing assistive technologies. It highlights the importance of IoT and intelligent sensors in fostering a more inclusive and empowered environment for the deaf and hard-of-hearing people. This review covers studies published between 2011 and 2024, highlighting advances in sensor technologies for assistive systems in this timeframe.

JVM optimization: An empirical analysis of JVM configurations for enhanced web application performance

2024-01-01T00:00:00Z

[EN]This research presents software for empirically analyzing Java Virtual Machine (JVM) parameter configurations to enhance web application performance. Using tools like JMeter and cAdvisor in a controlled hardware environment, it collects and analyzes performance metrics. Tailored JVM settings for high request loads improved CPU efficiency by 20% and reduced memory usage by 15% compared to standard configurations. For I/O intensive operations with large files, optimized JVM configurations decreased response times by 30% and CPU usage by 25%. These findings highlight the impact of tailored JVM settings on application responsiveness and resource management, providing valuable guidance for developers and engineers.

An analysis of the use of augmented reality and virtual reality as educational resources

2023-01-01T00:00:00Z

[EN]In recent years, the utilization of augmented reality (AR) and virtual reality (VR) has emerged as a transformative approach in education, revolutionizing traditional teaching methods. This study seeks to explore the efficacy of AR and VR as pedagogical resources for enhancing the teaching of the solar system. The research process involved the development of an application comprising two modules, AR and VR, which were evaluated to assess their impact on the teaching process. Furthermore, a comparative study was conducted to evaluate the immersiveness, interactivity, and ease of use offered by these technologies. The findings demonstrate that both AR and VR demonstrate promise in supporting the teaching process, with the VR module garnering particularly positive evaluations. However, it is crucial to acknowledge existing barriers in underprivileged communities, where public schools face limited investments in technology infrastructure. These limitations hinder the widespread implementation of such immersive experiences and their potential to foster new knowledge acquisition.

Edge Face Recognition System Based on One-Shot Augmented Learning

2022-01-01T00:00:00Z

[EN]There is growing concern among users of computer systems about how their data is handled. In this sense, IT (Information Technology) professionals are not unaware of this problem and are looking for solutions to meet the requirements and concerns of their users. During the last few years, various techniques and technologies have emerged that allow us to answer to the problem posed by users. Technologies such as edge computing and techniques such as one-shot learning and data augmentation enable progress in this regard. Thus, in this article, we propose the creation of a system that makes use of these techniques and technologies to solve the problem of face recognition and form a low-cost security system. The results obtained show that the combination of these techniques is effective in most of the face detection algorithms and allows an effective solution to the problem raised.

Active Actions in the Extraction of Urban Objects for Information Quality and Knowledge Recommendation with Machine Learning

2022-12-23T00:00:00Z

[EN]Due to the increasing urban development, it has become important for municipalities to permanently understand land use and ecological processes, and make cities smart and sustainable by implementing technological tools for land monitoring. An important problem is the absence of technologies that certify the quality of information for the creation of strategies. In this context, expressive volumes of data are used, requiring great effort to understand their structures, and then access information with the desired quality. This study are designed to provide an initial response to the need for mapping zones in the city of Itajaí (SC), Brazil. The solution proposes to aid object recognition employing object-based classifiers OneR, NaiveBayes, J48, IBk, and Hoeffding Tree algorithms used together with GeoDMA, and a first approach in the use of Region-based Convolutional Neural Network (R-CNN) and the YOLO algorithm. All this is to characterize vegetation zones, exposed soil zones, asphalt, and buildings within an urban and rural area. Through the implemented model for active identification of geospatial objects with similarity levels, it was possible to apply the data crossover after detecting the best classifier with accuracy (85%) and the kappa agreement coefficient (76%). The case study presents the dynamics of urban and rural expansion, where expressive volumes of data are obtained and submitted to different methods of cataloging and preparation to subsidize rapid control actions. Finally, the research describes a practical and systematic approach, evaluating the extraction of information to the recommendation of knowledge with greater scientific relevance. Allowing the methods presented to apply the calibration of values for each object, to achieve results with greater accuracy, which is proposed to help improve conservation and management decisions related to the zones within the city, leaving as a legacy the construction of a minimum technological infrastructure to support the decision.