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Dual-Band Reflectarray-Based Electromagnetic Skin to Provide Millimeter-Wave Coverage in the 28/60-GHz Bands

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Dual-Band Reflectarray-Based Electromagnetic Skin to Provide...

European Conference on Antennas and Propagation, EuCAP

作者： Jorge Vallejo Eduardo Martinez-de-Rioja Ana Arboleya Department of Signal Theory and Communications and Telematic Systems and Computing Universidad Rey Juan Carlos Madrid Spain

In this contribution, a low-profile dual-band smart electromagnetic skin (SES) is proposed to improve wireless communications in millimeter-wave 5G. The SES is based on a single-layer reflectarray panel that can operate simultaneously at the 28 and 60 GHz bands. The reflectarray unit-cell consists of a central lower frequency element which is surrounded by four higher frequency elements. The SES has been designed to provide a different coverage for each frequency (i.e., two separate beams with different pointing directions). A beamforming technique has been applied to broaden the beams in azimuth and achieve a wider coverage. The proposed device can be used to enable wireless communications in millimeter-wave 5G by providing enhanced coverage in nonline-of-sight (NLOS) scenarios.

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Towards the early detection of speech disorders in children applying manifold learning techniques: preliminary results

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Towards the early detection of speech disorders in children ...

International Conference on Technological Innovation and AI Research (ICTIAIR 2025)

作者： Malena Loza Felipe Grijalva Francisco Manuel Melgarejo-Meseguer José Luis Rojo-Álvarez Diego S. Benítez Universidad San Francisco de Quito USFQ Colegio de Cienciase Ingeniería Quito Ecuador Universidad Rey Juan Carlos Department of Signal Theory and Communications Telematics and Computing Systems Madrid Spain

ISBN: (数字)9781837243235

Speech disorders in children can significantly hinder their social interaction, academic performance, and overall development. Early detection is crucial for effective intervention, yet current diagnostic methods often overlook the potential of video-based analysis. This study explores the application of Principal Component Analysis (PCA) and Autoencoder (AE) techniques to analyse video data, focussing on facial movements during speech to facilitate the clustering of children with distinctive speech features. Using a database of 60 video recordings, embeddings were generated and evaluated on the basis of their ability to reconstruct the original data, the results visualised through t-SNE. PCA demonstrated superior performance with a Mean Squared Error (MSE) of 0.0023 for 78 dimensions, while AE achieved its lowest MSE of 0.0093 with 15 dimensions. In particular, embeddings with lower MSE showed better clustering tendencies. This study highlights the potential of integrating video-based analysis into machine learning frameworks to improve the accuracy and depth of speech disorder diagnostics.

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Irregular Temporal Classification of Multidrug Resistance Development in Intensive Care Unit Patients

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Irregular Temporal Classification of Multidrug Resistance De...

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Paula Martín-Palomeque Óscar Escudero-Arnanz Joaquín Álvarez-Rodríguez Cristina Soguero-Ruiz Dept. of Signal Theory and Communications Telematics and Computing Systems Rey Juan Carlos University Fuenlabrada Madrid Spain Intensive Care Department University Hospital of Fuenlabrada Fuenlabrada Madrid Spain

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

The rising incidence of Multidrug-Resistant (MDR) infections in Intensive Care Units (ICUs) presents a critical challenge to global healthcare, leading to adverse patient outcomes and increased costs. Early identification of MDR pathogens is crucial for timely implementation of isolation protocols and prevention of cross-infections. This study introduces a novel Deep Learning (DL) framework for temporal prediction of MDR status in ICU patients, addressing the complexities of irregular classification tasks. We utilize Electronic Health Records from 3,502 anonymized ICU patient records spanning 17 years at the University Hospital of Fuenlabrada, Madrid, Spain. Five advanced DL architectures are systematically evaluated: Vanilla Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Bidirectional LSTM, Gated Recurrent Unit, and Transformer, each adapted for irregular Multivariate Time Series (MTS) data. Notably, this is the first study to assess the impact of temporal window size on predictive performance and to adapt different RNN models for irregular temporal classification in MTS. Our results show that the LSTM model, when applied to a 4-day temporal window, achieves the highest predictive performance, with a mean Receiver Operating Characteristic Area Under the Curve of 79.78 ± 2.78. This study highlights the importance of early temporal windows in capturing critical indicators of MDR infections and significantly advances the state-of-the-art in MDR prediction, providing a robust foundation for more effective ICU management and potential reduction of MDR infections in healthcare settings.

关键词： Adaptation models Pathogens Protocols Prevention and mitigation Time series analysis Receivers Predictive models Logic gates Transformers Immune system

Multimodal Interpretable Data-Driven Models for Early Prediction of Antimicrobial Multidrug Resistance Using Multivariate Time-Series

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arXiv 2024年

作者： Martínez-Agüero, Sergio Marques, Antonio G. Mora-Jiménez, Inmaculada Alvárez-Rodríguez, Joaquín Soguero-Ruiz, Cristina Department of Signal Theory and Communications Telematics and Computing Systems Rey Juan Carlos University Madrid Fuenlabrada28942 Spain Intensive Care Department University Hospital of Fuenlabrada Madrid Fuenlabrada28942 Spain

Electronic health records (EHR) is an inherently multimodal register of the patient’s health status characterized by static data and multivariate time series (MTS). While MTS are a valuable tool for clinical prediction, their fusion with other data modalities can possibly result in more thorough insights and more accurate results. Deep neural networks (DNNs) have emerged as fundamental tools for identifying and defining underlying patterns in the healthcare domain. However, fundamental improvements in interpretability are needed for DNN models to be widely used in the clinical setting. In this study, we present an approach built on a collection of interpretable multimodal data-driven models that may anticipate and understand the emergence of antimicrobial multidrug resistance (AMR) germs in the intensive care unit (ICU) of the University Hospital of Fuenlabrada (Madrid, Spain). The profile and initial health status of the patient are modeled using static variables, while the evolution of the patient’s health status during the ICU stay is modeled using several MTS, including mechanical ventilation and antibiotics intake. The multimodal DNNs models proposed in this paper include interpretable principles in addition to being effective at predicting AMR and providing an explainable prediction support system for AMR in the ICU. Furthermore, our proposed methodology based on multimodal models and interpretability schemes can be leveraged in additional clinical problems dealing with EHR data, broadening the impact and applicability of our results. © 2024, CC BY.

关键词： Intensive care units

LM-IGTD: a 2D image generator for low-dimensional and mixed-type tabular data to leverage the potential of convolutional neural networks

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arXiv 2024年

作者： Gómez-Martínez, Vanesa Lara-Abelenda, Francisco J. Peiro-Corbacho, Pablo Chushig-Muzo, David Granja, Conceição Soguero-Ruiz, Cristina Department of Signal Theory and Communications Telematics and Computing Systems Rey Juan Carlos University Madrid28943 Spain Norwegian Centre for E-health Research University Hospital of North Norway Tromsø9008 Norway

Tabular data have been extensively used in different knowledge domains. Convolutional neural networks (CNNs) have been successfully used in many applications where important information about data is embedded in the order of features (images), outperforming predictive results of traditional models. Recently, several researchers have proposed transforming tabular data into images to leverage the potential of CNNs and obtain high results in predictive tasks such as classification and regression. In this paper, we present a novel and effective approach for transforming tabular data into images, addressing the inherent limitations associated with low-dimensional and mixed-type datasets. Our method, named Low Mixed-Image Generator for Tabular Data (LM-IGTD), integrates a stochastic feature generation process and a modified version of the IGTD. We introduce an automatic and interpretable end-to-end pipeline, enabling the creation of images from tabular data. A mapping between original features and the generated images is established, and post hoc interpretability methods are employed to identify crucial areas of these images, enhancing interpretability for predictive tasks. An extensive evaluation of the tabular-to-image generation approach proposed on 12 low-dimensional and mixed-type datasets, including binary and multi-class classification scenarios. In particular, our method outperformed all traditional ML models trained on tabular data in five out of twelve datasets when using images generated with LM-IGTD and CNN. In the remaining datasets, LM-IGTD images and CNN consistently surpassed three out of four traditional ML models, achieving similar results to the fourth model. © 2024, CC BY.

关键词： Image classification

On Speech Pre-emphasis as a Simple and Inexpensive Method to Boost Speech Enhancement

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arXiv 2024年

作者： López-Espejo, Iván Joglekar, Aditya Peinado, Antonio M. Jensen, Jesper Department of Signal Theory Telematics and Communications University of Granada Spain The University of Texas Dallas United States Oticon A/S Denmark Department of Electronic Systems Aalborg University Denmark

Pre-emphasis filtering, compensating for the natural energy decay of speech at higher frequencies, has been considered as a common pre-processing step in a number of speech processing tasks over the years. In this work, we demonstrate, for the first time, that pre-emphasis filtering may also be used as a simple and computationally-inexpensive way to leverage deep neural network-based speech enhancement performance. Particularly, we look into pre-emphasizing the estimated and actual clean speech prior to loss calculation so that different speech frequency components better mirror their perceptual importance during the training phase. Experimental results on a noisy version of the TIMIT dataset show that integrating the pre-emphasis-based methodology at hand yields relative estimated speech quality improvements of up to 4.6% and 3.4% for noise types seen and unseen, respectively, during the training phase. Similar to the case of pre-emphasis being considered as a default pre-processing step in classical automatic speech recognition and speech coding systems, the pre-emphasis-based methodology analyzed in this article may potentially become a default add-on for modern speech enhancement. Copyright © 2024, The Authors. All rights reserved.

关键词： Speech enhancement

Explainable Artificial Intelligence Techniques for Irregular Temporal Classification of Multidrug Resistance Acquisition in Intensive Care Unit Patients

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arXiv 2024年

作者： Escudero-Arnanz, Óscar Soguero-Ruiz, Cristina Álvarez-Rodríguez, Joaquín Marques, Antonio G. Department of Signal Theory and Communications Telematics and Computing Systems Rey Juan Carlos University Camino del Molino 5 Fuenlabrada Madrid28942 Spain University Hospital of Fuenlabrada Camino del Molino 2 Fuenlabrada Madrid28942 Spain

Antimicrobial Resistance represents a significant challenge in the Intensive Care Unit (ICU), where patients are at heightened risk of Multidrug-Resistant (MDR) infections—pathogens resistant to multiple antimicrobial agents. This study introduces a novel methodology that integrates Gated Recurrent Units (GRUs) with advanced intrinsic and post-hoc interpretability techniques for detecting the onset of MDR in patients across time. Within interpretability methods, we propose Explainable Artificial Intelligence (XAI) approaches to handle irregular Multivariate Time Series (MTS), introducing Irregular Time Shapley Additive Explanations (IT-SHAP), a modification of Shapley Additive Explanations designed for irregular MTS with Recurrent Neural Networks focused on temporal outputs. Our methodology aims to identify specific risk factors associated with MDR in ICU patients. GRU with Hadamard’s attention demonstrated high initial specificity and increasing sensitivity over time, correlating with increased nosocomial infection risks during prolonged ICU stays. XAI analysis, enhanced by Hadamard attention and IT-SHAP, identified critical factors such as previous non-resistant cultures, specific antibiotic usage patterns, and hospital environment dynamics. These insights suggest that early detection of at-risk patients can inform interventions such as preventive isolation and customized treatments, significantly improving clinical outcomes. The proposed GRU model for temporal classification achieved an average Receiver Operating Characteristic Area Under the Curve of 78.27 ± 1.26 over time, indicating strong predictive performance. In summary, this study highlights the clinical utility of our methodology, which combines predictive accuracy with interpretability, thereby facilitating more effective healthcare interventions by professionals. © 2024, CC BY.

关键词： Intensive care units

Dual-Band Metasurface-Based Radome for Millimeter-Wave 5G Applications

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Dual-Band Metasurface-Based Radome for Millimeter-Wave 5G Ap...

European Conference on Antennas and Propagation, EuCAP

作者： Roman Soroka Ana Arboleya Eduardo Martinez-De-Rioja M. E. De Cos Gómez Department of Signal Theory and Communications and Telematic Systems and Computing Universidad Rey Juan Carlos Madrid Spain Electrical Engineering Department TSC Universidad de Oviedo Gijón Spain

ISBN: (数字)9788831299107

ISBN: (纸本)9798350366327

This contribution presents the design of a dual-band metasurface-based radome (metaradome, MTR) for antenna protection applications in millimeter-wave 5G. The proposed MTR has two transmission bands at 28 GHz and 39 GHz. A parametric study has been carried out to characterize the frequency response of the MTR. Then, the designed MTR has been combined with a reflectarray-based Smart Electromagnetic Skin (SES) to analyze the impact of the MTR on the radiation performance of the SES: first, the MTR unit-cell has been combined with the SES unit-cell to analyze its performance; then, the complete SES has been loaded with the MTR and the radiation patterns of the structure have been computed. The simulated results confirm the potential of the proposed MTR to protect the SES without degrading its performance and allowing for a much more compact design than a conventional half-wavelength radome.

关键词： 5G mobile communication Parametric study Dual band Millimeter wave propagation Metasurfaces Skin Frequency response Radomes Protection Electromagnetics

Design of Multiband Large Reflectarray Antennas for Space Applications using Support Vector Machines

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Design of Multiband Large Reflectarray Antennas for Space Ap...

IEEE-APS Conference on Antennas and Propagation for Wireless communications

作者： Borja Imaz-Lueje Daniel Martinez-de-Rioja Álvaro F. Vaquero Jesús A. López-Fernández Marcos R. Pino Manuel Arrebola Department of Signal Theory and Communications and Telematic Systems and Computing Universidad Rey Juan Carlos Fuenlabrada Spain Information Processing and Telecommunications Center Universidad Politécnica de Madrid Madrid Spain Signal Theory and Communications Group Universidad de Oviedo Gijón Spain

ISBN: (数字)9798350360776

ISBN: (纸本)9798350360783

Modern satellite communications rely on high-gain antennas on board the satellite to establish deep-space communications, provide high-throughput links, or perform high-resolution radiometry. Conventional reflector antennas provide high gain beams, although their large aperture and doubly curved surface make them difficult to manufacture, mount on the satellite, and fold and deploy after the satellite launch.

关键词： Wireless communication Support vector machines Satellite antennas Satellite broadcasting Reflector antennas Radiometry Antennas and propagation Aperture antennas Deep-space communications