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Transient states to control the electromagnetic response of space-time dispersive media

Physical Review B 2024年第23期110卷 235125-235125页

作者： Pablo H. Zapata-Cano Salvador Moreno-Rodríguez Stamatios Amanatiadis Antonio Alex-Amor Zaharias D. Zaharis Carlos Molero School of Electrical and Computer Engineering Aristotle University of Thessaloniki 54124 Thessaloniki Greece Department of Signal Theory Telematics and Communications Research Centre for Information and Communication Technologies (CITIC-UGR) Universidad de Granada 18071 Granada Spain Department of Electrical and Systems Engineering University of Pennsylvania Philadelphia Pennsylvania 19104 USA

In this paper, we study the dynamic formation of transients when plane waves impinge on a dispersive slab that abruptly changes its electrical properties in time. The time-varying slab alternates between air and metal-like states, whose frequency dispersion is described by the Drude model. It is shown how the physics of this complex system can be well described with the joint combination of two terms: one associated with temporal refractions and the other associated with spatial refractions. To test the validity of the approach, some analytical results are compared with a self-implemented finite-difference time-domain method. Results show how the transients that occur after the abrupt temporal changes can shape the overall steady-state response of the space-time system. In fact, far from always being detrimental, these transient states can be conveniently used to perform frequency conversion or to amplify or attenuate the electromagnetic fields.

关键词： Electromagnetic transients

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Personalized Glucose Forecasting for People with Type 1 Diabetes Using Large Language Models

SSRN

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SSRN 2024年

作者： Lara-Abelenda, Francisco Jesus Chushig-Muzo, David Peiro-Corbacho, Pablo Wägner, Ana M. Soguero-Ruiz, Cristina Department of Signal Theory and Communications Telematics and Computing Systems Rey Juan Carlos University Madrid Spain Instituto Universitario de Investigaciones Biomédicas y Sanitarias Universidad de Las Palmas de Gran Canaria Las Palmas de Gran Canaria Spain

Type 1 diabetes (T1D) is an autoimmune disease that requires exogenous insulin via multiple daily injections (MDIs) or subcutaneous pumps to maintain targeted glucose levels. Despite the advances in continuous glucose monitoring (CGM), controlling glucose remains challenging. Large language model (LLM)-based models have produced impressive results in text processing, but their performance with diverse data modalities in real-world scenarios remains unexplored. The aim of this study is three-fold. First, to evaluate the effectiveness of LLM-based models for forecasting glucose using CGM data. Second, to compare the performance of LLMs and state-of-the-art models for predicting glucose in T1D individuals treated with MDIs and pumps. Lastly, to create a personalized approach based on patient-specific training and adaptive model selection. We considered three prediction horizons (PHs), including 60, 90 and 120 minutes and evaluated the performance using mean squared error (MAE) and root mean squared error (RMSE). For short-term PHs (60 and 90 minutes), the personalized approach achieved the best results, with an average MAE of 15.7 and 20.2 for MDIs, and a MAE of 15.2 and 17.2 for pumps. For long-term PH (120 minutes), TIDE obtained an MAE of 19.8 for MDIs, whereas Patch-TST obtained a MAE of 18.5. LLM-based models provided similar MAE values to novel ANN-based models but presented a reduced variability. Our work contributes to developing personalized glucose prediction models for enhancing glycemic control, thus reducing diabetes-related complications. © 2024, The Authors. All rights reserved.

关键词： Disease control

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Tri-Band Endfire Antenna Array with Wide Angles of Beam-Scanning Capability for 5G mmWave Mobile Communication

Tri-Band Endfire Antenna Array with Wide Angles of Beam-Scan...

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2023 IEEE International Workshop on Technologies for Defense and Security, TechDefense 2023

作者： Zidour, Ali Ayad, Mouloud Alibakhshikenari, Mohammad Basherlou, Haleh Jahanbakhsh Parchin, Naser Ojaroudi See, Chan Hwang University of m'Hamed Bougara Faculty of Technology Department of Electrical Systems Engineering Boumerdes Algeria University of Bouira Faculty of Sciences and Applied Sciences Department of Electrical Systems Bouira Algeria Universidad Carlos Iii de Madrid Leganés Department of Signal Theory and Communications Madrid Spain Edinburgh Napier University School of Computing Engineering and the Built Environment Edinburgh United Kingdom

ISBN: (纸本)9798350319392

In this paper, a novel multiband phased array antenna with endfire radiation pattern is designed at millimeter-wave (mmWave) frequency bands for 5G mobile applications. The proposed antenna element is a compact quasi-Yagi with a modified dipole driver to generate multiband operation, arranged on low-loss RO4003C materiel. The antenna array achieves excellent performance and covers wide frequency ranges of (24.15-29.8 GHz), (36.2-50 GHz) to support 5G NR bands;26, 28, 39 and the new allocated 48 GHz. The array can exhibit a maximum realized gain from 8.4 dBi to 11.8 dBi. Meanwhile, wide beam-scanning angles from - 68° to 65° at 28 GHz, from 40° to 41° at 39 GHz, and from - 33° to 35° at 48 GHz are realized. © 2023 IEEE.

关键词： Millimeter waves

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Radio Sensing with Large Intelligent Surface for 6G

Radio Sensing with Large Intelligent Surface for 6G

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International Conference on Acoustics, Speech, and signal Processing (ICASSP)

作者： Cristian J. Vaca-Rubio Pablo Ramirez-Espinosa Kimmo Kansanen Zheng-Hua Tan Elisabeth de Carvalho Department of Electronic Systems Aalborg University Aalborg Denmark Departament of Signal Theory Telematics and Communications University of Granada Granada Spain Norwegian University of Science and Technology Trondheim Norway

This paper leverages the potential of Large Intelligent Surfaces (LIS) for radio sensing in 6G wireless networks. By taking advantage of arbitrary communication signals occurring in the scenario, we apply direct processing to the output signal from the LIS to obtain a radio map that describes the physical presence of passive devices (scatterers, humans) which act as virtual sources due to the communication signal reflections. We then assess the usage of machine learning and computer vision methods including clustering, template matching and component labeling to extract meaningful information from these radio maps. As an exemplary use case, we evaluate this method for passive multi-human detection in an indoor setting. The results show that the presented method has high application potential as we are able to detect around 98% of humans passively even in quite unfavorable signal-to-Noise Ratio (SNR) conditions.

关键词： 6G mobile communication Computer vision Wireless networks signal processing algorithms Machine learning signal processing Sensors

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Low-Rank Tensor Completion for Heart Failure Exacerbation Detection in Multivariate Time Series with Missing Data

Low-Rank Tensor Completion for Heart Failure Exacerbation De...

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Annual IEEE Symposium on Computer-Based Medical systems

作者： Óscar Escudero-Arnanz Rosa Sicilia Cristina Soguero-Ruiz Inmaculada Mora-Jiménez Diana Lelli Claudio Pedone Antonio G. Marques Dept. Signal Theory & Communications Telematics & Computing Systems Rey Juan Carlos University Fuenlabrada Spain Department of Engineering Unit of Computer Systems & Bioinformatics Università Campus Bio-Medico di Roma Italy Operative Research Unit of Geriatrics Fondazione Policlinico Universitario Campus Bio-Medico Italy

ISBN: (数字)9798350384727

ISBN: (纸本)9798350384734

Heart failure exacerbations (HFE) represent a critical challenge in healthcare due to their significant role in global mortality. The rise of home and wearable devices capable of monitoring cardiac conditions provides valuable opportunities for data-driven analysis and HFE detection. However, these devices frequently generate low-quality measurements with irregular sampling frequencies and high rates of missing data. Our paper presents a methodology that processes these measurements as a three-dimensional tensor, applying a low-rank tensor completion scheme to manage missing data effectively, thus facilitating anomaly detection without necessitating data imputation. We validate our method on a dataset from 4 patients with chronic HF in the compensation phase, collected at the Hospital Fondazione Policlinico Universitario Campus Bio-Medico in Rome, Italy. Our results demonstrate the tensor-based method’s superiority over traditional techniques, highlighting its potential for detecting anomalies within complex multivariate time series data. This research emphasizes the critical role of advanced data analysis in enhancing HFE identification, which could lead to improved patient care and reduced hospitalization rates.

关键词： Tensors Data analysis Hospitals Time series analysis Imputation Data models Frequency measurement

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Reflectarray-based Intelligent Reflecting Surface to Improve mm-Wave 5G coverage in outdoor scenarios

Reflectarray-based Intelligent Reflecting Surface to Improve...

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2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022

作者： Vaquero, Alvaro F. Martinez-De-Rioja, Eduardo Arrebola, Manuel Carrasco, Eduardo Encinar, José A. Salman, Safa Achour, Maha Universidad de Oviedo Group of Signal Theory and Communications Department of Electrical Engineering Gijón33203 Spain Universidad Rey Juan Carlos Department of Signal Theory and Communications and Telematic Systems and Computing Fuenlabrada28942 Spain Universidad Politécnica de Madrid Information Processing and Telecommunications Center Madrid28040 Spain Metawave Corporation Palo AltoCA94303 United States

ISBN: (数字)9781665496582

ISBN: (纸本)9781665496582

This work presents the use of a reflectarray panel as an efficient and low-cost solution to improve millimeter-wave coverage in 5G scenarios. A dual-polarized shaped-beam is designed to radiate a flat-top beam to cover a blind zone without coverage. The design is carried out using a phase-only synthesis considering the beam specifications for the outdoor scenario. Then, a single-layer reflectarray element is used to perform a design at 27.7 GHz. This element provides an angularly-stable phase response at the operational frequency with an independent phase control in each linear polarization. The reflectarray panel shows a stable gain and a low both side-lobe level and cross-polarization within 1 GHz bandwidth. © 2022 IEEE.

关键词： Millimeter waves

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Polarizing Reflectarray Antenna in K and Ka Bands for Small Satellites

Polarizing Reflectarray Antenna in K and Ka Bands for Small ...

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Antennas and Propagation & USNC/URSI National Radio Science Meeting

作者： Juan Ruiz-Casado Daniel Martinez-De-Rioja Eduardo Martinez-De-Rioja Jose A. Encinar Information Processing and Telecommunications Center Universidad Politécnica de Madrid Madrid Spain Department of Signal Theory and Communications and Telematic Systems and Computing Universidad Rey Juan Carlos Fuenlabrada Spain

ISBN: (数字)9798350369908

ISBN: (纸本)9798350369915

This contribution describes a dual-band polarizing reflectarray antenna for small-satellite communications. The proposed antenna has been designed to generate a high-gain beam while converting the incident dual-linearly polarized field into dual-circular polarization in K and Ka bands. The unit-cell, based on orthogonal groups of dipoles, comprises two layers of metallization, where each layer is designed to independently operate at a different frequency. The polarization conversion process has been generalized to operate with non-stacked dual-polarization elements for each frequency, which allows to use a single layer per operating frequency. The simulated results show a maximum gain larger than 30 dBi and an axial ratio below 1.5 dB at both design frequencies (19.7 and 29.5 GHz).

关键词： Satellite antennas Dipole antennas Metallization Conferences Dual band Small satellites Reflector antennas

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Orthogonal projection for anomaly detection in networking datasets

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Journal of Ambient Intelligence and Humanized computing 2023年第6期14卷 7957-7966页

作者： Cortes-Polo, David Jimenez, Luis I. Paoletti, Mercedes E. Calle-Cancho, Jesus Rico-Gallego, Juan A. Department of Signal Theory and Communication University Rey Juan Carlos Cam. del Molino 5 Madrid Fuenlabrada28942 Spain Intelligent Systems Group Department of Computer Science University of Valladolid P.O de Belén 15 Valladolid47011 Spain Hyperspectral Computing Laboratory Department of Technology of Computers and Communication University of Extremadura Av. de la Universidad s/n Caceres10003 Spain Calle Sola 1 Extremadura Trujillo10200 Spain Computer and Telematics Systems Engineering University of Extremadura Av. de la Universidad s/n Caceres10003 Spain

In recent years, the impressive growth of new wireless technologies, together with the appearance of new requirements in applications and services, is progressively changing the use of networks. Due to the high mobility required, the network must adapt to the infrastructure to meet the demands of the users. As a result, service providers currently have to over-provision network capacity, which is costly. In addition, considering efficient resource planning in advance involves a lot of labor-intensive efforts. Consequently, network usage analysis is a very useful tool that allows network administrators to find patterns and anomalies. Whilst pattern detection provides administrators the ability to define the infrastructure, anomaly detection provides rich and valuable information for certain applications, for example, to avoid network saturation in urban areas during peak hours. This article proposes a new methodology based on orthogonal projections over Call Detail Records (CDR) for anomaly detection to help in the dynamic management of the network in an urban area. The method is evaluated in a real scenario provided by an Italian telecommunications operator, considering different locations in the Milan metropolitan area, differentiated by the geographic resolution of the data, reaching F1 scores above 0.8. In addition, a new ground truth is presented, hoping it will become a reference data set for the community, in the form of a set of locations that have been corroborated for use in evaluating anomaly detection techniques. © 2023, The Author(s).

关键词： Anomaly detection

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Single Reference Segmentation to Estimate T-Wave Alternans

Single Reference Segmentation to Estimate T-Wave Alternans

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Computers in Cardiology (CinC)

作者： E Sánchez-Carballo F M Melgarejo-Meseguer J L Rojo-Álvarez A García-Alberola Y Rudy Department of Signal Theory and Communications Telematics and Computing Systems Universidad Rey Juan Carlos Madrid Spain Arrhythmia Unit Hospital Universitario Virgen de la Arrixaca Murcia Spain Cardiac Bioelectricity and Arrhythmia Center Washington University in St. Louis USA

T-Wave Alternans (TWA) are an indicator of sudden cardiac death risk, and they measure the differences between consecutive T-waves in Electrocardiogram (ECG) studies. In this work, we used ECG Imaging (ECGI) data to scrutinize the spatial distribution of alternans along the torso and epicardium. ECGI signals were obtained from the torso of eight patients suffering from Long QT syndrome and from three control subjects, and epicardial signals were estimated through the inverse problem of ECG. We introduced a new method for segmenting T-waves, called the Single Reference Segmentation (SRS), and we analyzed its performance by estimating TWA and comparing it to the atomic segmentation method, consisting of performing the alignment of each signal in isolation. Our results showed that SRS improves the quality of TWA estimations, which could provide clinically useful representations of the spatial-temporal distribution of TWA using ECGI data.

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Unsupervised Accuracy Estimation for Brain-Computer Interfaces based on Auditory Attention Decoding

TechRxiv

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TechRxiv 2023年

作者： Lopez-Gordo, Miguel A. Geirnaert, Simon Bertrand, Alexander The University of Granada Department of Signal Theory Telematics and Communications Spain Spain STADIUS Center for Dynamical Systems Signal Processing and Data Analytics Belgium Leuven.AI – KU Leuven Institute for AI Belgium KU Leuven Department of Neurosciences Research Group ExpORL Belgium

Auditory attention decoding (AAD) algorithms process brain data such as electroencephalography (EEG) in order to decode to which of multiple competing sound sources a person attends. Example use cases are neuro-steered hearing aids or brain-computer interfaces (BCI) for patients with severe motor or cognitive impairments. Recently, it has been shown that it is possible to train such AAD decoders in an unsupervised setting, where there is no ground truth available regarding which of the sound sources is attended. However, in the absence of such ground-truth labels, it is difficult to quantify the accuracy of the decoders, in particular when working with patients who can neither communicate nor show any evidence of collaboration. In this paper, we use principles of digital communications to estimate the AAD accuracy during a competing talker listening task in a completely unsupervised manner. We show that our proposed unsupervised estimation technique can accurately determine the accuracy of the decoder in a transparent-for-the-user way, for different amounts of training data, decision window lengths, and amounts of estimation data. Furthermore, since different applications demand different targeted accuracies, our approach can estimate the required minimal amount of training required for any given target accuracy. In neuro-steered hearing aids, our approach could support time-adaptive decoding, dynamic gain control, and neurofeedback training. In BCIs, it could support a robust communication paradigm for caregivers and professionals to reach and gain insights into people who cannot communicate by any means. © 2023, CC BY-SA.

关键词： Decoding

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