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Deep learning in crowd counting:A survey

CAAI Transactions on Intelligence Technology 2024年第5期9卷 1043-1077页

作者： Lijia Deng Qinghua Zhou Shuihua Wang Juan Manuel Górriz Yudong Zhang School of Computing and Mathematical Sciences University of LeicesterLeicesterUK Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Signal Theory Networking and CommunicationsUniversity of GranadaGranadaSpain

Counting high-density objects quickly and accurately is a popular area of *** counting has significant social and economic value and is a major focus in artificial *** many advancements in this field,many of them are not widely known,especially in terms of research *** authors proposed a three-tier standardised dataset taxonomy(TSDT).The Taxonomy divides datasets into small-scale,large-scale and hyper-scale,according to different application *** theory can help researchers make more efficient use of datasets and improve the performance of AI algorithms in specific ***,the authors proposed a new evaluation index for the clarity of the dataset:average pixel occupied by each object(APO).This new evaluation index is more suitable for evaluating the clarity of the dataset in the object counting task than the image ***,the authors classified the crowd counting methods from a data-driven perspective:multi-scale networks,single-column networks,multi-column networks,multi-task networks,attention networks and weak-supervised networks and introduced the classic crowd counting methods of each *** authors classified the existing 36 datasets according to the theory of three-tier standardised dataset taxonomy and discussed and evaluated these *** authors evaluated the performance of more than 100 methods in the past five years on different levels of popular ***,progress in research on small-scale datasets has slowed *** are few new datasets and algorithms on small-scale *** studies focused on large or hyper-scale datasets appear to be reaching a saturation *** combined use of multiple approaches began to be a major research *** authors discussed the theoretical and practical challenges of crowd counting from the perspective of data,algorithms and computing *** field of crowd counting is moving towards combining multiple methods and requ

关键词： artificial intelligence computer vision image analysis image processing

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

Irregular Temporal Classification of Multidrug Resistance De...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

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

ISBN: (纸本)9798350386226

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. © 2024 IEEE.

关键词： Electronic health record

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Sequential Networks for Predicting the Clinical Risk of Chronic Patients Using Drug Dispensation

Sequential Networks for Predicting the Clinical Risk of Chro...

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17th International Joint Conference on Biomedical Engineering systems and Technologies, BIOSTEC 2024

作者： Hijosa-Guzmán, Daniel Jurado-Camino, Marıa Teresa Miguel-Bohoyo, Pablo de Mora-Jiménez, Inmaculada Dept. Signal Theory and Communications Telematics and Computing Systems Rey Juan Carlos University Madrid Spain University Hospital of Fuenlabrada Madrid Spain

Chronic diseases are one of the leading causes of death worldwide, with diabetes, hypertension, congestive heart failure, and chronic obstructive pulmonary disease among the most common ones. In this sense, the extraction of clinical patterns from the data recorded in the Electronic Health Record is of great interest and motivates research in models to predict the temporal evolution of the patient’s health status. Predictive models would be of great help in the treatment of chronic patients to carry out preventive policies. Our approach considers the Gated Recurrent Unit neural network to extract temporal patterns of drug dispensation and to predict the progression of Chronic Conditions (CCs) towards a more complex health status. Real-world data linked to chronic patients of a Spanish hospital were considered, obtaining the most probable health status among a set of 10, including single dominant or moderate CCs, significant CCs in multiple organ systems, and dominant CC in three or more organ systems. Accuracy rates above 70% for single dominant or moderate CCs and nearly 50% for significant/dominant conditions across multiple organs were obtained. These results show the potential of sequential networks to predict the clinical risk of chronic patients and support clinical decision-making. © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Electronic health record

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Naïve Bayes as a Probabilistic Tool for Monitoring the Health Status of Chronic Patients

Naïve Bayes as a Probabilistic Tool for Monitoring the Heal...

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17th International Joint Conference on Biomedical Engineering systems and Technologies, BIOSTEC 2024

作者： Martínez-Marquina, Laura Teresa Jurado-Camino, María Teresa Caballero-López-fando, Isabel Mora-Jiménez, Inmaculada Dept. Signal Theory and Communications Telematics and Computing Systems Rey Juan Carlos University Madrid Spain University Hospital of Fuenlabrada Madrid Spain

Chronic diseases have emerged as a pervasive global health concern, standing as a leading cause of mortality. Among these, prevalent conditions encompass diabetes, hypertension, congestive heart failure and chronic obstructive pulmonary disease. The large amount of data in Electronic Health Records is being exploited by machine learning schemes to design clinical decision support systems, usually of limited practical application because of lack of transparency. To overcome this issue and given the dynamic nature of the health-status over time, we propose here a patient health monitoring scheme based on a Näive Bayes approach because of its interpretability, minimal computational cost, and efficient handling of high-dimensional and unbalanced data. Our approach considers clinical codes (diagnosis and drugs) on real data collected by a Spanish hospital and provides a probability score for different chronic health-statuses. A gender-based approach has also been explored, exhibiting promising performance when there is a significant patient population for each sex. We conclude that pharmacological codes are more informative, although the best performance was obtained by using all the clinical codes and demographic features. Though a more exhaustive study on patient monitoring is necessary, the proposed NB scheme can be considered a proof of concept which has demonstrated to be a valuable tool and easily interpretable method. © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Electronic health record

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Space-time metallic metasurfaces for frequency conversion and beamforming

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Physical Review Applied 2024年第6期21卷 064018-064018页

作者： Salvador Moreno-Rodríguez Antonio Alex-Amor Pablo Padilla Juan F. Valenzuela-Valdés Carlos Molero Department of Signal Theory Telematics and Communications Research Centre for Information and Communication Technologies (CITIC-UGR) Department of Electrical and Systems Engineering

This paper details a class of metal-based space-time metasurfaces for application in wireless communications scenarios. Concretely, we describe space-time metasurfaces that periodically alternate their properties in time between three spatial states: “air,” “conductor,” and “grating.” We analyze the physics of these metastructures via a computationally efficient analytical technique based on the use of Floquet-Bloch series, integral equations, and circuit models. By doing so, we reveal features of these spatiotemporal metasurfaces: scattering parameters, field profiles, diffraction angles, and the nature of the space-time harmonics. The results, corroborated with a self-implemented numerical finite-difference time-domain approach, show the potential application of these space-time metasurfaces as beamformers acting in reflection, in transmission or both. The amplitude and direction of the diffracted orders can be electronically controlled with the parameters of the metasurface. Moreover, the intrinsic ability of time-modulated diffractive metasurfaces to mix and multiply frequencies is tested. We show how two different modulations can lead to the same diffraction angle but with different mixed output frequencies.

关键词： Beam control Frequency conversion Optoelectronics Wave scattering Metagratings Metasurfaces Wireless communication networks Finite-difference time-domain method

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Design of Multiband Large Reflectarray Antennas for Space Applications using Support Vector Machines 13

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

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13th IEEE-APS Topical Conference on Antennas and Propagation in Wireless communications, APWC 2024

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

ISBN: (纸本)9798350360776

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. © 2024 IEEE.

关键词： Satellite antennas

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Radio Sensing with Large Intelligent Surface for 6G 48

Radio Sensing with Large Intelligent Surface for 6G

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48th IEEE International Conference on Acoustics, Speech and signal Processing, ICASSP 2023

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

ISBN: (纸本)9781728163277

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. © 2023 IEEE.

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An empirical investigation into the altering health perspectives in the internet of health things

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International Journal of Information Technology (Singapore) 2023年第1期15卷 67-77页

作者： Bahbouh, Nour Mahmoud Compte, Sandra Sendra Valdes, Juan Valenzuela Sen, Adnan Ahmed Abi Department of Information and Communication Sciences Granada University Granada Spain Department of Communications Politecnica UPV Valencia Spain Department of Signal Theory Telematics and Communications Granada University Granada Spain Faculty of Computer and Information Systems King Abdulaziz University Jeddah Saudi Arabia

Healthcare is on top of the agenda of all governments in the world as it is related to the well-being of the people. Naturally, this domain has attracted the attention of many researchers globally, who have studied the development of its different phases, including E-Health and the Internet of Health Things (IoHT). In this paper, the difference between the recent concepts of healthcare (E-health, M-Health, S-Health, I-Health, U-Health, and IoHT/IoMT) is analyzed based on the main services, applications, and technologies in each concept. The paper has also studied the latest developments in IoHT, which are linked to existing phases of development. A classification of groups of services and constituents of IoHT, linked to the latest technologies, is also provided. In addition, challenges, and future scope of research in this domain concerning the wellbeing of the people in the face of ongoing COVID-19 and future pandemics are explored. © 2022, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management.

关键词： Covid-19 E-Health I-Health IoHT IoT M-Health S-Health U-Health

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Multi-graph Networks with Graph Pooling for COVID-19 Diagnosis

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Journal of Bionic Engineering 2024年第6期21卷 3179-3200页

作者： Chaosheng Tang Wenle Xu Junding Sun Shuihua Wang Yudong Zhang Juan Manuel Górriz School of Computer Science and Technology Henan Polytechnic UniversityHenanJiaozuo454003People’s Republic of China Department of Biological Sciences School of ScienceXi’an Jiaotong Liverpool UniversitySuzhou215123JiangsuChina School of Computing and Mathematical Sciences University of LeicesterLeicesterLE17RHUK Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz University21589JeddahSaudi Arabia Department of Signal Theory Networking and CommunicationsUniversity of Granada52005GranadaSpain

Convolutional Neural Networks(CNNs)have shown remarkable capabilities in extracting local features from images,yet they often overlook the underlying relationships between *** address this limitation,previous approaches have attempted to combine CNNs with Graph Convolutional Networks(GCNs)to capture global ***,these approaches typically neglect the topological structure information of the graph during the global feature extraction *** paper proposes a novel end-to-end hybrid architecture called the Multi-Graph Pooling Network(MGPN),which is designed explicitly for chest X-ray image *** approach sequentially combines CNNs and GCNs,enabling the learning of both local and global features from individual *** that different nodes contribute differently to the final graph representation,we introduce an NI-GTP module to enhance the extraction of ultimate global ***,we introduce a G-LFF module to fuse the local and global features effectively.

关键词： Convolutional neural networks Graph convolutional networks Graph pooling COVID-19

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Fast Surface Error Diagnosis for Radiotelescopes Using Linearized Microwave Holography

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IEEE Transactions on Antennas and Propagation 2025年第6期73卷 3527-3536页

作者： Romero, Celia Fonta Varela, Fernando Rodriguez Iraguen, Belen Galocha Lopez-Perez, Jose-Antonio Universidad Politécnica de Madrid Information Processing and Telecommunications Center Radiation Group Department of Signals Systems and Radiocommunications MadridE-28040 Spain Universidad Rey Juan Carlos Group of Microwave Engineering and Radiocommunication Systems Department of Signal Theory and Communications and Telematic Systems and Computing MadridE-28942 Spain Centro Astronómico de Yebes Dirección General del Instituto Geográfico Nacional Guadalajara19001 Spain

Radiotelescopes are typically composed of large reflector antennas located outdoors. These antennas are designed to achieve high levels of gain, which is possible due to their large size and RF systems designed for working at high frequencies. However, various sources of error (such as mounting imperfection or deformations caused by gravity and weather effects) are difficult to identify and can significantly degrade the performance of the radiotelescope, reducing its gain. The correct diagnosis of these errors has been a challenge in radioastronomy. The most widely used technique is microwave holography, in which mechanical errors are detected through long electromagnetic acquisitions. The measured far-field is processed to obtain the aperture field and thus the errors affecting the surface or the phase distribution. In this paper, a novel methodology is proposed to diagnose reflector errors using holography. The proposed approach defines a system that models all possible aperture errors as a combination of basis functions, directly relating the errors to the acquired far-field and reducing the number of samples required for analysis. © 1963-2012 IEEE.

关键词： Reflection

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