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In this paper, we propose an exact model-based method for near-field sources localization with a bistatic multiple input, multiple output (MIMO) radar system, and compare it with an approximated model-based method. The aim of this paper is to propose an efficient way to use the exact model of the received signals of near-field sources in order to eliminate the systematic error introduced by the use of approximated model in most existing near-field sources localization techniques. The proposed method uses parallel factor (PARAFAC) decomposition to deal with the exact model. Thanks to the exact model, the proposed method has better precision and resolution than the compared approximated model-based method. The simulation results show the performance of the proposed method.
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http://dx.doi.org/10.3390/s17040723 | DOI Listing |
Phys Med Biol
September 2025
BioMaps, Université Paris-Saclay, CNRS, Inserm, SHFJ, CEA, 4 Place du général Leclerc, Orsay, Île-de-France, 91401, FRANCE.
Deep learning has shown great promise for improving medical image reconstruction, including PET. However, concerns remain about the stability and robustness of these methods, especially when trained on limited data. This work aims to explore the use of the Plug-and-Play (PnP) framework in PET reconstruction to address these concerns.
View Article and Find Full Text PDFTraffic Inj Prev
September 2025
Chongqing Jianzhu College, Chongqing, P.R. China.
Purpose: The monotonous lighting environment in extra-long tunnels often induces mind-wandering in drivers. To address this issue, this study explores effective strategies to optimize tunnel lighting environments by configuring various background colors and special lighting zones to enhance the alertness of young drivers and ensure driving safety.
Methods: A virtual driving simulator was utilized to carry out the experiment.
PLoS One
September 2025
Mawlana Bhashani Science and Technology University, Tangail, Bangladesh.
Student dropout is a significant challenge in Bangladesh, with serious impacts on both educational and socio-economic outcomes. This study investigates the factors influencing school dropout among students aged 6-24 years, employing data from the 2019 Multiple Indicator Cluster Survey (MICS). The research integrates statistical analysis with machine learning (ML) techniques and explainable AI (XAI) to identify key predictors and enhance model interpretability.
View Article and Find Full Text PDFJ Med Internet Res
September 2025
School of Pharmacy, Sungkyunkwan University, Gyeonggi-do, Republic of Korea.
Background: Owing to the unique characteristics of digital health interventions (DHIs), a tailored approach to economic evaluation is needed-one that is distinct from that used for pharmacotherapy. However, the absence of clear guidelines in this area is a substantial gap in the evaluation framework.
Objective: This study aims to systematically review and compare the economic evaluation literature on DHIs and pharmacotherapy for the treatment of depression.
Cereb Cortex
August 2025
Section on Functional Imaging Methods & Functional MRI Core Facility, National Institute of Mental Health, 10 Center Drive, Rm 1D80, Bethesda, MD 20892, United States.
Statistical Parametric Mapping (SPM) has been profoundly influential to neuroimaging as it has fostered rigorous, statistically grounded structure for model-based inferences that have led to mechanistic insights about the human brain over the past 30 years. The statistical constructs shared with the world through SPM have been instrumental for deriving meaning from neuroimaging data; however, they require simplifying assumptions which can provide results that, while statistically sound, may not accurately reflect the mechanisms of brain function. A platform that fosters the exploration of the rich and varying neuronal and physiologic underpinnings of the measured signals and their associations to behavior and physiologic measures needs a different set of tools.
View Article and Find Full Text PDF