When insufficient samples in the spatial frequency domain could be effectively compensated by the modified CLEAN algorithm, a novel aperture-synthetic scheme of ghost imaging takes advantage of a superior speed of modulation and an enhancement on the spatial resolution. However, there still exist some imperfections in the modified CLEAN reconstructions, such as the fact that some omitted scatter noise still remains or the object contour may be incomplete. Therefore, we optimize the modified CLEAN algorithm by proposing a density clustering algorithm to overcome these drawbacks and improve the visual quality.
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http://dx.doi.org/10.1364/OE.440497 | DOI Listing |
Sensors (Basel)
January 2025
Guangdong Institute of Intelligence Science and Technology, Hengqin, Zhuhai 519031, China.
Electroencephalogram (EEG) signals are important bioelectrical signals widely used in brain activity studies, cognitive mechanism research, and the diagnosis and treatment of neurological disorders. However, EEG signals are often influenced by various physiological artifacts, which can significantly affect data analysis and diagnosis. Recently, deep learning-based EEG denoising methods have exhibited unique advantages over traditional methods.
View Article and Find Full Text PDFBMC Oral Health
January 2025
Department of Clinical Dentistry, Faculty of Medicine, University of Bergen, Bergen, Norway.
Background: In the last years, artificial intelligence (AI) has contributed to improving healthcare including dentistry. The objective of this study was to develop a machine learning (ML) model for early childhood caries (ECC) prediction by identifying crucial health behaviours within mother-child pairs.
Methods: For the analysis, we utilized a representative sample of 724 mothers with children under six years in Bangladesh.
Sci Rep
January 2025
School of Mechanical, Electrical, and Information Engineering, Putian University, Putian, 351100, China.
Noise label learning has attracted considerable attention owing to its ability to leverage large amounts of inexpensive and imprecise data. Sharpness aware minimization (SAM) has shown effective improvements in the generalization performance in the presence of noisy labels by introducing adversarial weight perturbations in the model parameter space. However, our experimental observations have shown that the SAM generalization bottleneck primarily stems from the difficulty of finding the correct adversarial perturbation amidst the noisy data.
View Article and Find Full Text PDFPLoS One
January 2025
School of Electrical Engineering, Zhejiang University, Hangzhou, China.
Adversarial training has become a primary method for enhancing the robustness of deep learning models. In recent years, fast adversarial training methods have gained widespread attention due to their lower computational cost. However, since fast adversarial training uses single-step adversarial attacks instead of multi-step attacks, the generated adversarial examples lack diversity, making models prone to catastrophic overfitting and loss of robustness.
View Article and Find Full Text PDFSci Rep
January 2025
Integrated Traditional and Western Medicine Hospital of Linping District, Hangzhou, 311100, China.
To explore the attitudes of healthcare professionals and the public on applying ChatGPT in clinical practice. The successful application of ChatGPT in clinical practice depends on technical performance and critically on the attitudes and perceptions of non-healthcare and healthcare. This study has a qualitative design based on artificial intelligence.
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