Imaging systems are widely used in many fields. However, there is an inherent compromise between field of view (FOV) and resolution. In this paper, we propose an optofluidic zoom system with increased FOV and less chromatic aberration, which can realize switching between large FOV and high resolution. The proposed system consists of a liquid prism, a zoom objective, an image sensor and image processing module, which can realize optical zoom and deflection. The proposed system achieves non-mechanical optical zoom from f = 40.5 mm to f = 84.0 mm. Besides, the angular resolution of zoom objective is up to 26"18 at f = 84.0 mm. The deflection range is ±10°, and the whole FOV of proposed system can reach up to 30.3°. The proposed system is compact and easy to machine. In addition, we reduce chromatic aberration produced by the liquid prism significantly. The proposed system can be used in monitor system, target tracking system, telescope system and so on.
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http://dx.doi.org/10.1364/OE.498096 | DOI Listing |
Med Phys
January 2025
Department of Oncology, The Affiliated Hospital of Southwest Medical University, Luzhou, China.
Background: Kidney tumors, common in the urinary system, have widely varying survival rates post-surgery. Current prognostic methods rely on invasive biopsies, highlighting the need for non-invasive, accurate prediction models to assist in clinical decision-making.
Purpose: This study aimed to construct a K-means clustering algorithm enhanced by Transformer-based feature transformation to predict the overall survival rate of patients after kidney tumor resection and provide an interpretability analysis of the model to assist in clinical decision-making.
Med Phys
January 2025
Department of Engineering Physics, Tsinghua University, Beijing, China.
Background: X-ray grating-based dark-field imaging can sense the small angle scattering caused by object's micro-structures. This technique is sensitive to the porous microstructure of lung alveoli and has the potential to detect lung diseases at an early stage. Up to now, a human-scale dark-field CT (DF-CT) prototype has been built for lung imaging.
View Article and Find Full Text PDFJ Imaging Inform Med
January 2025
School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA.
Vision transformer (ViT)and convolutional neural networks (CNNs) each possess distinct strengths in medical imaging: ViT excels in capturing long-range dependencies through self-attention, while CNNs are adept at extracting local features via spatial convolution filters. While ViT may struggle with capturing detailed local spatial information, critical for tasks like anomaly detection in medical imaging, shallow CNNs often fail to effectively abstract global context. This study aims to explore and evaluate hybrid architectures that integrate ViT and CNN to leverage their complementary strengths for enhanced performance in medical vision tasks, such as segmentation, classification, reconstruction, and prediction.
View Article and Find Full Text PDFSci Rep
January 2025
Instituto de Ingeniería Energética, Universitat Politècnica de València, Valencia, Spain.
Reliable prediction of photovoltaic power generation is key to the efficient management of energy systems in response to the inherent uncertainty of renewable energy sources. Despite advances in weather forecasting, photovoltaic power prediction accuracy remains a challenge. This study presents a novel approach that combines genetic algorithms and dynamic neural network structure refinement to optimize photovoltaic prediction.
View Article and Find Full Text PDFISA Trans
January 2025
Institute of Artificial Intelligence and Future Networks, Beijing Normal University at Zhuhai, Zhuhai, China; BNU-HKBU United International College Tangjiawan, Rd. JinTong 2000#, Zhuhai, China. Electronic address:
In this paper, a novel recursive hierarchical parametric identification method based on initial value optimization is proposed for Wiener-Hammerstein systems subject to stochastic measurement noise. By transforming the traditional Wiener-Hammerstein system model into a generalized form, the system model parameters are uniquely expressed for estimation. To avoid cross-coupling between estimating block-oriented model parameters, a hierarchical identification algorithm is presented by dividing the parameter vector into two subvectors containing the coupled and uncoupled terms for estimation, respectively.
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