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An interpolation computational ghost imaging (ICGI) method is proposed and demonstrated that is able to reduce the noise interference from a fluctuating source and background. The noise is estimated through periodic illuminations by a specific assay pattern during sampling, which is then used to correct the bucket detector signal. To validate this method simulations and experiments were conducted. Light source intensity and background lighting were randomly varied to modulate the noise. The results show that good quality images can be obtained, while with conventional computational ghost imaging (CGI) the reconstructed object is barely recognizable. The ICGI method offers a general approach applicable to all CGI techniques, which can attenuate the interference from source fluctuations, background light noise, dynamic scattering, and so on.
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http://dx.doi.org/10.1364/AO.57.006097 | DOI Listing |
Front Plant Sci
August 2025
College of Software, Shanxi Agricultural University, Taigu, China.
The challenge of efficiently detecting ripe and unripe strawberries in complex environments like greenhouses, marked by dense clusters of strawberries, frequent occlusions, overlaps, and fluctuating lighting conditions, presents significant hurdles for existing detection methodologies. These methods often suffer from low efficiency, high computational expenses, and subpar accuracy in scenarios involving small and densely packed targets. To overcome these limitations, this paper introduces YOLOv11-GSF, a real-time strawberry ripeness detection algorithm based on YOLOv11, which incorporates several innovative features: a Ghost Convolution (GhostConv) convolution method for generating rich feature maps through lightweight linear transformations, thereby reducing computational overhead and enhancing resource utilization; a C3K2-SG module that combines self-moving point convolution (SMPConv) and convolutional gated linear units (CGLU) to better capture the local features of strawberry ripeness; and a F-PIoUv2 loss function inspired by Focaler IoU and PIoUv2, utilizing adaptive penalty factors and interval mapping to expedite model convergence and optimize ripeness classification.
View Article and Find Full Text PDFISA Trans
August 2025
Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou, Zhejiang, 313001, PR China; School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, 611731, PR China; Laboratory for Microwave Spatial Inte
Failures in long-term tracking have been frequently reported, posing significant challenges for the practical implementation of UAV tracking systems. Previous research has often employed a metric based on the current tracking state to assess reliability, coupled with a time-consuming re-detection network designed to recover the lost target. However, this approach lacks sufficient robustness and flexibility when dealing with unknown factors present in complex tracking scenarios.
View Article and Find Full Text PDFFront Plant Sci
August 2025
College of Information and Electrical Engineering, Heilongjiang Bayi Agricultural University, Daqing, China.
Deep learning models for rice pest detection often face performance degradation in real-world field environments due to complex backgrounds and limited computational resources. Existing approaches suffer from two critical limitations: (1) inadequate feature representation under occlusion and scale variations, and (2) excessive computational costs for edge deployment. To overcome these limitations, this paper introduces GhostConv+CA-YOLOv8n, a lightweight object detection framework was proposed, which incorporates several innovative features: GhostConv replaces standard convolutional operations with computationally efficient ghost modules in the YOLOv8n's backbone structure, reducing parameters by 40,458 while maintaining feature richness; a Context Aggregation (CA) module is applied after the large and medium-sized feature maps were output by the YOLOv8n's neck structure.
View Article and Find Full Text PDFProc Natl Acad Sci U S A
August 2025
Centre for Translational Medicine and Parasitology, Department of Immunology and Microbiology, University of Copenhagen, Copenhagen 2200, Denmark.
pathology is driven by the accumulation of parasite-infected erythrocytes in blood capillaries. This sequestration process is mediated by the parasite's erythrocyte membrane protein 1 (PfEMP1) adhesins, which bind select endothelial cell receptors. A subset of PfEMP1 binding human endothelial protein C receptor (EPCR) through their cysteine-rich interdomain region alpha 1 (CIDRα1) domains drives the pathogenesis to severe malaria.
View Article and Find Full Text PDFPhys Rev E
July 2025
Max Planck Institute for Neurobiology of Behavior - caesar, Cellular Computations and Learning, Ludwig-Erhard-Allee 2, 53175 Bonn, Germany.
Many natural and engineered systems display oscillations that are characterized by multiple timescales. Typically, such systems are described as slow-fast systems, where the slow dynamics result from a hyperbolic slow manifold that guides the movement of the system's trajectories. Recently, we have provided an alternative description in which the slow timescale results from Lyapunov-unstable transient dynamics of connected dynamical ghosts that form a closed orbit termed ghost cycle.
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